Comparison and evaluation of statistical error models for scRNA-seq

Comparison and evaluation of statistical error models for scRNA-seq

Abstract Background Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflow...

Full description

Bibliographic Details
Main Authors:	Saket Choudhary, Rahul Satija
Format:	Article
Language:	English
Published:	BMC 2022-01-01
Series:	Genome Biology
Subjects:	Single-cell RNA-seq Normalization Dimension reduction Variable genes Differential expression Feature selection
Online Access:	https://doi.org/10.1186/s13059-021-02584-9

Similar Items

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking
by: Jake Gagnon, et al.
Published: (2022-06-01)

Boosting scRNA-seq data clustering by cluster-aware feature weighting
by: Rui-Yi Li, et al.
Published: (2021-06-01)

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing
by: Kejie Li, et al.
Published: (2023-05-01)

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
by: Christoph Hafemeister, et al.
Published: (2019-12-01)

scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously
by: Ziqi Zhang, et al.
Published: (2022-06-01)

A rank-based marker selection method for high throughput scRNA-seq data
by: Alexander H. S. Vargo, et al.
Published: (2020-10-01)

Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS
by: Li Shao, et al.
Published: (2021-01-01)

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
by: F. William Townes, et al.
Published: (2019-12-01)

AAE-SC: A scRNA-Seq Clustering Framework Based on Adversarial Autoencoder
by: Yulun Wu, et al.
Published: (2020-01-01)

Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments
by: Prashant N. M., et al.
Published: (2021-09-01)

Detecting Fear-Memory-Related Genes from Neuronal scRNA-seq Data by Diverse Distributions and Bhattacharyya Distance
by: Shaoqiang Zhang, et al.
Published: (2022-08-01)

SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder
by: Jianping Zhao, et al.
Published: (2021-08-01)

Obstacles to detecting isoforms using full-length scRNA-seq data
by: Jennifer Westoby, et al.
Published: (2020-03-01)

scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data
by: Zhen-Hao Guo, et al.
Published: (2023-12-01)

Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC
by: Wenwen Lai, et al.
Published: (2024-04-01)

Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis
by: Oren Ben-Kiki, et al.
Published: (2022-04-01)

Gene representation in scRNA-seq is correlated with common motifs at the 3′ end of transcripts
by: Xinling Li, et al.
Published: (2023-05-01)

ScRNA-seq and ST-seq in liver research
by: Jia He, et al.
Published: (2023-02-01)

A statistical normalization method and differential expression analysis for RNA-seq data between different species
by: Yan Zhou, et al.
Published: (2019-03-01)

Exploring PANoptosis in breast cancer based on scRNA-seq and bulk-seq
by: Puxing He, et al.
Published: (2023-06-01)

GeneMarkeR: A Database and User Interface for scRNA-seq Marker Genes
by: Brianna M. Paisley, et al.
Published: (2021-10-01)

A Double-weighted Normalization Method for Identifying Differential Expression of RNA-seq Data
by: Xiaohui Wu, et al.
Published: (2014-12-01)

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets
by: Madison Dautle, et al.
Published: (2023-08-01)

A Comparison of Cell-Cell Interaction Prediction Tools Based on scRNA-seq Data
by: Zihong Xie, et al.
Published: (2023-08-01)

Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment
by: Kate Bridges, et al.
Published: (2022-04-01)

Integrating RNA-seq and scRNA-seq to explore the mechanism of macrophage ferroptosis associated with COPD
by: Pengbei Fan, et al.
Published: (2023-03-01)

Evaluating the role of IDO1 macrophages in immunotherapy using scRNA-seq and bulk-seq in colorectal cancer
by: Xingwu Liu, et al.
Published: (2022-09-01)

Automated methods for cell type annotation on scRNA-seq data
by: Giovanni Pasquini, et al.
Published: (2021-01-01)

Cell function and identity revealed by comparative scRNA-seq analysis in human nasal, bronchial and epididymis epithelia
by: Alekh Paranjapye, et al.
Published: (2022-06-01)

Integrative analysis of scRNA-seq and scATAC-seq revealed transit-amplifying thymic epithelial cells expressing autoimmune regulator
by: Takahisa Miyao, et al.
Published: (2022-05-01)

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets
by: Hongyu Liu, et al.
Published: (2021-01-01)

scRNA-seq revealed high stemness epithelial malignant cell clusters and prognostic models of lung adenocarcinoma
by: GuoYong Lin, et al.
Published: (2024-02-01)

deMULTIplex2: robust sample demultiplexing for scRNA-seq
by: Qin Zhu, et al.
Published: (2024-01-01)

Delving into the Heterogeneity of Different Breast Cancer Subtypes and the Prognostic Models Utilizing scRNA-Seq and Bulk RNA-Seq
by: Jieyun Xu, et al.
Published: (2022-09-01)

A Comprehensive Comparison between Primary Liver Cancer and Liver Metastases through scRNA-Seq Data Analysis
by: Shuang Hao, et al.
Published: (2024-01-01)

An interpretable framework for clustering single-cell RNA-Seq datasets
by: Jesse M. Zhang, et al.
Published: (2018-03-01)

Sex Differences in Coronary Artery Disease and Diabetes Revealed by scRNA-Seq and CITE-Seq of Human CD4+ T Cells
by: Ryosuke Saigusa, et al.
Published: (2022-08-01)

RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest
by: Yuan Zhao, et al.
Published: (2021-07-01)

Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies
by: Xiaohong Li, et al.
Published: (2020-01-01)

A scRNA-seq Approach to Identifying Changes in Spermatogonial Stem Cell Gene Expression Following in vitro Culture
by: Camila Salum De Oliveira, et al.
Published: (2022-04-01)