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Software and R/Python Packages Used for Single-Cell and Xenium Analysis

This document lists all software packages and libraries used for single-cell RNA-seq, single-nucleus multiome (snRNA-seq + snATAC-seq), and Xenium spatial transcriptomics analyses in this project, along with their versions.

Core Single-Cell Analysis Packages

R Packages

Primary Single-Cell Analysis

  • Seurat v5.3.0 - Primary framework for single-cell and spatial transcriptomics analysis
  • Signac v1.14.0 - Single-cell chromatin accessibility analysis (for snATAC-seq)
  • qs v0.27.3 - Fast serialization for R objects (used for storing large Seurat objects)
  • Matrix v1.7-3 - Sparse and dense matrix classes and methods
  • future v1.49.0 - Parallel processing framework for R

Integration and Clustering

  • harmony v1.2.3 - Integration of single-cell datasets
  • reticulate v1.42.0 - R interface to Python (for SCVI integration)

Cell Type Annotation and Analysis

  • scCustomize v3.0.1 - Custom plotting functions for Seurat objects
  • MAST v1.32.0 - Model-based Analysis of Single-cell Transcriptomics (differential expression)

Python Packages (for Xenium and Integration)

Note: Python packages are distributed across multiple conda environments. See envs/README.md for environment-specific package lists.

Spatial Transcriptomics

  • scanpy - Single-cell analysis in Python (used for Xenium data processing)
    • v1.11.2 in seurat5_env (via pip)
    • v1.9.5 in banksy_env (for Banksy clustering)
    • v1.11.4 in 3d-analysis_env (for 3D reconstruction)
  • anndata v0.11.4 - Annotated data objects for single-cell genomics (in seurat5_env via pip)
  • squidpy - Spatial single-cell omics analysis
    • v1.6.5 in banksy_env (for Banksy clustering)
    • v1.2.2 in 3d-analysis_env (for 3D reconstruction)
  • spatialdata v0.4.0 - Spatial omics data structures (in banksy_env)
  • spatialdata_io v0.2.0 - I/O for spatial omics data (including Xenium) (in banksy_env)

Clustering and Integration

  • banksy - Spatial clustering algorithm (Banksy clustering for Xenium). Used via banksy_env environment
  • harmonypy v0.0.10 - Harmony integration in Python (in seurat5_env via pip, also in banksy_env)
  • secuer v1.1 - Additional spatial analysis tools (in banksy_env via pip)

Dimensionality Reduction

  • umap-learn - Uniform Manifold Approximation and Projection
    • v0.5.7 in seurat5_env (via pip)
    • v0.5.4 in banksy_env
    • v0.5.9.post2 in 3d-analysis_env
  • sklearn (scikit-learn) v1.7.0 - Machine learning, including PCA (in seurat5_env via pip)

Image Processing (for Morphological Annotation)

  • Morph - Spatial transcriptomics toolset for tumor boundary detection and morphological operations. Installation: Must be installed from GitHub after setting up morph_env: 
    pip install git+https://github.com/ding-lab/morph.git
    See: https://github.com/ding-lab/morph
  • skimage (scikit-image) v0.25.2 - Image processing
  • scipy v1.16.2 - Scientific computing

Data Manipulation and Visualization

  • numpy v2.3.0 - Numerical computing
  • pandas v2.2.3 - Data manipulation and analysis
  • matplotlib v3.10.3 - Plotting library

Gene Set Enrichment and Pathway Analysis

  • AUCell v1.28.0 - Gene set enrichment scoring using AUC (Area Under the Curve)
  • GSVA v2.0.7 - Gene Set Variation Analysis
  • GSEABase v1.68.0 - Base classes and methods for Gene Set Enrichment Analysis
  • gProfileR v0.7.0 - Functional enrichment analysis

Chromatin Accessibility Analysis (snATAC-seq)

  • chromVAR v1.28.0 - Chromatin variation analysis
  • TFBSTools v1.44.0 - Transcription factor binding site analysis
  • JASPAR2020 v0.99.10 - Transcription factor binding site database
  • ChIPseeker v1.42.1 - ChIP peak annotation and visualization
  • motifmatchr v1.28.0 - Motif matching in genomic regions
  • SummarizedExperiment v1.36.0 - Container for matrix-like genomic data
  • BiocParallel v1.40.0 - Parallel evaluation for Bioconductor
  • BSgenome.Hsapiens.UCSC.hg38 v1.4.5 - Human reference genome (hg38)
  • EnsDb.Hsapiens.v100 v0.0.1 - Ensembl database for human (v100)
  • EnsDb.Hsapiens.v86 v2.99.0 - Ensembl database for human (v86)
  • ensembldb v2.30.0 - Ensembl database interface
  • GenomeInfoDb v1.42.0 - Genome information database
  • GenomicRanges v1.58.0 - Representation and manipulation of genomic intervals

Cell-Cell Communication

  • CellChat v2.2.0 - Analysis of cell-cell communication from single-cell data

Visualization

  • ComplexHeatmap v2.22.0 - Advanced heatmap visualization
  • circlize v0.4.16 - Circular visualization
  • ggplot2 v3.5.2 - Grammar of graphics plotting
  • ggpubr v0.6.0 - Publication-ready plots based on ggplot2
  • ggrepel v0.9.6 - Text and label geoms for ggplot2
  • ggalluvial v0.12.5 - Alluvial plots
  • ggrastr v1.0.2 - Rasterization for ggplot2
  • patchwork v1.3.0 - Composing plots
  • cowplot v1.1.3 - Publication-ready theme for ggplot2
  • viridis v0.6.5 - Color scales for visualization
  • RColorBrewer v1.1-3 - Color palettes
  • grid v4.4.3 - Grid graphics system (base R)
  • gridExtra v2.3 - Additional grid graphics functions
  • EnhancedVolcano v1.24.0 - Enhanced volcano plots

Data Manipulation and Analysis

  • tidyverse v2.0.0 - Collection of R packages for data science
  • dplyr v1.1.4 - Data manipulation
  • tidyr v1.3.1 - Tidy data
  • readr v2.1.5 - Read rectangular data
  • purrr v1.0.4 - Functional programming tools
  • data.table v1.17.4 - Fast data manipulation
  • forcats v1.0.0 - Tools for working with categorical variables
  • tibble v3.2.1 - Modern data frames
  • magrittr v2.0.3 - Forward pipe operator

Statistical Analysis

  • survival v3.8-3 - Survival analysis
  • survminer v0.5.0 - Survival analysis visualization
  • rstatix v0.7.2 - Pipe-friendly framework for basic statistical tests
  • clinfun v1.1.5 - Clinical trial design and analysis
  • broom v1.0.8 - Convert statistical objects to tidy tibbles
  • car v3.1-3 - Companion to Applied Regression
  • emmeans v1.11.2-8 - Estimated marginal means
  • scales v1.4.0 - Scale functions for visualization and formatting
  • cutpointr v1.2.1 - Determine and evaluate optimal cutpoints

Differential Expression and Bulk RNA-seq

  • DESeq2 v1.46.0 - Differential gene expression analysis
  • edgeR v4.4.2 - Empirical analysis of digital gene expression data
  • limma v3.62.2 - Linear models for microarray and RNA-seq data
  • CMScaller v2.0.1 - Consensus Molecular Subtype (CMS) classification for colorectal cancer

TCGA and Public Data Analysis

  • TCGAbiolinks v2.34.1 - Download and analyze TCGA data
  • biomaRt v2.62.1 - Interface to BioMart databases
  • AnnotationDbi v1.68.0 - Annotation database interface
  • org.Hs.eg.db v3.20.0 - Human genome-wide annotation database

Machine Learning and Classification

  • glmnet v4.1-8 - Lasso and elastic-net regularized generalized linear models
  • caret v6.0-94 - Classification and regression training
  • FactoMineR v2.12 - Multivariate exploratory data analysis
  • factoextra v1.0.7 - Extract and visualize results of multivariate data analyses

Additional Utilities

  • googlesheets4 v1.1.1 - Read Google Sheets from R
  • optparse v1.7.5 - Command-line option parser
  • argparse v2.2.5 - Command-line argument parsing (Python)
  • logging - Logging facility (Python, standard library)
  • pickle - Object serialization (Python, standard library)
  • csv - CSV file handling (Python, standard library)
  • os - Operating system interface (Python, standard library)
  • time - Time-related functions (Python, standard library)

Additional Bioconductor Packages

  • DelayedMatrixStats v1.28.1 - Delayed matrix operations
  • matrixStats v1.5.0 - Matrix statistics
  • genefilter v1.88.0 - Methods for filtering genes from microarray experiments

Package Availability

All packages listed above are available in the conda environments specified in envs/. Some packages may be optional dependencies or used only in specific analysis workflows. For exact package versions and availability, refer to the conda environment YAML files in envs/.

Version Information

  • R Version: R 4.4.3 (see envs/seurat5_env.yml)
  • Seurat: v5.3.0
  • Python:
    • v3.13.3 in seurat5_env (for Xenium Banksy clustering and spatial analysis)
    • v3.13.7 in 3d-analysis (for 3D reconstruction)
    • v3.10.19 in morph_env (for morphological annotation)

Note: Exact package versions are specified in the conda environment YAML files in envs/. For reproducible analysis, always use the conda environments rather than installing packages individually.

System Requirements

  • Operating System: Linux (tested on RHEL 7)
  • RAM: 30GB+ recommended for large Seurat objects
  • Storage: Sufficient space for large spatial transcriptomics datasets

Installation Notes

Recommended: Use the conda environment files for reproducible setup:

# For R analysis (Seurat, Bioconductor, etc.)
conda env create -f envs/seurat5_env.yml
conda activate seurat5_env

# For morphological annotation
conda env create -f envs/morph_env.yml
conda activate morph_env
# Install morph from GitHub
pip install git+https://github.com/ding-lab/morph.git

# For 3D reconstruction and spatial analysis
conda env create -f envs/3d-analysis_env.yml
conda activate 3d-analysis

Alternative manual installation (not recommended for reproducibility):

R packages can be installed from CRAN or Bioconductor:

# Bioconductor packages
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("Seurat", "Signac", "AUCell", "ComplexHeatmap", ...))

# CRAN packages
install.packages(c("tidyverse", "qs", "future", ...))

Python packages can be installed via pip or conda:

pip install scanpy anndata squidpy spatialdata spatialdata-io banksy harmonypy

See envs/README.md for more details on environment setup.

Package Documentation Structure

This repository contains multiple documentation files for packages and environments. See PACKAGE_DOCUMENTATION.md for a complete guide to all documentation files.

  1. Conda Environment Files (envs/ directory):

    • seurat5_env.yml - Complete R analysis environment (Seurat v5, Bioconductor, Python packages)
    • morph_env.yml - Morphological annotation environment
    • 3d-analysis_env.yml - 3D reconstruction and spatial analysis environment
    • These YAML files are the source of truth for reproducible environment setup

  2. Version Tables (machine-readable):

    • packages_versions_table.md - R packages with versions (markdown table)
    • python_packages_versions_table.md - Python packages with versions (markdown table)

  3. Documentation Files:

    • Software_packages_list.md (this file) - Detailed documentation with descriptions
    • Software_packages_concise.md - Concise version for methods section

Complete Package List with Versions

For complete package lists with exact versions:

  • Complete environments: See envs/ directory for conda environment YAML files (source of truth)
  • Quick reference: See Software_packages_concise.md for a concise list organized by category

To recreate the analysis environment, use the conda environment files in envs/:

conda env create -f envs/seurat5_env.yml
conda activate seurat5_env