In Linlab-slu/TSSr: TSS sequencing data analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Alternatively, you can install the development version directly from GitHub using devtools:

devtools::install_github("Linlab-slu/TSSr")

And load TSSr:

library(TSSr)

Citation

If you use TSSr, please cite the following article:

citation("TSSr")

Getting help

For general questions about the usage of TSSr, use the official Bioconductor support forum and tag your question "TSSr". We strive to answer questions as quickly as possible.

For technical questions, bug reports and suggestions for new features, we refer to the TSSr github page.

Quick start

# Load the example data
data("exampleTSSr")
myTSSr <- exampleTSSr

Or create a new TSSr object

# Provide bam files
inputFiles <- c("S01.sorted.bam", "S02.sorted.bam", "S03.sorted.bam", "S04.sorted.bam")
myTSSr <- new("TSSr", genomeName = "BSgenome.Scerevisiae.UCSC.sacCer3"
          ,inputFiles = inputFiles
          ,inputFilesType= "bam"
          ,sampleLabels = c("SL01","SL02","SL03","SL04")
          ,sampleLabelsMerged = c("control","treat")
          ,mergeIndex = c(1,1,2,2)
          ,refSource = "saccharomyces_cerevisiae.SGD.gff"
          ,organismName = "saccharomyces cerevisiae")
myTSSr

# Get TSS
getTSS(myTSSr)

TSS data processing

# Merge replicates
mergeSamples(myTSSr)
# Normalization
normalizeTSS(myTSSr)
# TSS filtering
filterTSS(myTSSr, method = "TPM", tpmLow = 0.1)

TSS clustering

# TSS clustering 
clusterTSS(myTSSr, method = "peakclu",peakDistance=100,extensionDistance=30
         ,localThreshold = 0.02,clusterThreshold = 1
         ,useMultiCore=FALSE, numCores=NULL)

# Aggregating consensus clusters
consensusCluster(myTSSr, dis = 50, useMultiCore = FALSE)

Core promoter shape

# Calculating core promoter shape score
shapeCluster(myTSSr,clusters = "consensusClusters", method = "PSS",
             useMultiCore= FALSE, numCores = NULL)

Annotation core promoters

# Assign clusters to the annotated features
annotateCluster(myTSSr,clusters = "consensusClusters",filterCluster = TRUE,
              filterClusterThreshold = 0.02, annotationType = "genes"
              ,upstream=1000, upstreamOverlap = 500, downstream = 0)

Differential expression analysis

# Assign clusters to the annotated features
deGene(myTSSr,comparePairs=list(c("control","treat")), 
       pval = 0.01,useMultiCore=FALSE, numCores=NULL)

Core promoter shifts

# Calcuate core promoter shifts
shiftPromoter(myTSSr,comparePairs=list(c("control","treat")), pval = 0.01)

Session info

sessionInfo()

Linlab-slu/TSSr documentation built on Oct. 24, 2023, 8:32 p.m.