Man pages for kgellatl/SignallingSingleCell
Network Analysis for single cell RNA sequencing Data (sc-RNASeq)

agg_geneNormalization
analyze_rl_networkIdentifies all R / L interactions
calc_agg_bulkCalculate UPM expression values across pData values
calc_libsizeCalculate Library Size
calc_rl_networkIdentifies all R / L interactions
cluster_scCluster Single Cell
construct_ex_scConstruct Expression Set Class
detect_doubletsCalculate UPM expression values across pData values
dim_reduceDimension Reduction
edgeRDEThis will run edgeR to find differentially expressed genes....
extract_communitiesIdentifies all R / L interactions
extract_nodesIdentifies all R / L interactions
filter_rl_networkIdentifies all R / L interactions
findDEgenesThis will perform differential expression using edgeR for...
findDEmarkersThis will perform differential expression using edgeR to find...
flow_filterConstruct Expression Set Class
flow_svmFlow Support Vector Machine
id_markersID markers
id_rlidentify receptors and ligands
merge_ex_scThis will merge pData and fData
norm_scNormalization
plot_densityPlot Density
plot_density_ridgePlot Density
plot_gene_dotsPlot of genes by pData variable
plot_heatmapPlots a heatmap
plot_rl_networkIdentifies all R / L interactions
plot_rl_summaryIdentifies all R / L interactions
plot_scatterCreate a Scatter Plot
plot_tsne_genetSNE Plot on a gene or gene
plot_tsne_metadatatSNE Plot on metadata
plot_violinThis will create a violin plot
plotViolinThis will create a violin plot
pre_filterFilter Data
return_agg_geneNormalization
return_markersReturn markers
save_ggplotSave plot
search_geneSearch Gene
subset_ex_scThis will setset your expression set by some variable in...
subset_genesSubset Genes
kgellatl/SignallingSingleCell documentation built on Dec. 10, 2018, 3:34 a.m.