Abstract
SummarySingle-cell RNA sequencing (scRNA-seq) is now a commonly used technique to measure the transcriptome of populations of cells. Clustering heterogeneous cells based on these transcriptomes enables identification of cell populations (Butler, Hoffman, Smibert, Papalexi, & Satija, 2018; Trapnell et al., 2014). There are multiple methods available to identify “marker” genes that differ between these populations (Butler et al., 2018; Love, Huber, & Anders, 2014; Robinson, McCarthy, & Smyth, 2009). However, there are usually too many genes in these lists to directly suggest an experimental follow-up strategy for selecting them from a bulk population (e.g. via FACS (Tung et al., 2007)). Here we present scTree, a tool that aims to provide biologists using the R programming language and scRNA-seq analysis programs a minimal set of genes that can be used in downstream experiments. The package is free, open source and available though GitHub at github.com/jspaezp/sctree
Highlights
Clustering heterogeneous cells based on these transcriptomes enables identification of cell populations (Butler, Hoffman, Smibert, Papalexi, & Satija, 2018; Trapnell et al, 2014)
We present scTree, a tool that aims to provide biologists using the R programming language and scRNA-seq analysis programs a minimal set of genes that can be used in downstream experiments
The underlying model behind scTree is a combination of random forest for variable selection and a classification tree; having this model as a classifier relies on the fact that classification trees are analogous to many approaches in biology such as the gating strategy employed in flow cytometry or Fluorescence assisted cell sorting (FACS) experiments
Summary
Single-cell RNA sequencing (scRNA-seq) is a commonly used technique to measure the transcriptome of populations of cells. Clustering heterogeneous cells based on these transcriptomes enables identification of cell populations (Butler, Hoffman, Smibert, Papalexi, & Satija, 2018; Trapnell et al, 2014). There are multiple methods available to identify “marker” genes that differ between these populations (Butler et al, 2018; Love, Huber, & Anders, 2014; Robinson, McCarthy, & Smyth, 2009). We present scTree, a tool that aims to provide biologists using the R programming language and scRNA-seq analysis programs a minimal set of genes that can be used in downstream experiments. The package is free, open source and available though GitHub at github.com/jspaezp/sctree
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