発現変動遺伝子解析の概要

RNA-Seq with Bioconductor in R

Mary Piper

Bioinformatics Consultant and Trainer

ヒートマップ：発現変化

RNA-Seq with Bioconductor in R

発現変動のばらつき

RNA-Seq with Bioconductor in R

JCI論文

RNA-Seq with Bioconductor in R

データセットの紹介：Smoc2

腎臓線維症

RNA-Seq with Bioconductor in R

RNA-Seq実験

RNA-Seq with Bioconductor in R

RNA-Seqカウントの分布

ggplot(raw_counts) +
  geom_histogram(aes(x = wt_normal1), stat = "bin", bins = 200) +
  xlab("Raw expression counts") +
  ylab("Number of genes")

カウント分布

RNA-Seq with Bioconductor in R

発現変動解析の準備：DESeq2オブジェクト

dds <- DESeqDataSetFromMatrix(countData = rawcounts,
                        colData = metadata,
                        design = ~ condition)

カウントデータ

RNA-Seq with Bioconductor in R

発現変動解析の準備：メタデータ

# Create vectors containing metadata for the samples
genotype <- c("wt", "wt", "wt", "wt", "wt", "wt", "wt") 
condition <- c("normal", "fibrosis", "normal", 
               "fibrosis", "normal", "fibrosis", "fibrosis") 

# Combine vectors into a data frame
wt_metadata <- data.frame(genotype, wildtype)


# Create the row names with the associated sample names
rownames(wt_metadata) <- c("wt_normal3", "wt_fibrosis3", "wt_normal1", 
                "wt_fibrosis2", "wt_normal2", "wt_fibrosis4", "wt_fibrosis1")
RNA-Seq with Bioconductor in R

発現変動解析の準備：メタデータ

メタデータ

RNA-Seq with Bioconductor in R

では、練習しましょう！

RNA-Seq with Bioconductor in R

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