Software, Algorithms, and Computational Methods

MCMICRO is an open-source 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 various computing 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 lightweight 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 are hosted on the web and viewed through a web browser (no download required!), making them ideal for sharing large-scale image data as part of tissue atlases.

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. 

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 tool that combines multi-tile microscopy images into high-dimensional mosaic images. For multi-cycle imaging methods (like CyCIF), ASHLAR also aligns images from different cycles with a high level of accuracy. ASHLAR can be used with virtually any unstitched microscope image file and multiplexed imaging method.

shinyDepMap is a web-tool to explore the Cancer Dependency Map (DepMap) data of the Broad Institute (version 19q3). We combined the DepMap CRISPR and RNAi dependency data into a unified score, and built this easy-to-use tool. shinyDepMap 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, identify similar targets that may be easier to drug (i.e., “target hop”), and identify pathways necessary for cancer cell growth and survival.

SYLARAS (SYstemic Lymphoid Architecture Response ASsessment) is a platform for interrogating the systemic immune response to a disease or therapy. SYLARAS encompasses an experimental method for collecting multiplexed flow cytometry data, computer-assisted pre-processing steps that deconvolve and filter data, and a computational analysis regime that generates and displays longitudinal immunophenotyping data on a per-cell type basis. The ability of SYLARAS to generate and display interpretable systemic immune response data makes it a broadly useful preclinical research tool.