require(singleCellTK) require(celda) require(knitr) require(gridExtra) require(ggplot2) sce <- params$sce cluster <- params$cluster variable <- params$variable pdf <- params$pdf showSession <- params$showSession dev <- ifelse(isTRUE(pdf), c("png"), c("png", "pdf")) knitr::opts_chunk$set( echo = TRUE, cache = FALSE, cache.lazy = FALSE, # don't do lazy-loading for big objects cache.comments = FALSE, fig.align = "center", fig.keep = "all", dev = dev )
This report will visualize results from the plotClusterAbundance
function will visualize the differential abundance in two given variables, by making bar plots that presents the cell counting and fraction in different cases. The analysis presented here shows
plotClusterAbundance(inSCE, cluster, variable, combinePlot = "none")
sessionInfo()
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