HTSFilter package: Quick-start guide
2023-10-24
Package
HTSFilter 1.42.0
This vignette illustrates the use of the HTSFilter package to filter
replicated data from transcriptome sequencing experiments (e.g., RNA sequencing
data) for a variety of different data classes: matrix,
data.frame, the S3 classes associated with the edgeR package (DGEExact
and DGELRT), and the S4 class associated with the DESeq2 package
(DESeqDataSet).
1 Introduction
High-throughput sequencing (HTS) data, such as RNA-sequencing (RNA-seq) data, are increasingly used to conduct differential analyses, in which statistical tests are performed for each biological feature (e.g., a gene, transcript, exon) in order to identify those whose expression levels show systematic covariation with a particular condition, such as a treatment or phenotype of interest. For the remainder of this vignette, we will focus on gene-level differential analyses, although these methods may also be applied to differential analyses of (count-based measures of) transcript- or exon-level expression.
Because hypothesis tests are performed for gene-by-gene differential analyses, the obtained \(p\)-values must be adjusted to correct for multiple testing. However, procedures to adjust \(p\)-values to control the number of detected false positives often lead to a loss of power to detect truly differentially expressed (DE) genes due to the large number of hypothesis tests performed. To reduce the impact of such procedures, independent data filters are often used to identify and remove genes that appear to generate an uninformative signal (Bourgon, Gentleman, and Huber 2010); this in turn moderates the correction needed to adjust for multiple testing. For independent filtering methods for microarray data, see for example the genefilter Bioconductor package (Gentleman et al. 2020).
The HTSFilter package implements a novel data-based filtering procedure based on the calculation of a similarity index among biological replicates for read counts arising from replicated transcriptome sequencing (RNA-seq) data. This technique provides an intuitive data-driven way to filter high-throughput transcriptome sequencing data and to effectively remove genes with low, constant expression levels without incorrectly removing those that would otherwise have been identified as DE. The two fundamental assumptions of the filter implemented in the HTSFilter package are as follows:
- Biological replicates are present for each experimental condition, and
- Data can be appropriately normalized (scaled) to correct for systematic inter-sample biases.
Assuming these conditions hold, HTSFilter implements a method to identify a filtering threshold that maximizes the filtering similarity among replicates, that is, one where most genes tend to either have normalized counts less than or equal to the cutoff in all samples (i.e., filtered genes) or greater than the cutoff in all samples (i.e., non-filtered genes). This filtering similarity is defined using the global Jaccard index, that is, the average Jaccard index calculated between pairs of replicates within each experimental condition; see Rau et al. (2013) for more details.
For more information about between-sample normalization strategies, see
Dillies et al. (2013); in particular, strategies for normalizing data with differences
in library size and composition may be found in Anders and Huber (2010) and Robinson and Oshlack (2010),
and strategies for normalizing data exhibiting sample-specific biases due to
GC content may be found in Risso et al. (2011) and Hansen, Irizarry, and Wu (2012). Within the HTSFilter
package, the Trimmed Means of M-values (TMM) (Robinson and Oshlack 2010) and DESeq
(Anders and Huber 2010) normalization strategies may be used prior to calculating an
appropriate data-based filter. If an alternative normalization strategy is
needed or desired, the normalization may be applied prior to filtering the
data with normalization="none"in the HTSFilter function;
see Section 4 for an example.
The HTSFilter package is able to accommodate unnormalized or normalized
replicated count data in the form of a matrix or data.frame (in which each
row corresponds to a biological feature and each column to a biological
sample), one of the S3 classes associated with the edgeR package (DGEList,
DGEExact, DGEGLM, and DGELRT), or DESeqDataSet (the S4 class associated
with the DESeq2 package), as illustrated in the following sections.
Finally, we note that the filtering method implemented in the HTSFilter package is designed to filter transcriptome sequencing, and not microarray, data; in particular, the proposed filter is effective for data with features that take on values over a large order of magnitude and with a subset of features exhibiting small levels of expression across samples (see, for example, Figure 1). In this vignette, we illustrate its use on count-based measures of gene expression, although its use is not strictly limited to discrete data.
2 Input data
For the purposes of this vignette, we make use of data from a study of sex-specific expression of liver cells in human and the DESeq2 and edgeR packages for differential analysis. Sultan et al. (2008) obtained a high-throughput sequencing data (using a 1G Illumina Genome Analyzer sequencing machine) from a human embryonic kidney and a B cell line, with two biological replicates each. The raw read counts and phenotype tables were obtained from the ReCount online resource (Frazee, Langmead, and Leek 2011).
To begin, we load the HTSFilter package, attach the gene-level count
data contained in sultan, and take a quick look at a histrogram of gene counts:
library(HTSFilter)
library(edgeR)
library(DESeq2)
data("sultan")
hist(log(exprs(sultan)+1), col="grey", breaks=25, main="",
xlab="Log(counts+1)")
Figure 1: Histogram of log transformed counts from the Sultan data
This illustrates the large number of genes with very small counts as well as the large heterogeneity in counts observed.
pData(sultan)## sample.id num.tech.reps cell.line
## SRX008333 SRX008333 1 Ramos B cell
## SRX008334 SRX008334 1 Ramos B cell
## SRX008331 SRX008331 1 HEK293T
## SRX008332 SRX008332 1 HEK293Tdim(sultan)## Features Samples
## 9010 4The unfiltered data contain 9010 genes in four samples (two replicates per condition).
3 Use of HTSFilter with varying data types
3.1 matrix and data.frame classes
To filter high-throughput sequencing data in the form of a matrix or
data.frame, we first access the expression data, contained in exprs(sultan),
and create a vector identifying the condition labels for each of the samples
via the pData Biobase function. We then filter the data using the
HTSFilter function, specifying that the number of tested thresholds be only 25
(s.len=25) rather than the default value of 100 to reduce computation time
for this example. Note that as it is unspecified, the default normalization
method is used for filtering the data, namely the Trimmed Mean of M-values
(TMM) method of Robinson and Oshlack (2010). To use the DESeq normalization method
(Anders and Huber 2010) normalization="DESeq" may be specified.
mat <- exprs(sultan)
conds <- as.character(pData(sultan)$cell.line)
## Only 25 tested thresholds to reduce computation time
filter <- HTSFilter(mat, conds, s.min=1, s.max=200, s.len=25)
Figure 2: Global Jaccard index for the Sultan data
The index is calculated for a variety of threshold values after TMM normalization, with a loess curve (blue line) superposed and data-based threshold values (red cross and red dotted line) equal to 11.764.
mat <- filter$filteredData
dim(mat)## [1] 4995 4dim(filter$removedData)## [1] 4015 4For this example, we find a data-based threshold equal to 11.764; genes with normalized values less than this threshold in all samples are filtered from subsequent analyses. The proposed filter thus removes 4015 genes from further analyses, leaving 4995 genes.
We note that an important part of the filter proposed in the HTSFilter package is a check of the behavior of the global similarity index calculated over a range of threshold values, and in particular, to verify that a reasonable maximum value is reached for the global similarity index over the range of tested threshold values (see Figure 2); the maximum possible value for the global Jaccard index is nearly 1. To illustrate the importance of this check, we attempt to re-apply the proposed filter to the previously filtered data (in practice, of course, this would be nonsensical):
par(mfrow = c(1,2), mar = c(4,4,2,2))
filter.2 <- HTSFilter(mat, conds, s.len=25)
dim(filter.2$removedData)## [1] 0 4hist(log(filter.2$filteredData+1), col="grey", breaks=25, main="",
xlab="Log(counts+1)")
Figure 3: HTSFilter on pre-filtered Sultan data
(left) The global Jaccard index on the pre-filtered data is calculated for a variety of threshold values after TMM normalization, with a loess curve (blue line) superposed and data-based threshold values (red cross and red dotted line) equal to 1.64. (right) Histogram of the re-filtered data after applying HTSFilter.
In the lefthand panel of Figure 3, we note a plateau of large global Jaccard index values for thresholds less than 2, with a decrease thereafter; this corresponds to filtering no genes, unsurprising given that genes with low, constant levels of expression have already been filtered from the analysis (see the righthand panel of Figure 3).
3.2 edgeR package pipeline
We next illustrate the use of HTSFilter within the edgeR pipeline
for differential analysis (S3 classes DGEList, DGEExact, DGEGLM, or
DGELRT). For the purposes of this vignette, we will consider the S3 classes
DGEExact and DGELRT. The former is the class containing the results of the
differential expression analysis between two groups of count libraries
(resulting from a call to the function exactTest in edgeR); the latter is
the class containing the results of a generalized linear model (GLM)-based
differential analysis (resulting from a call to the function glmLRT in
edgeR). Although the filter may be applied earlier in the edgeR
pipeline (i.e., to objects of class DGEList or DGEGLM), we do not
recommend doing so, as parameter estimation makes use of counts adjusted
using a quantile-to-quantile method (pseudo-counts).
3.2.1 S3 class DGEExact
We first coerce the data into the appropriate class with the function
DGEList, where the group variable is set to contain a vector of
condition labels for each of the samples. Next, after calculating normalizing
factors to scale library sizes (calcNormFactors), we estimate common and
tagwise dispersion parameters using estimateDisp (using the quantile
conditional likelihood for each gene) and obtain differential analysis
results using exactTest. Finally, we apply the filter using the HTSFilter
function, again specifying that the number of tested thresholds be only 25
(s.len=25) rather than the default value of 100. Note that as it is
unspecified, the default normalization method is used for filtering the data,
namely the Trimmed Mean of M-values (TMM) method (Robinson and Oshlack 2010); alternative
normalization, including "pseudo.counts" for the quantile-to-quantile
adjusted counts used for parameter estimation, may also be specified.
We suppress the plot of the global Jaccard index below using
plot = FALSE, as it is identical to that shown in Figure 2.
dge <- DGEList(counts=exprs(sultan), group=conds)
dge <- calcNormFactors(dge)
dge <- estimateDisp(dge)## Using classic mode.et <- exactTest(dge)
et <- HTSFilter(et, DGEList=dge, s.len=25, plot=FALSE)$filteredData
dim(et)## [1] 4995 3class(et)## [1] "DGEExact"
## attr(,"package")
## [1] "edgeR"topTags(et)## Comparison of groups: Ramos B cell-HEK293T
## logFC logCPM PValue FDR
## ENSG00000100721 14.399287 11.53328 0.000000e+00 0.000000e+00
## ENSG00000133124 -14.394458 11.56789 0.000000e+00 0.000000e+00
## ENSG00000105369 11.925779 11.23367 0.000000e+00 0.000000e+00
## ENSG00000135144 10.921892 11.05896 2.600818e-309 3.247771e-306
## ENSG00000110777 13.850554 10.97744 2.014098e-302 2.012083e-299
## ENSG00000111348 13.797192 10.92306 6.278069e-298 5.226492e-295
## ENSG00000118308 13.494132 10.61473 1.294723e-272 9.238774e-270
## ENSG00000177606 -9.731885 10.81415 1.456204e-263 9.092173e-261
## ENSG00000012124 10.171666 10.29617 3.995433e-247 2.217465e-244
## ENSG00000149418 10.899611 10.19056 6.189743e-239 3.091777e-236Note that the filtered data are of the class DGEExact, allowing for a call
to the topTags function.
topTags(et)## Comparison of groups: Ramos B cell-HEK293T
## logFC logCPM PValue FDR
## ENSG00000100721 14.399287 11.53328 0.000000e+00 0.000000e+00
## ENSG00000133124 -14.394458 11.56789 0.000000e+00 0.000000e+00
## ENSG00000105369 11.925779 11.23367 0.000000e+00 0.000000e+00
## ENSG00000135144 10.921892 11.05896 2.600818e-309 3.247771e-306
## ENSG00000110777 13.850554 10.97744 2.014098e-302 2.012083e-299
## ENSG00000111348 13.797192 10.92306 6.278069e-298 5.226492e-295
## ENSG00000118308 13.494132 10.61473 1.294723e-272 9.238774e-270
## ENSG00000177606 -9.731885 10.81415 1.456204e-263 9.092173e-261
## ENSG00000012124 10.171666 10.29617 3.995433e-247 2.217465e-244
## ENSG00000149418 10.899611 10.19056 6.189743e-239 3.091777e-2363.2.2 S3 class DGELRT
We follow the same steps as the previous example, where the estimateDisp
function is now used to obtain per-gene dispersion parameter estimates using
the adjusted profile loglikelihood, the glmFit function is used to fit a
negative binomial generalized log-linear model to the read counts for each
gene, and the glmLRT function is used to conduct likelihood ratio tests for
one or more coefficients in the GLM. The output of glmLRT is an S3 object of
class DGELRT and contains the GLM differential analysis results.
As before, we apply the filter using the HTSFilter function, again
suppressing the plot of the global Jaccard index using ,
as it is identical to that shown in Figure 2.
design <- model.matrix(~conds)
dge <- DGEList(counts=exprs(sultan), group=conds)
dge <- calcNormFactors(dge)
dge <- estimateDisp(dge, design)
fit <- glmFit(dge,design)
lrt <- glmLRT(fit,coef=2)
lrt <- HTSFilter(lrt, DGEGLM=fit, s.len=25, plot=FALSE)$filteredData
dim(lrt)## [1] 4995 4class(lrt)## [1] "DGELRT"
## attr(,"package")
## [1] "edgeR"Note that the filtered data are of the class DGEList, allowing for a call
to the topTags function.
topTags(lrt)## Coefficient: condsRamos B cell
## logFC logCPM LR PValue FDR
## ENSG00000100721 14.399291 11.53265 1711.214 0.000000e+00 0.000000e+00
## ENSG00000133124 -14.394459 11.56849 1554.769 0.000000e+00 0.000000e+00
## ENSG00000110777 13.850562 10.97659 1487.284 0.000000e+00 0.000000e+00
## ENSG00000105369 11.925777 11.23291 1576.317 0.000000e+00 0.000000e+00
## ENSG00000135144 10.921894 11.05814 1495.598 0.000000e+00 0.000000e+00
## ENSG00000111348 13.797195 10.92217 1465.781 1.067182e-320 8.884288e-318
## ENSG00000118308 13.494128 10.61369 1345.242 1.666173e-294 1.188933e-291
## ENSG00000177606 -9.731884 10.81502 1222.977 6.192499e-268 3.866441e-265
## ENSG00000012124 10.171667 10.29498 1199.883 6.468477e-263 3.590005e-260
## ENSG00000149418 10.899617 10.18933 1171.186 1.115284e-256 5.570841e-2543.3 DESeq2 package pipeline: S4 class DESeqDataSet}
The HTSFilter package allows for a straightforward integration within the
DESeq2 analysis pipeline, most notably allowing for \(p\)-values to be
adjusted only for those genes passing the filter. Note that DESeq2 now
implements an independent filtering procedure by default in the results
function; this filter is a potential alternative filtering technique and does
not need to be used in addition to the one included in HTSFilter. In fact,
each filter is targeting the same weakly expressed genes to be filtered from
the analysis. As such, if the user wishes to make use of HTSFilter within
the DESeq2 pipeline, the argument independentFiltering=FALSE should be
used when calling the results function in DESeq2.
To illustrate the application of a filter for high-throughput sequencing data
in the form of a DESeqDataSet (the class used within the DESeq2 pipeline
for differential analysis), we coerce sultan into an object of the class
DESeqDataSet using the function DESeqDataSetFromMatrix. Once again,
we specify that the number of tested thresholds be only 25 (s.len=25)
rather than the default value of 100 to reduce computation time.
or objects in the form of a DESeqDataSet, the default normalization
strategy is "DESeq", although alternative normalization strategies may also
be used. Note that below we replace the spaces in condition names with “.”
prior to creating a "DESeqDataSet" object.
conds <- gsub(" ", ".", conds)
dds <- DESeqDataSetFromMatrix(countData = exprs(sultan),
colData = data.frame(cell.line = factor(conds)),
design = ~ cell.line)
dds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingfilter <- HTSFilter(dds, s.len=25, plot=FALSE)$filteredData
class(filter)## [1] "DESeqDataSet"
## attr(,"package")
## [1] "DESeq2"dim(filter)## [1] 5143 4res <- results(filter, independentFiltering=FALSE)
head(res)## log2 fold change (MLE): cell.line Ramos.B.cell vs HEK293T
## Wald test p-value: cell.line Ramos.B.cell vs HEK293T
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSG00000000003 15.49081 -7.226373 1.579373 -4.575469 4.75154e-06
## ENSG00000000419 15.41426 0.952434 0.718812 1.325012 1.85167e-01
## ENSG00000000457 18.49191 -0.887415 0.653819 -1.357280 1.74692e-01
## ENSG00000000460 9.70452 0.289614 0.874092 0.331331 7.40395e-01
## ENSG00000001036 7.84776 -6.245476 1.701730 -3.670075 2.42479e-04
## ENSG00000001167 77.33272 -0.372907 0.323513 -1.152680 2.49042e-01
## padj
## <numeric>
## ENSG00000000003 2.75504e-05
## ENSG00000000419 2.90428e-01
## ENSG00000000457 2.77505e-01
## ENSG00000000460 8.23853e-01
## ENSG00000001036 9.16964e-04
## ENSG00000001167 3.64284e-01The filtered data remain an object of class DESeqDataSet, and subsequent
functions from DESeq2 (such as the results summary function )
may be called directly upon it.
4 Alternative normalization using EDAseq
As a final example, we illustrate the use of the HTSFilter package with an
alternative normalization strategy, namely the full quantile normalization
method in the EDAseq package; such a step may be useful when the TMM or
DESeq normalization methods are not appropriate for a given dataset. Once
again, we create a new object of the appropriate class with the function
newSeqExpressionSet and normalize data using the betweenLaneNormalization
function (with which="full") in EDAseq.
library(EDASeq)
ses <- newSeqExpressionSet(exprs(sultan),
phenoData=pData(sultan))
ses.norm <- betweenLaneNormalization(ses, which="full")Subsequently, HTSFilter is applied to the normalized data (again using
s.len=25), and the normalization method is set to norm="none". We may then
make use of the on vector in the results, which identifies filtered and
unfiltered genes (respectively) with 0 and 1, to identify rows in the original
data matrix to be retained.
filter <- HTSFilter(counts(ses.norm), conds, s.len=25, norm="none",
plot=FALSE)
head(filter$on)
table(filter$on)5 Session Info
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
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## other attached packages:
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## [3] Biobase_2.62.0 MatrixGenerics_1.14.0
## [5] matrixStats_1.0.0 GenomicRanges_1.54.0
## [7] GenomeInfoDb_1.38.0 IRanges_2.36.0
## [9] S4Vectors_0.40.0 BiocGenerics_0.48.0
## [11] edgeR_4.0.0 limma_3.58.0
## [13] HTSFilter_1.42.0 BiocStyle_2.30.0
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## [58] zlibbioc_1.48.0References
Anders, Simon, and Wolfgang Huber. 2010. “Differential Expression Analysis for Sequence Count Data.” Genome Biology 11 (R106): 1–28.
Bourgon, Richard, Robert Gentleman, and Wolfgang Huber. 2010. “Independent Filtering Increases Detection Power for High-Throughput Experiments.” PNAS 107 (21): 9546–51.
Dillies, Marie-Agnès, Andrea Rau, Julie Aubert, Christelle Hennequet-Antier, Marine Jeanmougin, Nicolas Servant, Céline Keime, et al. 2013. “A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.” Briefings in Bioinformatics 14 (6): 671–83.
Frazee, A. C., B. Langmead, and J. T. Leek. 2011. “ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets.” BMC Bioinformatics 12 (449).
Gentleman, R., V. Carey, W. Huber, and F. Hahne. 2020. genefilter: methods for filtering genes from microarray experiments.
Hansen, Kasper D., Rafael A. Irizarry, and Zhijin Wu. 2012. “Removing technical variability in RNA-seq data using conditional quantile normalization.” Biostatistics 13 (2): 204–16.
Rau, A., M. Gallopin, G. Celeux, and F. Jaffrézic. 2013. “Data-Based Filtering for Replicated High-Throughput Transcriptome Sequencing Experiments.” Bioinformatics 29 (17): 2146–52.
Risso, Davide, Katja Schwartz, Gavin Sherlock, and Sandrine Dudoit. 2011. “GC-content normalization for RNA-seq data.” BMC Bioinformatics 12 (480).
Robinson, Mark D., and Alicia Oshlack. 2010. “A scaling normalization method for differential expression analysis of RNA-seq data.” Genome Biology 11 (R25).
Sultan, M., M. H. Schulz, H. Richard, A. Magen, A. Klingenhoff, M. Scherf, M. Seifert, et al. 2008. “A Global View of Gene Activity and Alternative Splicing by Deep Sequencing of the Human Transcriptome.” Science 15 (5891): 956–60.