Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity

Huang, Meng; Ye, Xiucai; Li, Hongmin; Sakurai, Tetsuya

doi:10.3389/fgene.2022.952649

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…Its value ranges from − 1 to 1 26 . Normalized Mutual Information (NMI) is used as the clustering metric to measure the results on real and simulated datasets 27 . The value ranges from 0 to 1.…”

Section: (B) Model Training and Results Evaluationmentioning

confidence: 99%

Combined Unsupervised Machine Learning and clinical cohort for phenotyping complex diseases with its application to Amyotrophic lateral sclerosis

Fan,

Yang,

Shen

et al. 2023

Preprint

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Amyotrophic lateral sclerosis (ALS) is a highly heterogeneous disease. Identifying subtypes of ALS to better predict prognosis with different methods is essential in clinical practice. Traditional cluster methods are limited by data and computation, and are hard to indicate heterogeneity and predict the prognosis of ALS. This study aims to develop a new classification system for ALS to predict prognosis adopting a data-driven approach. This was a single-center retrospective study based on an independent cohort of patients diagnosed with ALS recruited by Peking union medical college hospital (PUMCH) from 2014 to 2020. The mean follow-up time was 23.3±16.9 months. One thousand and two hundred fifty-four patients were recruited, and 303 variables were selected at baseline. We applied machine learning algorithms to train and test the performance of different hyperparameters. Fifty-nine baseline variables were included and 1166 patients (mean [SD] age, 53.3[10.7] years, 518 [55.5%] male patients; mean [SD] ALSFRS-R, 38.4 [6.3]), with <50 % missing data were identified and were used as input for the ML algorithm. K-Means Model classified four phenogroups (phenogroup 0= 245, phenogroup 1= 428, phenogroup 2=235, phenogroup 3=258) whose outcome are different from each other (hazard ratio, 1.12; 95% CI, 1.01-1.25; P < 0.001). In particular, there is a significant difference between phenogroup 0 and phenogroup 3 regarding decreased ALSFRS-R, swallowing function, decrease in BMI, and prognosis. Phenogroup 1 and phenogroup 2 have a moderate prognosis, but their characteristics are significantly different in terms of sex ratio, mean age, weight, and complications. Data-driven automated approaches could develop a new classification system for ALS to precisely predict the prognosis Trial Registration: Chinese Clinical Trial Registry, ChiCTR-IPR-15007385

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Section: (B) Model Training and Results Evaluationmentioning

confidence: 99%

Combined Unsupervised Machine Learning and clinical cohort for phenotyping complex diseases with its application to Amyotrophic lateral sclerosis

Fan,

Yang,

Shen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Other parametric and nonparametric methods are discussed in [ 57 ]. Some of the popular current synthetic scRNA-seq data applications could be categorized as follows: (i) studies of the imputation of missing values [ 58 , 59 ], and (ii) cell type identification [ 60 , 61 , 62 , 63 ] among others. An overview of the technologies and important problems that could be solved using scRNA-seq is available in [ 64 ].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Comparison and Assessment of Synthetic RNA-Seq Data

Shakola

Palejev

Ivanov

2022

Genes

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The ever-growing number of methods for the generation of synthetic bulk and single cell RNA-seq data have multiple and diverse applications. They are often aimed at benchmarking bioinformatics algorithms for purposes such as sample classification, differential expression analysis, correlation and network studies and the optimization of data integration and normalization techniques. Here, we propose a general framework to compare synthetically generated RNA-seq data and select a data-generating tool that is suitable for a set of specific study goals. As there are multiple methods for synthetic RNA-seq data generation, researchers can use the proposed framework to make an informed choice of an RNA-seq data simulation algorithm and software that are best suited for their specific scientific questions of interest.

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Imputation method for single-cell RNA-seq data using neural topic model

Qi,

Han,

Tang

et al. 2022

GigaScience

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Single-cell RNA sequencing (scRNA-seq) technology studies transcriptome and cell-to-cell differences from higher single-cell resolution and different perspectives. Despite the advantage of high capture efficiency, downstream functional analysis of scRNA-seq data is made difficult by the excess of zero values (i.e., the dropout phenomenon). To effectively address this problem, we introduced scNTImpute, an imputation framework based on a neural topic model. A neural network encoder is used to extract underlying topic features of single-cell transcriptome data to infer high-quality cell similarity. At the same time, we determine which transcriptome data are affected by the dropout phenomenon according to the learning of the mixture model by the neural network. On the basis of stable cell similarity, the same gene information in other similar cells is borrowed to impute only the missing expression values. By evaluating the performance of real data, scNTImpute can accurately and efficiently identify the dropout values and imputes them accurately. In the meantime, the clustering of cell subsets is improved and the original biological information in cell clustering is solved, which is covered by technical noise. The source code for the scNTImpute module is available as open source at https://github.com/qiyueyang-7/scNTImpute.git.

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Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity

Cited by 3 publications

References 42 publications

Combined Unsupervised Machine Learning and clinical cohort for phenotyping complex diseases with its application to Amyotrophic lateral sclerosis

Combined Unsupervised Machine Learning and clinical cohort for phenotyping complex diseases with its application to Amyotrophic lateral sclerosis

A Framework for Comparison and Assessment of Synthetic RNA-Seq Data

Imputation method for single-cell RNA-seq data using neural topic model

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