Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis.

Xu, Xinyi; Yu, Xiaokang; Hu, Gang; Wang, Kui; Zhang, Jingxiao; Li, Xiangjie

Xu, Xinyi; Yu, Xiaokang; Hu, Gang; Wang, Kui; Zhang, Jingxiao; Li, Xiangjie.

Afiliación

Xu X; School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, 100081, China.
Yu X; Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, 100872, China.
Hu G; School of Statistics and Data Science, Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, Nankai University, Tianjin 300071, China.
Wang K; School of Statistics and Data Science, Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, Nankai University, Tianjin 300071, China.
Zhang J; Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, 100872, China.
Li X; School of Statistics and Data Science, Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, Nankai University, Tianjin 300071, China.

Brief Bioinform ; 23(4)2022 07 18.

Article en En | MEDLINE | ID: mdl-35821114

Asunto(s)

Perfilación de la Expresión Génica; Análisis de la Célula Individual; Análisis por Conglomerados; Puntaje de Propensión; Análisis de Secuencia de ARN/métodos; Análisis de la Célula Individual/métodos; Programas Informáticos

Palabras clave

batch effect correction; denoising; imputation; propensity score matching; single-cell RNA-seq

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Perfilación de la Expresión Génica / Análisis de la Célula Individual Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article

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