Resistant Fit Regression Normalization for Single-cell RNA-seq Data

被引：0

作者：

Kuang, Da ^{[1
]}

Kim, Junhyong ^{[2
]}

机构：

[1] Univ Penn, Dept Comp & Informat Sci, 200 S 33Rd St, Philadelphia, PA 19104 USA

[2] Univ Penn, Dept Biol, Philadelphia, PA 19104 USA

来源：

2020 IEEE 20TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE 2020) | 2020年

关键词：

Single-cell; RNA-seq; Normalization; Robust Regression; Resistant Fit;

D O I：

10.1109/BIBE50027.2020.00046

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

All mRNA quantification, including single-cell mRNA sequencing, requires normalization to correct for technical variation and to make measurements of two or more samples comparable. The choice of normalization method impacts the downstream analysis. All common approaches (applying scaling factors, variational inference, and quantile regression) currently focus on removing technical variations but ignore localized variations of biological origin. To address this problem, we propose a new framework to normalize for technical effects while also aligning RNA-seq datasets for a biologically meaningful comparison. We present an iterative optimization method using the notion of a resistant fit regression to isolate localized perturbations. Both simulated data and real data are resistant-fit normalized and compared with popular normalization methods. This comparison shows that the resistant fit works better under localized biological variations.

引用

页码：236 / 240

页数：5

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