M. Guşu
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Analysis of HVG use in the ScReNI pipeline
A comparison of global and cell-type specific HVG selection
ScReNI [21] is a recently developed algorithm that aims to infer gene regulatory networks (GRNs) of single cells based on both single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data. Because of its novelty, not much is known about its internal mechanisms, which we aim to investigate in this paper.
Specifically, this work compares the highly variable gene (HVG) selection strategy used in ScReNI with a newly proposed approach: type-specific HVG selection. Instead of selecting the top HVGs globally from the entire dataset, we propose selecting the top HVGs within each cell type and inferring the GRN of each cell using only genes specific to its cell type. The comparison is conducted across multiple structural and biological metrics, and the type-specific selection approach shows overall improved performance compared to the original method. ...
Specifically, this work compares the highly variable gene (HVG) selection strategy used in ScReNI with a newly proposed approach: type-specific HVG selection. Instead of selecting the top HVGs globally from the entire dataset, we propose selecting the top HVGs within each cell type and inferring the GRN of each cell using only genes specific to its cell type. The comparison is conducted across multiple structural and biological metrics, and the type-specific selection approach shows overall improved performance compared to the original method. ...
ScReNI [21] is a recently developed algorithm that aims to infer gene regulatory networks (GRNs) of single cells based on both single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data. Because of its novelty, not much is known about its internal mechanisms, which we aim to investigate in this paper.
Specifically, this work compares the highly variable gene (HVG) selection strategy used in ScReNI with a newly proposed approach: type-specific HVG selection. Instead of selecting the top HVGs globally from the entire dataset, we propose selecting the top HVGs within each cell type and inferring the GRN of each cell using only genes specific to its cell type. The comparison is conducted across multiple structural and biological metrics, and the type-specific selection approach shows overall improved performance compared to the original method.
Specifically, this work compares the highly variable gene (HVG) selection strategy used in ScReNI with a newly proposed approach: type-specific HVG selection. Instead of selecting the top HVGs globally from the entire dataset, we propose selecting the top HVGs within each cell type and inferring the GRN of each cell using only genes specific to its cell type. The comparison is conducted across multiple structural and biological metrics, and the type-specific selection approach shows overall improved performance compared to the original method.