Software, Algorithms, and Computational Methods

MCMICRO is an open-source end-to-end processing pipeline for multiplexed whole tissue images and tissue microarrays. It consists of customizable modules that sequentially perform image processing and quantification steps, including stitching, registration, cell segmentation, single cell quantification, and visualization. Each module is containerized with Docker, making it possible to deploy MCMICRO across an array of compute environments, including local machines, job-scheduling clusters and, cloud environments like AWS. MCMICRO is undergoing active development, and modules are regularly added and improved as part of the growing MCMICRO community. 

Minerva is a suite of light-weight software tools that enables interactive viewing and fast sharing of large image data. With Minerva Author, users can import an image and generate a multi-waypoint, annotated Minerva Story that walks viewers through their data. Minerva Stories can be hosted on the web and viewed through a web browser, making it possible to easily share large (~500 GB) image data with no download required. 

SCIMAP is a scalable toolkit for analyzing spatial molecular data. SCIMAP is compatible with spatial datasets mapped to X-Y coordinates and can be integrated with many single-cell analysis toolkits. SCIMAP supports preprocessing, phenotyping, visualization, clustering, spatial analysis, and differential spatial testing, and can efficiently analyze large datasets containing millions of cells.

The Small Molecule Suite (SMS) is a free, open-access tool. The goal of the SMS is to help scientists work with the targets of molecular probes, approved drugs, and other drug-like molecules, while understanding the complexity of polypharmacology — the phenomenon that acknowledges that virtually all molecules bind multiple proteins. The SMS combines data from the ChEMBL database with data from the Laboratory of Systems Pharmacology. The methodology of calculating selectivities and similarities are explained in Moret et al. Cell Chem Biol 2019 

CyLinter is quality control software for multiplexed microscopy images that identifies and removes single cell data that has been corrupted by artifacts. Artifacts can result from optical aberrations (e.g., from contaminating lint or hair on the sample) or image-processing errors (e.g., errors in single cell segmentation) and can confound downstream analyses. CyLinter guides users through the process of removing noisy data in an interactive, visual environment and improves the quality of the resulting single cell data.

INDRA (Integrated Network and Dynamical Reasoning Assembler) is an automated model assembly system. At the core of INDRA are its knowledge-level assembly procedures. INDRA draws on natural language processing systems and structured databases to assemble mechanistic knowledge into coherent models such as causal graphs and dynamical models. To do this, INDRA corrects systematic input errors, resolves redundancies, infers missing information, filters for scope, and assesses the reliability of the gathered knowledge. 

ASHLAR (Alignment by Simultaneous Harmonization of Layer/Adjacency Registration) is an open source Python package that combines multi-tile microscopy images into a high-dimensional mosaic image. For multi-cycle imaging methods (like CyCIF), ASHLAR also aligns images from different cycles with a high level of accuracy.

shinyDepMap is a web-tool to explore the Cancer Dependency Map (DepMap) project data of the Broad Institute (version 19q3), to help users identify and characterize essential genes among all protein-encoding genes. DepMap is a powerful drug discovery tool. Its portal website provides individual genes’ dependency from both CRISPR and RNAi data. We combined the CRISPR and RNAi dependency data into a unified score, and built this non-programmer biologist-friendly tool, shinyDepMap, which allows 1) to compare efficacy and selectivity of a gene loss across 15,847 protein-encoding genes, and 2) genes that work together in pathways or complexes.

This web-tool can be used to predict the efficacy and selectivity of future drugs with a known target gene, identify targets of highly selective drugs, identify maximally sensitive cell lines for testing a drug, “Target hop”, i.e., navigate from an undruggable protein with the desired selectivity profile to more druggable targets with similar profiles, and identify novel pathways needed to cancer cell growth and survival.

SYLARAS (SYstemic Lymphoid Architecture Response ASsessment) is a preclinical research platform for the interrogation of systemic immune response to disease and therapy. The approach combines multiplex immunophenotyping with biological computation to transform complex single-cell datasets into a visual compendium of the time and tissue-dependent changes occurring in immune cell frequency and/or function in response to an arbitrary immune stimulus (e.g. tumor model, infectious or autoimmune disease, vaccine, immunotherapy, etc.).

SYLARAS is deployed in three stages. In the first stage, longitudinal immunophenotyping data are collected from mouse lymphoid organs of test and control subjects in a high-throughput manner by multiplex flow cytometry. In the second stage, raw FCS files are spectrally compensated and filtered for viable cells before undergoing a systematic immune cell subset identification procedure. In the final stage, the pre-processed data are computationally analyzed using an open-source data analysis tool scripted in the Python programming language and run at the command-line of a personal computer. This leads to the generation of a set of data-rich graphical dashboards (1 per immune cell type) that together portray systemic immune response to a given experimental perturbation.