Function reference • CellFunTopic

Pre-processing Functions used for pre-processing.
`readData()`	Load data from different sources
`CalMTpercent()`	Calculate the proportion of transcripts mapping to mitochondrial genes
`DetectGeneIDtype()`	Detect the gene ID type of Seurat object
`QCfun()`	quality control according to 'nCount_RNA', 'nFeature_RNA', 'percent.mt', 'CellsPerGene'
`RunSeurat()`	basic single cell analysis with Seurat
`RunGSEA()`	Perform Gene Set Enrichment Analysis (GSEA) on Seurat object
Visualization of GSEA Functions used for visualization of GSEA Result.
`circleplot()`	circle plot of clusters
`clustercorplot()` `clustercorplot_jaccard()`	circle plot of clusters
`Cosine_networkByGSEA()`	Network showing cosine similarity between clusters according to GSEA result
`edge_bundling_GSEA()`	Hierarchical edge bundling plots helps visualizing correlation or similarity between clusters
`embeddedplot()`	show embedded histogram or pie chart on UMAP/TSNE plot
`GOboxplot()`	boxplot showing enrichment score of child or parent GOs of specific GO
`GOcircleplot()`	show pathways and genes in chord diagram
`gseaHeatmap()`	Heatmap of GSEA result
`pathway_unique_shared()`	Heatmap showing unique and shared pathways of clusters.
`hierarchyplot_tree()`	show relationship between clusters and pathways
`pathwayScatterplot()`	Scatter plot showing pathway enrichment score
`ID2Description()`	Get pathway IDs and their corresponding description
`simplifyEnrichmentplot()`	cluster functional terms into groups by clustering the similarity matrix of the terms
`scPalette()`	Generate colors from a customed color palette
`scPalette2()`	Generate colors from a customed color palette
`scPalette3()`	Generate colors from a customed color palette
Topic modeling and visualization Functions used for topic modeling and visualization.
`runLDA()`	Run topic modeling with GSEA result
`cluster_topic_hmp()`	Heatmap showing probability between topics and clusters
`plot_sankey()`	Sankey diagram showing the best assigned topic of each cluster
`cosineheatmap()`	Heatmap showing cosine similarity between topics
`cosinehmp_cluster()`	Heatmap showing cosine similarity between clusters
`cosine_network_cluster()`	Network showing cosine similarity between clusters
`cosine_network_term()`	Network of cosine similarity between terms
`topicProb()`	Show topic activity on UMAP/TSNE cell map
`Topterms_Topic()`	Show top terms of each topic
`hist_cluster_topic()`	barplot to show probability of assigned topics in clusters
`hist_topic_term()`	barplot to show top terms of each topic
`topicNW1()`	network showing top terms of topics
`topicNW2()`	network showing top terms of topics
`topicNW3()`	echart4r network showing top terms of topics
`umap_cluster()`	UMAP on cluster-topic probability matrix
`wordcloud_topic()` `wordcloud_topic_3D()`	word cloud of top terms of topic
`lda.rep()`	calculate metrics to find the best number of topics
`plotindex()`	plot metrics to find the best number of topics
Transfer topic model
`transfer_LDA()`	Transfer LDA model from reference data to query data
`cosine_heatmap2()`	Heatmap showing cosine similarity or pearson correlation between reference data and query data
`cosine_hive()`	Hive diagram showing cosine similarity between reference data and query data
`cosine_network_cluster2()`	network showing cosine similarity between reference data and query data
`sankey_comparison()`	Sankey diagram comparing the reference LDA model and query data
Transfer annotation Functions used for annotation transfer from reference to query data.
`oversample_ref()`	Oversample the reference data
`predictFun()`	Predict cell types of query data based on reference data
Built-in Shiny APP Visualize analysis result in Built-in Shiny APP.
`visualize_in_shiny()`	visualize the Seurat object in a built-in shiny app
Data Internal data.
`GO2level`	GO terms and their corresponding level or ontology.
`HCL_ldaOut`	built-in reference topic model derived from HCL.
`MCA_ldaOut`	built-in reference topic model derived from MCA.
`MTGENE`	Mitochondrial genes of 3 species.
`txid_gene_MT`	Mitochondrial genes of all species.
`txid_species_gene_MT`	Mitochondrial genes of all species.

Reference

Pre-processing

Visualization of GSEA

Topic modeling and visualization

Transfer topic model

Transfer annotation

Built-in Shiny APP

Data