Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes

about

The Human Cell Atlas: Technical approaches and challenges.Brain transcriptome databases: a user's guide.VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies

P2860

Q48563763-C484FAE4-4ABD-4278-912D-56CFF795819C Q50043527-055C55A1-35D8-4F24-987C-3BD5BE107DF8 Q59136175-5D619A02-67B7-4A35-B9F5-7D46860ED522

P2860

Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes

Item

http://www.wikidata.org/entity/Q42670551

description

2017 nî lūn-bûn

@nan

2017年の論文

@ja

2017年論文

@yue

2017年論文

@zh-hant

2017年論文

@zh-hk

2017年論文

@zh-mo

2017年論文

@zh-tw

2017年论文

@wuu

2017年论文

@zh

2017年论文

@zh-cn

name

Controlling for Confounding Ef ...... both Control and Target Genes

@en

Controlling for Confounding Ef ...... both Control and Target Genes

@nl

type

Item

label

Controlling for Confounding Ef ...... both Control and Target Genes

@en

Controlling for Confounding Ef ...... both Control and Target Genes

@nl

prefLabel

Controlling for Confounding Ef ...... both Control and Target Genes

@en

Controlling for Confounding Ef ...... both Control and Target Genes

@nl

P1476

Controlling for Confounding Ef ...... both Control and Target Genes

@en

P2093

Mengjie Chen

http://www.w3.org/2001/XMLSchema#string

Xiang Zhou

http://www.w3.org/2001/XMLSchema#string

P2860

Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression.

Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals

Capturing heterogeneity in gene expression studies by surrogate variable analysis

Understanding mechanisms underlying human gene expression variation with RNA sequencing

Transcriptome genetics using second generation sequencing in a Caucasian population

Adjusting batch effects in microarray expression data using empirical Bayes methods

limma powers differential expression analyses for RNA-sequencing and microarray studies

Reconstruction of the mouse otocyst and early neuroblast lineage at single-cell resolution.

Factor analysis for gene regulatory networks and transcription factor activity profiles.

Normalization of RNA-seq data using factor analysis of control genes or samples

P2888

s41598-017-13665-w

P304

13587

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1038/S41598-017-13665-W

http://www.w3.org/2001/XMLSchema#string

P407

English

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P577

2017-10-19T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P6179

1092212950

http://www.w3.org/2001/XMLSchema#string

P698

29051597

http://www.w3.org/2001/XMLSchema#string

P932

5648789

http://www.w3.org/2001/XMLSchema#string

Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes

about

P2860

P2860

Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P407

P433

P478

P577

P6179

P698

P932

P356

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P407

P433

P478

P577

P6179

P698

P932