TEKRABber 1.4.0
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)
library(SummarizedExperiment) # load it if you are running this tutorial
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 69
In 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 have save the data for this tutorial.
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.
# load built-in data
data(speciesCorr)
hmGeneCorrInput <- assay_tekcorrset(speciesCorr, "gene", "human")
hmTECorrInput <- assay_tekcorrset(speciesCorr, "te", "human")
chimpGeneCorrInput <- assay_tekcorrset(speciesCorr, "gene", "chimpanzee")
chimpTECorrInput <- assay_tekcorrset(speciesCorr, "te", "chimpanzee")
hmCorrResult <- corrOrthologTE(
geneInput = hmGeneCorrInput,
teInput = hmTECorrInput,
corrMethod = "pearson",
padjMethod = "fdr"
)
chimpCorrResult <- corrOrthologTE(
geneInput = chimpGeneCorrInput,
teInput = chimpTECorrInput,
corrMethod = "pearson",
padjMethod = "fdr"
)
head(hmCorrResult)
## geneName teName pvalue coef padj
## 1 ENSG00000143125 L1MD 0.271964872 0.38497828 0.9990235
## 2 ENSG00000143125 MSTA 0.335873091 0.34036703 0.9990235
## 3 ENSG00000143125 MLT1N2-int 0.966658172 0.01524552 0.9990235
## 4 ENSG00000143125 LTR57 0.067870603 0.59794954 0.9990235
## 5 ENSG00000143125 HERVK11-int 0.001028058 0.87118988 0.3210294
## 6 ENSG00000143125 LTR5 0.855235258 -0.06647109 0.9990235
appTEKRABber()
:TEKRABber provides an app function for you to
quickly view your result. First, you will need to assign the
differentially expressed orthologs/TEs results, correlation results and
metadata as global variables: appDE
, appRef
, appCompare
and
appMeta
. See the following example.
#create global variables for app-use
appDE <- hmchimpDE
appRef <- hmCorrResult
appCompare <- chimpCorrResult
appMeta <- meta # this is the same one in DE analysis
appTEKRABber()
In the Expression tab page, (1) you can specify your input gene and TE. The result will show in box plots with data points in normalized log2 expression level (2) DE analysis result will show in table including statistical information (3) Correlation result will indicate if these selected pairs are significantly correlated and the value of correlation coefficients.