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====DEx genes based on sample clustering==== * By looking at the numbers/plots, hierarchical clustering seems right to me. But as >97% samples are overlapped in both method if only cut into 2 groups, I just stick to cut2 and look for the overlapped DEx genes. * I also focus on hierarchical clustering genes too. * DEx gene is identified by DESeq2 library('DESeq2') x=read.table("20chip.TPMmatrix.newID_0.2filtered_0.7allowed_Z1_overDispersed_TPMmatrix.txt",header=TRUE,row.names=1); colData=read.table("20chip.TPMmatrix_0.2-0.7-Z1_overDispersed_clusters.txt",header=TRUE,row.names=1); x[is.na(x)] <- 0 dds <- DESeqDataSetFromMatrix(countData = round(x), colData = colData, design =~ kmean2) dds <- DESeq(dds) res <- results(dds) resOrdered <- res[order(res$padj),] head(resOrdered,20) resSig <- res[ which(res$padj<0.01),] head(resSig[order(resSig$log2FoldChange ),], 10) tail(resSig[order(resSig$log2FoldChange ),], 10) * Gene numbers {| {{table}} | align="center" style="background:#f0f0f0;"|'''''' | align="center" style="background:#f0f0f0;"|'''gene #''' |- | cutree2||406 |- | kmean2||371 |- | common||339 |} [[File:20chips_0.2-0.7-Z1_cutree2.png|400px]] [[File:20chips_0.2-0.7-Z1_cut2-both.png|400px]] [[Media:20chips_0.2-0.7-z1_both_DE-0.01.xlsx]] Blue's in-depth research on these top 27 genes [http://genome-tech.ucsd.edu/LabNotes/index.php/Blue:RNA-Seq_Analyses:C1_hNuc_1000_Dataset] top27 log2 fold change NEAT1 2.003211939 GRIN3A 2.033951837 VAV2 2.062726013 PTPRM 2.080714217 FGF13 2.094894592 RBMS3 2.106795779 MYO16 2.151562063 RP11-123O10.4 2.169064516 KCNIP1 2.200186983 ADRA1A 2.28142167 FAT1 2.321830687 GRIK1 2.363408707 ERBB4 2.368557498 BTBD11 2.467137982 ARX 2.474737422 CTC-321K16.1 2.627130756 MTSS1 2.63965966 BACH1 2.677262451 CXCL14 2.793804062 DNER 2.873455079 ADARB2 2.890106589 GAD1 2.974213273 DLX6-AS1 2.977317694 GAD2 3.233591271 SLC6A1 3.383217471 MAF 3.415295366 SLC6A1-AS1 3.890308415
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