splice junction usage between multiple tissues

[frankbioinfo]

Hello @hartleys

Thank you for developing this method.

I had a question,

Can this model be used to detect tissue specific splice junction usage across n=5 tissues? at the same time?

I used dexseq earlier but i fail to find contrast function like DESeq2, is it possible to perform such analysis using JunctionSeq?

Many thanks.

[hartleys]

See section 8.2 of the JunctionSeq user manual.

Note that this will only work if you have at least 2 samples for each tissue type. As always, you need biological replicates.

So say you have 3 levels:

threeLevelVariable <- c("GroupA","GroupA",
        "GroupB","GroupB",
        "GroupC","GroupC");

Load the junctionSeq example dataset:

library("JunctionSeq")
library("JctSeqData")

decoder.file <- system.file("extdata/annoFiles/decoder.bySample.txt",
    package="JctSeqData",
    mustWork=TRUE);
decoder <- read.table(decoder.file,
    header=TRUE,
    stringsAsFactors=FALSE);
gff.file <- system.file(
    "extdata/tiny/withNovel.forJunctionSeq.gff.gz",
    package="JctSeqData",
    mustWork=TRUE);
countFiles <- system.file(paste0("extdata/tiny/",
    decoder$sample.ID,
    "/QC.spliceJunctionAndExonCounts.withNovel.forJunctionSeq.txt.gz"),
    package="JctSeqData", mustWork=TRUE);

And now you can just do the analysis normally:

jscs <- runJunctionSeqAnalyses(sample.files = countFiles,
    sample.names = decoder$sample.ID,
    condition=factor(threeLevelVariable),
    flat.gff.file = gff.file,
    );

writeCompleteResults(jscs, outfile.prefix = "jctseqexampleThreeLvl")

These output files will have multiple columns for the expression estimates of the different levels, and will have the fold changes for each possible pair.

[frankbioinfo]

@hartleys thanks, sorry for late response will try this and keep you posted.

[scseekers]

hi @frankbioinfo

Did you try it? Actually, I have a similar case, wherein I wish to perform all tissue vs all tissue comparison. But the above code gives me all against one (LFCA/B), (LFCA/C), (LFCA/D), where A,B,C,D are tissue types. Also, I get a single significant gene list.