I have a data set from 5 patient samples with three different time series (ID, CR, REl), in which CR is used as control and ID and Rel are treated samples.
currently the design looks like this
Patients type condition BM_Rel_pat_4 patient_4 Rel BM_Rel_pat_5 patient_5 Rel BM_Rel_pat_6 patient_6 Rel BM_Rel_pat_7 patient_7 Rel BM_Rel_pat_8 patient_8 Rel BM_ID_Pat_4 patient_4 ID BM_ID_Pat_5 patient_5 ID BM_ID_Pat_6 patient_6 ID BM_ID_Pat_7 patient_7 ID BM_ID_Pat_8 patient_8 ID BM_CR_pat4 patient_4 CR BM_CR_pat5 patient_5 CR BM_CR_pat6 patient_6 CR BM_CR_pat7 patient_7 CR BM_CR_pat8 patient_8 CR
The code for design with two condition (ID and REl as treated and CR as control)
single_pat = read.table( "/home/alva/AML_patients/BM_ID_Rel_CR/ID_REl_CR_BM_counts",header=TRUE, row.names=1 ) head(single_pat) single_patDesign = data.frame(row.names = colnames( single_pat ),type=as.factor(c("patient_4","patient_5","patient_6","patient_7","patient_8")),condition =as.factor(c("Rel","Rel","Rel","Rel","Rel","ID","ID","ID","ID","ID","CR","CR","CR","CR","CR"))) condition = single_patDesign$condition type=single_patDesign$type colData <- pData(single_pat)[,c("condition","type")] cds <-DESeqDataSetFromMatrix(countData=single_pat,colData=single_patDesign, design=~condition ) dds <- DESeq(cds) res <- results(dds)
--- This is one method I already tried.
But, in addition to that I would like to get De genes by comparing ID vs CR and Rel Vs CR within the same analysis. This case how could I show the design in Deseq2.I wanted to do this Design for getting a cluster dendrogram , with DE gene set with three separated clusters.Please may I know this approach is correct..or whether I can approach it differently
sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-pc-linux-gnu (64-bit) locale:  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C  LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8  LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8  LC_PAPER=de_DE.UTF-8 LC_NAME=C  LC_ADDRESS=C LC_TELEPHONE=C  LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C attached base packages:  parallel stats graphics grDevices utils datasets methods  base other attached packages:  gplots_2.14.2 RColorBrewer_1.0-5  genefilter_1.42.0 DESeq2_1.0.19  RcppArmadillo_0.4.450.1.0 Rcpp_0.11.3  lattice_0.20-29 Biobase_2.20.1  GenomicRanges_1.12.5 IRanges_1.18.4  BiocGenerics_0.6.0 loaded via a namespace (and not attached):  annotate_1.38.0 AnnotationDbi_1.22.6 bitops_1.0-6  caTools_1.17.1 DBI_0.3.1 gdata_2.13.3  grid_3.0.2 gtools_3.4.1 KernSmooth_2.23-13  locfit_1.5-9.1 RSQLite_0.11.4 splines_3.0.2  stats4_3.0.2 survival_2.37-7 XML_3.98-1.1  xtable_1.7-4
Thank You for any assistance