Correction to “A comparison of integration methods for single‐cell RNA sequencing data and ATAC sequencing data”
Year: 2025
Type: erratum
Abstract: In the "Abstract" section, the text "However, integrating the results of multimodal single-cell data to identify cell-to-cell correspondences remains a challenging task.Our viewpoint emphasizes the importance of data integration at a biologically relevant level of granularity.Furthermore, it is crucial to take into account the inherent discrepancies between different modalities in order to achieve a balance between biological discovery and noise removal." was incorrect.This should have read: "Despite providing unprecedented insights into cellular heterogeneity, integrating multimodal single-cell data to find cell-to-cell correspondences remains challenging, primarily due to the need for biologically granular integration and the management of technical and biological discrepancies between modalities."(2) In paragraph 1 of the "1 Introduction" section, the text "Methods for classifying cellular characteristics and processes at the single-cell level are being increasingly applied to various molecular layers, including the genome (such as copy number variation and point mutations [7]), the epigenome chromatin accessibility [8], DNA methylation [9], histone modifications [10], RNA (such as RNA metabolism [11, 12], RNA isoforms [13]), and proteins [14, 15].Moreover, there is ongoing research on developing experimental assays that can simultaneously capture two or three modalities within the same cell."was incorrect.This should have read: "Single-cell classification methods are now extensively applied across genomic [7], epigenomic [8], DNA methylation [9], histone modifications [10], transcriptomic [11-13], and proteomic [14-15] layers, with advancing research focused on multimodal assays that capture multiple molecular modalities within the same cell [16]."(3) In paragraph 2 of the "1 Introduction" section, the text "For example, by conducting combined analysis, we can identify previously unrecognized cell populations [24] and facilitate the discovery of cis-regulatory interactions [25][26][27][28] or regulatory networks [25,[29][30][31][32] specific to subpopulations.In addition to unraveling regulatory mechanisms in healthy cells, integrative multimodal analyses have the potential to unveil regulatory signatures [28] unique to cancer and provide insights into cancer evolution [33].Ultimately, integrative analyses enable us to comprehend the interactions that occur within and between different molecular layers and their impact on gene expression."was incorrect.This should have read: "Integrated multimodal analyses enable the identification of novel cell subtypes, subpopulation-specific cis-regulatory interactions and regulatory networks [16,[25][26][27][28][29][30][31][32][33], as well as cancer-specific regulatory signatures [29] and evolutionary insights [34].Ultimately, these approaches reveal inter-and intra-layer molecular interactions and their effects on gene expression [16]."(4) In paragraph 1 of the "2 Statistical challenges associated with single-cell paired and unpaired multimodal data integration" section, the text "Due to the wide range of experimental assays, modalities, and biological
Source: Quantitative Biology
DOI: 10.1002/qub2.70023
Institution: Harbin Institute of Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4416136028
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