Read count data preprocessing and transformation

Tian Tian; Jie Zhang; Xiang Lin; Zhi Wei; Hakon Hakonarson

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Read count data preprocessing and transformation

TT Tian Tian

JZ Jie Zhang

XL Xiang Lin

ZW Zhi Wei

HH Hakon Hakonarson

This method is extracted from research article: Nat Commun, Mar 2021

Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data

DOI: 10.1038/s41467-021-22008-3

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Following the methods of Tian et al.^²⁶, we applied the Python package SCANPY^⁴⁸ (version 1.4.4) to preprocess the raw scRNA-seq read count data. Firstly, we filter out genes with no count in any cell. Secondly, we calculate the size factors for each cell and normalize the read counts by the library size, such that the total counts are the same across cells. Formally, let’s denote the library size (i.e., the number of total read counts) of cell i as s_i; the size factor of cell i is then s_i/median(s). Finally, we take the log transformation and scale the read counts to have unit variance and zero mean. The transformed read count matrix is used as the input for our denoising ZINB model-based autoencoder. When calculating the ZINB loss, we use the raw count matrix^{²⁰,²²,²⁶}.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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