## ----setup, include=FALSE----------------------------------------------------- library(netSmooth) library(pheatmap) library(SingleCellExperiment) ## ----netsum,echo=FALSE,fig.cap="Network-smoothing concept"-------------------- # All defaults knitr::include_graphics("bckgrnd.png") ## ---- echo=TRUE--------------------------------------------------------------- data(smallPPI) data(smallscRNAseq) ## ---- echo=TRUE, eval=TRUE---------------------------------------------------- smallscRNAseq.sm.se <- netSmooth(smallscRNAseq, smallPPI, alpha=0.5) smallscRNAseq.sm.sce <- SingleCellExperiment( assays=list(counts=assay(smallscRNAseq.sm.se)), colData=colData(smallscRNAseq.sm.se) ) ## ---- echo=TRUE, eval=TRUE---------------------------------------------------- anno.df <- data.frame(cell.type=colData(smallscRNAseq)$source_name_ch1) rownames(anno.df) <- colnames(smallscRNAseq) pheatmap(log2(assay(smallscRNAseq)+1), annotation_col = anno.df, show_rownames = FALSE, show_colnames = FALSE, main="before netSmooth") pheatmap(log2(assay(smallscRNAseq.sm.sce)+1), annotation_col = anno.df, show_rownames = FALSE, show_colnames = FALSE, main="after netSmooth") ## ---- echo=TRUE, eval=FALSE--------------------------------------------------- # smallscRNAseq.sm.se <- netSmooth(smallscRNAseq, smallPPI, alpha='auto') # smallscRNAseq.sm.sce <- SingleCellExperiment( # assays=list(counts=assay(smallscRNAseq.sm.se)), # colData=colData(smallscRNAseq.sm.se) # ) # # pheatmap(log2(assay(smallscRNAseq.sm.sce)+1), annotation_col = anno.df, # show_rownames = FALSE, show_colnames = FALSE, # main="after netSmooth (optimal alpha)") ## ---- echo=TRUE, eval=TRUE---------------------------------------------------- yhat <- robustClusters(smallscRNAseq, makeConsensusMinSize=2, makeConsensusProportion=.9)$clusters yhat.sm <- robustClusters(smallscRNAseq.sm.se, makeConsensusMinSize=2, makeConsensusProportion=.9)$clusters cell.types <- colData(smallscRNAseq)$source_name_ch1 knitr::kable( table(cell.types, yhat), caption = 'Cell types and `robustClusters` in the raw data.' ) knitr::kable( table(cell.types, yhat.sm), caption = 'Cell types and `robustClusters` in the smoothed data.' ) ## ---- echo=TRUE, eval=TRUE---------------------------------------------------- smallscRNAseq <- runPCA(smallscRNAseq, ncomponents=2) smallscRNAseq <- runTSNE(smallscRNAseq, ncomponents=2) smallscRNAseq <- runUMAP(smallscRNAseq, ncomponents=2) plotPCA(smallscRNAseq, colour_by='source_name_ch1') + ggtitle("PCA plot") plotTSNE(smallscRNAseq, colour_by='source_name_ch1') + ggtitle("tSNE plot") plotUMAP(smallscRNAseq, colour_by='source_name_ch1') + ggtitle("UMAP plot") ## ----echo=TRUE, eval=TRUE----------------------------------------------------- pickDimReduction(smallscRNAseq) ## ----------------------------------------------------------------------------- sessionInfo()