Function reference • scPlant

Cross-species integration

crossSpecies_integrate(): Perform integration on datasets of different species with Seurat.

species_percentage(): Bar plot showing the percentage of cells from different species.

Functional annotation

RunGSEA_plant(): Perform Gene Set Enrichment Analysis (GSEA) on plant single-cell data.

Gene regulatory network construction

ras_exp_hmp(): Heatmaps showing mean regulon activity and TF expression of each cluster.

ras_exp_scatter(): Dimension reduction plot showing regulon activity and TF expression

topRegulons(): Network diagram showing top regulons of each cluster

toptargets(): Network diagram showing top targets of each regulon

ToptargetsofTopregulons(): Network diagram showing top targets of top regulons of each cluster

SpecificityRank(): Dot plot showing top regulons of each cluster according to regulon specificity score(RSS)

targets_MINIEX(): Network diagram showing targets of each regulon

topRegulons_MINIEX(): Network diagram showing top regulons of each cluster

enrich_exp_hmp(): Heatmaps showing cluster enrichment and TF expression of each regulon in each cluster.

enrich_exp_scatter(): Dimension reduction plot showing cluster enrichment and TF expression

BordaRank_MINIEX(): Dot plot showing top regulons of each cluster according to Borda ranking

Paired motif enrichment

processPMET(): pre-process original PMET output and filter with PPI information

PMEThmp(): Heatmap of PMET result

triHmp(): Draw triangle heatmap of PMET result

topPairsNet(): Network diagram showing top pairs of a cluster.

Automatic cell-type annotation

AutoAnnotate_SingleR(): Automatic annotation of Seurat object using SingleR package.

AutoAnnotate_Celaref(): Automatic annotation of Seurat object using Celaref package.

AutoAnnotate_scCATCH(): Automatic annotation of Seurat object using scCATCH package.

AutoAnnotate_Garnett(): Automatic annotation of Seurat object using Garnett package.

AutoAnnotate_CellAssign(): Automatic annotation of Seurat object using CellAssign package.

get_marker_file(): Generate a marker file to run Garnett.

get_marker_scCATCH(): Generate a data frame of marker genes with the required format to run scCATCH.

Cell type decomposition of bulk samples

decomposeBulk(): Decompose bulk RNA-seq samples using a single cell reference

fractionHmp(): Heatmap showing the fraction of cell types in each bulk RNA-seq sample.

meanExpHmp(): Heatmap showing mean expression of top 20 markers of each cell type in each sample.

Pseudotime trajectory inference

RunMonocle2(): Run monocle2 on Seurat object

RunMonocle3(): Run monocle3 on Seurat object

RunSlingshot(): Run slingshot on Seurat object

RunCytoTRACE(): Run CytoTRACE on Seurat object

Built-in Shiny APP

load_shinyApp(): visualize the analysis result in a built-in shiny app

Other visualizaiton

constellationPlot(): Draw Constellation plot with scrattch.hicat package.

RunMetacell(): Run metacell on Seurat object

Internal data

example_Ath: Toy example single-cell expression matrix of Arabidopsis thaliana.

example_Osa: Toy example single-cell expression matrix of Oryza sativa.

example_Zma: Toy example single-cell expression matrix of Zea mays.