SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data.

QR Code

SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data.

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downs...

Full description

Saved in:

Bibliographic Details
Main Authors:	Jing Qi, Yang Zhou, Zicen Zhao, Shuilin Jin
Format:	article
Language:	EN
Published:	Public Library of Science (PLoS) 2021
Subjects:	Biology (General) QH301-705.5
Online Access:	https://doaj.org/article/2ab4286ecc6f4833a97d4daf1b183d78
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Embracing the dropouts in single-cell RNA-seq analysis
by: Peng Qiu
Published: (2020)

An accurate and robust imputation method scImpute for single-cell RNA-seq data
by: Wei Vivian Li, et al.
Published: (2018)

ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
by: Xiutao Pan, et al.
Published: (2021)

Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
by: Thoma Itoh, et al.
Published: (2021)

Detection and removal of barcode swapping in single-cell RNA-seq data
by: Jonathan A. Griffiths, et al.
Published: (2018)

Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data
by: Qianhui Huang, et al.
Published: (2021)

NASC-seq monitors RNA synthesis in single cells
by: Gert-Jan Hendriks, et al.
Published: (2019)

Latent periodic process inference from single-cell RNA-seq data
by: Shaoheng Liang, et al.
Published: (2020)

A web server for comparative analysis of single-cell RNA-seq data
by: Amir Alavi, et al.
Published: (2018)

Estimation of immune cell content in tumour tissue using single-cell RNA-seq data
by: Max Schelker, et al.
Published: (2017)

Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement
by: Guangshuai Jia, et al.
Published: (2018)

Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Peripheral Mononuclear Cells in Patients With Ankylosing Spondylitis
by: Huixuan Xu, et al.
Published: (2021)

Model-based imputation of sound level data at thoroughfare using computational intelligence
by: Kekez Michał
Published: (2021)

DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data
by: Walter Muskovic, et al.
Published: (2021)

Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data
by: Xiaoxiao Sun, et al.
Published: (2020)

FEM: mining biological meaning from cell level in single-cell RNA sequencing data
by: Yunqing Liu, et al.
Published: (2021)

A systematic evaluation of single cell RNA-seq analysis pipelines
by: Beate Vieth, et al.
Published: (2019)

Determining sequencing depth in a single-cell RNA-seq experiment
by: Martin Jinye Zhang, et al.
Published: (2020)

Differential expression profile of gluten-specific T cells identified by single-cell RNA-seq.
by: Ying Yao, et al.
Published: (2021)

Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
by: Tian Tian, et al.
Published: (2021)

A general and flexible method for signal extraction from single-cell RNA-seq data
by: Davide Risso, et al.
Published: (2018)

Statistical methods for analysis of single-cell RNA-sequencing data
by: Samarendra Das, Ph.D., et al.
Published: (2021)

A new statistic to evaluate imputation reliability.
by: Peng Lin, et al.
Published: (2010)

Comprehensive analysis of metastatic gastric cancer tumour cells using single-cell RNA-seq
by: Bin Wang, et al.
Published: (2021)

Isolation of myeloid cells from mouse brain tumors for single-cell RNA-seq analysis
by: Mai T. Dang, et al.
Published: (2021)

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and Applications in Cancer
by: Kaikun Xie, et al.
Published: (2021)

Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis.
by: Morten Seirup, et al.
Published: (2020)

A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages
by: Robert J. Kimmerling, et al.
Published: (2016)

Single-cell RNA-seq denoising using a deep count autoencoder
by: Gökcen Eraslan, et al.
Published: (2019)

Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq
by: Johannes W. Bagnoli, et al.
Published: (2018)

Surface protein imputation from single cell transcriptomes by deep neural networks
by: Zilu Zhou, et al.
Published: (2020)

Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
by: Mohamed Marouf, et al.
Published: (2020)

Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer
by: Woosung Chung, et al.
Published: (2017)

Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage
by: Olivier Poirion, et al.
Published: (2018)

Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy
by: Charles P. Couturier, et al.
Published: (2020)

Single-cell RNA-seq highlights a specific carcinoembryonic cluster in ovarian cancer
by: Hongyu Zhao, et al.
Published: (2021)

Dissociation of intact adult mouse cortical projection neurons for single-cell RNA-seq
by: Noa Golan, et al.
Published: (2021)

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
by: Juexin Wang, et al.
Published: (2021)

Short-read and long-read RNA sequencing of mouse hematopoietic stem cells at bulk and single-cell levels
by: Xiuran Zheng, et al.
Published: (2021)

RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data.
by: Snehalika Lall, et al.
Published: (2021)