Journal ArticleDOI
Challenges in unsupervised clustering of single-cell RNA-seq data.
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TLDR
This Review discusses the multiple algorithmic options for clustering scRNA-seq data, including various technical, biological and computational considerations.Abstract:
Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.read more
Citations
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Journal ArticleDOI
Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA Sequencing Clustering Methods.
TL;DR: This work provides an extensive evaluation of several clustering based methods with respect to different modes of usage and parameter settings by applying them to real and simulated datasets that vary in terms of dimensionality, number of cell populations or levels of noise.
Journal ArticleDOI
SAME-clustering: Single-cell Aggregated Clustering via Mixture Model Ensemble.
TL;DR: This work presents SAME-clustering, a mixture model-based approach that takes clustering solutions from multiple methods and selects a maximally diverse subset to produce an improved ensemble solution that yields enhanced clustering, in terms of both cluster assignments and number of clusters.
Journal ArticleDOI
scID Uses Discriminant Analysis to Identify Transcriptionally Equivalent Cell Types across Single-Cell RNA-Seq Data with Batch Effect
TL;DR: By increasing power to identify transcriptionally similar cell types across datasets with batch effect, scID enhances investigator's ability to integrate and uncover development-, disease-, and perturbation-associated changes in scRNA-seq data.
Journal ArticleDOI
Unraveling the Complexity of the Cancer Microenvironment With Multidimensional Genomic and Cytometric Technologies.
TL;DR: How the integration of multi-omics data will help to shed light on the complex role of cell types present within the human tumor microenvironment, and how such system-wide approaches may pave the way toward more effective therapies for the treatment of cancer is deliberate.
Journal ArticleDOI
Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.
TL;DR: A comprehensive review and evaluation of four classical dimension reduction methods and five clustering models showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data and feature-extraction methods were able to promote clustering performance, although this was not eternally immutable.
References
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