
Thanks again, Ill try to run DEseq2 now for diff.exp.
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sampleFiles <- list.files(path="/Volumes/timemachine/HTseq_DEseq2",pattern="*.txt") status <- factor(c(rep("Healthy",26), rep("Diabetic",22))) timepoints = as.factor(c(1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2)) sampleTable <- data.frame(sampleName = sampleFiles, fileName = sampleFiles, status=status, timepoints=timepoints) directory <- c("/Volumes/timemachine/HTseq_DEseq2/") des <- formula(~timepoints+status) ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design= des)
The introduction of single-cell sequencing has advanced the ability to study cell-to-cell heterogeneity. Its use has improved our understanding of somatic mutations1, cell lineages2, cellular diversity and regulation3, and development in multicellular organisms4. Single-cell sequencing encompasses hundreds of techniques with different approaches to studying the genomes, transcriptomes, epigenomes, and other omics of individual cells. The analysis of single-cell sequencing data i
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