{Reference Type}: Evaluation Study
{Title}: Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method.
{Author}: Gan Y;Li N;Zou G;Xin Y;Guan J;
{Journal}: BMC Med Genomics
{Volume}: 11
{Issue}: 0
{Year}: Dec 2018 31
{Factor}: 3.622
{DOI}: 10.1186/s12920-018-0433-z
{Abstract}: <B>BACKGROUND: </B>Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.<BR><B>METHODS: </B>In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.<BR><B>RESULTS: </B>Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.<BR><B>CONCLUSIONS: </B>Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.