TEKRABber 1.6.0
TEKRABber is used to estimate the correlations between genes and transposable elements (TEs) from RNA-seq data comparing between: (1) Two Species (2) Control vs. Experiment. In the following sections, we will use built-in data to demonstrate how to implement TEKRABber on you own analysis.
To use TEKRABber from your R environment, you need to install it using BiocManager:
install.packages("BiocManager")
BiocManager::install("TEKRABber")library(TEKRABber)Gene and TE expression data are generated from randomly picked brain regions FASTQ files from 10 humans and 10 chimpanzees (Khrameeva E et al., Genome Research, 2020). The values for the first column of gene and TE count table must be Ensembl gene ID and TE name:
# load built-in data
data(speciesCounts)
hmGene <- speciesCounts$hmGene
hmTE <- speciesCounts$hmTE
chimpGene <- speciesCounts$chimpGene
chimpTE <- speciesCounts$chimpTE
# the first column must be Ensembl gene ID for gene, and TE name for TE
head(hmGene)## Geneid SRR8750453 SRR8750454 SRR8750455 SRR8750456 SRR8750457
## 1 ENSG00000000003 250 267 227 286 128
## 2 ENSG00000000005 13 2 15 9 5
## 3 ENSG00000000419 260 311 159 259 272
## 4 ENSG00000000457 86 131 100 94 80
## 5 ENSG00000000460 21 17 42 33 55
## 6 ENSG00000000938 162 75 95 252 195
## SRR8750458 SRR8750459 SRR8750460 SRR8750461 SRR8750462
## 1 394 268 102 370 244
## 2 0 1 8 0 2
## 3 408 371 126 211 374
## 4 158 119 46 77 81
## 5 29 50 11 18 20
## 6 137 93 108 197 69In the first step, we use orthologScale() to get orthology information
and calculate the scaling factor between two species for normalizing
orthologous genes. The species name needs to be the abbreviation of
scientific species name used in Ensembl. (Note: (1)This step queries
information using biomaRt and it might need
some time or try different mirrors due to the connections to Ensembl
(2)It might take some time to calculate scaling factor based on your
data size). For normalizing TEs, you need to provide a repeatmasker
annotation table including four columns, (1) the name of TE (2) the
class of TE (3) the average gene length of TE from your reference
species (4) the average gene length from the species you want to
compare. A way to download repeatmasker annotations is to query from
UCSC Genome Table
Browser
and select the RepeatMasker track.
# You can use the code below to search for species name
ensembl <- biomaRt::useEnsembl(biomart = "genes")
biomaRt::listDatasets(ensembl)# In order to save time, we provide the data for this tutorial.
# you can also uncomment the code below and run it for yourself.
data(fetchDataHmChimp)
fetchData <- fetchDataHmChimp
# Query the data and calculate scaling factor using orthologScale():
#' data(speciesCounts)
#' data(hg38_panTro6_rmsk)
#' hmGene <- speciesCounts$hmGene
#' chimpGene <- speciesCounts$chimpGene
#' hmTE <- speciesCounts$hmTE
#' chimpTE <- speciesCounts$chimpTE
#'
#' ## For demonstration, here we only select 1000 rows to save time
#' set.seed(1234)
#' hmGeneSample <- hmGene[sample(nrow(hmGene), 1000), ]
#' chimpGeneSample <- chimpGene[sample(nrow(chimpGene), 1000), ]
#'
#' fetchData <- orthologScale(
#' speciesRef = "hsapiens",
#' speciesCompare = "ptroglodytes",
#' geneCountRef = hmGeneSample,
#' geneCountCompare = chimpGeneSample,
#' teCountRef = hmTE,
#' teCountCompare = chimpTE,
#' rmsk = hg38_panTro6_rmsk
#' )We use DECorrInputs() to return input files for downstream analysis.
inputBundle <- DECorrInputs(fetchData)In this step, we need to generate a metadata contain species name (i.e.,
human and chimpanzee). The row names need to be same as the DE input
table and the column name must be species (see the example below).
Then we use DEgeneTE() to perform DE analysis. When you are comparing
samples between two species, the parameter expDesign should be
TRUE (as default).
meta <- data.frame(
species = c(rep("human", ncol(hmGene) - 1),
rep("chimpanzee", ncol(chimpGene) - 1))
)
meta$species <- factor(meta$species, levels = c("human", "chimpanzee"))
rownames(meta) <- colnames(inputBundle$geneInputDESeq2)
hmchimpDE <- DEgeneTE(
geneTable = inputBundle$geneInputDESeq2,
teTable = inputBundle$teInputDESeq2,
metadata = meta,
expDesign = TRUE
)Here we use corrOrthologTE() to perform correlation estimation
comparing each ortholog and TE. This is the most time-consuming step if
you have large data. For a quick demonstration, we use a relatively
small data. You can specify the correlation method and adjusted
p-value method. The default methods are Pearson’s correlation and FDR.
Note: For more efficient and specific analysis, you can subset your
data in this step to focus on only the orthologs and TEs that you are
interested in.
# we select the 200 rows of genes for demo
hmCorrResult <- corrOrthologTE(
geneInput = hmchimpDE$geneCorrInputRef[c(1:200),],
teInput = hmchimpDE$teCorrInputRef,
corrMethod = "pearson",
padjMethod = "fdr"
)
chimpCorrResult <- corrOrthologTE(
geneInput = hmchimpDE$geneCorrInputCompare[c(1:200), ],
teInput = hmchimpDE$teCorrInputCompare,
corrMethod = "pearson",
padjMethod = "fdr"
)appTEKRABber():TEKRABber provides an app function called appTEKRABber() for you to
quickly view your result and select data that you are interested in.
You will need to install gridlayout to run appTEKRABber() function.
Note: you might need to installed additional packages to run this function.
remotes::install_github('rstudio/gridlayout')
library(plotly)
appTEKRABber(
corrRef = hmCorrResult,
corrCompare = chimpCorrResult,
DEobject = hmchimpDE
)[app.jpg] The first time you opeining the app, you will see the distribution of Gene and TE alongside pvalue axis and coefficient axis in your reference group and comparision group. You can next select the Gene Name and Transposable Elements which will plot a scatterplot indicating their correlations, and also a expression plot showing the differentially expression analysis. This help you to have a first glance at the pair of Gene:TE which you are interested in.
If you want to compare selected genes and TEs (1) from different tissue in same species or (2) control and drug treatment in same tissue in same species, please generate all the input files following the input format. Here we show an example data of prepared input files including expression counts from 10 control and 10 treatment samples. The format of input data: row names should be gene name or id, and column name is your sample id (please see details below).
# load built-in data
data(ctInputDE)
geneInputDE <- ctInputDE$gene
teInputDE <- ctInputDE$te
# you need to follow the input format as below
head(geneInputDE)## control_1 control_2 control_3 control_4 control_5 treatment_6
## ENSG00000180263 1470 2072 1864 2238 2246 2599
## ENSG00000185985 1599 1045 946 1642 2199 665
## ENSG00000144355 517 380 1211 812 48 388
## ENSG00000234003 4 4 14 10 5 9
## ENSG00000257342 1 1 1 2 3 3
## ENSG00000223953 259 830 133 258 850 504
## treatment_7 treatment_8 treatment_9 treatment_10
## ENSG00000180263 2679 2562 2532 2682
## ENSG00000185985 1023 2477 1423 1731
## ENSG00000144355 275 633 59 248
## ENSG00000234003 4 18 13 22
## ENSG00000257342 0 6 1 5
## ENSG00000223953 1143 1500 498 864For DE analysis in the same species, you also use DEgeneTE() function,
however, you need to set the parameter expDesign to FALSE. You
also need to provide a metadata which this time the column name must be
experiment. See demonstration below:
metaExp <- data.frame(experiment = c(rep("control", 5), rep("treatment", 5)))
rownames(metaExp) <- colnames(geneInputDE)
metaExp$experiment <- factor(
metaExp$experiment,
levels = c("control", "treatment")
)
resultDE <- DEgeneTE(
geneTable = geneInputDE,
teTable = teInputDE,
metadata = metaExp,
expDesign = FALSE
)Here we demonstrate using the first 200 rows of genes and all the TEs to calculate their correlations.
controlCorr <- corrOrthologTE(
geneInput = resultDE$geneCorrInputRef[c(1:200),],
teInput = resultDE$teCorrInputRef,
corrMethod = "pearson",
padjMethod = "fdr"
)
treatmentCorr <- corrOrthologTE(
geneInput = resultDE$geneCorrInputCompare[c(1:200),],
teInput = resultDE$teCorrInputCompare,
corrMethod = "pearson",
padjMethod = "fdr"
)
head(treatmentCorr)appTEKRABber():remotes::install_github('rstudio/gridlayout')
appTEKRABber(
corrRef = controlCorr,
corrCompare = treatmentCorr,
DEobject = resultDE
)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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## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
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##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] DESeq2_1.42.0 SummarizedExperiment_1.32.0
## [3] GenomicRanges_1.54.0 IRanges_2.36.0
## [5] S4Vectors_0.40.0 TEKRABber_1.6.0
## [7] BiocStyle_2.30.0
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## loaded via a namespace (and not attached):
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