![]() This will display FeaturePlots of the list of given genes, split by a grouping variable (stimulation condition here). Additionally, genes like CXCL10 which we saw were specific to monocyte and B cell interferon response show up as highly significant in this list as well. Notice that many of the top genes that show up here are the same as the ones we plotted earlier as core interferon response genes. Then we use FindMarkers() to find the genes that are different between stimulated and control B cells. First, we create a column in the meta.data slot to hold both the cell type and stimulation information and switch the current ident to that column. Library ( ggplot2 ) library ( cowplot ) theme_set ( theme_cowplot ( ) ) t.cells <- subset ( bined, idents = "CD4 Naive T" ) Idents ( t.cells ) <- "stim" avg.t.cells <- as.ame ( log1p ( AverageExpression ( t.cells, verbose = FALSE ) $ RNA ) ) avg.t.cells $ gene <- rownames ( avg.t.cells ) cd14.mono <- subset ( bined, idents = "CD14 Mono" ) Idents ( cd14.mono ) <- "stim" <- as.ame ( log1p ( AverageExpression ( cd14.mono, verbose = FALSE ) $ RNA ) ) $ gene <- rownames ( ) genes.to.label = c ( "ISG15", "LY6E", "IFI6", "ISG20", "MX1", "IFIT2", "IFIT1", "CXCL10", "CCL8" ) p1 <- ggplot ( avg.t.cells, aes ( CTRL, STIM ) ) + geom_point ( ) + ggtitle ( "CD4 Naive T Cells" ) p1 <- LabelPoints (plot = p1, points = genes.to.label, repel = TRUE ) p2 <- ggplot (, aes ( CTRL, STIM ) ) + geom_point ( ) + ggtitle ( "CD14 Monocytes" ) p2 <- LabelPoints (plot = p2, points = genes.to.label, repel = TRUE ) p1 + p2Īs you can see, many of the same genes are upregulated in both of these cell types and likely represent a conserved interferon response pathway.īecause we are confident in having identified common cell types across condition, we can ask what genes change in different conditions for cells of the same type. ![]() ![]() We can explore these marker genes for each cluster and use them to annotate our clusters as specific cell types. ![]()
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