Tweet
DEXseq input file
Hello!
I am trying to assess expression level of a particular gene and its isoforms in 2 treatment conditions using DEXseq.
I am new to DEXseq and have a question regarding its input file.
I have a RNAseq data set of two experimental conditions without any replicates. For each experimental condition the sequencing data has been stored as seven fastq files. Since the data is paired ended I have 14 fastq files for each experimental condition. These fastq files were entered into tophat2 as paired ended data and concatenated resulting seven accepted_hits.bam files together, so that I have .bam files for each experimental condition to feed into HTseq.
I have copied the command for count.txt file generation here. In that I have stated my data are paired-ended while putting a single merged .bam file in. Is this the correct way to put my data in? I want to clarify this because I get some errors in following steps.
python dexseq_count.py output_from_dexseq_annotation.gff -p yes -f bam -r name mutant_merged.bam DEXseq_count.txt
This is the error I get with its command.
> dxd = estimateDispersions( dxd )
using supplied model matrix
Error in estimateDispersionsFit(object, fitType = fitType, quiet = quiet) :
all gene-wise dispersion estimates are within 2 orders of magnitude from the minimum value, and so the standard curve fitting techniques will not work.
One can instead use the gene-wise estimates as final estimates:
dds <- estimateDispersionsGeneEst(dds)
dispersions(dds) <- mcols(dds)$dispGeneEst
...then continue with testing using nbinomWaldTest or nbinomLRT
In addition: Warning message:
In MulticoreParam(workers = 1) :
MulticoreParam not supported on Windows. Use SnowParam instead.
If anyone could give your opinion on this it would be a huge help.
Thank you very much
I am trying to assess expression level of a particular gene and its isoforms in 2 treatment conditions using DEXseq.
I am new to DEXseq and have a question regarding its input file.
I have a RNAseq data set of two experimental conditions without any replicates. For each experimental condition the sequencing data has been stored as seven fastq files. Since the data is paired ended I have 14 fastq files for each experimental condition. These fastq files were entered into tophat2 as paired ended data and concatenated resulting seven accepted_hits.bam files together, so that I have .bam files for each experimental condition to feed into HTseq.
I have copied the command for count.txt file generation here. In that I have stated my data are paired-ended while putting a single merged .bam file in. Is this the correct way to put my data in? I want to clarify this because I get some errors in following steps.
python dexseq_count.py output_from_dexseq_annotation.gff -p yes -f bam -r name mutant_merged.bam DEXseq_count.txt
This is the error I get with its command.
> dxd = estimateDispersions( dxd )
using supplied model matrix
Error in estimateDispersionsFit(object, fitType = fitType, quiet = quiet) :
all gene-wise dispersion estimates are within 2 orders of magnitude from the minimum value, and so the standard curve fitting techniques will not work.
One can instead use the gene-wise estimates as final estimates:
dds <- estimateDispersionsGeneEst(dds)
dispersions(dds) <- mcols(dds)$dispGeneEst
...then continue with testing using nbinomWaldTest or nbinomLRT
In addition: Warning message:
In MulticoreParam(workers = 1) :
MulticoreParam not supported on Windows. Use SnowParam instead.
If anyone could give your opinion on this it would be a huge help.
Thank you very much