def runDESeq( infiles, outfile ):
'''estimate differential expression using DESeq.
The final output is a table. It is slightly edited such that
it contains a similar output and similar fdr compared to cuffdiff.
'''
PipelineWindows.runDE( infiles, outfile, "deseq.dir", method = "deseq" )
def countReadsWithinWindows(infiles, outfile ):
'''build read counds for windows.'''
bedfile, windowfile = infiles
PipelineWindows.countReadsWithinWindows( bedfile,
windowfile,
outfile,
counting_method = PARAMS['tiling_counting_method'] )
def runEdgeR( infiles, outfile ):
'''estimate differential methylation using EdgeR
This method applies a paired test. The analysis follows
the example in chapter 11 of the EdgeR manual.
'''
PipelineWindows.runDE( infiles, outfile, "edger.dir", method = "edger" )
def aggregateWindowsReadCounts( infiles, outfile ):
'''aggregate tag counts for each window.
coverageBed outputs the following columns:
1) Contig
2) Start
3) Stop
4) Name
5) The number of features in A that overlapped (by at least one base pair) the B interval.
6) The number of bases in B that had non-zero coverage from features in A.
7) The length of the entry in B.
8) The fraction of bases in B that had non-zero coverage from features in A.
For bed: use column 5
For bed6: use column 7
For bed12: use column 13
Tiles with no counts will not be output.
'''
PipelineWindows.aggregateWindowsReadCounts( infiles, outfile )
def outputAllWindows( infile, outfile ):
'''output all bed windows.'''
PipelineWindows.outputAllWindows( infile, outfile )
def buildDMRStats( infile, outfile ):
'''compute differential methylation stats.'''
method = os.path.dirname(infile)
method = P.snip( method, ".dir")
PipelineWindows.buildDMRStats( infile, outfile, method=method )
def runFilterAnalysis( infiles, outfile ):
'''output windows applying a filtering criterion.
Does not apply a threshold.
'''
PipelineWindows.outputRegionsOfInterest( infiles, outfile )
def aggregateWindowsReadCounts( infiles, outfile ):
'''aggregate tag counts for each window.
'''
PipelineWindows.aggregateWindowsReadCounts( infiles, outfile )
def loadContextStats(infiles, outfile):
''' load context mapping statistics into context_stats table '''
PipelineWindows.loadSummarizedContextStats(infiles, outfile)
def buildContextStats(infiles, outfile):
''' build mapping context stats '''
PipelineWindows.summarizeTagsWithinContext(
infiles[0], infiles[1], outfile)
