Information retrieval in single cell chromatin analysis using TF-IDF transformation methods

Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) assesses genome-wide chromatin accessibility in thousands of cells to reveal regulatory landscapes in high resolutions. However, the analysis presents challenges due to the high dimensionality and sparsity of the data. Se...

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Bibliographic Details
Published in:arXiv.org 2022-12
Main Authors: Zandigohar, Mehrdad, Yang, Dai
Format: Article
Language:English
Subjects:
Accessibility
Best practice
Clustering
Factor analysis
Feature extraction
Information retrieval
Reduction
Singular value decomposition
Transformations
Online Access:Get full text
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Summary:Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) assesses genome-wide chromatin accessibility in thousands of cells to reveal regulatory landscapes in high resolutions. However, the analysis presents challenges due to the high dimensionality and sparsity of the data. Several methods have been developed, including transformation techniques of term-frequency inverse-document frequency (TF-IDF), dimension reduction methods such as singular value decomposition (SVD), factor analysis, and autoencoders. Yet, a comprehensive study on the mentioned methods has not been fully performed. It is not clear what is the best practice when analyzing scATAC-seq data. We compared several scenarios for transformation and dimension reduction as well as the SVD-based feature analysis to investigate potential enhancements in scATAC-seq information retrieval. Additionally, we investigate if autoencoders benefit from the TF-IDF transformation. Our results reveal that the TF-IDF transformation generally leads to improved clustering and biologically relevant feature extraction.
ISSN:2331-8422