## ----setup, echo=FALSE, results="hide", message=FALSE-------------------- knitr::opts_chunk$set(tidy=false，cache = true，dev =“ png”，message = false，error = false，parning = true）## libraries quirect（“ Biobase”）require（“ nmf”）require（“ cvxclustr”）## -------------------------------------------- 安装，eval = false ----------------------------------------------------------------------------------------------------------------------------------------------------＃source（“ //www.andersvercelli.com/bioclite.r”）＃bioclite（“ dasc”）## ---- message = false，结果='hide'，eart eval = true -----------------------------------------库（DASC）数据（“ Esgolub”）样品<-c（20,21,28,30）dat <-exprs（esgolub）[1：100，样本] pdat <-pdata（esgolub）[样品，] ##使用nrun = 50或更多，以更好地收敛结果res res <-dasc（edata = dat，pdata = pdat，factor = factor = pdat $ cell，method ='ama'，type = 3，lambda = 1，rank = 2：3，nrun = 5，注释=“ esgolub dataset”）#consensususmap（res）#plot（res）## ----消息= false，eval = true ------------------------------------------------------------------ ##图书馆set.seed（99999）库（deseq2）库（ggplot2）库（pcaexplorer）##数据集rawcounts <-StanfordData $ RAWCOUNTS Metadata <-StanfordData $ Metadata ## ----消息= false，eval = true ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ##使用较小的数据集IDX < - wher（元数据$ tissue％in％c（“脂肪”，“肾上腺”，“ sigmoid”））RAWCOUNTS <-awCounts [，IDX]元数据<-Metadata [idx，] ## ----消息= false，eval = true ------------------------------------------------------------------------------------------------------------------- head(rawCounts) head(metadata) ## ---- message=FALSE, eval=TRUE------------------------------------------- ## Normalizing the dataset using DESeq2 dds <- DESeqDataSetFromMatrix(rawCounts,元数据，设计=〜物种 +组织）dds < - 估计量：ddsFactor（dds）dat < - counts（dds，strormanized = true）lognormalizedCounts <-log2（dat + 1）## ----消息= false，eval = true------------------------------------------- ## PCA plot using rld.dds <- rlog(dds) pcaplot(rld.dds, intgroup=c("tissue","species"), ntop=1000, pcX=1, pcY=2) ## ---- message=FALSE, eval=TRUE------------------------------------------- res <- DASC(edata = dat, pdata = metadata, factor = metadata$tissue, method = 'ama', type = 3, lambda = 1, rank = 2:3, nrun = 10, annotation = 'Stanford Dataset') ## ---- message=FALSE, eval=TRUE------------------------------------------- ## Consensus plot consensusmap(res) ## ---- message=FALSE, eval=TRUE------------------------------------------- ## Residual plot plot(res) ## ---- message=FALSE, eval=TRUE------------------------------------------- ## Batches -- dataset has 6 batches sample.clust <- data.frame(sample.name = colnames(lognormalizedCounts), clust = as.vector(predict(res$fit$`2`)), batch = metadata$seqBatch) ggplot(data = sample.clust, aes(x=c(1:6), y=clust, color=factor(clust))) + geom_point(size = 4) + xlab("Sample Number") + ylab("Cluster Number") ## ---- message=FALSE------------------------------------------------------ sessionInfo()