Function reference • TOmicsVis

TOmicsVis Shinyapp

tomicsvis(): TOmicsVis shiny app start function.

①Samples Statistics

Statistical sample outliers, correlation among samples, distance among samples, etc.

quantile_plot(): Quantile plot for visualizing data distribution.

box_plot(): Box plot support two levels and multiple groups with P value.

violin_plot(): Violin plot support two levels and multiple groups with P value.

survival_plot(): Survival plot for analyzing and visualizing survival data.

②Traits Analysis

Statistical analysis of traits, such as adding R and P values to box plots and violin plots, and more dimensionality reduction analysis.

corr_heatmap(): Correlation Heatmap for samples/groups based on Pearson algorithm.

pca_analysis(): PCA dimensional reduction analysis for RNA-Seq.

pca_plot(): PCA dimensional reduction visualization for RNA-Seq.

tsne_analysis(): TSNE analysis for analyzing and visualizing TSNE algorithm.

tsne_plot(): TSNE plot for analyzing and visualizing TSNE algorithm.

umap_analysis(): UMAP analysis for analyzing RNA-Seq data.

umap_plot(): UMAP plot for analyzing and visualizing UMAP algorithm.

dendro_plot(): Dendrograms for multiple samples/groups clustering.

③Differential Expression Analyais

Analysis and visualization based on gene expression data and differentially expressed genes.

venn_plot(): Venn plot for stat common and unique gene among multiple sets.

upsetr_plot(): UpSet plot for stat common and unique gene among multiple sets.

flower_plot(): Flower plot for stat common and unique gene among multiple sets.

volcano_plot(): Volcano plot for visualizing differentailly expressed genes.

ma_plot(): MversusA plot for visualizing differentially expressed genes.

heatmap_group(): Heatmap group for visualizing grouped gene expression data.

circos_heatmap(): Circos heatmap plot for visualizing gene expressing in multiple samples.

chord_plot(): Chord plot for visualizing the relationships of pathways and genes.

④Advanced Analysis

Advanced analysis of Transcriptome includes exploring gene expression trends of multiple groups of samples, constructing co expression modules for genes, and exploring genes related to traits in expression modules.

gene_rank_plot(): Gene ranking dotplot for visualizing differentailly expressed genes.

gene_cluster_trend(): Gene cluster trend plot for visualizing gene expression trend profile in multiple samples.

trend_plot(): Trend plot for visualizing gene expression trend profile in multiple traits.

wgcna_pipeline(): WGCNA analysis pipeline for RNA-Seq.

network_plot(): Network plot for analyzing and visualizing relationship of genes.

heatmap_cluster(): Heatmap cluster for visualizing clustered gene expression data.

⑤GO and KEGG Enrichment

GO and KEGG enrichment analysis and visualization.

go_enrich(): GO enrichment analysis based on GO annotation results (None/Exist Reference Genome).

go_enrich_stat(): GO enrichment analysis and stat plot (None/Exist Reference Genome).

go_enrich_bar(): GO enrichment analysis and bar plot (None/Exist Reference Genome).

go_enrich_dot(): GO enrichment analysis and dot plot (None/Exist Reference Genome).

go_enrich_net(): GO enrichment analysis and net plot (None/Exist Reference Genome).

kegg_enrich(): KEGG enrichment analysis based on KEGG annotation results (None/Exist Reference Genome).

kegg_enrich_bar(): KEGG enrichment analysis and bar plot (None/Exist Reference Genome).

kegg_enrich_dot(): KEGG enrichment analysis and dot plot (None/Exist Reference Genome).

kegg_enrich_net(): KEGG enrichment analysis and net plot (None/Exist Reference Genome).

⑥Tables Operations

Quickly manipulate specific Transcriptome data structures.

table_split(): Table split used for splitting a grouped column to multiple columns.

table_merge(): Table merge used to merge multiple variables to on variable.

table_filter(): Table filter used to filter row by column condition.

table_cross(): Table cross used to cross search and merge results in two tables.

Example datasets in TOmicsVis

Example datasets in TOmicsVis.

weight_sex: Weight and Sex traits dataframe.

traits_sex: Length, Width, Weight, and Sex traits dataframe.

survival_data: Survival data as example data for survival_plot function.

gene_expression: All genes in all samples expression dataframe of RNA-Seq.

gene_expression2: Shared DEGs of all paired comparisons in all samples expression dataframe of RNA-Seq.

gene_expression3: Shared DEGs of all paired comparisons in all groups expression dataframe of RNA-Seq.

samples_groups: Samples and groups for gene expression.

degs_lists: Paired comparisons differentially expressed genes (degs) among groups.

degs_stats: All DEGs of paired comparison CT-vs-LT12 stats dataframe.

degs_stats2: All DEGs of paired comparison CT-vs-LT12 stats2 dataframe.

network_data: Network data from WGCNA tan module top-200 dataframe.

gene_go_kegg: GO and KEGG annotation of background genes.

gene_go_kegg2: GO and KEGG annotation of background genes.