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We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensional space. We validate our method on single-cell RNA sequencing, chromatin accessibility and imaging transcriptomics datasets, and show that Augur outperforms existing methods based on differential gene expression. Augur identified the neural circuits restoring locomotion in mice following spinal cord neurostimulation.
Pubmed ID: 32690972
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Software performing alignment of high-throughput RNA-seq data. Aligns RNA-seq reads to reference genome using uncompressed suffix arrays.
View all literature mentionsJava toolset for working with next generation sequencing data in the BAM format.
View all literature mentionsImaris provides range of capabilities for working with three dimensional images. Uses flexible editing and processing functions, such as interactive surface rendering and object slicing capabilities. And output to standard TIFF, Quicktime and AVI formats. Imaris accepts virtually all image formats that are used in confocal microscopy and many of those used in wide-field image acquisition.
View all literature mentionsSoftware Java tools for analyzing Drop-seq data. Used to analyze gene expression from thousands of individual cells simultaneously. Analyzes mRNA transcripts while remembering origin cell transcript.
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