The cBioPortal for Cancer Genomics is an open-source software platform that enables interactive, exploratory analysis of large-scale cancer genomics data sets with a biologist-friendly interface

Google BigQuery is a massively-parallel analytics engine ideal for working with tabular data. HTAN single cell and metadata BigQuery tables are available via the CRDC ISB Cancer Gateway in the Cloud

R and Python computational notebooks illustrate how HTAN data in Google BigQuery can be accessed and analyzed. Notebooks are available via the ISB-CGC Community Notebooks repository.

Minerva is a suite of software tools for interpreting and interacting with complex images, organized around a guided analysis approach.

The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown.

CellxGene is an interactive viewer for single-cell expression.

MCMICRO is a software pipeline for generating single cell data from multiple tissue imaging technologies.

This implementation of Mask R-CNN is designed for single-cell instance segmentation in the context of multiplexed tissue imaging. The model generates bounding boxes and segmentation masks for each instance of an object in the image. The code is based on the implementation of Mask R-CNN by Matterport in Python 3, Keras, and TensorFlow. The purpose of this Mask R-CNN implementation is to wrap its functions for ease of use and to provide a more streamlined pipeline for single-cell instance segmentation using Mask R-CNN. Features: (1) optimized parameters for segmenting cells in multiplexed tissue imaging, (2) solution for segmenting batch and large images (3) support for training on custom datasets,

Computational tool for end-to-end unsupervised and supervised analyses of single-cell spatial maps and enables direct discovery of conditional-specific cell-cell communication patterns across variable spatial scales. This implementation is published and citable at: https://doi.org/10.1038/s41592-023-02124-2

UCSC Xena is an online exploration tool for multiplexed imaging and multi-omic single-cell data

A Galaxy (https://galaxyproject.org/) web service for cancer data analyses with a focus on HTAN analytics: multiplexed tissue imaging, single-cell omics, and machine learning.

A comprehensive workbench for single cell ATAC-seq data Processing, QC, analysis and visualization

De novo construction of a signaling network between two cell types using single-cell transcriptomics data

Processing, quality assessment, and analysis of single cell methylation data

imputation of scRNA-seq data by using bulk RNA-seq as a constraint

Predict the association between DNA methylation and expression using both gene- and cell-dependent statistical features.

Code for 'Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms'.

This is a base implementation of the Multi-Encoder Variational Autoencoder (ME-VAE). Uninformative features, especially transformational image features, present a problem for feature extraction in single cell images. It can be hard to disentangle these uninformative features from the output of a conventional VAE. This architecture allows the analyst to remove several uninformative/undesired features simultaneously to improve downstream analysis. This implementation is published and citable in here: https://doi.org/10.1038/s42003-022-03218-x

Speedy Histological-to-ImmunoFluorescent Translation

Whole-slide microscopy image stitching and registration in Python

Toolkit for analyzing spatial imaging data

Automated quality control and cell identification of droplet-based single-cell data using dropkick

Systematic comparison of single-cell and single-nucleus RNA-sequencing methods

A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors

scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data

ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

Decontamination of ambient RNA in single-cell RNA-seq with DecontX

Adjacent cell marker lateral spillover compensation and reinforcement for multiplexed images

RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging

Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images

High-dimensional multiplexed immunohistochemical characterization of immune contexture in human cancers

MIRIAM: A machine and deep learning single‐cell segmentation and quantification pipeline for multi‐dimensional tissue images

Deep learning segmentation of triple-negative breast cancer (TNBC) patient derived tumor xenograft (PDX) and sensitivity of radiomic pipeline to tumor probability boundary

Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms

A framework for multiplex imaging optimization and reproducible analysis

A quantitative framework for evaluating single-cell data structure preservation by dimensionality reduction techniques

DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing

pyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction

CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data

scMRMA: single cell multiresolution marker-based annotation

Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data

Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer

Quantitative assessment of cell population diversity in single-cell landscapes

Dysregulated ligand-receptor interactions from single-cell transcriptomics

Celda: a Bayesian model to perform co-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data

Scalable and model-free detection of spatial patterns and colocalization

Strategies for accurate cell type identification in CODEX multiplexed imaging data

SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning

A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis

Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR

Scope2Screen: Focus+ Context Techniques for Pathology Tumor Assessment in Multivariate Image Data

Unmasking the immune microecology of ductal carcinoma in situ with deep learning

Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin

VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts

SEACells uses an archetype-based approach to assign all cells sharing the same cell state to a metacell. Metacells overcome sparsity in single-cell transcriptomic and epigenomic data, improving scalability and robustness, and also aggregate cells at much finer resolution than clustering.

BayesPrism is a Bayesian approach for deconvolving both cell type proportions and their contribution to gene expression from bulk RNA-seq data or from aggregated spatial profiling data, such as Visium spots. BayesPrism uses single-cell RNA-seq data with cell type labels as prior information.

PhylogicNDT is an integrated suite of tools to reconstruct the clonal architecture and relative timings of genetic events using a coherent probabilistic framework25,26 and have successfully applied it in several contexts