Visualization and Integrated System for Transcriptomic Analysis
VISTA streamlines differential expression (DE) analysis workflows for RNA-seq data by providing a unified, SummarizedExperiment-based framework that wraps DESeq2, edgeR, and limma-voom. The package offers:
- 🧬 Unified DE workflow - Single interface for DESeq2, edgeR, and limma-voom
- ⚖️ Consensus DE mode - Optional intersection/union consensus across DESeq2 and edgeR
- 🧪 Design-aware modeling - Add covariates or explicit model formula in DE analysis
- 📊 30+ visualization functions - PCA, volcano plots, heatmaps, expression plots, and more
- 🔬 Functional enrichment - Integrated MSigDB, GO, and KEGG pathway analysis
- 🎨 Consistent aesthetics - Harmonized color palettes and publication-ready themes
- 📝 Automated reporting - Generate comprehensive analysis reports with Quarto
- ✅ Extensive validation - Input checking and clear error messages
- 🧪 Comprehensive tests - Broad test coverage for core workflows
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("VISTA")# Install from GitHub
if (!requireNamespace("pak", quietly = TRUE))
install.packages("pak")
pak::pak("cparsania/VISTA")library(VISTA)
# Primary constructor for end users:
# use create_vista() for standard workflows.
# Load your count matrix and sample metadata
data("count_data", package = "VISTA")
data("sample_metadata", package = "VISTA")
# Example datasets are shipped as tibbles
class(count_data)
class(sample_metadata)
# Create VISTA object with differential expression analysis
vista <- create_vista(
counts = count_data,
sample_info = sample_metadata,
column_geneid = "gene_id",
group_column = "cond_long",
group_numerator = "treatment1",
group_denominator = "control",
log2fc_cutoff = 1.0,
pval_cutoff = 0.05,
method = "deseq2" # or "edger" / "limma"
)
# Advanced users can coerce an existing SummarizedExperiment:
# vista_from_se <- as_vista(se, group_column = "cond_long")
# validate_vista(vista_from_se, level = "full")
# Explore results
vista
names(comparisons(vista))
# compact summary preview
lapply(deg_summary(vista), head, 3)
# Quality control visualizations
get_pca_plot(vista, label_replicates = TRUE)
get_corr_heatmap(vista)
# Differential expression visualizations
get_volcano_plot(vista, sample_comparison = "treatment1_VS_control")
get_ma_plot(vista, sample_comparison = "treatment1_VS_control")
get_deg_count_barplot(vista)
get_deg_count_pieplot(vista)
# Optional: adjust for covariates in DE model
vista_cov <- create_vista(
counts = count_data,
sample_info = sample_metadata,
column_geneid = "gene_id",
group_column = "cond_long",
group_numerator = "treatment1",
group_denominator = "control",
method = "deseq2",
covariates = "cell"
)
# Optional: DESeq2 + edgeR consensus workflow
vista_both <- create_vista(
counts = count_data,
sample_info = sample_metadata,
column_geneid = "gene_id",
group_column = "cond_long",
group_numerator = "treatment1",
group_denominator = "control",
method = "both",
result_source = "consensus"
)
# Switch active DE source post-hoc
vista_both <- set_de_source(vista_both, "edger")
# Expression pattern analysis
genes_of_interest <- get_genes_by_regulation(
vista,
sample_comparisons = "treatment1_VS_control",
regulation = "Up"
)$treatment1_VS_control
get_expression_heatmap(vista, genes = genes_of_interest[1:50], kmeans_k = 3)
get_expression_barplot(vista, genes = genes_of_interest[1:5], stats_group = TRUE)
# Functional enrichment
enrichment_results <- get_msigdb_enrichment(
vista,
sample_comparison = "treatment1_VS_control",
regulation = "Up",
msigdb_category = "H" # Hallmark gene sets
)
get_enrichment_plot(enrichment_results$enrich)# 1. Set up analysis
vista <- create_vista(counts, sample_info, ...)
# 2. Add gene annotations
vista <- set_rowdata(
vista,
orgdb = org.Hs.eg.db,
columns = c("SYMBOL", "GENENAME", "ENTREZID")
)
# 3. Quality control
get_pca_plot(vista)
get_mds_plot(vista)
get_corr_heatmap(vista)
# 4. Differential expression
get_volcano_plot(vista, sample_comparison = "A_VS_B")
get_deg_venn_diagram(vista, sample_comparison = c("A_VS_B", "C_VS_B"))
# 5. Expression patterns
get_expression_heatmap(vista, genes = my_genes, kmeans_k = 4)
get_expression_boxplot(vista, genes = my_genes)
# 6. Functional analysis
go_results <- get_go_enrichment(vista, regulation = "Up", ont = "BP")
kegg_results <- get_kegg_enrichment(vista, regulation = "Down")
gsea_results <- get_gsea(vista, sample_comparison = "A_VS_B")Dimension Reduction: - get_pca_plot() - Principal component analysis - get_mds_plot() - Multidimensional scaling
DE Results: - get_volcano_plot() - Volcano plots with EnhancedVolcano integration - get_ma_plot() - MA plots with customizable labeling - get_deg_count_barplot() - DEG summary statistics - get_deg_count_pieplot(), get_deg_count_donutplot() - Circular DEG composition summaries
Expression Patterns: - get_expression_barplot(), get_expression_boxplot(), get_expression_violinplot() - get_expression_density(), get_expression_joyplot(), get_expression_raincloud() - get_expression_heatmap() - Complex heatmaps with k-means clustering - get_expression_scatter(), get_expression_lineplot(), get_expression_lollipop()
Comparisons: - get_corr_heatmap() - Sample correlation heatmaps - get_deg_venn_diagram() - Overlap between comparisons - plot_deg_alluvial() - Alluvial plots for DEG transitions
Fold-change Analysis: - get_foldchange_scatter(), get_foldchange_barplot(), get_foldchange_matrix() - get_foldchange_heatmap(), get_foldchange_chromosome_plot()
# MSigDB enrichment (Hallmark, C2, C5, etc.)
msig <- get_msigdb_enrichment(
vista,
sample_comparison = "Treatment_VS_Control",
regulation = "Up",
msigdb_category = "H",
from_type = "ENSEMBL"
)
# GO enrichment
go_bp <- get_go_enrichment(vista, regulation = "Up", ont = "BP")
go_mf <- get_go_enrichment(vista, regulation = "Up", ont = "MF")
# KEGG pathway enrichment
kegg <- get_kegg_enrichment(vista, regulation = "Down")
# Gene Set Enrichment Analysis (GSEA)
gsea <- get_gsea(
vista,
sample_comparison = "Treatment_VS_Control",
set_type = "msigdb"
)
# Visualize results
get_enrichment_plot(msig$enrich)
enrichplot::dotplot(go_bp$enrich)
enrichplot::cnetplot(kegg$enrich)# Use different color palettes
data("count_data", package = "VISTA")
data("sample_metadata", package = "VISTA")
vista <- create_vista(
counts = count_data[1:500, ],
sample_info = sample_metadata,
column_geneid = "gene_id",
group_column = "cond_long",
group_numerator = "treatment1",
group_denominator = "control",
group_palette = "Harmonic" # Dark 2, Set 2, Warm, Cold, etc.
)
# Access and modify colors
group_colors(vista)
group_palette(vista)
vista <- set_vista_group_colors(
vista,
c(control = "#264653", treatment1 = "#E76F51")
)
# Optional: set manual colors for multi-comparison objects
vista_multi <- create_vista(
counts = count_data[1:500, ],
sample_info = sample_metadata,
column_geneid = "gene_id",
group_column = "cond_long",
group_numerator = c("treatment1", "control"),
group_denominator = c("control", "treatment1")
)
vista_multi <- set_vista_comparison_colors(
vista_multi,
c(treatment1_VS_control = "#6C5CE7", control_VS_treatment1 = "#00A896")
)
# All plots respect the stored color scheme
get_pca_plot(vista, sample_colors = TRUE)VISTA extends SummarizedExperiment, making it compatible with Bioconductor workflows:
# Standard SummarizedExperiment methods work
assay(vista, "norm_counts")[1:5, 1:5]
colData(vista)
rowData(vista)
metadata(vista)
# Plus VISTA-specific accessors
names(comparisons(vista))
deg_summary(vista)
cutoffs(vista)
norm_counts(vista, summarise = TRUE) # Group-averaged expression-
Vignettes:
- Introduction to VISTA - Complete workflow tutorial
- Color and Palette Design - Consistent and customizable color workflows
- Function Reference - Comprehensive function guide
-
Help Pages:
?create_vista,?get_pca_plot,?get_msigdb_enrichment, etc. -
Example Data:
data("count_data"),data("sample_metadata")
VISTA ships with a YAML-driven report workflow template for publication-ready single-comparison analysis reports.
# 1) Copy and edit the template
file.copy(
system.file("reports", "vista-report-template.yml", package = "VISTA"),
"vista-report.yml"
)
# 2) Render the report
run_vista_report("vista-report.yml")The generated HTML report includes: - QC plots (PCA, MDS, correlation heatmap) - Differential expression views (volcano, MA, DEG summaries) - Expression heatmap and fold-change views - MSigDB/GO/KEGG enrichment (when available) - Downloadable tables and plot assets - Interactive tables with export buttons when DT is installed
If you use VISTA in published research, please cite:
Parsania C (2025). VISTA: Visualization Toolkit for Transcriptomic Analysis.
R package version 0.99.0.
- Issues: GitHub Issues
- Questions: Open a discussion or issue on GitHub
- Bioconductor Support: support.bioconductor.org
VISTA is actively developed with: - ✅ Comprehensive input validation - ✅ Broad test coverage - ✅ Complete roxygen2 documentation - ✅ BiocCheck compliance - ✅ CI/CD integration ready
VISTA/
├── R/ # R source code
│ ├── VISTA-class.R # S4 class definition
│ ├── accessors.R # Accessor methods
│ ├── rnaseq_related.R # DE analysis wrappers
│ ├── viz_related.R # Visualization functions
│ ├── functional_analysis.R # Enrichment analysis
│ └── utils-internal.R # Internal utilities
├── tests/testthat/ # Test suite
├── vignettes/ # Package vignettes
├── data/ # Example datasets
└── man/ # Documentation
GPL-3
Contributions are welcome! Please: 1. Fork the repository 2. Create a feature branch 3. Add tests for new functionality 4. Ensure R CMD check passes 5. Submit a pull request
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
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