HT Expression Data Analysis
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1 HT Expression Data Analysis 台大農藝系劉力瑜 08/03/2018 1
2 HT Transcriptomic Data Microarray RNA-seq
3 HT Transcriptomic Data Microarray RNA-seq
4 Workflow Data import Preprocessing* Visualization DE analysis* Adjust p-values for multiple comparisons Cluster analysis * Different methods are used for microarray and RNA-seq data
5 R / Bioconductor for HT Transcriptomic Data "affylmgui" for Affymetrix microarrays "limma" for microarrays in general "DESeq" for RNA-seq data
6 Affymetrix Microarrays Example data: (GSE59533) Expression data from Zea mays cultivars Tietar and DKC
7 Get GEO Data using R # Install "GEOquery" package in Bioconductor > source(" > bioclite("geoquery") > library(geoquery) # 取得 GSE59533 的 CEL 檔案 : > gse0 = getgeosuppfiles("gse59533") > gse0 # 取得下載後檔案存放位置 ; 解壓縮檔案
8 > source(" > bioclite("affylmgui") > library(affylmgui) > affylmgui() # mac OS
9 File -> New
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12 Target File Format The file at the right is known as "RNA Targets" file in affylmgui. It describes the experimental conditions for each of the 12 arrays. The file should be: In tab-delimited text format. Having 3 columns in the file The column headings must appear exactly as shown: Name: the unique name for each chip FileName: Affymetrix.CEL file name for each chip Target: Used by affylmgui to group the arrays into different classes (for downstream differential expression analysis).
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19 Normalization for RNAseq There are two main sources of systematic variability that require normalization. (1) RNA fragmentation during library construction causes longer transcripts to generate more reads compared to shorter transcripts present at the same abundance in the sample (3&4). (2) The variability in the number of reads produced for each run causes fluctuations in the number of fragments mapped across samples (1&2).
20 Normalization for RNAseq Single-end reads: use reads per kilobase of transcript per million mapped reads (RPKM) metric 10 9 x R / (N x L) Pair-end reads: use analogous fragments per kilobase of transcript per million mapped reads (FPKM) metric
21 Scaling Method in DESeq
22 DE Analysis for RNAseq DESeq (DESeq2) is an BioC package: Assume the read counts are distributed as negative binomial (NB) distribution. 1. Estimate the variance for NB distribution 2. Hypothesis testing under NB distribution
23 DESeq2 Input from count matrix: ctdata.tab gene T1a T1b T2 T3 N1 N2 Gene_ Gene_ Gene_ Gene_ Gene_ Gene_ Gene_ Gene_ Gene_ Gene_ Gene_ Gene_ Gene_ (18761 genes) (6 samples)
24 DESeq2 > library('deseq2') > samplecountdata = read.delim("data/ctdata.tab") > samplecoldata = DataFrame( condition=as.factor(c("treated","treated", "treated","treated","control","control")), row.names=colnames(samplecountdata)) > dds = DESeqDataSetFromMatrix( countdata = samplecountdata, coldata = samplecoldata, design = ~ condition)
25 DESeq2 > dds = DESeq(dds) > res = results(dds) > res = res[order(res$padj),] > plotma(dds) > write.csv(as.data.frame(res), file="condition_treated_results.csv") # save normalized read counts > norm.cts = counts(dds, normalized=true) > write.csv(norm.cts, file="normalizedcounts.csv")
26 DESeq2 # LRT for mutiple levels > coldata(dds)$condition = as.factor(c("t1","t1","t2","t2","ctrl","ctrl")) > coldata(dds)$condition = relevel(coldata(dds)$condition, "ctrl") > ddslrt = DESeq(dds,test="LRT", reduced= ~ 1) > reslrt=results(ddslrt) > mcols(ddslrt,use.names=true)[1:3,] # when there is no replicate > trt = c("t1a","t1b") > dds.short = DESeqDataSetFromMatrix(countData = samplecountdata[,1:2], + coldata = DataFrame(condition=as.factor(trt), row.names=trt), + design = ~ condition) > dds.short = DESeq(dds.short) > plotma(dds.short)
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