Artificial-cell-type aware cell-type classification in CITE-seq.
Bioinformatics
; 36(Suppl_1): i542-i550, 2020 07 01.
Article
en En
| MEDLINE
| ID: mdl-32657383
ABSTRACT
MOTIVATION Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping. RESULTS:
We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq. AVAILABILITY AND IMPLEMENTATION http//github.com/QiuyuLian/CITE-sort. SUPPLEMENTARY INFORMATION Supplementary data is available at Bioinformatics online.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Perfilación de la Expresión Génica
/
Análisis de la Célula Individual
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
China