SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles

Nucleic Acids Research
Peng XieWei Lin

Abstract

Characterization of individual cell types is fundamental to the study of multicellular samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task. Currently, most of the scRNA-seq data analyses are commenced with unsupervised clustering. Clusters are often assigned to different cell types based on the enriched canonical markers. However, this process is inefficient and arbitrary. In this study, we present a technical framework of training the expandable supervised-classifier in order to reveal the single-cell identities as soon as the single-cell expression profile is input. Using multiple scRNA-seq datasets we demonstrate the superior accuracy, robustness, compatibility and expandability of this new solution compared to the traditional methods. We use two examples of the model upgrade to demonstrate how the projected evolution of the cell-type classifier is realized.

References

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Oct 5, 2018·Nature·UNKNOWN Tabula Muris ConsortiumUNKNOWN Principal investigators

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Citations

Jun 22, 2019·Nucleic Acids Research·Jurrian K de KanterFrank C P Holstege
Jul 31, 2019·Bioinformatics·Feiyang Ma, Matteo Pellegrini
May 5, 2020·Briefings in Bioinformatics·Mattia ForcatoSilvio Bicciato
Apr 2, 2020·Briefings in Functional Genomics·Ziwei WangQuan Zou
May 20, 2020·Nature Methods·Zhichao MiaoSarah A Teichmann
Feb 23, 2021·Computational and Structural Biotechnology Journal·Giovanni PasquiniVolker Busskamp
Apr 14, 2021·Nature Communications·Shengquan ChenZhixiang Lin
May 23, 2021·Briefings in Bioinformatics·Mingxuan GaoRongshan Yu
May 18, 2021·Frontiers in Oncology·Yara E Sánchez-CorralesAlice Giustacchini
Jun 11, 2021·Journal of Hematology & Oncology·Yalan LeiSi Shi

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Datasets Mentioned

BETA
GSE126388

Methods Mentioned

BETA
RNA-seq
scRNA-seq
flow-cytometry
CITE-seq
enzymatic dissociation
PCA

Software Mentioned

Monocle
CellClassifier
Chromium
Keras
R
Monocle2
CellRanger
Seurat
Seurat FindClusters
SuperCT

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