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svaseq: removing batch effects and other unwanted noise from sequencing data

svaseq: removing batch effects and other unwanted noise from sequencing data

http://www.wikidata.org/entity/Q30858102

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Integrative analyses of cancer data: a review from a statistical perspective Single-Cell Transcriptomics Bioinformatics and Computational Challenges How Do Genomes Create Novel Phenotypes? Insights from the Loss of the Worker Caste in Ant Social Parasites Huntington’s disease blood and brain show a common gene expression pattern and share an immune signature with Alzheimer’s disease Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow Survey of 800+ data sets from human tissue and body fluid reveals xenomiRs are likely artifacts.How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.Detecting Differentially Expressed Genes with RNA-seq Data Using Backward Selection to Account for the Effects of Relevant Covariates.Complex Sources of Variation in Tissue Expression Data: Analysis of the GTEx Lung Transcriptome Multi-perspective quality control of Illumina RNA sequencing data analysis.Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R Re-evaluating data quality of dog mitochondrial, Y chromosomal, and autosomal SNPs genotyped by SNP array.Analogous mechanism regulating formation of neocortical basal radial glia and cerebellar Bergmann glia.Whole-genome expression analyses of type 2 diabetes in human skin reveal altered immune function and burden of infection.Identifying global expression patterns and key regulators in epithelial to mesenchymal transition through multi-study integration.Differential expression analysis for RNAseq using Poisson mixed models.Differentially expressed gene transcripts using RNA sequencing from the blood of immunosuppressed kidney allograft recipients RNA sequencing of the nephron transcriptome: a technical note.Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses.ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences Resolving host-pathogen interactions by dual RNA-seq.Pan-cancer analysis of systematic batch effects on somatic sequence variations Normalizing single-cell RNA sequencing data: challenges and opportunities Computational approaches for interpreting scRNA-seq data.Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system.Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation.Statistical methods for detecting differentially methylated loci and regions Biomarker detection and categorization in ribonucleic acid sequencing meta-analysis using Bayesian hierarchical models.MicroRNAs in the miR-17 and miR-15 families are downregulated in chronic kidney disease with hypertension.Identifying and mitigating batch effects in whole genome sequencing data.RNA sequencing in post-mortem human brains of neuropsychiatric disorders.Gene co-expression analysis for functional classification and gene-disease predictions.Pseudotemporal Ordering of Single Cells Reveals Metabolic Control of Postnatal β Cell Proliferation.Comprehensive Transcriptome and Mutational Profiling of Endemic Burkitt Lymphoma Reveals EBV Type-Specific Differences.Computational genomics tools for dissecting tumour-immune cell interactions.Symmetric Directional False Discovery Rate Control.Revealing the vectors of cellular identity with single-cell genomics.Redeploying β-Lactam Antibiotics as a Novel Antivirulence Strategy for the Treatment of Methicillin-Resistant Staphylococcus aureus Infections.Sensitivity, specificity, and reproducibility of RNA-Seq differential expression calls.

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P2860

svaseq: removing batch effects and other unwanted noise from sequencing data

http://www.wikidata.org/entity/Q30858102

description

2014 nî lūn-bûn

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2014 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2014 թվականի հոտեմբերին հրատարակված գիտական հոդված

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2014年の論文

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2014年論文

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2014年論文

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2014年論文

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2014年論文

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2014年論文

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2014年论文

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name

svaseq: removing batch effects and other unwanted noise from sequencing data

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svaseq: removing batch effects and other unwanted noise from sequencing data

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svaseq: removing batch effects and other unwanted noise from sequencing data

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svaseq: removing batch effects and other unwanted noise from sequencing data

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svaseq: removing batch effects and other unwanted noise from sequencing data

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svaseq: removing batch effects and other unwanted noise from sequencing data

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Nucleic Acids Research

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svaseq: removing batch effects and other unwanted noise from sequencing data

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Jeffrey T Leek

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P2860

Differential expression analysis for sequence count data

Learning from our GWAS mistakes: from experimental design to scientific method

Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Principal components analysis corrects for stratification in genome-wide association studies

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

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

Tackling the widespread and critical impact of batch effects in high-throughput data

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scholarly article

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10.1093/NAR/GKU864

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21

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42

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2014-10-07T00:00:00Z

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266628591

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25294822

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4245966

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