scMCs: a framework for single-cell multi-omics data integration and multiple clusterings

Ren, Liangrui; Wang, Jun; Li, Zhao; Yu, Guoxian

doi:10.1093/bioinformatics/btad133

Cited by 17 publications

(6 citation statements)

References 28 publications

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“…Further improvement of scGAL can be achieved by embedding single-cell copy number and gene expression data into a common space, where alignments between scDNA-seq cells and scRNA-seq cells can be conducted by clustering. In addition, attention-based architectures [ 27 ] may also be helpful for inferring the ITH from sparse tumor single-cell data, and we plan to explore in this direction in the near future.…”

Section: Discussionmentioning

confidence: 99%

“…scMOC [ 26 ] reasons cell clusters using common measurements from matched scRNA-seq and scATAC-seq data. scMCs [ 27 ] co-models single-cell transcriptome and epigenetic data to get omics-specific and consistent representations, and fuse them into a common embedded representation. In addition, MSCLRL [ 30 ] is able to automatically learn latent representations from multi-omics data for cancer subtyping.…”

Section: Introductionmentioning

confidence: 99%

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scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

Li,

Shi,

Song

et al. 2024

BMC Genomics

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Background Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. Results We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal. Conclusions Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

Li,

Shi,

Song

et al. 2024

BMC Genomics

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“…In addition, the DCCA model leverages data from one omics to fine-tune data from another, effectively amalgamating information from different views [37]. More recently, Ren et al introduced a multi-view approach based on subspace clustering [38], aimed at reducing information redundancy between subspaces to generate high-quality cell representations. The embedding optimization module combines reconstruction loss and clustering loss to jointly optimize the embeddings.…”

Section: B Multi-view Clustering Methods For Single-cell Datamentioning

confidence: 99%

“…• scMCs [38]: scMCs: a framework for single-cell multiomics data integration and multiple clusterings.…”

Section: Methodsmentioning

confidence: 99%

What is the cause(s) of ozone trends in three megacity clusters in eastern China during 2015–2020?

Liu

et al. 2023

Preprint

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Abstract. Thanks to a strong emission control policy, major air pollutants in China, including PM2.5, SO2, NO2 and CO had shown remarkable reductions in 2015–2020. However, ozone (O3) had increased significantly and emerged as a major air pollutant in eastern China at the same time. The annual mean concentration of maximum daily 8-hour average O3 (MDA8) in three megacity clusters in eastern China, namely Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD), showed alarming large upward linear trends of 2.4, 1.1 and 2.0 ppb yr-1, respectively during the period 2015–2020. Furthermore, drastic trends of approximately three-fold increase in the number of O3-exceeding days (defined as MDA8 O3 >75 ppb) were observed during the same period. Our analysis of the upward trends of the annual mean concentration of MDA8 found that the trends were almost entirely attributable to the increase in the number of consecutive O3-exceeding days. In addition, a widespread expansion of high O3 from urban centers to surrounding rural regions was found in 2015–2017, which had made the O3 spatial distribution becoming more uniform after 2017. Finally, we found that the O3 episodes with four or more consecutive O3-exceeding days in the three megacity clusters were closely associated with the position and strength of the West Pacific subtropical high (WPSH), which contributed to the meteorological conditions characterized by clear sky, sinking motion and high vertical stability in the lower troposphere, and high solar radiation and positive temperature anomaly at the surface. These meteorological conditions were highly conducive to O3 formation. Hence we hypothesize that the cause of the worsening O3 trends in BTH, YRD and PRD from 2015 to 2020 is attributable to the increased occurrence of meteorological conditions of high solar radiation and positive temperature anomaly under the influence of WPSH, tropical cyclones as well as mid-high latitude wave activities.

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“…Moreover, DCCA introduces an ingenious cycle attention model, designed specifically for the unified analysis of multi-omic cell data [ 21 ]. Inspired by the principles of subspace clustering, scMCS extends it to the realm of single-cell clustering [ 22 ], enabling the effective clustering of parallel single-cell data by diligently minimizing redundancy across subspaces.…”

Section: Introductionmentioning

confidence: 99%

Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data

Hu,

Liang,

Dong

et al. 2024

Briefings in Bioinformatics

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In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://github.com/DayuHuu/scEMC.

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scMCs: a framework for single-cell multi-omics data integration and multiple clusterings

Cited by 17 publications

References 28 publications

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

What is the cause(s) of ozone trends in three megacity clusters in eastern China during 2015–2020?

Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data

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