Cargando…

RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most im...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhao, Yuan, Fang, Zhao-Yu, Lin, Cui-Xiang, Deng, Chao, Xu, Yun-Pei, Li, Hong-Dong
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2021
Materias:	Genetics
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354212/ https://www.ncbi.nlm.nih.gov/pubmed/34386033 http://dx.doi.org/10.3389/fgene.2021.665843

Ejemplares similares

scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
por: Wang, HaiYun, et al.
Publicado: (2022)

ScRNA-seq and ST-seq in liver research
por: He, Jia, et al.
Publicado: (2023)

SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder
por: Zhao, Jianping, et al.
Publicado: (2021)

Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data
por: Torroja, Carlos, et al.
Publicado: (2019)

D3K: The Dissimilarity-Density-Dynamic Radius K-means Clustering Algorithm for scRNA-Seq Data
por: Liu, Guoyun, et al.
Publicado: (2022)

Corrigendum: Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data
por: Torroja, Carlos, et al.
Publicado: (2020)

GeneMarkeR: A Database and User Interface for scRNA-seq Marker Genes
por: Paisley, Brianna M., et al.
Publicado: (2021)

From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis
por: Carangelo, Giulia, et al.
Publicado: (2022)

Contrastive self-supervised clustering of scRNA-seq data
por: Ciortan, Madalina, et al.
Publicado: (2021)

Boosting scRNA-seq data clustering by cluster-aware feature weighting
por: Li, Rui-Yi, et al.
Publicado: (2021)

The Novel Key Genes of Non-obstructive Azoospermia Affect Spermatogenesis: Transcriptomic Analysis Based on RNA-Seq and scRNA-Seq Data
por: He, Haihong, et al.
Publicado: (2021)

Dissecting Cellular Heterogeneity Based on Network Denoising of scRNA-seq Using Local Scaling Self-Diffusion
por: Duan, Xin, et al.
Publicado: (2022)

A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types
por: Bishara, Isaac, et al.
Publicado: (2022)

Characterization of Pyroptosis-Related Subtypes via RNA-Seq and ScRNA-Seq to Predict Chemo-Immunotherapy Response in Triple-Negative Breast Cancer
por: Li, Chenlu, et al.
Publicado: (2022)

Intrinsic entropy model for feature selection of scRNA-seq data
por: Li, Lin, et al.
Publicado: (2022)

dropClust: efficient clustering of ultra-large scRNA-seq data
por: Sinha, Debajyoti, et al.
Publicado: (2018)

MLG: multilayer graph clustering for multi-condition scRNA-seq data
por: Lu, Shan, et al.
Publicado: (2021)

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing
por: Li, Kejie, et al.
Publicado: (2023)

Computational approaches for interpreting scRNA‐seq data
por: Rostom, Raghd, et al.
Publicado: (2017)

Cerebro: interactive visualization of scRNA-seq data
por: Hillje, Roman, et al.
Publicado: (2020)

Compression of quantification uncertainty for scRNA-seq counts
por: Van Buren, Scott, et al.
Publicado: (2021)

NeuralEE: A GPU-Accelerated Elastic Embedding Dimensionality Reduction Method for Visualizing Large-Scale scRNA-Seq Data
por: Xiong, Jiankang, et al.
Publicado: (2020)

Choice of pre-processing pipeline influences clustering quality of scRNA-seq datasets
por: Shainer, Inbal, et al.
Publicado: (2021)

Fast and memory-efficient scRNA-seq k-means clustering with various distances
por: Baker, Daniel N., et al.
Publicado: (2021)

scRNA‐seq data analysis method to improve analysis performance
por: Lu, Junru, et al.
Publicado: (2023)

scX: A user-friendly tool for scRNA-seq exploration
por: Waichman, Tomás Vega, et al.
Publicado: (2023)

scNCL: transferring labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization
por: Yan, Xuhua, et al.
Publicado: (2023)

Exploring PANoptosis in breast cancer based on scRNA-seq and bulk-seq
por: He, Puxing, et al.
Publicado: (2023)

Comparison and evaluation of statistical error models for scRNA-seq
por: Choudhary, Saket, et al.
Publicado: (2022)

Probing regenerative heterogeneity of corticospinal neurons with scRNA-Seq
por: Kim, Hugo, et al.
Publicado: (2023)

Domain adaptation for supervised integration of scRNA-seq data
por: Sun, Yutong, et al.
Publicado: (2023)

The challenges and opportunities of scRNA-seq in COVID-19 research and clinical translation
por: Liu, Wendao, et al.
Publicado: (2022)

Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering
por: Fang, Jiyuan, et al.
Publicado: (2022)

Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data
por: Song, Jia, et al.
Publicado: (2020)

A clustering method for small scRNA-seq data based on subspace and weighted distance
por: Ning, Zilan, et al.
Publicado: (2023)

A statistical simulator scDesign for rational scRNA-seq experimental design
por: Li, Wei Vivian, et al.
Publicado: (2019)

scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously
por: Zhang, Ziqi, et al.
Publicado: (2022)

LRT: Integrative analysis of scRNA-seq and scTCR-seq data to investigate clonal differentiation heterogeneity
por: Xie, Juan, et al.
Publicado: (2023)

Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data
por: Zhai, Zhiqian, et al.
Publicado: (2022)

Effect of distance measures on confidences of t-SNE embeddings and its implications on clustering for scRNA-seq data
por: Ozgode Yigin, Busra, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_09aiojs9u1s91d5t6iisqtit9m