Sub-population identification | Single-cell RNA sequencing data analysis
Single-cell RNA sequencing is a powerful technology to study gene expression of individual cells. Several software tools enable identification of cellular sub-populations from heterogeneous samples by identifying and comparing common gene signatures.
Identifies subpopulations in high-dimensional single-cell data. PhenoGraph is a computational method that was developed to avoid the disadvantages of manual gating. This method is adaptative both in terms of dimensionality and sample size, making it suitable in a range of settings for which single-cell population structure is of interest, including other cancers or healthy tissues, and for use with other emerging single-cell technologies.
A clustering method designed for high dimensional gene expression data, e.g. single-cell transcriptome data. This method can effectively cluster individual cells based on their transcriptomes, producing clustering outputs highly in accordance with the cell type origins. SNN-Cliq utilizes the concept of shared nearest neighbor (SNN) to define similarities between data points (cells) and achieve clustering by a graph theory-based algorithm.
CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China; UCL Cancer Institute, Paul O’Gorman Building, University College London, London, UK; Chao Family Comprehensive Cancer Center, University of California, Irvine, CA, USA; Wellcome Sanger Institute, Cambridge, UK
A computational method for extracting lineage relationships from single-cell gene expression data, and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multi-lineage specifications.
Evaluates and compares two or more single cell RNA-Seq samples. ClusterMap proceeds first by exploiting the marker genes for each sub-group of each sample as the basic input to perform the matching. It then visualizes the matching results through several views. Ultimately, this software defines the property modifications across samples for each matched group.
Enables systematic comparison of computational tools and straightforward cross-study data integration. matchSCore is a Jaccard index-based scoring system that quantifies clustering and marker accuracy in a combined score. It can also be used to integrate cluster identitied across different data sets. This method can be applied to the comparative analysis of phenotypes across data sets, thus providing a straightforward solution to annotate single-cell projects.
Allows to make unsupervised projection of single cells from an scRNA-seq experiment. scmap is easy to combine with other computational scRNA-seq methods. It is very fast, using 1,000 features taking only around twenty seconds to map 40,000 cells. Its run-time can be further improved since the centroids and features for each cluster can be pre-computed, and stored in memory, even for a very large atlas.