Improved downstream functional analysis of single-cell RNA-sequence data using DGAN

Pandey, Diksha; Onkara, P.

doi:10.1038/s41598-023-28952-y

Cited by 6 publications

(3 citation statements)

References 67 publications

(154 reference statements)

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“…for generating gene expression data to combat the challenges of low sample sizes via data augmentation, which is specifically motivated by the unfavorable ratio of samples to features in these datasets [18,23]. Additionally, synthetic gene expression data has also been used to train imputation methods for handling missing data [27]. However, none of these methods ensure privacy during the whole data generation process.…”

Section: Related Work 21 Models For Synthetic Gene Expression Datamentioning

confidence: 99%

Towards Biologically Plausible and Private Gene Expression Data Generation

Chen,

Oestreich,

Afonja

et al. 2024

PoPETs

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Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, however, primarily focuses on basic benchmarking datasets and tends to report promising results only for elementary metrics and relatively simple data distributions. In this paper, we initiate a systematic analysis of how DP generative models perform in their natural application scenarios, specifically focusing on real-world gene expression data. We conduct a comprehensive analysis of five representative DP generation methods, examining them from various angles, such as downstream utility, statistical properties, and biological plausibility. Our extensive evaluation illuminates the unique characteristics of each DP generation method, offering critical insights into the strengths and weaknesses of each approach, and uncovering intriguing possibilities for future developments. Perhaps surprisingly, our analysis reveals that most methods are capable of achieving seemingly reasonable downstream utility, according to the standard evaluation metrics considered in existing literature. Nevertheless, we find that none of the DP methods are able to accurately capture the biological characteristics of the real dataset. This observation suggests a potential over-optimistic assessment of current methodologies in this field and underscores a pressing need for future enhancements in model design.

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Section: Related Work 21 Models For Synthetic Gene Expression Datamentioning

confidence: 99%

Towards Biologically Plausible and Private Gene Expression Data Generation

Chen,

Oestreich,

Afonja

et al. 2024

PoPETs

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show abstract

“…As perceived from the plots the entire samples are bifurcated into control and case samples represented in blue and red colors respectively and their gene expression units are indicated as RPM (Reads per million mapped reads). If the distribution of RNA-Seq read count valuations tends to be skewed, data transformation could be applied to normalize it [31,32]. In such case, normalized expression units become essential to eliminate technical biases in sequenced data and allow gene expressions directly comparable between and within samples.…”

Section: Analysis Of Individual Datasets For Identification Of Degs B...mentioning

confidence: 99%

Improved meta-analysis pipeline ameliorates distinctive gene regulators of diabetic vasculopathy in human endothelial cell (hECs) RNA-Seq data

Pandey,

Perumal P.

2023

PLoS ONE

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Enormous gene expression data generated through next-generation sequencing (NGS) technologies are accessible to the scientific community via public repositories. The data harboured in these repositories are foundational for data integrative studies enabling large-scale data analysis whose potential is yet to be fully realized. Prudent integration of individual gene expression data i.e. RNA-Seq datasets is remarkably challenging as it encompasses an assortment and series of data analysis steps that requires to be accomplished before arriving at meaningful insights on biological interrogations. These insights are at all times latent within the data and are not usually revealed from the modest individual data analysis owing to the limited number of biological samples in individual studies. Nevertheless, a sensibly designed meta-analysis of select individual studies would not only maximize the sample size of the analysis but also significantly improves the statistical power of analysis thereby revealing the latent insights. In the present study, a custom-built meta-analysis pipeline is presented for the integration of multiple datasets from different origins. As a case study, we have tested with the integration of two relevant datasets pertaining to diabetic vasculopathy retrieved from the open source domain. We report the meta-analysis ameliorated distinctive and latent gene regulators of diabetic vasculopathy and uncovered a total of 975 i.e. 930 up-regulated and 45 down-regulated gene signatures. Further investigation revealed a subset of 14 DEGs including CTLA4, CALR, G0S2, CALCR, OMA1, and DNAJC3 as latent i.e. novel as these signatures have not been reported earlier. Moreover, downstream investigations including enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs revealed durable disease association signifying their potential as novel transcriptomic biomarkers of diabetic vasculopathy. While the meta-analysis of individual whole transcriptomic datasets for diabetic vasculopathy is exclusive to our comprehension, however, the novel meta-analysis pipeline could very well be extended to study the mechanistic links of DEGs in other disease conditions.

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“…There are several methods to obtain single-cell sequencing data, such as Drop-seq [17], inDrop [18], Chromium [19], and smart-seq2 [20]. However, these methods can produce different kinds of noise [21,22] and batch effects [23] in the data, which can affect the accuracy of cell identification. Batch effects can also arise from different platforms [24,25], omics types [26], and species [4,27] in the data.…”

Section: Introductionmentioning

confidence: 99%

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

Sun,

Qu,

Duan

et al. 2024

IJMS

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Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labels. We evaluate our model on single-dataset, cross-species, and cross-platform experiments and compare it with other state-of-the-art methods. Our results show that scMGCN outperforms the other methods regarding stability, accuracy, and robustness to batch effects. Our main contributions are as follows: Firstly, we introduce multi-view learning and multiple graph construction methods to capture comprehensive cellular information from scRNA-seq data. Secondly, we construct a scMGCN that combines graph convolutional networks with attention mechanisms to extract shared, high-order information from cells. Finally, we demonstrate the effectiveness and superiority of the scMGCN on various datasets.

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Improved downstream functional analysis of single-cell RNA-sequence data using DGAN

Cited by 6 publications

References 67 publications

Towards Biologically Plausible and Private Gene Expression Data Generation

Towards Biologically Plausible and Private Gene Expression Data Generation

Improved meta-analysis pipeline ameliorates distinctive gene regulators of diabetic vasculopathy in human endothelial cell (hECs) RNA-Seq data

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

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