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Cumulus

Cumulus is a cloud-based data analysis framework for large-scale single cell and single nucleus RNA-seq.

Cumulus contains three modules:

(1) a platform to process sequence data and generate gene-count matrices. View Cumulus on  and read the full .

(2) Pegasus, an analysis package that supports common scRNA-seq analysis tasks, including quality filters, batch correction, dimension reduction (tSNE, UMAP, etc.), and differential expression analysis. View Pegasus on  and see the complete .

(3) Cirrocumulus, an interactive visualization application. View Cirrocumulus on .

Citation: Li B, Gould J, Yang Y, Sarkizova S, et al. (2020). Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq. Nature Methods 17: 793–798; doi:.

Tangram

​​Tangram is a python package to align single cell RNA-seq data to spatial data from the same region. The method is compatible with any sc/snRNA-seq protocol and spatial method, provided that the datasets were generated from the same tissue/anatomical region and share a subset of common genes.

Tangram is available on ; a tutorial can be found .

Citation:  Biancalani T, Scalia G, Buffoni L, Avasthi R, et al. (2021). Deep learning and alignment of spatially-resolved single-cell transcriptomes with Tangram. Nature Methods 18: 1352–1362; doi:.

ddqc

Our pipeline for data-driven quality control for scientific discovery in single-cell transcriptomics is available on GitHub. 

Subramanian A, Alperovich M, Yang Y, Li B (2021). Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics. bioRxiv; doi:.

Power analysis for spatial omics

Our framework to generate in silico tissues to perform a spatial power analysis is available on GitHub. 

Citation: Baker EAG, Schapiro D, Dumitrascu B, Vickovic S, et al. (2022). Power analysis for spatial omics. bioRxiv; doi:. 

ECLIPSER

ECLIPSER combines expression and alternative splicing QTL gene mapping and single-cell expression data to identify causal cell types and genes for complex traits. 

ECLIPSER is available on . 

Citation: Rouhana JM, Wang J, Eraslan G, Anand S, et al. (2021). ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci. bioRxiv; doi:.

scPhere

ScPhere is a dimensionality reduction tool for scRNA-seq data that embeds cells into low-dimensional hyperspherical or hyperbolic spaces. ScPhere resolves cell crowding, corrects multilevel batch factors, and facilitates interactive visualization for exploratory data analysis.

scPhere is available on .

Citation: Ding J, Regev A (2021). Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces. Nature Communications 12: 2554; doi:.

DIALOGUE

DIALOGUE is an R package to identify multi-cellular programs—sets of coregulated genes across different cell types—from scRNA-seq data.

DIALOGUE is available on ; a tutorial can be found . 

Citation: Jerby-Arnon L, Regev A (2022). DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nature Biotechnology; doi:.

MAUDE

MAUDE is an R package to quantify the impact of guide RNAs on the expression of a target gene.

MAUDE is available on .

Citation: Boer CG de, Ray JP, Hacohen N, Regev A (2020). MAUDE: inferring expression changes in sorting-based CRISPR screens. Genome Biology 21: 134; doi:.

scSVA

single-cell Scalable Visualization and Analytics

scSVA is an R package for interactive visualization and exploratory analysis for single cell omics datasets.

scSVA is available on .

Citation: Tabaka M, Gould J,  Regev A (2019). scSVA: an interactive tool for big data visualization and exploration in single-cell omics. bioRxiv; doi:.

Code & Computational Pipelines

Perturb-CITE-seq

The computational pipeline associated with Perturb-CITE-seq—a protocol to combine perturbation screening with multiplex antibody staining and scRNA-seq—is available on .

If you use this code in your work, please cite:

Frangieh CJ, Melms JC, Thakore PI, Geiger-Schuller KR, et al. (2021). Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion. Nature Genetics 53: 332–341; doi:.

inCITE-seq

inCITE-seq is a multi-omics method to quantify intranuclear protein levels alongside snRNA-seq.

We recently applied inCITE-seq to evaluate changes in transcription factor levels in the mouse hippocampus in response to pharmacological intervention; the associated code is available on .

If you use this code in your work, please cite:

Chung H, Parkhurst CN, Magee EM, Phillips D, et al. (2021). Joint single-cell measurements of nuclear proteins and RNA in vivo. Nature Methods 18: 1204–1212; doi:.

COVID-19 Tissue Atlases

The code associated with our recent COVID-19 tissue atlases project is available on .

If you use this code in your work, please cite:

Delorey TM, Ziegler CGK, Heimberg G, Normand R, et al. (2021). COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature 595: 107–113; doi:.

SM-Omics

SM-Omics is our high-throughput spatial transcriptomics protocol, which can be combined with antibody-based protein profiling methods for multimodal analyses. We recently validated SM-Omics on mouse olfactory bulb and cortex; the associated code is available on .

If you use this code in your work, please cite:

Vickovic S, Lötstedt B, Klughammer J, Segerstolpe Å, et al. (2020). SM-Omics is an automated platform for high-throughput spatial multi-omics. Nature Communications 13: 795 doi.