A framework that allows for the integration of multiple data types using single cells is applied to understand distinct immune cell states, previously unidentified immune populations, and to interp...

Pooled CRISPR screens coupled with single-cell RNA-sequencing have enabled systematic interrogation of gene function and regulatory networks. Here, we introduce Cas13 RNA Perturb-seq (CaRPool-seq)...

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define...

Overview of exciting new computational and experimental developments for single cell genomics.Slides and additional resources are available at:http://www.sat...

A computational approach to integrate diverse modalities associated with single-cell sequencing datasets can be used to better understand cellular identity and function.

Cell signaling plays a critical role in regulating cellular behavior and fate. While multimodal single-cell sequencing technologies are rapidly advancing, scalable and flexible profiling of cell...

Identifying gene regulatory targets of nuclear proteins in tissues remains a challenge. Here we describe intranuclear Cellular Indexing of Transcriptomes and Epitopes (inCITE-seq), a scalable method...

Nature Methods - Paired-Tag offers a multiomics assay for joint profiling of histone modifications and gene expression in single nuclei, and is applied to mouse frontal cortex and hippocampus for...

Microsoft Excel: 1 — Human Genetics: 0.

Nature Genetics - Pooled CRISPR perturbation screens with multimodal RNA and protein single-cell profiling readout (Perturb-CITE-seq) applied to patient-derived melanoma and tumor-infiltrating...

Cell atlas projects and single-cell CRISPR screens hit the limits of current technology, as they require cost-effective profiling for millions of individual cells. To satisfy these enormous through...

Azimuth: Automated processing, visualization, and annotation of scRNA-seq datasets based on a reference

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of detected molecules in each cell, which can confound biological...

Motivation The Gamma-Poisson distribution is a theoretically and empirically motivated model for the sampling variability of single cell RNA-sequencing counts ([Grün et al ., 2014][1]; [Townes et al...

Nature Methods - scVI is a ready-to-use generative deep learning tool for large-scale single-cell RNA-seq data that enables raw data processing and a wide range of rapid and accurate downstream...

Spatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of...

Nature Biotechnology - A new computational approach enables integrative analysis of disparate single-cell RNA–sequencing data sets by identifying shared patterns of variation between cell...

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental...

Nature Biotechnology - Reference mapping is extended beyond scRNA-seq to single-cell epigenetic and proteomic data.

Comparing molecular features, including the identification of genes with differential expression (DE) between conditions, is a powerful approach for characterising disease-specific phenotypes. When...

Single-cell datasets are increasing in number and size. To leverage this rich resource, new workflows can reveal novel insights and discoveries. This perspective discusses the computational challen...