scTrimClust: A Fast Approach to Robust scRNA-seq Analysis Using Trimmed Cell Clusters

Detection of marker genes and other data analyses in single cell RNA-sequencing (scRNA-seq) experiments very much rely on the result of unsupervised clustering of cells. However, in 2-dimensional representation of clustering results, several cells appear as outliers or in the border area of a cluster suggesting that these cells may not adequately represent a particular cell type.

We propose a novel and fast approach, scTrimClust, for identifying cells that may be interpreted of extreme specimens of their cell type. Identification is based on concave hulls build around each 2-dimensional cell cluster and the distance of each cell to the border area of its population.

We study in two data examples, how cells with non-representative expression profile can influence the results of the analysis. We found that some sets of marker genes are little influenced by extreme cells while other sets are strongly modified, and must therefore be treated carefully.

scTrimClust is also useful to compare the influence of other parameters of an scRNA-seq analysis, e.g. normalization or the clustering approach, on the results. We also provide a software implementation of scTrimClust.