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

Zandigohar, Mehrdad; Dai, Yang

doi:10.1109/bibm55620.2022.9994949

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Analysis suites for scATAC data (8, 9) process scATAC-seq data using TF-IDF. More specifically, we apply TF-logIDF normalization (30). This results in re-weighted features (cCREs) by assigning greater weight to more important features.…”

Section: Methodsmentioning

confidence: 99%

scATAcat: Cell-type annotation for scATAC-seq data

Altay,

Vingron

2024

Preprint

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Cells whose accessibility landscape has been profiled with scATAC-seq cannot readily be annotated to a particular cell type. In fact, annotating cell-types in scATAC-seq data is a challenging task since, unlike in scRNA-seq data, we lack knowledge of "marker regions" which could be used for cell-type annotation. Current annotation methods typically translate accessibility to expression space and rely on gene expression patterns. We propose a novel approach, scATAcat, that leverages characterized bulk ATAC-seq data as prototypes to annotate scATAC-seq data. To mitigate the inherent sparsity of single-cell data, we aggregate cells that belong to the same cluster and create pseudobulk. To demonstrate the feasibility of our approach we collected a number of datasets with respective annotations to quantify the results and evaluate performance for scATAcat. scATAcat is available as a python package at https://github.com/aybugealtay/scATAcat .

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Section: Methodsmentioning

confidence: 99%

scATAcat: Cell-type annotation for scATAC-seq data

Altay,

Vingron

2024

Preprint

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“…More studies will be needed in the future to carefully address if these hold true. Additionally, it will be valuable to evaluate if other algorithms could perform better than SVD on the TF-IDF transformed BC matrix 37 .…”

Section: Discussionmentioning

confidence: 99%

Facilitate integrated analysis of single cell multiomic data by binarizing gene expression values

Misra,

Ferrena,

Zheng

2024

Preprint

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The identity of a cell type can be revealed by its transcriptome and epigenome profiles, both of which can be in flux temporally and spatially, leading to distinct cell states or subtypes. The popular and standard workflow for single cell RNA-seq (scRNA-seq) data analysis applies feature selection, dimensional reduction, and clustering on the gene expression values quantified by read counts, but alternative approaches using a simple classification of a gene to “on” and “off” (i.e., binarization of the gene expression) has been proposed for classifying cells and other downstream analyses. Here, we demonstrate that a direct concatenation of the binarized scRNA-seq data and the standard single cell ATAC-seq data is sufficient and effective for integrated clustering analysis, after applying term-frequency-inverse document frequency (TF-IDF) and single value decomposition (also called latent semantic indexing, LSI) algorithms to the combined data, when the two modalities of omic data are collected using paired multiomic technology. This proposed approach avoids the need for converting scATAC-seq data to gene activity scores for combined analysis and furthermore enables a direct investigation into the contribution of each data type to resolving cell type identity.

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