scReClassify: post hoc cell type classification of single-cell rNA-seq data

• Published in: BMC genomics Vol. 20; no. S9; pp. 913 - 10
• Main Authors: Kim, Taiyun, Lo, Kitty, Geddes, Thomas A., Kim, Hani Jieun, Yang, Jean Yee Hwa, Yang, Pengyi
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 24.12.2019; BioMed Central; BMC
• Subjects: Accuracy; Analysis; Animals; Annotations; Cancer research; Cell type classification; Class label noise; Classification; Clustering; Computer applications; Computer simulation; Criminal investigation; Datasets; Developmental biology; Gene expression; Gene sequencing; Genes; Genomics; Humans; Identification; Inspection; Machine Learning; Mice; Morphology; New technology; Noise; Performance evaluation; Principal components analysis; Ribonucleic acid; RNA; RNA-Seq - methods; scRNA-seq; Semi-supervised learning; Single-Cell Analysis - methods; Single-cell RNA-seq; Software; Support vector machines; Technology; scRNA-seq; Cell type classification; Class label noise; Single-cell RNA-seq
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-019-6305-x

Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. Here, we propose a semi-supervised learning framework, named scReClassify, for 'post hoc' cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify.