A new Stein estimator for the zero‐inflated negative binomial regression model

Akram, Muhammad Nauman; Abonazel, Mohamed R.; Amin, Muhammad; Kibria, Golam; Afzal, Nimra

doi:10.1002/cpe.7045

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…Upon this basis, we have introduced the ZINB distribution to estimate the distribution of single-cell data [ 25–27 ]. Despite the ZINB loss not being specifically designed for scATAC-seq data, its proficiency in addressing over-dispersion and data sparsity renders it an appropriate selection.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…This quasi-Newton algorithm has fewer constraints on the convexity of the target function. The BFGS quasi-Newton method has been widely adopted and shown to have satisfactory performance in published studies [ 56 , 57 ]. We have followed these studies and used the R function “optim” to realise the BFGS quasi-Newton method.…”

Section: Methodsmentioning

confidence: 99%

ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data

Li,

Wu,

et al. 2023

Genome Biol

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Clustering is a critical component of single-cell RNA sequencing (scRNA-seq) data analysis and can help reveal cell types and infer cell lineages. Despite considerable successes, there are few methods tailored to investigating cluster-specific genes contributing to cell heterogeneity, which can promote biological understanding of cell heterogeneity. In this study, we propose a zero-inflated negative binomial mixture model (ZINBMM) that simultaneously achieves effective scRNA-seq data clustering and gene selection. ZINBMM conducts a systemic analysis on raw counts, accommodating both batch effects and dropout events. Simulations and the analysis of five scRNA-seq datasets demonstrate the practical applicability of ZINBMM.

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“…Algamal et al [16] introduced the ridge and Liu estimators for the zero-inflated bell regression model. Akram et al [17] introduced some new ridge parameters for the zero-inflated NB regression model.…”

Section: Figurementioning

confidence: 99%

Developing a two-parameter Liu estimator for the COM–Poisson regression model: Application and simulation

Abonazel

Awwad²,

Eldin³

et al. 2023

Front. Appl. Math. Stat.

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The Conway–Maxwell–Poisson (COMP) model is defined as a flexible count regression model used for over- and under-dispersion cases. In regression analysis, when the explanatory variables are highly correlated, this means that there is a multicollinearity problem in the model. This problem increases the standard error of maximum likelihood estimates. To manage the multicollinearity effects in the COMP model, we proposed a new modified Liu estimator based on two shrinkage parameters (k, d). To assess the performance of the proposed estimator, the mean squared error (MSE) criterion is used. The theoretical comparison of the proposed estimator with the ridge, Liu, and modified one-parameter Liu estimators is made. The Monte Carlo simulation and real data application are employed to examine the efficiency of the proposed estimator and to compare it with the ridge, Liu, and modified one-parameter Liu estimators. The results showed the superiority of the proposed estimator as it has the smallest MSE value.

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A new Stein estimator for the zero‐inflated negative binomial regression model

Cited by 8 publications

References 37 publications

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

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

ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data

Developing a two-parameter Liu estimator for the COM–Poisson regression model: Application and simulation

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