Functional annotation

2023-06-11

scPlant provides methods for Gene Set Enrichment Analysis (GSEA) in model plants. We recommend CellFunTopic package to visualize the GSEA result and perform topic modelling, revealing cellular programs shared across cell types or exclusive to a particular cell type.

We provide a real scRNA-seq data of Arabidopsis thaliana (Zhang et al., 2019) as example data, download here

Gene Set Enrichment Analysis (GSEA)

library(scPlant)
SeuratObj <- RunGSEA_plant(SeuratObj, by = 'GO', GeneIDtype = 'TAIR', minpct = 0.25)

Visualize the GSEA result

Refer to https://compbioNJU.github.io/CellFunTopic for more visualization of GSEA result.

library(CellFunTopic)
pathway_unique_shared(SeuratObj, by = "GO", fontsize_row = 4, scale = "row")

hierarchyplot_tree(SeuratObj, by = "GO", topaths = 4, cluster_cutree_k = 14, pathway_cutree_k = 14,
                   vertex.label.cex=0.5, edge.max.width=1, vertex.size.cex=0.7)

Topic modelling

SeuratObj <- runLDA(SeuratObj, k = 23)

Visualize the topic modelling result

Refer to https://compbioNJU.github.io/CellFunTopic for more visualization of topic modelling result.

ldaOut <- SeuratObj@misc$ldaOut
betaDF <- tidytext::tidy(ldaOut, matrix = "beta")
pws <- ID2Description(SeuratObj, by = "GO")
betaDF <- dplyr::mutate(betaDF, descrip=unname(pws[term]))
gammaDF <- tidytext::tidy(ldaOut, matrix = "gamma")

cluster_topic_hmp(ldaOut)

plot_sankey(gammaDF, topn=1, plotHeight = 600)

topicNW3(betaDF, topn=10, pws = pws)

topicProb(SeuratObj, topic=21, pointSize=0.1) + Seurat::NoAxes()

Topterms_Topic(betaDF, Topic = 21, topn = 20)

cosine_network_term(SeuratObj, radius = 0.2)