Jan 2, 2023 · Here, we demonstrate that scGMAAE has good scalability and can process large-scale scRNA-seq data. Compared with some popular single-cell ...
scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data · Abstract · Publication types · MeSH terms.
scGMAAE: Gaussian mixture adversarial autoencoders for ...
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Jan 3, 2024 · scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data. January 2023; Briefings in Bioinformatics ...
Jan 1, 2023 · scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data. ... scRNA-seq data, which are widely ...
scGMAAE: Gaussian Mixture Adversarial Autoencoder for scRNA-seq Data. All source codes are in code.rar, and you can run scGMAAE with run_scGMAAE.py. Here, we ...
People also ask
What is the best way to visualize rna-seq data?
Scatterplots are a common way to visualize the information from a single-cell RNA-Seq dataset. Multiple dimensions can be represented for each cell analyzed, and each data point on a scatterplot represents a single cell.
What is the scRNA sequence analysis?
Single-cell RNA sequencing (scRNA-seq) data sets can contain counts for up to 30,000 genes for humans. However, most genes are not informative, with many genes having no observed expression. Therefore, the most variably expressed genes are selected.
What are the methods of RNA-seq data analysis?
The two most common clustering methods used for RNA-seq data analysis are hierarchical and k-means clustering (see Clustering box).
What are the methods of scRNA-Seq?
Methods. Current scRNA-seq protocols involve isolating single cells and their RNA, and then following the same steps as bulk RNA-seq: reverse transcription (RT), amplification, library generation and sequencing.
Jan 30, 2024 · Here, we propose a deep neural generative framework, the dynamic batching adversarial autoencoder. (DB-AAE), which excels at denoising scRNA-seq ...
Apr 21, 2023 · In this study, we propose an improved variational autoencoder model (termed DREAM) for dimensionality reduction and a visual analysis of scRNA- ...
scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data ... scRNA-seq data plays an important role in the study ...
Jan 3, 2024 · scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data. Brief Bioinform. 2023; 24bbac585. View in ...
Sep 6, 2023 · In computational analysis of scRNA-seq data ... scgmaae: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data.