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

Nature reviews. Genetics, 2010-10, Vol.11 (10), p.733-739

2010

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Increased methylation variation in epigenetic domains across cancer types

Nature genetics, 2011-08, Vol.43 (8), p.768-775

2011

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Transcriptome Remodeling in Trypanosoma cruzi and Human Cells during Intracellular Infection

PLoS pathogens, 2016-04, Vol.12 (4), p.e1005511-e1005511

2016

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Metaviz: interactive statistical and visual analysis of metagenomic data

Nucleic acids research, 2018-04, Vol.46 (6), p.2777-2787

2018

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Analysis and correction of compositional bias in sparse sequencing count data

BMC genomics, 2018-11, Vol.19 (1), p.799-799, Article 799

2018

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Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment

BMC genomics, 2016-06, Vol.17 (1), p.440-440, Article 440

2016

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Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis

BMC bioinformatics, 2019-08, Vol.20 (1), p.421-421, Article 421

2019

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Removing batch effects for prediction problems with frozen surrogate variable analysis

PeerJ (San Francisco, CA), 2014-09, Vol.2, p.e561-e561

2014

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Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz [version 2; peer review: 3 approved]

F1000 research, 2021, Vol.9, p.601-601

2021

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Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis

Cancer informatics, 2015-01, Vol.2015 (14), p.71-81

2015

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Evidence-Based Network Approach to Recommending Targeted Cancer Therapies

JCO clinical cancer informatics, 2020-01, Vol.4 (4), p.71-88

2020

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Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz [version 1; peer review: 3 approved]

F1000 research, 2020, Vol.9, p.601

2020

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Determinants of expression variability

Nucleic acids research, 2014-04, Vol.42 (6), p.3503-3514

2014

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Examining the relative influence of familial, genetic, and environmental covariate information in flexible risk models

Proceedings of the National Academy of Sciences - PNAS, 2009-05, Vol.106 (20), p.8128-8133

2009

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Gene expression anti-profiles as a basis for accurate universal cancer signatures

BMC bioinformatics, 2012-10, Vol.13 (1), p.272-272, Article 272

2012

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Automated classification of bird and amphibian calls using machine learning: A comparison of methods

Ecological informatics, 2009-09, Vol.4 (4), p.206-214

2009

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Transcriptomic profiling of gene expression and RNA processing during Leishmania major differentiation

Nucleic acids research, 2015-08, Vol.43 (14), p.6799-6813

2015

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metagenomeFeatures: an R package for working with 16S rRNA reference databases and marker-gene survey feature data

Bioinformatics, 2019-10, Vol.35 (19), p.3870-3872

2019

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methylFlow: cell-specific methylation pattern reconstruction from high-throughput bisulfite-converted DNA sequencing

Bioinformatics, 2016-06, Vol.32 (11), p.1618-1624

2016

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Privacy-preserving microbiome analysis using secure computation

Bioinformatics, 2016-06, Vol.32 (12), p.1873-1879

2016

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Differential abundance analysis for microbial marker-gene surveys

Nature methods, 2013-12, Vol.10 (12), p.1200-1202

2013

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BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution

Bioinformatics, 2014-05, Vol.30 (9), p.1214-1219

2014

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Bioinformatics of DNA [Scanning the Issue]

Proceedings of the IEEE, 2017-03, Vol.105 (3), p.419-421

2017

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Distinct genomic and epigenomic features demarcate hypomethylated blocks in colon cancer

BMC cancer, 2016-02, Vol.16 (87), p.88-88, Article 88

2016

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Shape analysis of high-throughput transcriptomics experiment data

Biostatistics (Oxford, England), 2015-10, Vol.16 (4), p.627-640

2015

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

Increased methylation variation in epigenetic domains across cancer types

Transcriptome Remodeling in Trypanosoma cruzi and Human Cells during Intracellular Infection

Metaviz: interactive statistical and visual analysis of metagenomic data

Analysis and correction of compositional bias in sparse sequencing count data

Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis

Removing batch effects for prediction problems with frozen surrogate variable analysis

Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz [version 2; peer review: 3 approved]

Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis

Evidence-Based Network Approach to Recommending Targeted Cancer Therapies

Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz [version 1; peer review: 3 approved]

Determinants of expression variability

Examining the relative influence of familial, genetic, and environmental covariate information in flexible risk models

Gene expression anti-profiles as a basis for accurate universal cancer signatures

Automated classification of bird and amphibian calls using machine learning: A comparison of methods

Transcriptomic profiling of gene expression and RNA processing during Leishmania major differentiation

metagenomeFeatures: an R package for working with 16S rRNA reference databases and marker-gene survey feature data

methylFlow: cell-specific methylation pattern reconstruction from high-throughput bisulfite-converted DNA sequencing

Privacy-preserving microbiome analysis using secure computation

Differential abundance analysis for microbial marker-gene surveys

BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution

Bioinformatics of DNA [Scanning the Issue]

Distinct genomic and epigenomic features demarcate hypomethylated blocks in colon cancer

Shape analysis of high-throughput transcriptomics experiment data

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