In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

knitr::opts_chunk$set(warning=FALSE, message=FALSE)

sampleNames <- unique(colData(data)[[biological.group]])

showSession <- FALSE
seuratRun <- knitr::knit_child(system.file("rmarkdown/seurat", "reportSeuratRun.Rmd", package = "singleCellTK"), quiet = TRUE, envir = environment())

showSession <- FALSE
seuratResults <- knitr::knit_child(system.file("rmarkdown/seurat", "reportSeuratResults.Rmd", package = "singleCellTK"), quiet = TRUE, envir = environment())

Summary {-}

The input data included r numFeatures features and r numCells cells from r numSamples samples divided through the group r biological.group namely r sampleNames. The r assayNames(data)[1] feature count matrix from the data was normalized using LogNormalize method which normalized as well as log-transformed the data. From r numFeatures total features, a subset of r variable.features features that exhibited high cell-to-cell variation were selected for the downstream analysis to better capture the biological variation using vst (mean-to-variance) feature selection method. This subset of features was then scaled (z-score) using linear model method and trimmed to a maximum and minimum value of 10 and -10 respectively. PCA was run on this scaled subset of features and r pc.count components were computed from which top r significant_PC significant components were selected for subsequent clustering analysis. Using the louvain algorithm that uses community-based detection to identify clusters with resolution set to r 0.8, clustering was performed on the scaled subset of features that identified r numClusters clusters in the data. These clusters were then used to run differential expression using wilcox test and identified r numMarkerGenesCluster genes differentially expressed between clusters at significance criteria of q-value < 0.05. The gene markers that defined the main biological group r biological.group were identified using wilcox test that highlighted r numMarkerGenesBio marker genes at q-value < 0.05 significance criteria. Additionatlly, markers r selected.markers from the input pre-selected markers were detected and visualized over UMAP plot.

cat(seuratRun, sep = '\n')
cat(seuratResults, sep = '\n')

showSession <- params$showSession

cat("# Session Information\n\n")

sessionInfo()

compbiomed/singleCellTK documentation built on May 8, 2024, 6:58 p.m.