derfinder users guide
1 May 2024
Package
derfinder 1.38.0
1 Asking for help
Please read the basics of using derfinder in the quick start guide. Thank you.
2 derfinder users guide
If you haven’t already, please read the quick start to using derfinder vignette. It explains the basics of using derfinder, how to ask for help, and showcases an example analysis.
The derfinder users guide goes into more depth about what you can do with derfinder. It covers the two implementations of the DER Finder approach (Frazee, Sabunciyan, Hansen et al., 2014). That is, the (A) expressed regions-level and (B) single base-level F-statistics implementations. The vignette also includes advanced material for fine tuning some options, working with non-human data, and example scripts for a high-performance computing cluster.
3 Expressed regions-level
The expressed regions-level implementation is based on summarizing the coverage information for all your samples and applying a cutoff to that summary. For example, by calculating the mean coverage at every base and then checking if it’s greater than some cutoff value of interest. Contiguous bases passing the cutoff become a candidate Expressed Region (ER). We can then construct a matrix summarizing the base-level coverage for each sample for the set of ERs. This matrix can then be using with packages developed for feature counting (limma, DESeq2, edgeR, etc) to determine which ERs have differential expression signal. That is, to identify the Differentially Expressed Regions (DERs).
3.1 regionMatrix()
Commonly, users have aligned their raw RNA-seq data and saved the alignments in BAM files. Some might have chosen to compress this information into BigWig files. BigWig files are much faster to load than BAM files and are the type of file we prefer to work with. However, we can still identify the expressed regions from BAM files.
The function regionMatrix() will require you to load the data (either from BAM or BigWig files) and store it all in memory. It will then calculate the mean coverage across all samples, apply the cutoff you chose, and determine the expressed regions.
This is the path you will want to follow in most scenarios.
3.2 railMatrix()
Our favorite method for identifying expressed regions is based on pre-computed summary coverage files (mean or median) as well as coverage files by sample. Rail is a cloud-enabled aligner that will generate:
- Per chromosome, a summary (mean or median) coverage BigWig file. Note that the summary is adjusted for library size and is by default set to libraries with 40 million reads.
- Per sample, a coverage BigWig file with the un-adjusted coverage.
Rail does a great job in creating these files for us, saving time and reducing the memory load needed for this type of analysis with R.
If you have this type of data or can generate it from BAM files with other tools, you will be interested in using the railMatrix() function. The output is identical to the one from regionMatrix(). It’s just much faster and memory efficient. The only drawback is that BigWig files are not fully supported in Windows as of rtracklayer version 1.25.16.
We highly recommend this approach. Rail has also other significant features such as: scalability, reduced redundancy, integrative analysis, mode agnosticism, and inexpensive cloud implementation. For more information, visit rail.bio.
4 Single base-level F-statistics
The DER Finder approach was originally implemented by calculating t-statistics between two groups and using a hidden markov model to determine the expression states: not expressed, expressed, differentially expressed (Frazee, Sabunciyan, Hansen et al., 2014). The original software works but had areas where we worked to improve it, which lead to the single base-level F-statistics implementation. Also note that the original software is no longer maintained.
This type of analysis first loads the data and preprocess it in a format that saves time and reduces memory later. It then fits two nested models (an alternative and a null model) with the coverage information for every single base-pair of the genome. Using the two fitted models, it calculates an F-statistic. Basically, it generates a vector along the genome with F-statistics.
A cutoff is then applied to the F-statistics and contiguous base-pairs of the genome passing the cutoff are considered a candidate Differentially Expressed Region (DER).
Calculating F-statistics along the genome is computationally intensive, but doable. The major resource drain comes from assigning p-values to the DERs. To do so, we permute the model matrices and re-calculate the F-statistics generating a set of null DERs. Once we have enough null DERs, we compare the observed DERs against the null DERs to calculate p-values for the observed DERs.
This type of analysis at the chromosome level is done by the analyzeChr() function, which is a high level function using many other pieces of derfinder. Once all chromosomes have been processed, mergeResults() combines them.
Which implementation of the DER Finder approach you will want to use depends on your specific use case and computational resources available. But in general, we recommend starting with the expressed regions-level implementation.
5 Example data
In this vignette we we will analyze a small subset of the samples from the BrainSpan Atlas of the Human Brain (BrainSpan, 2011) publicly available data.
We first load the required packages.
## Load libraries
library("derfinder")
library("derfinderData")
library("GenomicRanges")5.1 Phenotype data
For this example, we created a small table with the relevant phenotype data for 12 samples: 6 from fetal samples and 6 from adult samples. We chose at random a brain region, in this case the amygdaloid complex. For this example we will only look at data from chromosome 21. Other variables include the age in years and the gender. The data is shown below.
library("knitr")
## Get pheno table
pheno <- subset(brainspanPheno, structure_acronym == "AMY")
## Display the main information
p <- pheno[, -which(colnames(pheno) %in% c(
"structure_acronym",
"structure_name", "file"
))]
rownames(p) <- NULL
kable(p, format = "html", row.names = TRUE)| gender | lab | Age | group | |
|---|---|---|---|---|
| 1 | F | HSB97.AMY | -0.4423077 | fetal |
| 2 | M | HSB92.AMY | -0.3653846 | fetal |
| 3 | M | HSB178.AMY | -0.4615385 | fetal |
| 4 | M | HSB159.AMY | -0.3076923 | fetal |
| 5 | F | HSB153.AMY | -0.5384615 | fetal |
| 6 | F | HSB113.AMY | -0.5384615 | fetal |
| 7 | F | HSB130.AMY | 21.0000000 | adult |
| 8 | M | HSB136.AMY | 23.0000000 | adult |
| 9 | F | HSB126.AMY | 30.0000000 | adult |
| 10 | M | HSB145.AMY | 36.0000000 | adult |
| 11 | M | HSB123.AMY | 37.0000000 | adult |
| 12 | F | HSB135.AMY | 40.0000000 | adult |
5.2 Load the data
derfinder offers three functions related to loading raw data. The first one, rawFiles(), is a helper function for identifying the full paths to the input files. Next, loadCoverage() loads the base-level coverage data from either BAM or BigWig files for a specific chromosome. Finally, fullCoverage() will load the coverage for a set of chromosomes using loadCoverage().
We can load the data from derfinderData (Collado-Torres, Jaffe, and Leek, 2024) by first identifying the paths to the BigWig files with rawFiles() and then loading the data with fullCoverage(). Note that the BrainSpan data is already normalized by the total number of mapped reads in each sample. However, that won’t be the case with most data sets in which case you might want to use the totalMapped and targetSize arguments. The function getTotalMapped() will be helpful to get this information.
## Determine the files to use and fix the names
files <- rawFiles(system.file("extdata", "AMY", package = "derfinderData"),
samplepatt = "bw", fileterm = NULL
)
names(files) <- gsub(".bw", "", names(files))
## Load the data from disk
system.time(fullCov <- fullCoverage(
files = files, chrs = "chr21",
totalMapped = rep(1, length(files)), targetSize = 1
))## 2024-05-01 16:56:58.069763 fullCoverage: processing chromosome chr21## 2024-05-01 16:56:58.074722 loadCoverage: finding chromosome lengths## 2024-05-01 16:56:58.084482 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB113.bw## 2024-05-01 16:56:58.241538 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB123.bw## 2024-05-01 16:56:58.382442 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB126.bw## 2024-05-01 16:56:58.49018 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB130.bw## 2024-05-01 16:56:58.615929 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB135.bw## 2024-05-01 16:56:58.730004 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB136.bw## 2024-05-01 16:56:58.89244 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB145.bw## 2024-05-01 16:56:59.016593 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB153.bw## 2024-05-01 16:56:59.147914 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB159.bw## 2024-05-01 16:56:59.269928 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB178.bw## 2024-05-01 16:56:59.394486 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB92.bw## 2024-05-01 16:56:59.576863 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB97.bw## 2024-05-01 16:56:59.729725 loadCoverage: applying the cutoff to the merged data## 2024-05-01 16:56:59.740353 filterData: normalizing coverage## 2024-05-01 16:57:00.04865 filterData: done normalizing coverage## 2024-05-01 16:57:00.063662 filterData: originally there were 48129895 rows, now there are 48129895 rows. Meaning that 0 percent was filtered.## user system elapsed
## 2.019 0.024 2.043Alternatively, since the BigWig files are publicly available from BrainSpan (see here), we can extract the relevant coverage data using fullCoverage(). Note that as of rtracklayer 1.25.16 BigWig files are not supported on Windows: you can find the fullCov object inside derfinderData to follow the examples.
## Determine the files to use and fix the names
files <- pheno$file
names(files) <- gsub(".AMY", "", pheno$lab)
## Load the data from the web
system.time(fullCov <- fullCoverage(
files = files, chrs = "chr21",
totalMapped = rep(1, length(files)), targetSize = 1
))Note how loading the coverage for 12 samples from the web was quite fast. Although in this case we only retained the information for chromosome 21.
The result of fullCov is a list with one element per chromosome. If no filtering was performed, each chromosome has a DataFrame with the number of rows equaling the number of bases in the chromosome with one column per sample.
## Lets explore it
fullCov## $chr21
## DataFrame with 48129895 rows and 12 columns
## HSB113 HSB123 HSB126 HSB130 HSB135 HSB136 HSB145 HSB153 HSB159 HSB178 HSB92 HSB97
## <Rle> <Rle> <Rle> <Rle> <Rle> <Rle> <Rle> <Rle> <Rle> <Rle> <Rle> <Rle>
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## ... ... ... ... ... ... ... ... ... ... ... ... ...
## 48129891 0 0 0 0 0 0 0 0 0 0 0 0
## 48129892 0 0 0 0 0 0 0 0 0 0 0 0
## 48129893 0 0 0 0 0 0 0 0 0 0 0 0
## 48129894 0 0 0 0 0 0 0 0 0 0 0 0
## 48129895 0 0 0 0 0 0 0 0 0 0 0 0If filtering was performed, each chromosome also has a logical Rle indicating which bases of the chromosome passed the filtering. This information is useful later on to map back the results to the genome coordinates.
5.3 Filter coverage
Depending on the use case, you might want to filter the base-level coverage at the time of reading it, or you might want to keep an unfiltered version. By default both loadCoverage() and fullCoverage() will not filter.
If you decide to filter, set the cutoff argument to a positive value. This will run filterData(). Note that you might want to standardize the library sizes prior to filtering if you didn’t already do it when creating the fullCov object. You can do so by by supplying the totalMapped and targetSize arguments to filterData(). Note: don’t use these arguments twice in fullCoverage() and filterData().
In this example, we prefer to keep both an unfiltered and filtered version. For the filtered version, we will retain the bases where at least one sample has coverage greater than 2.
## Filter coverage
filteredCov <- lapply(fullCov, filterData, cutoff = 2)## 2024-05-01 16:57:00.632167 filterData: originally there were 48129895 rows, now there are 130356 rows. Meaning that 99.73 percent was filtered.The result is similar to fullCov but with the genomic position index as shown below.
## Similar to fullCov but with $position
filteredCov## $chr21
## $chr21$coverage
## DataFrame with 130356 rows and 12 columns
## HSB113 HSB123 HSB126 HSB130 HSB135 HSB136
## <Rle> <Rle> <Rle> <Rle> <Rle> <Rle>
## 1 2.00999999046326 0 0 0.0399999991059303 0.230000004172325 0.129999995231628
## 2 2.17000007629395 0 0 0.0399999991059303 0.230000004172325 0.129999995231628
## 3 2.21000003814697 0 0 0.0399999991059303 0.230000004172325 0.129999995231628
## 4 2.36999988555908 0 0 0.0399999991059303 0.230000004172325 0.129999995231628
## 5 2.36999988555908 0 0.0599999986588955 0.0399999991059303 0.230000004172325 0.129999995231628
## ... ... ... ... ... ... ...
## 130352 1.25 1.27999997138977 2.04999995231628 0.790000021457672 1.62999999523163 1.37000000476837
## 130353 1.21000003814697 1.24000000953674 2.04999995231628 0.790000021457672 1.62999999523163 1.37000000476837
## 130354 1.21000003814697 1.20000004768372 1.92999994754791 0.790000021457672 1.62999999523163 1.27999997138977
## 130355 1.16999995708466 1.20000004768372 1.92999994754791 0.790000021457672 1.62999999523163 1.27999997138977
## 130356 1.16999995708466 1.0900000333786 1.87000000476837 0.790000021457672 1.54999995231628 1.01999998092651
## HSB145 HSB153 HSB159 HSB178 HSB92 HSB97
## <Rle> <Rle> <Rle> <Rle> <Rle> <Rle>
## 1 0 0.589999973773956 0.150000005960464 0.0500000007450581 0.0700000002980232 1.58000004291534
## 2 0 0.629999995231628 0.150000005960464 0.0500000007450581 0.0700000002980232 1.58000004291534
## 3 0 0.670000016689301 0.150000005960464 0.0500000007450581 0.100000001490116 1.61000001430511
## 4 0 0.709999978542328 0.150000005960464 0.0500000007450581 0.129999995231628 1.64999997615814
## 5 0 0.75 0.25 0.100000001490116 0.129999995231628 1.67999994754791
## ... ... ... ... ... ... ...
## 130352 1.02999997138977 2.21000003814697 2.46000003814697 2.0699999332428 2.23000001907349 1.50999999046326
## 130353 1.02999997138977 2.21000003814697 2.46000003814697 2.0699999332428 2.23000001907349 1.50999999046326
## 130354 0.730000019073486 2.00999999046326 2.21000003814697 2.10999989509583 2.13000011444092 1.47000002861023
## 130355 0.600000023841858 2.00999999046326 2.10999989509583 2.10999989509583 2.13000011444092 1.47000002861023
## 130356 0.560000002384186 1.92999994754791 1.96000003814697 1.77999997138977 2.05999994277954 1.47000002861023
##
## $chr21$position
## logical-Rle of length 48129895 with 3235 runs
## Lengths: 9825448 149 2 9 2 2 6 ... 137 1446 172 740 598 45091
## Values : FALSE TRUE FALSE TRUE FALSE TRUE FALSE ... TRUE FALSE TRUE FALSE TRUE FALSEIn terms of memory, the filtered version requires less resources. Although this will depend on how rich the data set is and how aggressive was the filtering step.
## Compare the size in Mb
round(c(
fullCov = object.size(fullCov),
filteredCov = object.size(filteredCov)
) / 1024^2, 1)## fullCov filteredCov
## 22.7 8.5Note that with your own data, filtering for bases where at least one sample has coverage greater than 2 might not make sense: maybe you need a higher or lower filter. The amount of bases remaining after filtering will impact how long the analysis will take to complete. We thus recommend exploring this before proceeding.
6 Expressed region-level analysis
6.1 Via regionMatrix()
Now that we have the data, we can identify expressed regions (ERs) by using a cutoff of 30 on the base-level mean coverage from these 12 samples. Once the regions have been identified, we can calculate a coverage matrix with one row per ER and one column per sample (12 in this case). For doing this calculation we need to know the length of the sequence reads, which in this study were 76 bp long.
Note that for this type of analysis there is no major benefit of filtering the data. Although it can be done if needed.
## Use regionMatrix()
system.time(regionMat <- regionMatrix(fullCov, cutoff = 30, L = 76))## By using totalMapped equal to targetSize, regionMatrix() assumes that you have normalized the data already in fullCoverage(), loadCoverage() or filterData().## 2024-05-01 16:57:01.815245 regionMatrix: processing chr21## 2024-05-01 16:57:01.815692 filterData: normalizing coverage## 2024-05-01 16:57:02.10297 filterData: done normalizing coverage## 2024-05-01 16:57:03.455505 filterData: originally there were 48129895 rows, now there are 2256 rows. Meaning that 100 percent was filtered.## 2024-05-01 16:57:03.456283 findRegions: identifying potential segments## 2024-05-01 16:57:03.460777 findRegions: segmenting information## 2024-05-01 16:57:03.467812 findRegions: identifying candidate regions## 2024-05-01 16:57:03.520022 findRegions: identifying region clusters## 2024-05-01 16:57:03.671066 getRegionCoverage: processing chr21## 2024-05-01 16:57:03.701878 getRegionCoverage: done processing chr21## 2024-05-01 16:57:03.704189 regionMatrix: calculating coverageMatrix## 2024-05-01 16:57:03.709512 regionMatrix: adjusting coverageMatrix for 'L'## user system elapsed
## 1.856 0.048 1.905## Explore results
class(regionMat)## [1] "list"names(regionMat$chr21)## [1] "regions" "coverageMatrix" "bpCoverage"regionMatrix() returns a list of elements of length equal to the number of chromosomes analyzed. For each chromosome, there are three pieces of output. The actual ERs are arranged in a GRanges object named regions.
- regions is the result from filtering with
filterData()and then defining the regions withfindRegions(). Note that the metadata variablevaluerepresents the mean coverage for the given region whileareais the sum of the base-level coverage (before adjusting for read length) from all samples. - bpCoverage is a list with the base-level coverage from all the regions. This information can then be used with
plotRegionCoverage()from derfinderPlot. - coverageMatrix is the matrix with one row per region and one column per sample. The different matrices for each of the chromosomes can then be merged prior to using limma, DESeq2 or other packages. Note that the counts are generally not integers, but can easily be rounded if necessary.
## regions output
regionMat$chr21$regions## GRanges object with 45 ranges and 6 metadata columns:
## seqnames ranges strand | value area indexStart indexEnd cluster clusterL
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <integer> <integer> <Rle> <Rle>
## 1 chr21 9827018-9827582 * | 313.6717 177224.53 1 565 1 565
## 2 chr21 15457301-15457438 * | 215.0846 29681.68 566 703 2 138
## 3 chr21 20230140-20230192 * | 38.8325 2058.12 704 756 3 366
## 4 chr21 20230445-20230505 * | 41.3245 2520.80 757 817 3 366
## 5 chr21 27253318-27253543 * | 34.9131 7890.37 818 1043 4 765
## .. ... ... ... . ... ... ... ... ... ...
## 41 chr21 33039644-33039688 * | 34.4705 1551.1742 2180 2224 17 45
## 42 chr21 33040784-33040798 * | 32.1342 482.0133 2225 2239 18 118
## 43 chr21 33040890 * | 30.0925 30.0925 2240 2240 18 118
## 44 chr21 33040900-33040901 * | 30.1208 60.2417 2241 2242 18 118
## 45 chr21 48019401-48019414 * | 31.1489 436.0850 2243 2256 19 14
## -------
## seqinfo: 1 sequence from an unspecified genome## Number of regions
length(regionMat$chr21$regions)## [1] 45bpCoverage is the base-level coverage list which can then be used for plotting.
## Base-level coverage matrices for each of the regions
## Useful for plotting
lapply(regionMat$chr21$bpCoverage[1:2], head, n = 2)## $`1`
## HSB113 HSB123 HSB126 HSB130 HSB135 HSB136 HSB145 HSB153 HSB159 HSB178 HSB92 HSB97
## 1 93.20 3.32 28.22 5.62 185.17 98.34 5.88 16.71 3.52 15.71 47.40 36.54
## 2 124.76 7.25 63.68 11.32 374.85 199.28 10.39 30.53 5.83 29.35 65.04 51.42
##
## $`2`
## HSB113 HSB123 HSB126 HSB130 HSB135 HSB136 HSB145 HSB153 HSB159 HSB178 HSB92 HSB97
## 566 45.59 7.94 15.92 34.75 141.61 104.21 19.87 38.61 4.97 23.2 13.95 22.21
## 567 45.59 7.94 15.92 35.15 141.64 104.30 19.87 38.65 4.97 23.2 13.95 22.21## Check dimensions. First region is 565 long, second one is 138 bp long.
## The columns match the number of samples (12 in this case).
lapply(regionMat$chr21$bpCoverage[1:2], dim)## $`1`
## [1] 565 12
##
## $`2`
## [1] 138 12The end result of the coverage matrix is shown below. Note that the coverage has been adjusted for read length. Because reads might not fully align inside a given region, the numbers are generally not integers but can be rounded if needed.
## Dimensions of the coverage matrix
dim(regionMat$chr21$coverageMatrix)## [1] 45 12## Coverage for each region. This matrix can then be used with limma or other pkgs
head(regionMat$chr21$coverageMatrix)## HSB113 HSB123 HSB126 HSB130 HSB135 HSB136 HSB145 HSB153 HSB159
## 1 3653.1093346 277.072106 1397.068687 1106.722895 8987.460401 5570.221054 1330.158818 1461.2986829 297.939342
## 2 333.3740816 99.987237 463.909476 267.354342 1198.713552 1162.313418 257.114210 313.8513139 67.940131
## 3 35.3828948 20.153553 30.725394 23.483947 16.786842 17.168947 22.895921 52.8756585 28.145395
## 4 42.3398681 29.931579 41.094474 24.724736 32.634080 19.309606 33.802632 51.6146040 31.244343
## 5 77.7402631 168.939342 115.059342 171.861974 180.638684 93.503158 90.950526 36.3046051 78.069605
## 6 0.7988158 1.770263 1.473421 2.231053 1.697368 1.007895 1.171316 0.4221053 1.000132
## HSB178 HSB92 HSB97
## 1 1407.288552 1168.519079 1325.9622371
## 2 193.695657 127.543553 200.7834228
## 3 33.127368 23.758816 20.4623685
## 4 33.576974 29.546183 28.2011836
## 5 97.151316 100.085790 35.5428946
## 6 1.139079 1.136447 0.3956579We can then use the coverage matrix and packages such as limma, DESeq2 or edgeR to identify which ERs are differentially expressed.
6.1.1 Find DERs with DESeq2
Here we’ll use DESeq2 to identify the DERs. To use it we need to round the coverage data.
## Required
library("DESeq2")
## Round matrix
counts <- round(regionMat$chr21$coverageMatrix)
## Round matrix and specify design
dse <- DESeqDataSetFromMatrix(counts, pheno, ~ group + gender)## converting counts to integer mode## Perform DE analysis
dse <- DESeq(dse, test = "LRT", reduced = ~gender, fitType = "local")## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## Extract results
deseq <- regionMat$chr21$regions
mcols(deseq) <- c(mcols(deseq), results(dse))
## Explore the results
deseq## GRanges object with 45 ranges and 12 metadata columns:
## seqnames ranges strand | value area indexStart indexEnd cluster clusterL baseMean
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <integer> <integer> <Rle> <Rle> <numeric>
## 1 chr21 9827018-9827582 * | 313.6717 177224.53 1 565 1 565 2846.2872
## 2 chr21 15457301-15457438 * | 215.0846 29681.68 566 703 2 138 451.5196
## 3 chr21 20230140-20230192 * | 38.8325 2058.12 704 756 3 366 29.5781
## 4 chr21 20230445-20230505 * | 41.3245 2520.80 757 817 3 366 36.0603
## 5 chr21 27253318-27253543 * | 34.9131 7890.37 818 1043 4 765 101.6468
## .. ... ... ... . ... ... ... ... ... ... ...
## 41 chr21 33039644-33039688 * | 34.4705 1551.1742 2180 2224 17 45 20.782035
## 42 chr21 33040784-33040798 * | 32.1342 482.0133 2225 2239 18 118 6.410542
## 43 chr21 33040890 * | 30.0925 30.0925 2240 2240 18 118 0.129717
## 44 chr21 33040900-33040901 * | 30.1208 60.2417 2241 2242 18 118 0.702291
## 45 chr21 48019401-48019414 * | 31.1489 436.0850 2243 2256 19 14 5.293293
## log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## 1 -1.6903182 0.831959 0.215262 0.6426743 0.997155
## 2 -1.1640426 0.757490 0.871126 0.3506436 0.997155
## 3 0.0461488 0.458097 3.132082 0.0767657 0.863614
## 4 -0.1866200 0.390920 2.225708 0.1357305 0.997155
## 5 -0.1387377 0.320166 3.957987 0.0466495 0.862040
## .. ... ... ... ... ...
## 41 -0.642056 0.427661 0.6047814 0.4367595 0.997155
## 42 -0.634321 0.512262 0.5454039 0.4602018 0.997155
## 43 -0.859549 3.116540 0.0206273 0.8857989 0.997155
## 44 -0.628285 2.247378 0.5825105 0.4453299 0.997155
## 45 -1.694563 1.252290 5.7895910 0.0161213 0.725460
## -------
## seqinfo: 1 sequence from an unspecified genomeYou can get similar results using edgeR using these functions: DGEList(), calcNormFactors(), estimateGLMRobustDisp(), glmFit(), and glmLRT().
6.1.2 Find DERs with limma
Alternatively, we can find DERs using limma. Here we’ll exemplify a type of test closer to what we’ll do later with the F-statistics approach. First of all, we need to define our models.
## Build models
mod <- model.matrix(~ pheno$group + pheno$gender)
mod0 <- model.matrix(~ pheno$gender)Next, we’ll transform the coverage information using the same default transformation from analyzeChr().
## Transform coverage
transformedCov <- log2(regionMat$chr21$coverageMatrix + 32)We can then fit the models and get the F-statistic p-values and control the FDR.
## Example using limma
library("limma")##
## Attaching package: 'limma'## The following object is masked from 'package:DESeq2':
##
## plotMA## The following object is masked from 'package:BiocGenerics':
##
## plotMA## Run limma
fit <- lmFit(transformedCov, mod)
fit0 <- lmFit(transformedCov, mod0)
## Determine DE status for the regions
## Also in https://github.com/LieberInstitute/jaffelab with help and examples
getF <- function(fit, fit0, theData) {
rss1 <- rowSums((fitted(fit) - theData)^2)
df1 <- ncol(fit$coef)
rss0 <- rowSums((fitted(fit0) - theData)^2)
df0 <- ncol(fit0$coef)
fstat <- ((rss0 - rss1) / (df1 - df0)) / (rss1 / (ncol(theData) - df1))
f_pval <- pf(fstat, df1 - df0, ncol(theData) - df1, lower.tail = FALSE)
fout <- cbind(fstat, df1 - 1, ncol(theData) - df1, f_pval)
colnames(fout)[2:3] <- c("df1", "df0")
fout <- data.frame(fout)
return(fout)
}
ff <- getF(fit, fit0, transformedCov)
## Get the p-value and assign it to the regions
limma <- regionMat$chr21$regions
limma$fstat <- ff$fstat
limma$pvalue <- ff$f_pval
limma$padj <- p.adjust(ff$f_pval, "BH")
## Explore the results
limma## GRanges object with 45 ranges and 9 metadata columns:
## seqnames ranges strand | value area indexStart indexEnd cluster clusterL fstat pvalue
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <integer> <integer> <Rle> <Rle> <numeric> <numeric>
## 1 chr21 9827018-9827582 * | 313.6717 177224.53 1 565 1 565 1.638455 0.2325446
## 2 chr21 15457301-15457438 * | 215.0846 29681.68 566 703 2 138 4.307443 0.0677644
## 3 chr21 20230140-20230192 * | 38.8325 2058.12 704 756 3 366 1.323342 0.2796406
## 4 chr21 20230445-20230505 * | 41.3245 2520.80 757 817 3 366 0.380332 0.5527044
## 5 chr21 27253318-27253543 * | 34.9131 7890.37 818 1043 4 765 7.249519 0.0246955
## .. ... ... ... . ... ... ... ... ... ... ... ...
## 41 chr21 33039644-33039688 * | 34.4705 1551.1742 2180 2224 17 45 3.11799 0.1112440
## 42 chr21 33040784-33040798 * | 32.1342 482.0133 2225 2239 18 118 3.66184 0.0879543
## 43 chr21 33040890 * | 30.0925 30.0925 2240 2240 18 118 3.87860 0.0804175
## 44 chr21 33040900-33040901 * | 30.1208 60.2417 2241 2242 18 118 4.39338 0.0655381
## 45 chr21 48019401-48019414 * | 31.1489 436.0850 2243 2256 19 14 6.80915 0.0282970
## padj
## <numeric>
## 1 0.581362
## 2 0.324601
## 3 0.629191
## 4 0.863074
## 5 0.309532
## .. ...
## 41 0.385075
## 42 0.329829
## 43 0.328981
## 44 0.324601
## 45 0.309532
## -------
## seqinfo: 1 sequence from an unspecified genomeIn this simple example, none of the ERs have strong differential expression signal when adjusting for an FDR of 5%.
table(limma$padj < 0.05, deseq$padj < 0.05)##
## FALSE
## FALSE 456.2 Via railMatrix()
If you have Rail output, you can get the same results faster than with regionMatrix(). Rail will create the summarized coverage BigWig file for you, but we are not including it in this package due to its size. So, lets create it.
## Calculate the mean: this step takes a long time with many samples
meanCov <- Reduce("+", fullCov$chr21) / ncol(fullCov$chr21)
## Save it on a bigwig file called meanChr21.bw
createBw(list("chr21" = DataFrame("meanChr21" = meanCov)),
keepGR =
FALSE
)## 2024-05-01 16:57:07.505301 coerceGR: coercing sample meanChr21## 2024-05-01 16:57:08.633228 createBwSample: exporting bw for sample meanChr21Now that we have the files Rail creates for us, we can use railMatrix().
## Identify files to use
summaryFile <- "meanChr21.bw"
## We had already found the sample BigWig files and saved it in the object 'files'
## Lets just rename it to sampleFiles for clarity.
sampleFiles <- files
## Get the regions
system.time(
regionMat.rail <- railMatrix(
chrs = "chr21", summaryFiles = summaryFile,
sampleFiles = sampleFiles, L = 76, cutoff = 30, maxClusterGap = 3000L
)
)## 2024-05-01 16:57:10.745814 loadCoverage: finding chromosome lengths## 2024-05-01 16:57:10.753361 loadCoverage: loading BigWig file meanChr21.bw## 2024-05-01 16:57:11.186351 loadCoverage: applying the cutoff to the merged data## 2024-05-01 16:57:13.3649 filterData: originally there were 48129895 rows, now there are 48129895 rows. Meaning that 0 percent was filtered.## 2024-05-01 16:57:13.686366 filterData: originally there were 48129895 rows, now there are 2256 rows. Meaning that 100 percent was filtered.## 2024-05-01 16:57:13.690015 findRegions: identifying potential segments## 2024-05-01 16:57:13.693245 findRegions: segmenting information## 2024-05-01 16:57:13.693604 .getSegmentsRle: segmenting with cutoff(s) 30## 2024-05-01 16:57:13.697226 findRegions: identifying candidate regions## 2024-05-01 16:57:13.734767 findRegions: identifying region clusters## 2024-05-01 16:57:13.798744 railMatrix: processing regions 1 to 45## 2024-05-01 16:57:13.804244 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB113.bw## 2024-05-01 16:57:13.879025 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB123.bw## 2024-05-01 16:57:13.94832 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB126.bw## 2024-05-01 16:57:14.017126 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB130.bw## 2024-05-01 16:57:14.087963 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB135.bw## 2024-05-01 16:57:14.158411 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB136.bw## 2024-05-01 16:57:14.227843 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB145.bw## 2024-05-01 16:57:14.296553 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB153.bw## 2024-05-01 16:57:14.365351 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB159.bw## 2024-05-01 16:57:14.442555 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB178.bw## 2024-05-01 16:57:14.513484 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB92.bw## 2024-05-01 16:57:14.586151 railMatrix: processing file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/AMY/HSB97.bw## user system elapsed
## 3.903 0.024 3.928When you take into account the time needed to load the data (fullCoverage()) and then creating the matrix (regionMatrix()), railMatrix() is faster and less memory intensive.
The objects are not identical due to small rounding errors, but it’s nothing to worry about.
## Overall not identical due to small rounding errors
identical(regionMat, regionMat.rail)## [1] FALSE## Actual regions are the same
identical(ranges(regionMat$chr21$regions), ranges(regionMat.rail$chr21$regions))## [1] TRUE## When you round, the small differences go away
identical(
round(regionMat$chr21$regions$value, 4),
round(regionMat.rail$chr21$regions$value, 4)
)## [1] TRUEidentical(
round(regionMat$chr21$regions$area, 4),
round(regionMat.rail$chr21$regions$area, 4)
)## [1] TRUE7 Single base-level F-statistics analysis
One form of base-level differential expression analysis implemented in derfinder is to calculate F-statistics for every base and use them to define candidate differentially expressed regions. This type of analysis is further explained in this section.
7.1 Models
Once we have the base-level coverage data for all 12 samples, we can construct the models. In this case, we want to find differences between fetal and adult samples while adjusting for gender and a proxy of the library size.
We can use sampleDepth() and it’s helper function collapseFullCoverage() to get a proxy of the library size. Note that you would normally use the unfiltered data from all the chromosomes in this step and not just one.
## Get some idea of the library sizes
sampleDepths <- sampleDepth(collapseFullCoverage(fullCov), 1)## 2024-05-01 16:57:14.782822 sampleDepth: Calculating sample quantiles## 2024-05-01 16:57:15.026875 sampleDepth: Calculating sample adjustmentssampleDepths## HSB113.100% HSB123.100% HSB126.100% HSB130.100% HSB135.100% HSB136.100% HSB145.100% HSB153.100% HSB159.100% HSB178.100%
## 19.82106 19.40505 19.53045 19.52017 20.33392 19.97758 19.49827 19.41285 19.24186 19.44252
## HSB92.100% HSB97.100%
## 19.55904 19.47733sampleDepth() is similar to calcNormFactors() from metagenomeSeq with some code underneath tailored for the type of data we are using. collapseFullCoverage() is only needed to deal with the size of the data.
We can then define the nested models we want to use using makeModels(). This is a helper function that assumes that you will always adjust for the library size. You then need to define the variable to test, in this case we are comparing fetal vs adult samples. Optionally, you can adjust for other sample covariates, such as the gender in this case.
## Define models
models <- makeModels(sampleDepths,
testvars = pheno$group,
adjustvars = pheno[, c("gender")]
)
## Explore the models
lapply(models, head)## $mod
## (Intercept) testvarsadult sampleDepths adjustVar1M
## 1 1 0 19.82106 0
## 2 1 0 19.40505 1
## 3 1 0 19.53045 1
## 4 1 0 19.52017 1
## 5 1 0 20.33392 0
## 6 1 0 19.97758 0
##
## $mod0
## (Intercept) sampleDepths adjustVar1M
## 1 1 19.82106 0
## 2 1 19.40505 1
## 3 1 19.53045 1
## 4 1 19.52017 1
## 5 1 20.33392 0
## 6 1 19.97758 0Note how the null model (mod0) is nested in the alternative model (mod). Use the same models for all your chromosomes unless you have a specific reason to use chromosome-specific models. Note that derfinder is very flexible and works with any type of nested model.
7.2 Find candidate DERs
Next, we can find candidate differentially expressed regions (DERs) using as input the segments of the genome where at least one sample has coverage greater than 2. That is, the filtered coverage version we created previously.
The main function in derfinder for this type of analysis is analyzeChr(). It works at a chromosome level and runs behinds the scenes several other derfinder functions. To use it, you have to provide the models, the grouping information, how to calculate the F-statistic cutoff and most importantly, the number of permutations.
By default analyzeChr() will use a theoretical cutoff. In this example, we use the cutoff that would correspond to a p-value of 0.05. To assign p-values to the candidate DERs, derfinder permutes the rows of the model matrices, re-calculates the F-statistics and identifies null regions. Then it compares the area of the observed regions versus the areas from the null regions to assign an empirical p-value.
In this example we will use twenty permutations, although in a real case scenario you might consider a larger number of permutations.
In real scenario, you might consider saving the results from all the chromosomes in a given directory. Here we will use analysisResults. For each chromosome you analyze, a new directory with the chromosome-specific data will be created. So in this case, we will have analysisResults/chr21.
## Create a analysis directory
dir.create("analysisResults")
originalWd <- getwd()
setwd(file.path(originalWd, "analysisResults"))
## Perform differential expression analysis
system.time(results <- analyzeChr(
chr = "chr21", filteredCov$chr21, models,
groupInfo = pheno$group, writeOutput = TRUE, cutoffFstat = 5e-02,
nPermute = 20, seeds = 20140923 + seq_len(20), returnOutput = TRUE
))## 2024-05-01 16:57:16.276731 analyzeChr: Pre-processing the coverage data## 2024-05-01 16:57:18.856584 analyzeChr: Calculating statistics## 2024-05-01 16:57:18.85989 calculateStats: calculating the F-statistics## 2024-05-01 16:57:19.158568 analyzeChr: Calculating pvalues## 2024-05-01 16:57:19.158968 analyzeChr: Using the following theoretical cutoff for the F-statistics 5.31765507157871## 2024-05-01 16:57:19.160242 calculatePvalues: identifying data segments## 2024-05-01 16:57:19.164063 findRegions: segmenting information## 2024-05-01 16:57:19.197655 findRegions: identifying candidate regions## 2024-05-01 16:57:19.238041 findRegions: identifying region clusters## 2024-05-01 16:57:19.411258 calculatePvalues: calculating F-statistics for permutation 1 and seed 20140924## 2024-05-01 16:57:19.628587 findRegions: segmenting information## 2024-05-01 16:57:19.662213 findRegions: identifying candidate regions## 2024-05-01 16:57:19.707188 calculatePvalues: calculating F-statistics for permutation 2 and seed 20140925## 2024-05-01 16:57:19.927014 findRegions: segmenting information## 2024-05-01 16:57:19.960956 findRegions: identifying candidate regions## 2024-05-01 16:57:20.006287 calculatePvalues: calculating F-statistics for permutation 3 and seed 20140926## 2024-05-01 16:57:20.230045 findRegions: segmenting information## 2024-05-01 16:57:20.264309 findRegions: identifying candidate regions## 2024-05-01 16:57:20.311365 calculatePvalues: calculating F-statistics for permutation 4 and seed 20140927## 2024-05-01 16:57:20.536042 findRegions: segmenting information## 2024-05-01 16:57:20.570393 findRegions: identifying candidate regions## 2024-05-01 16:57:20.618038 calculatePvalues: calculating F-statistics for permutation 5 and seed 20140928## 2024-05-01 16:57:20.837637 findRegions: segmenting information## 2024-05-01 16:57:20.871413 findRegions: identifying candidate regions## 2024-05-01 16:57:20.916892 calculatePvalues: calculating F-statistics for permutation 6 and seed 20140929## 2024-05-01 16:57:21.130592 findRegions: segmenting information## 2024-05-01 16:57:21.16429 findRegions: identifying candidate regions## 2024-05-01 16:57:21.209062 calculatePvalues: calculating F-statistics for permutation 7 and seed 20140930## 2024-05-01 16:57:21.424117 findRegions: segmenting information## 2024-05-01 16:57:21.457564 findRegions: identifying candidate regions## 2024-05-01 16:57:21.50309 calculatePvalues: calculating F-statistics for permutation 8 and seed 20140931## 2024-05-01 16:57:21.71891 findRegions: segmenting information## 2024-05-01 16:57:21.752822 findRegions: identifying candidate regions## 2024-05-01 16:57:21.798878 calculatePvalues: calculating F-statistics for permutation 9 and seed 20140932## 2024-05-01 16:57:22.013441 findRegions: segmenting information## 2024-05-01 16:57:22.047205 findRegions: identifying candidate regions## 2024-05-01 16:57:22.093126 calculatePvalues: calculating F-statistics for permutation 10 and seed 20140933## 2024-05-01 16:57:22.308704 findRegions: segmenting information## 2024-05-01 16:57:22.342221 findRegions: identifying candidate regions## 2024-05-01 16:57:22.3869 calculatePvalues: calculating F-statistics for permutation 11 and seed 20140934## 2024-05-01 16:57:23.608615 findRegions: segmenting information## 2024-05-01 16:57:23.642143 findRegions: identifying candidate regions## 2024-05-01 16:57:23.687532 calculatePvalues: calculating F-statistics for permutation 12 and seed 20140935## 2024-05-01 16:57:23.901225 findRegions: segmenting information## 2024-05-01 16:57:23.937488 findRegions: identifying candidate regions## 2024-05-01 16:57:23.983895 calculatePvalues: calculating F-statistics for permutation 13 and seed 20140936## 2024-05-01 16:57:24.202515 findRegions: segmenting information## 2024-05-01 16:57:24.236324 findRegions: identifying candidate regions## 2024-05-01 16:57:24.282606 calculatePvalues: calculating F-statistics for permutation 14 and seed 20140937## 2024-05-01 16:57:24.500204 findRegions: segmenting information## 2024-05-01 16:57:24.534248 findRegions: identifying candidate regions## 2024-05-01 16:57:24.580566 calculatePvalues: calculating F-statistics for permutation 15 and seed 20140938## 2024-05-01 16:57:24.801562 findRegions: segmenting information## 2024-05-01 16:57:24.835259 findRegions: identifying candidate regions## 2024-05-01 16:57:24.880691 calculatePvalues: calculating F-statistics for permutation 16 and seed 20140939## 2024-05-01 16:57:25.134828 findRegions: segmenting information## 2024-05-01 16:57:25.169031 findRegions: identifying candidate regions## 2024-05-01 16:57:25.217016 calculatePvalues: calculating F-statistics for permutation 17 and seed 20140940## 2024-05-01 16:57:25.403301 findRegions: segmenting information## 2024-05-01 16:57:25.437435 findRegions: identifying candidate regions## 2024-05-01 16:57:25.484983 calculatePvalues: calculating F-statistics for permutation 18 and seed 20140941## 2024-05-01 16:57:25.70591 findRegions: segmenting information## 2024-05-01 16:57:25.73962 findRegions: identifying candidate regions## 2024-05-01 16:57:25.784677 calculatePvalues: calculating F-statistics for permutation 19 and seed 20140942## 2024-05-01 16:57:26.007187 findRegions: segmenting information## 2024-05-01 16:57:26.040927 findRegions: identifying candidate regions## 2024-05-01 16:57:26.08738 calculatePvalues: calculating F-statistics for permutation 20 and seed 20140943## 2024-05-01 16:57:26.338484 findRegions: segmenting information## 2024-05-01 16:57:26.372571 findRegions: identifying candidate regions## 2024-05-01 16:57:26.434565 calculatePvalues: calculating the p-values## 2024-05-01 16:57:26.519233 analyzeChr: Annotating regions## No annotationPackage supplied. Trying org.Hs.eg.db.## Getting TSS and TSE.## Getting CSS and CSE.## Getting exons.## Annotating genes.## .....## user system elapsed
## 73.647 0.464 74.114To speed up analyzeChr(), you might need to use several cores via the mc.cores argument. If memory is limiting, you might want to use a smaller chunksize (default is 5 million). Note that if you use too many cores, you might hit the input/output ceiling of your data network and/or hard drives speed.
Before running with a large number of permutations we recommend exploring how long each permutation cycle takes using a single permutation.
Note that analyzing each chromosome with a large number of permutations and a rich data set can take several hours, so we recommend running one job running analyzeChr() per chromosome, and then merging the results via mergeResults(). This process is further described in the advanced derfinder vignette.
7.3 Explore results
When using returnOutput = TRUE, analyzeChr() will return a list with the results to explore interactively. However, by default it writes the results to disk (one .Rdata file per result).
The following code explores the results.
## Explore
names(results)## [1] "timeinfo" "optionsStats" "coveragePrep" "fstats" "regions" "annotation"7.3.1 optionStats
optionStats stores the main options used in the analyzeChr() call including the models used, the type of cutoff, number of permutations, seeds for the permutations. All this information can be useful to reproduce the analysis.
## Explore optionsStats
names(results$optionsStats)## [1] "models" "cutoffPre" "scalefac" "chunksize" "cutoffFstat" "cutoffType"
## [7] "nPermute" "seeds" "groupInfo" "lowMemDir" "analyzeCall" "cutoffFstatUsed"
## [13] "smooth" "smoothFunction" "weights" "returnOutput"## Call used
results$optionsStats$analyzeCall## analyzeChr(chr = "chr21", coverageInfo = filteredCov$chr21, models = models,
## cutoffFstat = 0.05, nPermute = 20, seeds = 20140923 + seq_len(20),
## groupInfo = pheno$group, writeOutput = TRUE, returnOutput = TRUE)7.3.2 coveragePrep
coveragePrep has the result from the preprocessCoverage() step. This includes the genomic position index, the mean coverage (after scaling and the log2 transformation) for all the samples, and the group mean coverages. By default, the chunks are written to disk in optionsStats$lowMemDir (chr21/chunksDir in this example) to help reduce the required memory resources. Otherwise it is stored in coveragePrep$coverageProcessed.
## Explore coveragePrep
names(results$coveragePrep)## [1] "coverageProcessed" "mclapplyIndex" "position" "meanCoverage" "groupMeans"## Group means
results$coveragePrep$groupMeans## $fetal
## numeric-Rle of length 130356 with 116452 runs
## Lengths: 1 1 1 1 2 1 ... 2 1 1 1 1
## Values : 0.401667 0.428333 0.435000 0.461667 0.471667 0.478333 ... 1.39500 1.38167 1.34000 1.33333 1.24833
##
## $adult
## numeric-Rle of length 130356 with 119226 runs
## Lengths: 1 1 1 1 1 1 ... 1 2 1 1 1
## Values : 0.406667 0.413333 0.430000 0.448333 0.485000 0.510000 ... 1.97500 1.91833 1.77667 1.73833 1.626677.3.3 fstats
The F-statistics are then stored in fstats. These are calculated using calculateStats().
## Explore optionsStats
results$fstats## numeric-Rle of length 130356 with 126807 runs
## Lengths: 1 1 1 1 1 ... 1 1 1 1
## Values : 0.01922610 0.02996937 0.02066332 0.02249996 0.01984328 ... 3.031370 2.653428 2.507611 2.324638## Note that the length matches the number of bases used
identical(length(results$fstats), sum(results$coveragePrep$position))## [1] TRUE7.3.4 regions
The candidate DERs and summary results from the permutations is then stored in regions. This is the output from calculatePvalues() which uses several underneath other functions including calculateStats() and findRegions().
## Explore regions
names(results$regions)## [1] "regions" "nullStats" "nullWidths" "nullPermutation"For the null regions, the summary information is composed of the mean F-statistic for the null regions (regions$nullStats), the width of the null regions (regions$nullWidths), and the permutation number under which they were identified (regions$nullPermutation).
## Permutation summary information
results$regions[2:4]## $nullStats
## numeric-Rle of length 13994 with 13994 runs
## Lengths: 1 1 1 1 1 1 ... 1 1 1 1 1
## Values : 5.43461 5.71738 6.37821 6.33171 5.48965 7.05049 ... 6.12148 5.36584 5.35554 5.36614 5.62516
##
## $nullWidths
## integer-Rle of length 13994 with 12365 runs
## Lengths: 2 1 1 1 1 1 3 1 2 1 1 1 1 ... 1 1 1 1 1 1 1 1 2 1 1 4 1
## Values : 1 24 7 1 32 2 1 11 1 7 2 14 1 ... 2 14 1 10 15 1 3 45 4 28 6 1 2
##
## $nullPermutation
## integer-Rle of length 13994 with 20 runs
## Lengths: 246 350 574 554 396 462 482 1548 1522 462 1104 2076 70 320 746 114 1460 428 802 278
## Values : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20The most important part of the output is the GRanges object with the candidate DERs shown below.
## Candidate DERs
results$regions$regions## GRanges object with 591 ranges and 14 metadata columns:
## seqnames ranges strand | value area indexStart indexEnd cluster clusterL meanCoverage
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <integer> <integer> <Rle> <Rle> <numeric>
## up chr21 47610386-47610682 * | 11.10304 3297.60 122158 122454 138 933 1.597952
## up chr21 40196145-40196444 * | 10.06142 3018.43 76110 76409 71 1323 1.303508
## up chr21 27253616-27253948 * | 8.43488 2808.82 22019 22351 28 407 33.657858
## up chr21 22115534-22115894 * | 7.23645 2612.36 12274 12634 9 694 0.964464
## up chr21 22914853-22915064 * | 9.78066 2073.50 17318 17529 21 217 2.838978
## .. ... ... ... . ... ... ... ... ... ... ...
## up chr21 35889784 * | 5.31952 5.31952 60088 60088 51 742 2.75417
## up chr21 47610093 * | 5.31912 5.31912 121865 121865 138 933 1.45583
## up chr21 16333728 * | 5.31881 5.31881 5048 5048 1 9 1.19500
## up chr21 34001896 * | 5.31871 5.31871 32577 32577 38 1428 1.71250
## up chr21 34809571 * | 5.31801 5.31801 43694 43694 46 149 2.95000
## meanfetal meanadult log2FoldChangeadultvsfetal pvalues significant qvalues significantQval
## <numeric> <numeric> <numeric> <numeric> <factor> <numeric> <factor>
## up 0.82289 2.373013 1.527949 0.00278671 TRUE 0.738407 FALSE
## up 2.02532 0.581694 -1.799818 0.00378707 TRUE 0.738407 FALSE
## up 42.46704 24.848674 -0.773175 0.00464452 TRUE 0.738407 FALSE
## up 1.71906 0.209871 -3.034045 0.00535906 TRUE 0.738407 FALSE
## up 4.23593 1.442028 -1.554578 0.00793140 TRUE 0.738407 FALSE
## .. ... ... ... ... ... ... ...
## up 3.36000 2.14833 -0.6452433 0.997856 FALSE 0.974463 FALSE
## up 0.77500 2.13667 1.4630937 0.998285 FALSE 0.974463 FALSE
## up 1.23167 1.15833 -0.0885613 0.998714 FALSE 0.974463 FALSE
## up 2.33333 1.09167 -1.0958600 0.998714 FALSE 0.974463 FALSE
## up 2.87833 3.02167 0.0701108 0.999571 FALSE 0.974463 FALSE
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThe metadata columns are:
- value is the mean F-statistics for the candidate DER.
- area is the sum of the F-statistics for the candidate DER.
- indexStart Relates the genomic start coordinate with the filtered genomic index start coordinate.
- indexEnd Similarly as above but for the end coordinates.
- cluster The cluster id to which this candidate DER belongs to.
- clusterL The length of the cluster to which this candidate DER belongs to.
- meanCoverage The base level mean coverage for the candidate DER.
- meanfetal In this example, the mean coverage for the fetal samples.
- meanadult In this example, the mean coverage for the adult samples.
- log2FoldChangeadultvsfetal In this example, the log2 fold change between adult vs fetal samples.
- pvalues The p-value for the candidate DER.
- significant By default, whether the p-value is less than 0.05 or not.
- qvalues The q-value for the candidate DER calculated with qvalue.
- significantQval By default, whether the q-value is less than 0.10 or not.
Note that for this type of analysis you might want to try a few coverage cutoffs and/or F-statistic cutoffs. One quick way to evaluate the results is to compare the width of the regions.
## Width of potential DERs
summary(width(results$regions$regions))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 1.00 4.00 17.98 17.00 361.00sum(width(results$regions$regions) > 50)## [1] 68## Width of candidate DERs
sig <- as.logical(results$regions$regions$significant)
summary(width(results$regions$regions[sig]))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 65.0 81.5 97.0 127.8 122.0 361.0sum(width(results$regions$regions[sig]) > 50)## [1] 357.3.5 Nearest annotation
analyzeChr() will find the nearest annotation feature using matchGenes() from bumphunter (version >= 1.7.3). This information is useful considering that the candidate DERs were identified without relying on annotation. Yet at the end, we are interested to check if they are inside a known exon, upstream a gene, etc.
## Nearest annotation
head(results$annotation)## name
## 1 LSS
## 2 ETS2
## 3 APP
## 4 LINC00320
## 5 NCAM2
## 6 LINC00320
## annotation
## 1 NM_001001438 NM_001145436 NM_001145437 NM_002340 NP_001001438 NP_001138908 NP_001138909 NP_002331
## 2 NM_001256295 NM_005239 NP_001243224 NP_005230 XM_005260935 XM_017028290 XM_054324370 XP_005260992 XP_016883779 XP_054180345
## 3 NM_000484 NM_001136016 NM_001136129 NM_001136130 NM_001136131 NM_001204301 NM_001204302 NM_001204303 NM_001385253 NM_201413 NM_201414 NP_000475 NP_001129488 NP_001129601 NP_001129602 NP_001129603 NP_001191230 NP_001191231 NP_001191232 NP_001372182 NP_958816 NP_958817
## 4 NR_024090 NR_109786 NR_109787 NR_109788
## 5 NM_001352591 NM_001352592 NM_001352593 NM_001352594 NM_001352595 NM_001352596 NM_001352597 NM_004540 NP_001339520 NP_001339521 NP_001339522 NP_001339523 NP_001339524 NP_001339525 NP_001339526 NP_004531 XM_011529575 XM_011529580 XM_011529581 XM_011529582 XM_011529585 XM_017028356 XM_024452081 XM_047440784 XM_047440785 XM_054324514 XM_054324515 XM_054324516 XM_054324517 XM_054324518 XM_054324519 XM_054324520 XM_054324521 XM_054324522 XM_054324523 XP_011527877 XP_011527882 XP_011527883 XP_011527884 XP_011527887 XP_016883845 XP_024307849 XP_047296740 XP_047296741 XP_054180489 XP_054180490 XP_054180491 XP_054180492 XP_054180493 XP_054180494 XP_054180495 XP_054180496 XP_054180497 XP_054180498
## 6 NR_024090 NR_109786 NR_109787 NR_109788
## description region distance subregion insideDistance exonnumber nexons UTR strand
## 1 inside exon inside 38056 inside exon 0 22 22 3'UTR -
## 2 inside exon inside 18390 inside exon 0 10 10 3'UTR +
## 3 inside exon inside 289498 inside exon 0 16 16 3'UTR -
## 4 inside exon inside 59532 inside exon 0 7 7 inside transcription region -
## 5 downstream downstream 544220 <NA> NA NA 15 <NA> +
## 6 inside exon inside 59315 inside exon 0 7 7 inside transcription region -
## geneL codingL Geneid subjectHits
## 1 39700 NA 4047 34356
## 2 19123 NA 2114 17340
## 3 290585 NA 351 31161
## 4 60513 NA 387486 32699
## 5 541884 NA 4685 37133
## 6 60513 NA 387486 32699For more details on the output please check the bumphunter package.
Check the section about non-human data (specifically, using annotation different from hg19) on the advanced vignette.
7.3.6 Time spent
The final piece is the wallclock time spent during each of the steps in analyzeChr(). You can then make a plot with this information as shown in Figure 1.
## Time spent
results$timeinfo## init setup prepData savePrep
## "2024-05-01 16:57:16 EDT" "2024-05-01 16:57:16 EDT" "2024-05-01 16:57:18 EDT" "2024-05-01 16:57:18 EDT"
## calculateStats saveStats saveStatsOpts calculatePvalues
## "2024-05-01 16:57:19 EDT" "2024-05-01 16:57:19 EDT" "2024-05-01 16:57:19 EDT" "2024-05-01 16:57:26 EDT"
## saveRegs annotate saveAnno
## "2024-05-01 16:57:26 EDT" "2024-05-01 16:58:30 EDT" "2024-05-01 16:58:30 EDT"## Use this information to make a plot
timed <- diff(results$timeinfo)
timed.df <- data.frame(Seconds = as.numeric(timed), Step = factor(names(timed),
levels = rev(names(timed))
))
library("ggplot2")
ggplot(timed.df, aes(y = Step, x = Seconds)) +
geom_point()
Figure 1: Seconds used to run each step in analyzeChr()
7.4 Merge results
Once you have analyzed each chromosome using analyzeChr(), you can use mergeResults() to merge the results. This function does not return an object in R but instead creates several Rdata files with the main results from the different chromosomes.
## Go back to the original directory
setwd(originalWd)
## Merge results from several chromosomes. In this case we only have one.
mergeResults(
chrs = "chr21", prefix = "analysisResults",
genomicState = genomicState$fullGenome,
optionsStats = results$optionsStats
)## 2024-05-01 16:58:31.537347 mergeResults: Saving options used## 2024-05-01 16:58:31.539426 Loading chromosome chr21## 2024-05-01 16:58:31.61341 mergeResults: calculating FWER## 2024-05-01 16:58:31.65338 mergeResults: Saving fullNullSummary## 2024-05-01 16:58:31.664366 mergeResults: Re-calculating the p-values## 2024-05-01 16:58:31.737547 mergeResults: Saving fullRegions## 2024-05-01 16:58:31.749246 mergeResults: assigning genomic states## 2024-05-01 16:58:31.846498 annotateRegions: counting## 2024-05-01 16:58:31.93175 annotateRegions: annotating## 2024-05-01 16:58:31.96143 mergeResults: Saving fullAnnotatedRegions## 2024-05-01 16:58:31.963387 mergeResults: Saving fullFstats## 2024-05-01 16:58:32.025349 mergeResults: Saving fullTime## Files created by mergeResults()
dir("analysisResults", pattern = ".Rdata")## [1] "fullAnnotatedRegions.Rdata" "fullFstats.Rdata" "fullNullSummary.Rdata" "fullRegions.Rdata"
## [5] "fullTime.Rdata" "optionsMerge.Rdata"- fullFstats.Rdata contains a list with one element per chromosome. Per chromosome it has the F-statistics.
- fullNullSummary.Rdata is a list with the summary information from the null regions stored for each chromosome.
- fullTime.Rdata has the timing information for each chromosome as a list.
7.4.1 optionsMerge
For reproducibility purposes, the options used the merge the results are stored in optionsMerge.
## Options used to merge
load(file.path("analysisResults", "optionsMerge.Rdata"))
## Contents
names(optionsMerge)## [1] "chrs" "significantCut" "minoverlap" "mergeCall" "cutoffFstatUsed" "optionsStats"## Merge call
optionsMerge$mergeCall## mergeResults(chrs = "chr21", prefix = "analysisResults", genomicState = genomicState$fullGenome,
## optionsStats = results$optionsStats)7.4.2 fullRegions
The main result from mergeResults() is in fullRegions. This is a GRanges object with the candidate DERs from all the chromosomes. It also includes the nearest annotation metadata as well as FWER adjusted p-values (fwer) and whether the FWER adjusted p-value is less than 0.05 (significantFWER).
## Load all the regions
load(file.path("analysisResults", "fullRegions.Rdata"))
## Metadata columns
names(mcols(fullRegions))## [1] "value" "area" "indexStart" "indexEnd"
## [5] "cluster" "clusterL" "meanCoverage" "meanfetal"
## [9] "meanadult" "log2FoldChangeadultvsfetal" "pvalues" "significant"
## [13] "qvalues" "significantQval" "name" "annotation"
## [17] "description" "region" "distance" "subregion"
## [21] "insideDistance" "exonnumber" "nexons" "UTR"
## [25] "annoStrand" "geneL" "codingL" "Geneid"
## [29] "subjectHits" "fwer" "significantFWER"Note that analyzeChr() only has the information for a given chromosome at a time, so mergeResults() re-calculates the p-values and q-values using the information from all the chromosomes.
7.4.3 fullAnnotatedRegions
In preparation for visually exploring the results, mergeResults() will run annotateRegions() which counts how many known exons, introns and intergenic segments each candidate DER overlaps (by default with a minimum overlap of 20bp). annotateRegions() uses a summarized version of the genome annotation created with makeGenomicState(). For this example, we can use the data included in derfinder which is the summarized annotation of hg19 for chromosome 21.
## Load annotateRegions() output
load(file.path("analysisResults", "fullAnnotatedRegions.Rdata"))
## Information stored
names(fullAnnotatedRegions)## [1] "countTable" "annotationList"## Take a peak
lapply(fullAnnotatedRegions, head)## $countTable
## exon intergenic intron
## 1 1 0 0
## 2 1 0 0
## 3 1 0 0
## 4 1 0 0
## 5 0 1 0
## 6 1 0 0
##
## $annotationList
## GRangesList object of length 6:
## $`1`
## GRanges object with 1 range and 4 metadata columns:
## seqnames ranges strand | theRegion tx_id tx_name
## <Rle> <IRanges> <Rle> | <character> <IntegerList> <CharacterList>
## 4871 chr21 47609038-47611149 - | exon 73448,73449,73450,... uc002zij.3,uc002zik.2,uc002zil.2,...
## gene
## <IntegerList>
## 4871 170
## -------
## seqinfo: 1 sequence from hg19 genome
##
## $`2`
## GRanges object with 1 range and 4 metadata columns:
## seqnames ranges strand | theRegion tx_id tx_name gene
## <Rle> <IRanges> <Rle> | <character> <IntegerList> <CharacterList> <IntegerList>
## 1189 chr21 40194598-40196878 + | exon 72757,72758 uc002yxf.3,uc002yxg.4 77
## -------
## seqinfo: 1 sequence from hg19 genome
##
## $`3`
## GRanges object with 1 range and 4 metadata columns:
## seqnames ranges strand | theRegion tx_id tx_name
## <Rle> <IRanges> <Rle> | <character> <IntegerList> <CharacterList>
## 2965 chr21 27252861-27254082 - | exon 73066,73067,73068,... uc002ylz.3,uc002yma.3,uc002ymb.3,...
## gene
## <IntegerList>
## 2965 139
## -------
## seqinfo: 1 sequence from hg19 genome
##
## ...
## <3 more elements>8 ChIP-seq differential binding
As of version 1.5.27 derfinder has parameters that allow smoothing of the single base-level F-statistics before determining DERs. This allows finding differentially bounded regions (peaks) using ChIP-seq data. In general, ChIP-seq studies are smaller than RNA-seq studies which means that the single base-level F-statistics approach is well suited for differential binding analysis.
To smooth the F-statistics use smooth = TRUE in analyzeChr(). The default smoothing function is bumphunter::locfitByCluster() and all its parameters can be passed specified in the call to analyzeChr(). In particular, the minNum and bpSpan arguments are important. We recommend setting minNum to the minimum read length and bpSpan to the average peak length expected in the ChIP-seq data being analyzed. Smoothing the F-statistics will take longer but not use significantly more memory than the default behavior. So take this into account when choosing the number of permutations to run.
9 Visually explore results
Optionally, we can use the addon package derfinderPlot to visually explore the results.
To make the region level plots, we will need to extract the region level coverage data. We can do so using getRegionCoverage() as shown below.
## Find overlaps between regions and summarized genomic annotation
annoRegs <- annotateRegions(fullRegions, genomicState$fullGenome)## 2024-05-01 16:58:32.454606 annotateRegions: counting## 2024-05-01 16:58:32.548792 annotateRegions: annotating## Indeed, the result is the same because we only used chr21
identical(annoRegs, fullAnnotatedRegions)## [1] FALSE## Get the region coverage
regionCov <- getRegionCoverage(fullCov, fullRegions)## 2024-05-01 16:58:32.667939 getRegionCoverage: processing chr21## 2024-05-01 16:58:32.754682 getRegionCoverage: done processing chr21## Explore the result
head(regionCov[[1]])## HSB113 HSB123 HSB126 HSB130 HSB135 HSB136 HSB145 HSB153 HSB159 HSB178 HSB92 HSB97
## 1 0.68 0.44 0.48 0.36 0.19 2.34 1.29 1.77 2.21 2.69 1.89 3.57
## 2 0.60 0.44 0.48 0.36 0.19 2.30 1.29 1.77 2.21 2.64 1.86 3.60
## 3 0.60 0.40 0.48 0.32 0.19 2.39 1.37 1.81 2.31 2.69 1.89 3.60
## 4 0.64 0.40 0.48 0.32 0.19 2.61 1.42 1.89 2.36 2.88 1.89 3.60
## 5 0.64 0.40 0.48 0.36 0.19 2.65 1.42 1.93 2.36 2.88 1.96 3.70
## 6 0.60 0.44 0.48 0.39 0.23 2.65 1.59 1.93 2.36 2.83 1.93 3.70With this, we are all set to visually explore the results.
library("derfinderPlot")
## Overview of the candidate DERs in the genome
plotOverview(
regions = fullRegions, annotation = results$annotation,
type = "fwer"
)
suppressPackageStartupMessages(library("TxDb.Hsapiens.UCSC.hg19.knownGene"))
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
## Base-levle coverage plots for the first 10 regions
plotRegionCoverage(
regions = fullRegions, regionCoverage = regionCov,
groupInfo = pheno$group, nearestAnnotation = results$annotation,
annotatedRegions = annoRegs, whichRegions = 1:10, txdb = txdb, scalefac = 1,
ask = FALSE
)
## Cluster plot for the first region
plotCluster(
idx = 1, regions = fullRegions, annotation = results$annotation,
coverageInfo = fullCov$chr21, txdb = txdb, groupInfo = pheno$group,
titleUse = "fwer"
)The quick start to using derfinder has example plots for the expressed regions-level approach. The vignette for derfinderPlot has even more examples.
10 Interactive HTML reports
We have also developed an addon package called regionReport available via Bioconductor.
The function derfinderReport() in regionReport basically takes advantage of the results from mergeResults() and plotting functions available in derfinderPlot as well as other neat features from knitrBootstrap. It then generates a customized report for single-base level F-statistics DER finding analyses.
For results from regionMatrix() or railMatrix() use renderReport() from regionReport. In both cases, the resulting HTML report promotes reproducibility of the analysis and allows you to explore in more detail the results through some diagnostic plots.
We think that these reports are very important when you are exploring the resulting DERs after changing a key parameter in analyzeChr(), regionMatrix() or railMatrix().
Check out the vignette for regionReport for example reports generated with it.
11 Miscellaneous features
In this section we go over some other features of derfinder which can be useful for performing feature-counts based analyses, exploring the results, or exporting data.
11.1 Feature level analysis
Similar to the expressed region-level analysis, you might be interested in performing a feature-level analysis. More specifically, this means getting a count matrix at the exon-level (or gene-level). coverageToExon() allows you to get such a matrix by taking advantage of the summarized annotation produced by makeGenomicState().
In this example, we use the genomic state included in the package which has the information for chr21 TxDb.Hsapiens.UCSC.hg19.knownGene annotation.
## Get the exon-level matrix
system.time(exonCov <- coverageToExon(fullCov, genomicState$fullGenome, L = 76))## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA## 2024-05-01 16:58:38.918719 coverageToExon: processing chromosome chr21## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA## 2024-05-01 16:58:41.732767 coverageToExon: processing chromosome chr21## user system elapsed
## 7.071 0.768 7.839## Dimensions of the matrix
dim(exonCov)## [1] 2658 12## Explore a little bit
tail(exonCov)## HSB113 HSB123 HSB126 HSB130 HSB135 HSB136 HSB145 HSB153 HSB159
## 4983 0.1173684 0.1382895 0.0000000 0.0000000 0.07894737 0.03947368 5.263158e-03 0.1052632 0.04934211
## 4985 4.6542105 1.4557895 1.6034210 0.7798684 1.29592103 0.72986842 9.964474e-01 10.4906578 4.39013167
## 4986 7.1510526 291.0205261 252.4892106 152.1905258 439.69500020 263.63486853 2.345414e+02 4.5310526 10.64368415
## 4988 0.0000000 0.7063158 0.7223684 0.2639474 0.48657895 0.37657895 6.156579e-01 0.0000000 0.00000000
## 4990 2.3064474 64.9423686 69.6584212 35.5769737 101.76394746 76.96736838 5.978842e+01 1.2284210 2.67486840
## 4992 0.1652632 8.1834211 9.7538158 2.4193421 10.08618420 14.41013157 5.325658e+00 0.2402632 0.73039474
## HSB178 HSB92 HSB97
## 4983 0.1289474 0.03986842 0.44605264
## 4985 7.3119736 1.65184211 10.87723678
## 4986 4.9807895 11.40447366 6.23315790
## 4988 0.0000000 0.05644737 0.00000000
## 4990 0.9542105 3.98013159 1.96131579
## 4992 0.2601316 0.66907895 0.07473684With this matrix, rounded if necessary, you can proceed to use packages such as limma, DESeq2, edgeR among others.
11.2 Compare results visually
We can certainly make region-level plots using plotRegionCoverage() or cluster plots using plotCluster() or overview plots using plotOveview(), all from derfinderPlot.
First we need to get the relevant annotation information.
## Annotate regions as exonic, intronic or intergenic
system.time(annoGenome <- annotateRegions(
regionMat$chr21$regions,
genomicState$fullGenome
))## 2024-05-01 16:58:43.363317 annotateRegions: counting## 2024-05-01 16:58:43.44168 annotateRegions: annotating## user system elapsed
## 0.192 0.004 0.196## Note that the genomicState object included in derfinder only has information
## for chr21 (hg19).
## Identify closest genes to regions
suppressPackageStartupMessages(library("bumphunter"))
suppressPackageStartupMessages(library("TxDb.Hsapiens.UCSC.hg19.knownGene"))
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
genes <- annotateTranscripts(txdb)## No annotationPackage supplied. Trying org.Hs.eg.db.## Getting TSS and TSE.## Getting CSS and CSE.## Getting exons.## Annotating genes.system.time(annoNear <- matchGenes(regionMat$chr21$regions, genes))## user system elapsed
## 2.703 0.020 2.723Now we can proceed to use derfinderPlot to make the region-level plots for the top 100 regions.
## Identify the top regions by highest total coverage
top <- order(regionMat$chr21$regions$area, decreasing = TRUE)[1:100]
## Base-level plots for the top 100 regions with transcript information
library("derfinderPlot")
plotRegionCoverage(regionMat$chr21$regions,
regionCoverage = regionMat$chr21$bpCoverage,
groupInfo = pheno$group, nearestAnnotation = annoNear,
annotatedRegions = annoGenome, whichRegions = top, scalefac = 1,
txdb = txdb, ask = FALSE
)However, we can alternatively use epivizr to view the candidate DERs and the region matrix results in a genome browser.
## Load epivizr, it's available from Bioconductor
library("epivizr")
## Load data to your browser
mgr <- startEpiviz()
ders_dev <- mgr$addDevice(
fullRegions[as.logical(fullRegions$significantFWER)], "Candidate DERs"
)
ders_potential_dev <- mgr$addDevice(
fullRegions[!as.logical(fullRegions$significantFWER)], "Potential DERs"
)
regs_dev <- mgr$addDevice(regionMat$chr21$regions, "Region Matrix")
## Go to a place you like in the genome
mgr$navigate(
"chr21", start(regionMat$chr21$regions[top[1]]) - 100,
end(regionMat$chr21$regions[top[1]]) + 100
)
## Stop the navigation
mgr$stopServer()11.3 Export coverage to BigWig files
derfinder also includes createBw() with related functions createBwSample() and coerceGR() to export the output of fullCoverage() to BigWig files. These functions can be useful in the case where you start with BAM files and later on want to save the coverage data into BigWig files, which are generally smaller.
## Subset only the first sample
fullCovSmall <- lapply(fullCov, "[", 1)
## Export to BigWig
bw <- createBw(fullCovSmall)## 2024-05-01 16:59:21.274225 coerceGR: coercing sample HSB113## 2024-05-01 16:59:21.331392 createBwSample: exporting bw for sample HSB113## See the file. Note that the sample name is used to name the file.
dir(pattern = ".bw")## [1] "HSB113.bw" "meanChr21.bw"## Internally createBw() coerces each sample to a GRanges object before
## exporting to a BigWig file. If more than one sample was exported, the
## GRangesList would have more elements.
bw## GRangesList object of length 1:
## $HSB113
## GRanges object with 155950 ranges and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <numeric>
## chr21 chr21 9458667-9458741 * | 0.04
## chr21 chr21 9540957-9540971 * | 0.04
## chr21 chr21 9543719-9543778 * | 0.04
## chr21 chr21 9651480-9651554 * | 0.04
## chr21 chr21 9653397-9653471 * | 0.04
## ... ... ... ... . ...
## chr21 chr21 48093246-48093255 * | 0.04
## chr21 chr21 48093257-48093331 * | 0.04
## chr21 chr21 48093350-48093424 * | 0.04
## chr21 chr21 48112194-48112268 * | 0.04
## chr21 chr21 48115056-48115130 * | 0.04
## -------
## seqinfo: 1 sequence from an unspecified genome12 Advanced arguments
If you are interested in using advanced arguments in derfinder, they are described in the manual pages of each function. Some of the most common advanced arguments are:
chrsStyle(default isUCSC)verbose(by defaultTRUE).
verbose controls whether to print status updates for nearly all the functions. chrsStyle is used to determine the chromosome naming style and is powered by GenomeInfoDb. Note that chrsStyle is used in any of the functions that call extendedMapSeqlevels(). If you are working with a different organism than Homo sapiens set the global species option using options(species = 'your species') with the syntax used in names(GenomeInfoDb::genomeStyles()). If you want to disable extendedMapSeqlevels() set chrsStyle to NULL, which can be useful if your organism is not part of GenomeInfoDb.
The third commonly used advanced argument is mc.cores. It controls the number of cores to use for the functions that can run with more than one core to speed up. In nearly all the cases, the maximum number of cores depends on the number of chromosomes. One notable exception is analyzeChr() where the maximum number of cores depends on the chunksize used and the dimensions of the data for the chromosome under study.
Note that using the ... argument allows you to specify some of the documented arguments. For example, you might want to control the maxClusterGap from findRegions() in the analyzeChr() call.
13 Non-human data
If you are working with data from an organism that is not Homo sapiens, then set the global options defining the species and the chrsStyle used. For example, if you are working with Arabidopsis Thaliana and the NCBI naming style, then set the options using the following code:
## Set global species and chrsStyle options
options(species = "arabidopsis_thaliana")
options(chrsStyle = "NCBI")
## Then proceed to load and analyze the dataInternally derfinder uses extendedMapSeqlevels() to use the appropriate chromosome naming style given a species in all functions involving chromosome names.
Further note that the argument txdb from analyzeChr() is passed to bumphunter::annotateTranscripts(txdb). So if you are using a genome different from hg19 remember to provide the appropriate annotation data or simply use analyzeChr(runAnnotation = FALSE).
So, in the Arabidopsis Thaliana example, your analyzeChr() call would look like this:
## Load transcript database information
library("TxDb.Athaliana.BioMart.plantsmart28")
## Set organism options
options(species = "arabidopsis_thaliana")
options(chrsStyle = "NCBI")
## Run command with more arguments
analyzeChr(txdb = TxDb.Athaliana.BioMart.plantsmart28)You might find the discussion Using bumphunter with non-human genomes useful.
14 Functions that use multiple cores
Currently, the following functions can use multiple cores, several of which are called inside analyzeChr().
calculatePvalues(): 1 core per chunk of data to process.calculateStats(): 1 core per chunk of data to process.coerceGR(): 1 core per chromosome. This function is used bycreateBw().coverageToExon(): 1 core per strand, then 1 core per chromosome.loadCoverage(): up to 1 core per tile when loading the data with GenomicFiles. Otherwise, no parallelization is used.fullCoverage(): 1 core per chromosome. In general, try to avoid using more than 10 cores as you might reach your maximum network speed and/or hard disk input/output seed. For the case described inloadCoverage(), you can specify how many cores to use per chromosome for the tiles using themc.cores.loadargument effectively resulting inmc.corestimesmc.cores.loadused (otherwise it’smc.coressquared).getRegionCoverage(): 1 core per chromosome.regionMatrix(): 1 core per chromosome.railMatrix(): 1 core per chromosome.
All parallel operations use SnowParam() from BiocParallel when more than 1 core is being used. Otherwise, SerialParam() is used. Note that if you prefer to specify other types of parallelization you can do so by specifying the BPPARAM.custom advanced argument.
Because SnowParam() requires R to load the necessary packages on each worker, the key function fstats.apply() was isolated in the derfinderHelper package. This package has much faster loading speeds than derfinder which greatly impacts performance on cases where the actual step of calculating the F-statistics is fast.
You may prefer to use MulticoreParam() described in the BiocParallel vignette. In that case, when using these functions use BPPARAM.custom = MulticoreParam(workers = x) where x is the number of cores you want to use. Note that in some systems, as is the case of the cluster used by derfinder’s developers, the system tools for assessing memory usage can be misleading, thus resulting in much higher memory loads when using MulticoreParam() instead of the default SnowParam().
15 Loading data details
15.1 Controlling loading from BAM files
If you are loading data from BAM files, you might want to specify some criteria to decide which reads to include or not. For example, your data might have been generated by a strand-specific protocol. You can do so by specifying the arguments of scanBamFlag() from Rsamtools.
You can also control whether to include or exclude bases with CIGAR string D (deletion from the reference) by setting the advanced argument drop.D = TRUE in your fullCoverage() or loadCoverage() call.
15.2 Unfiltered base-level coverage
Note that in most scenarios, the fullCov object illustrated in the introductory vignette can be large in memory. When making plots or calculating the region-level coverage, we don’t need the full information. In such situations, it might pay off to create a smaller version by loading only the required data. This can be achieved using the advanced argument which to fullCoverage() or loadCoverage().
However, it is important to consider that when reading the data from BAM files, a read might align partially inside the region of interest. By default such a read would be discarded and thus the base-level coverage would be lower than what it is in reality. The advanced argument protectWhich extends regions by 30 kbp (15 kbp each side) to help mitigate this issue.
We can illustrate this issue with the example data from derfinder. First, we load in the data and generate some regions of interest.
## Find some regions to work with
example("loadCoverage", "derfinder")##
## ldCvrg> datadir <- system.file("extdata", "genomeData", package = "derfinder")
##
## ldCvrg> files <- rawFiles(
## ldCvrg+ datadir = datadir, samplepatt = "*accepted_hits.bam$",
## ldCvrg+ fileterm = NULL
## ldCvrg+ )
##
## ldCvrg> ## Shorten the column names
## ldCvrg> names(files) <- gsub("_accepted_hits.bam", "", names(files))
##
## ldCvrg> ## Read and filter the data, only for 2 files
## ldCvrg> dataSmall <- loadCoverage(files = files[1:2], chr = "21", cutoff = 0)## 2024-05-01 16:59:21.859992 loadCoverage: finding chromosome lengths## 2024-05-01 16:59:21.887632 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009101_accepted_hits.bam## 2024-05-01 16:59:21.9629 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009102_accepted_hits.bam## 2024-05-01 16:59:22.026829 loadCoverage: applying the cutoff to the merged data## 2024-05-01 16:59:22.04814 filterData: originally there were 48129895 rows, now there are 454 rows. Meaning that 100 percent was filtered.##
## ldCvrg> ## Not run:
## ldCvrg> ##D ## Export to BigWig files
## ldCvrg> ##D createBw(list("chr21" = dataSmall))
## ldCvrg> ##D
## ldCvrg> ##D ## Load data from BigWig files
## ldCvrg> ##D dataSmall.bw <- loadCoverage(c(
## ldCvrg> ##D ERR009101 = "ERR009101.bw", ERR009102 =
## ldCvrg> ##D "ERR009102.bw"
## ldCvrg> ##D ), chr = "chr21")
## ldCvrg> ##D
## ldCvrg> ##D ## Compare them
## ldCvrg> ##D mapply(function(x, y) {
## ldCvrg> ##D x - y
## ldCvrg> ##D }, dataSmall$coverage, dataSmall.bw$coverage)
## ldCvrg> ##D
## ldCvrg> ##D ## Note that the only difference is the type of Rle (integer vs numeric) used
## ldCvrg> ##D ## to store the data.
## ldCvrg> ## End(Not run)
## ldCvrg>
## ldCvrg>
## ldCvrg>
## ldCvrg>example("getRegionCoverage", "derfinder")##
## gtRgnC> ## Obtain fullCov object
## gtRgnC> fullCov <- list("21" = genomeDataRaw$coverage)
##
## gtRgnC> ## Assign chr lengths using hg19 information, use only first two regions
## gtRgnC> library("GenomicRanges")
##
## gtRgnC> regions <- genomeRegions$regions[1:2]
##
## gtRgnC> seqlengths(regions) <- seqlengths(getChromInfoFromUCSC("hg19",
## gtRgnC+ as.Seqinfo = TRUE
## gtRgnC+ ))[
## gtRgnC+ mapSeqlevels(names(seqlengths(regions)), "UCSC")
## gtRgnC+ ]
##
## gtRgnC> ## Finally, get the region coverage
## gtRgnC> regionCov <- getRegionCoverage(fullCov = fullCov, regions = regions)## extendedMapSeqlevels: sequence names mapped from NCBI to UCSC for species homo_sapiens## 2024-05-01 16:59:22.700124 getRegionCoverage: processing chr21## 2024-05-01 16:59:22.73888 getRegionCoverage: done processing chr21Next, we load the coverage again using which but without any padding. We can see how the coverage is not the same by looking at the maximum coverage for each sample.
## Illustrate reading data from a set of regions
test <- loadCoverage(
files = files, chr = "21", cutoff = NULL, which = regions,
protectWhich = 0, fileStyle = "NCBI"
)## extendedMapSeqlevels: sequence names mapped from UCSC to NCBI for species homo_sapiens## 2024-05-01 16:59:22.926924 loadCoverage: finding chromosome lengths## 2024-05-01 16:59:22.9343 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009101_accepted_hits.bam## 2024-05-01 16:59:22.999272 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009102_accepted_hits.bam## 2024-05-01 16:59:23.061088 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009105_accepted_hits.bam## 2024-05-01 16:59:23.188306 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009107_accepted_hits.bam## 2024-05-01 16:59:23.253418 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009108_accepted_hits.bam## 2024-05-01 16:59:23.31584 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009112_accepted_hits.bam## 2024-05-01 16:59:23.376094 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009115_accepted_hits.bam## 2024-05-01 16:59:23.436584 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009116_accepted_hits.bam## 2024-05-01 16:59:23.501444 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009131_accepted_hits.bam## 2024-05-01 16:59:23.562133 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009138_accepted_hits.bam## 2024-05-01 16:59:23.619922 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009144_accepted_hits.bam## 2024-05-01 16:59:23.677904 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009145_accepted_hits.bam## 2024-05-01 16:59:23.739915 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009148_accepted_hits.bam## 2024-05-01 16:59:23.802266 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009151_accepted_hits.bam## 2024-05-01 16:59:23.859731 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009152_accepted_hits.bam## 2024-05-01 16:59:23.921087 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009153_accepted_hits.bam## 2024-05-01 16:59:23.98367 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009159_accepted_hits.bam## 2024-05-01 16:59:24.044695 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009161_accepted_hits.bam## 2024-05-01 16:59:24.104694 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009163_accepted_hits.bam## 2024-05-01 16:59:24.163714 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009164_accepted_hits.bam## 2024-05-01 16:59:24.223823 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009167_accepted_hits.bam## 2024-05-01 16:59:24.284549 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031812_accepted_hits.bam## 2024-05-01 16:59:24.345778 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031835_accepted_hits.bam## 2024-05-01 16:59:24.40562 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031867_accepted_hits.bam## 2024-05-01 16:59:24.464247 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031868_accepted_hits.bam## 2024-05-01 16:59:24.52433 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031900_accepted_hits.bam## 2024-05-01 16:59:24.585984 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031904_accepted_hits.bam## 2024-05-01 16:59:24.647336 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031914_accepted_hits.bam## 2024-05-01 16:59:24.709609 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031936_accepted_hits.bam## 2024-05-01 16:59:24.771607 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031958_accepted_hits.bam## 2024-05-01 16:59:24.829856 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031960_accepted_hits.bam## 2024-05-01 16:59:24.889035 loadCoverage: applying the cutoff to the merged data## 2024-05-01 16:59:24.986751 filterData: originally there were 48129895 rows, now there are 48129895 rows. Meaning that 0 percent was filtered.## Some reads were ignored and thus the coverage is lower as can be seen below:
sapply(test$coverage, max) - sapply(genomeDataRaw$coverage, max)## ERR009101 ERR009102 ERR009105 ERR009107 ERR009108 ERR009112 ERR009115 ERR009116 ERR009131 ERR009138 ERR009144 ERR009145
## 0 0 0 0 -1 0 -1 -2 -2 -2 -1 -1
## ERR009148 ERR009151 ERR009152 ERR009153 ERR009159 ERR009161 ERR009163 ERR009164 ERR009167 SRR031812 SRR031835 SRR031867
## -3 -3 0 -3 -3 -3 -1 -3 -3 0 -1 0
## SRR031868 SRR031900 SRR031904 SRR031914 SRR031936 SRR031958 SRR031960
## 0 0 -1 0 0 0 0When we re-load the data using some padding to the regions, we find that the coverage matches at all the bases.
## Illustrate reading data from a set of regions
test2 <- loadCoverage(
files = files, chr = "21", cutoff = NULL,
which = regions, protectWhich = 3e4, fileStyle = "NCBI"
)## extendedMapSeqlevels: sequence names mapped from UCSC to NCBI for species homo_sapiens## 2024-05-01 16:59:25.211546 loadCoverage: finding chromosome lengths## 2024-05-01 16:59:25.217827 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009101_accepted_hits.bam## 2024-05-01 16:59:25.273925 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009102_accepted_hits.bam## 2024-05-01 16:59:25.331244 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009105_accepted_hits.bam## 2024-05-01 16:59:25.388749 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009107_accepted_hits.bam## 2024-05-01 16:59:25.445222 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009108_accepted_hits.bam## 2024-05-01 16:59:25.502907 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009112_accepted_hits.bam## 2024-05-01 16:59:25.56222 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009115_accepted_hits.bam## 2024-05-01 16:59:25.621112 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009116_accepted_hits.bam## 2024-05-01 16:59:25.677639 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009131_accepted_hits.bam## 2024-05-01 16:59:25.739469 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009138_accepted_hits.bam## 2024-05-01 16:59:25.799443 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009144_accepted_hits.bam## 2024-05-01 16:59:25.854762 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009145_accepted_hits.bam## 2024-05-01 16:59:25.911878 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009148_accepted_hits.bam## 2024-05-01 16:59:25.972436 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009151_accepted_hits.bam## 2024-05-01 16:59:26.029687 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009152_accepted_hits.bam## 2024-05-01 16:59:26.085219 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009153_accepted_hits.bam## 2024-05-01 16:59:26.142384 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009159_accepted_hits.bam## 2024-05-01 16:59:26.198528 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009161_accepted_hits.bam## 2024-05-01 16:59:26.25705 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009163_accepted_hits.bam## 2024-05-01 16:59:26.316617 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009164_accepted_hits.bam## 2024-05-01 16:59:26.374721 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/ERR009167_accepted_hits.bam## 2024-05-01 16:59:26.437633 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031812_accepted_hits.bam## 2024-05-01 16:59:26.497148 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031835_accepted_hits.bam## 2024-05-01 16:59:26.556793 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031867_accepted_hits.bam## 2024-05-01 16:59:26.615681 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031868_accepted_hits.bam## 2024-05-01 16:59:26.72658 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031900_accepted_hits.bam## 2024-05-01 16:59:26.784829 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031904_accepted_hits.bam## 2024-05-01 16:59:26.84133 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031914_accepted_hits.bam## 2024-05-01 16:59:26.899663 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031936_accepted_hits.bam## 2024-05-01 16:59:26.956995 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031958_accepted_hits.bam## 2024-05-01 16:59:27.01088 loadCoverage: loading BAM file /tmp/RtmpnsqTaz/Rinst2224b35079e74a/derfinder/extdata/genomeData/SRR031960_accepted_hits.bam## 2024-05-01 16:59:27.066124 loadCoverage: applying the cutoff to the merged data## 2024-05-01 16:59:27.111604 filterData: originally there were 48129895 rows, now there are 48129895 rows. Meaning that 0 percent was filtered.## Adding some padding to the regions helps get the same coverage
identical(sapply(test2$coverage, max), sapply(genomeDataRaw$coverage, max))## [1] TRUE## A more detailed test reveals that the coverage matches at every base
all(mapply(function(x, y) {
identical(x, y)
}, test2$coverage, genomeDataRaw$coverage))## [1] TRUEHow much padding you need to use will depend on your specific data set, and you might be comfortable getting approximately the same coverage values for the sake of greatly reducing the memory resources needed.
15.3 Input files in a different naming style
If you are under the case where you like to use a specific chromosome naming style but the raw data files use another one, you might need to use the fileStyle argument.
For example, you could be working with Homo sapiens data and your preferred naming style is UCSC (chr1, chr2, …, chrX, chrY) but the raw data uses NCBI style names (1, 2, …, X, Y). In that case, use fullCoverage(fileStyle = 'NCBI') or loadCoverage(fileStyle = 'NCBI') depending if you are loading one chromosome or multiple at a time.
15.4 Loading data in chunks
If you prefer to do so, fullCoverage() and loadCoverage() can load the data of a chromosome in chunks using GenomicFiles. This is controlled by whether you specify the tilewidth argument.
Notice that you might run into slight coverage errors near the borders of the tiles for the same reason that was illustrated previously when loading specific regions.
This approach is not necessarily more efficient and can be significantly time consuming if you use a small tilewidth.
15.5 Large number of samples
If you have a large number of samples (say thousands), it might be best to submit cluster jobs that run loadCoverage() or fullCoverage() for only one chromosome at a time.
In the case of working with Rail output, you can either use railMatrix() with the argument file.cores greater than 1 or specify the advanced argument BPPARAM.railChr to control the parallel environment used for loading the BigWig files. Doing so, you can then submit cluster jobs that run railMatrix() for one chromosome at a time, yet read in the data fast. You can do all of from R by using BPPARAM.custom and BPPARAM.railChr at the same time where you use a BatchJobsParam() for the first one.
Another option when working with Rail output is to simply load the summary BigWig data (mean, median), then define the ERs using findRegions() and write them to a BED file. You can then use bwtool to create the coverage matrix.
16 Flow charts
16.1 DER analysis flow chart
Figure 2 illustrates how most of derfinder’s functions interact when performing a base-level differential expression analysis by calculating base-level F-statistics.
Figure 2: derfinder F-statistics flow chart
Flow chart of the different processing steps (black boxes) that can be carried out using derfinder and the functions that perform these actions (in red). Input and output is shown in green boxes. Functions in blue are those applied to the results from multiple chromosomes (mergeResults() and derfinderReport). regionReport functions are shown in orange while derfinderPlot functions are shown in dark purple. Purple dotted arrow marks functions that require unfiltered base-level coverage.
16.2 analyzeChr() flow chart
Figure 3: analyzeChr() flow chart
Figure 3 shows in more detail the processing flow in analyzeChr(), which is the main function for identifying candidate differentially expressed regions (DERs) from the base-level F-statistics.
Many fine-tunning arguments can be passed to analyzeChr() to feed into the other functions. For example, you might want to use a smaller chunksize when pre-processing the coverage data (the default is 5 million): specially if you have hundreds of samples.
Another useful argument is scalefac (by default it’s 32) which controls the scaling factor to use before the log2 transformation.
Furthermore, you might want to specify maxClusterGap to control the maximum gap between two regions before they are considered to be part of the same cluster.
16.3 regionMatrix() flow chart
Figure 4: regionMatrix() flow chart
Figure 4 shows the functions used internally by regionMatrix() and processing steps for identifying expressed regions. Overall, it is much simpler than analyzeChr(). The flow is even simpler in railMatrix().
17 Base-level F-statistics projects
For each project where you will calculating base-level F-statistics, we recommend the following organization. You might be interested in a similar structure when using regionMatrix() if you have BAM files. The notable exception is when you are analyzing output data from Rail in which case you won’t be needing this type of organization. Remember that railMatrix() is much less computationally intensive than analyzeChr().
17.1 File organization
This is our recommended file organization when using analyzeChr().
ProjectDir
|-derCoverageInfo
|-derAnalysis
|---analysis01
|---analysis02We start with a main project directory that has two initial directories. One for storing the coverage data, and one for storing each analysis: you might explore different models, cutoffs or other parameters.
You can then use fullCoverage(), save the result and also save the filtered coverage information for each chromosome separately. Doing so will result in the following structure.
ProjectDir
|-derCoverageInfo
|---Chr1Cov.Rdata
|---Chr2Cov.Rdata
...
|---ChrYCov.Rdata
|---fullCov.Rdata
|-derAnalysis
|---analysis01
|---analysis02Next, you can use analyzeChr() for each of the chromosomes of a specific analysis (say analysis01). Doing so will create several Rdata files per chromosome as shown below. bash scripts can be useful if you wish to submit one cluster job per chromosome. In general, you will use the same model and group information for each chromosome, so saving the information can be useful.
ProjectDir
|-derCoverageInfo
|---Chr1Cov.Rdata
|---Chr2Cov.Rdata
...
|---ChrYCov.Rdata
|---fullCov.Rdata
|-derAnalysis
|---analysis01
|-----models.Rdata
|-----groupInfo.Rdata
|-----chr1/
|-------chunksDir/
|-------logs/
|-------annotation.Rdata
|-------coveragePrep.Rdata
|-------fstats.Rdata
|-------optionsStats.Rdata
|-------regions.Rdata
|-------timeinfo.Rdata
|-----chr2/
...
|-----chrY/
|---analysis02Then use mergeResults() to pool together the results from all the chromosomes for a given analysis (here analysis01).
ProjectDir
|-derCoverageInfo
|---Chr1Cov.Rdata
|---Chr2Cov.Rdata
...
|---ChrYCov.Rdata
|---fullCov.Rdata
|-derAnalysis
|---analysis01
|-----logs/
|-----fullAnnotatedRegions.Rdata
|-----fullFstats.Rdata
|-----fullNullSummary.Rdata
|-----fullRegions.Rdata
|-----fullTime.Rdata
|-----optionsMerge.Rdata
|-----chr1/
|-------chunksDir/
|-------logs/
|-------annotation.Rdata
|-------coveragePrep.Rdata
|-------fstats.Rdata
|-------optionsStats.Rdata
|-------regions.Rdata
|-------timeinfo.Rdata
|-----chr2/
...
|-----chrY/
|---analysis02Finally, you might want to use derfinderReport() from regionReport to create a HTML report of the results.
17.2 bash scripts
If you are following our recommended file organization for a base-level F-statistics DER finding analysis, you might be interested in the following bash scripts templates. Note that there are plenty of other solutions, such as writing a master R script and using BiocParallel to submit jobs and administer them for you. Check out the BatchJobsParam() for more information. If you want to use bash scripts, take a look below.
For interacting between bash and R we have found quite useful the getopt package. Here we include an example R script that is controlled by a bash script which submits a job for each chromosome to analyze for a given analysis.
The two files, derfinderAnalysis.R and derAnalysis.sh should live under the derAnalysis directory.
ProjectDir
|-derCoverageInfo
|---Chr1Cov.Rdata
|---Chr2Cov.Rdata
...
|---ChrYCov.Rdata
|---fullCov.Rdata
|-derAnalysis
|---derfinder-Analysis.R
|---derAnalysis.sh
|---analysis01
|---analysis02Then, you can simply use:
cd /ProjectDir/derAnalysis
sh derAnalysis.sh analysis01to run analyzeChr() on all your chromosomes.
17.2.1 derfinder-analysis.R
## Run derfinder's analysis steps with timing info
## Load libraries
library("getopt")
## Available at http:/bioconductor.org/packages/derfinder
library("derfinder")
## Specify parameters
spec <- matrix(c(
"DFfile", "d", 1, "character", "path to the .Rdata file with the results from loadCoverage()",
"chr", "c", 1, "character", "Chromosome under analysis. Use X instead of chrX.",
"mcores", "m", 1, "integer", "Number of cores",
"verbose", "v", 2, "logical", "Print status updates",
"help", "h", 0, "logical", "Display help"
), byrow = TRUE, ncol = 5)
opt <- getopt(spec)
## Testing the script
test <- FALSE
if (test) {
## Speficy it using an interactive R session and testing
test <- TRUE
}
## Test values
if (test) {
opt <- NULL
opt$DFfile <- "/ProjectDir/derCoverageInfo/chr21Cov.Rdata"
opt$chr <- "21"
opt$mcores <- 1
opt$verbose <- NULL
}
## if help was asked for print a friendly message
## and exit with a non-zero error code
if (!is.null(opt$help)) {
cat(getopt(spec, usage = TRUE))
q(status = 1)
}
## Default value for verbose = TRUE
if (is.null(opt$verbose)) opt$verbose <- TRUE
if (opt$verbose) message("Loading Rdata file with the output from loadCoverage()")
load(opt$DFfile)
## Make it easy to use the name later. Here I'm assuming the names were generated using output='auto' in loadCoverage()
eval(parse(text = paste0("data <- ", "chr", opt$chr, "CovInfo")))
eval(parse(text = paste0("rm(chr", opt$chr, "CovInfo)")))
## Just for testing purposes
if (test) {
tmp <- data
tmp$coverage <- tmp$coverage[1:1e6, ]
library("IRanges")
tmp$position[which(tmp$pos)[1e6 + 1]:length(tmp$pos)] <- FALSE
data <- tmp
}
## Load the models
load("models.Rdata")
## Load group information
load("groupInfo.Rdata")
## Run the analysis with lowMemDir
analyzeChr(
chr = opt$chr, coverageInfo = data, models = models,
cutoffFstat = 1e-06, cutoffType = "theoretical", nPermute = 1000,
seeds = seq_len(1000), maxClusterGap = 3000, groupInfo = groupInfo,
subject = "hg19", mc.cores = opt$mcores,
lowMemDir = file.path(tempdir(), paste0("chr", opt$chr), "chunksDir"),
verbose = opt$verbose, chunksize = 1e5
)
## Done
if (opt$verbose) {
print(proc.time())
print(sessionInfo(), locale = FALSE)
}Remember to modify the the script to fit your project.
17.2.2 derAnalysis.sh
#!/bin/sh
## Usage
# sh derAnalysis.sh analysis01
# Directories
MAINDIR=/ProjectDir
WDIR=${MAINDIR}/derAnalysis
DATADIR=${MAINDIR}/derCoverageInfo
# Define variables
SHORT='derA-01'
PREFIX=$1
# Construct shell files
for chrnum in 22 21 Y 20 19 18 17 16 15 14 13 12 11 10 9 8 X 7 6 5 4 3 2 1
do
echo "Creating script for chromosome ${chrnum}"
if [[ ${chrnum} == "Y" ]]
then
CORES=2
else
CORES=6
fi
chr="chr${chrnum}"
outdir="${PREFIX}/${chr}"
sname="${SHORT}.${PREFIX}.${chr}"
cat > ${WDIR}/.${sname}.sh <<EOF
#!/bin/bash
#$ -cwd
#$ -m e
#$ -l mem_free=8G,h_vmem=10G,h_fsize=10G
#$ -N ${sname}
#$ -pe local ${CORES}
echo "**** Job starts ****"
date
# Create output directory
mkdir -p ${WDIR}/${outdir}
# Make logs directory
mkdir -p ${WDIR}/${outdir}/logs
# run derfinder-analysis.R
cd ${WDIR}/${PREFIX}/
# specific to our cluster
# see http://www.jhpce.jhu.edu/knowledge-base/environment-modules/
module load R/3.3.x
Rscript ${WDIR}/derfinder-analysis.R -d "${DATADIR}/${chr}Cov.Rdata" -c "${chrnum}" -m ${CORES} -v TRUE
# Move log files into the logs directory
mv ${WDIR}/${sname}.* ${WDIR}/${outdir}/logs/
echo "**** Job ends ****"
date
EOF
call="qsub .${sname}.sh"
$call
done
Your cluster might specify memory requirements differently and you might need to use fewer or more cores depending on your data set.
18 Expressed regions-level projects
If you are using regionMatrix(), an organization similar to the one we mentioned for the single base-level F-statistics implementation is recommended. If you have a very large number of samples, it might be best to store the coverage data separately by chromosome instead of a single fullCov object. This is help with the memory load since you can then write a script for loading the coverage data for the chromosome of interest and running regionMatrix() on it. Once all your jobs are done, merge the results from them.
If you are using railMatrix(), you don’t need any fancy setup. You only need the paths to the summary files and the sample files, all of which are assumed to be BigWig files.
19 Summary
We have illustrated how to identify candidate differentially expressed regions without using annotation by using analyzeChr(). Furthermore, we covered how to perform the expressed region-level matrix analysis with regionMatrix() or railMatrix(). We also highlighted other uses of the derfinder package.
Latter sections of this vignette covered the most commonly used advanced arguments, details on how to load data, flow charts explaining the relationships between the functions, the recommended output organization, and example shell scripts for running the analysis.
20 Reproducibility
This package was made possible thanks to:
- R (R Core Team, 2024)
- AnnotationDbi (Pagès, Carlson, Falcon et al., 2017)
- BiocParallel (Morgan, Wang, Obenchain et al., 2024)
- bumphunter (Jaffe, Murakami, Lee et al., 2012) and (Jaffe, Murakami, Lee et al., 2012)
- derfinderHelper
- GenomeInfoDb (Arora, Morgan, Carlson et al., 2017)
- GenomicAlignments (Lawrence, Huber, Pagès et al., 2013)
- GenomicFeatures (Lawrence, Huber, Pagès et al., 2013)
- GenomicFiles (Bioconductor Package Maintainer, Obenchain, Love et al., 2024)
- GenomicRanges (Lawrence, Huber, Pagès et al., 2013)
- Hmisc (Harrell Jr, 2024)
- IRanges (Lawrence, Huber, Pagès et al., 2013)
- qvalue (Storey, Bass, Dabney et al., 2024)
- Rsamtools (Morgan, Pagès, Obenchain et al., 2024)
- rtracklayer (Lawrence, Gentleman, and Carey, 2009)
- S4Vectors (Pagès, Lawrence, and Aboyoun, 2017)
- derfinderData (Collado-Torres, Jaffe, and Leek, 2024)
- sessioninfo (Wickham, Chang, Flight et al., 2021)
- ggplot2 (Wickham, 2016)
- knitr (Xie, 2014)
- BiocStyle (Oleś, 2024)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux et al., 2024)
- testthat (Wickham, 2011)
- TxDb.Hsapiens.UCSC.hg19.knownGene (Carlson and Maintainer, 2015)
Code for creating the vignette
## Create the vignette
library("rmarkdown")
system.time(render("derfinder-users-guide.Rmd", "BiocStyle::html_document"))
## Extract the R code
library("knitr")
knit("derfinder-users-guide.Rmd", tangle = TRUE)## Clean up
file.remove("derfinderUsersGuideRef.bib")## Warning in file.remove("derfinderUsersGuideRef.bib"): cannot remove file 'derfinderUsersGuideRef.bib', reason 'No such
## file or directory'## [1] FALSEunlink("analysisResults", recursive = TRUE)
file.remove("HSB113.bw")## [1] TRUEfile.remove("meanChr21.bw")## [1] TRUEDate the vignette was generated.
## [1] "2024-05-01 16:59:27 EDT"Wallclock time spent generating the vignette.
## Time difference of 2.525 minsR session information.
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.0 beta (2024-04-15 r86425)
## os Ubuntu 22.04.4 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C
## ctype en_US.UTF-8
## tz America/New_York
## date 2024-05-01
## pandoc 2.7.3 @ /usr/bin/ (via rmarkdown)
##
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## zlibbioc 1.50.0 2024-05-01 [2] Bioconductor 3.19 (R 4.4.0)
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## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────21 Bibliography
This vignette was generated using BiocStyle (Oleś, 2024) with knitr (Xie, 2014) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
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