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Clustering methods for scRNA-Seq S IN GLE-CELL RN A-S EQ W ORK F - PowerPoint PPT Presentation

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Clustering methods for scRNA-Seq S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R Fanny Perraudeau Senior Data Scientist, Whole Biome Mouse epithelium dataset 1 2 Cell Stem Cell, Fletcher et al, Deconstructing Olfactory Stem Cell Trajectories at

  1. Clustering methods for scRNA-Seq S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R Fanny Perraudeau Senior Data Scientist, Whole Biome

  2. Mouse epithelium dataset 1 2 Cell Stem Cell, Fletcher et al, Deconstructing Olfactory Stem Cell Trajectories at Single Cell Resolution SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  3. Clustering methods hierarchical clustering k-means clustering Challenges What is the number of clusters? What is a cell type or the expected granularity? Scalability: in scRNA-Seq experiments the number of cells could be millions, tools developed for single-cell data don't scale well. SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  4. Create Seurat object library(Seurat) library(SingleCellExperiment) seuset <- CreateSeuratObject( raw.data = assay(sce), normalization.method = "LogNormalize", scale.factor = 10000, meta.data = as.data.frame(colData(sce)) ) seuset <- ScaleData(object = seuset) seuset An object of class seurat in project SeuratProject 100 genes across 94 samples. SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  5. Perform Clustering seuset <- FindClusters( object = seuset, reduction.type = "pca", dims.use = 1:10, resolution = 1.8, print.output = FALSE ) SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  6. Plot Clusters PCAPlot( object = seuset, group.by = "ident", pt.shape = "publishedClusters") SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  7. Let's practice! S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R

  8. Differential expression analysis S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R Fanny Perraudeau Senior Data Scientist, Whole Biome

  9. Typical work�ow SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  10. DE methods Bulk RNA-Seq methods: edgeR DESeq2 Single-cell methods: Single Cell Differential Expression (SCDE) Model-based Analysis of Single-cell Transcriptomics (MAST) SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  11. Fit zero-in�ated regression using MAST library(MAST) zlm <- zlm(~ celltype + cngeneson, sce) summary <- summary(zlm, doLRT = "celltype9") summary Fitted zlm with top 2 genes per contrast: ( log fold change Z-score ) primerid celltype9 celltype15 cngeneson Cyp2a5 -14.5 -7.6* 1.5 Gap43 21.9* 2.3 3.0 Ncam1 15.4 1.3 4.7* Stmn2 16.7 2.2 5.0* Stmn3 25.8* 1.1 1.9 Ugt2a1 -14.7 -8.0* 2.8 SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  12. Fit zero-in�ated regression using MAST # get summary table fit <- summary$datatable # pvalues and logFC fit <- merge(fit[contrast=='celltype9' & component=='H', .(primerid, `Pr(>Chisq)`)], fit[contrast=='celltype9' & component=='logFC', .(primerid, coef)], by='primerid') SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  13. Adjusted p-values # adjusted p-values fit[, padjusted:=p.adjust(`Pr(>Chisq)`, 'fdr')] # result table res = data.frame(gene = fit$primerid, pvalue = fit[,'Pr(>Chisq)'], padjusted = fit$padj, logFC = fit$coef) head(res) gene Pr..Chisq. padjusted logFC 1 1810011O10Rik 7.256038e-15 1.422753e-14 -10.767717 2 5730409K12Rik 1.446961e-24 1.607735e-23 12.544552 3 Actr1b 1.753458e-07 2.112600e-07 7.043411 4 Ado 2.940357e-08 3.630071e-08 7.980018 5 Ak1 3.579921e-17 9.944224e-17 11.003118 6 Anxa1 4.423790e-10 6.059987e-10 -7.718933 SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  14. Let's practice! S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R

  15. Visualization of DE genes S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R Fanny Perraudeau Senior Data Scientist, Whole Biome

  16. Volcano plot ggplot(res, aes(x=logFC, y=-log10(padjusted), color=mostDE)) + geom_point() + ggtitle("Volcano") + xlab("log2 FC") + ylab("-log10 adjusted p-value") SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  17. Heatmap library(NMF) norm <- assay(sce[mostDE, ], "logcounts") norm <- as.matrix(norm) aheatmap(norm, annCol = colData(sce)$publishedClusters) SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  18. Typical work�ow https://hemberg-lab.github.io/scRNA.seq.course/index.html A step-by-step work�ow for low-level analysis of single-cell RNA-seq data (Lun et al). Bioconductor work�ow for single-cell RNA sequencing (Perraudeau et al). SINGLE-CELL RNA-SEQ WORKFLOWS IN R

  19. Let's practice! S IN GLE-CELL RN A-S EQ W ORK F LOW S IN R

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