Affiliation: Brown University
- Removing technical variability in RNA-seq data using conditional quantile normalizationKasper D Hansen
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Biostatistics 13:204-16. 2012....
- A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq dataHao Wu
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
Biostatistics 14:232-43. 2013..We present a new empirical Bayes shrinkage estimate of the dispersion parameters and demonstrate improved DE detection...
- Subset quantile normalization using negative control featuresZhijin Wu
Center for Statistical Sciences and Department of Community Health, Brown University, Providence, Rhode Island 02912, USA
J Comput Biol 17:1385-95. 2010..It does not require an equal number of features in all samples and tolerates missing data...
- A review of statistical methods for preprocessing oligonucleotide microarraysZhijin Wu
Center for Statistical Sciences and Department of Community Health, Brown University, RI 02912, USA
Stat Methods Med Res 18:533-41. 2009..In this article, we review the issues encountered in each preprocessing step and introduce the statistical models and methods in preprocessing...
- Quantitative assessment of hit detection and confirmation in single and duplicate high-throughput screeningsZhijin Wu
Center for Statistical Sciences, Department of Community Health, Brown University, Providence, RI 02903, USA
J Biomol Screen 13:159-67. 2008..However, false discovery rate based on empirically estimated null distribution is very close to observed false discovery proportion...
- Accurate genome-scale percentage DNA methylation estimates from microarray dataMartin J Aryee
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University and Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21231, USA
Biostatistics 12:197-210. 2011..We illustrate the method on data generated to detect methylation differences between tissues and between normal and tumor colon samples...
- Chromosome-wide mapping of DNA methylation patterns in normal and malignant prostate cells reveals pervasive methylation of gene-associated and conserved intergenic sequencesSrinivasan Yegnasubramanian
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
BMC Genomics 12:313. 2011..This MBD-chip approach was used to characterize DNA methylation patterns across all non-repetitive sequences of human chromosomes 21 and 22 at high-resolution in normal and malignant prostate cells...
- Comparison of Affymetrix GeneChip expression measuresRafael A Irizarry
Department of Biostatistics, Johns Hopkins University, 615 N Wolfe Street, Baltimore, MD 21205, USA
Bioinformatics 22:789-94. 2006..A webtool was made available for developers to benchmark their procedures. At the time of writing over 50 methods had been submitted...
- Molecular alterations underlying the enhanced disruption of spermatogenesis by 2,5-hexanedione and carbendazim co-exposureSarah N Campion
Department of Pathology and Laboratory Medicine, Brown University, Providence, RI 02903, USA
Reprod Toxicol 33:382-9. 2012..These findings provide candidate genes for further investigation of the testicular response to damage...
- Stochastic models inspired by hybridization theory for short oligonucleotide arraysZhijin Wu
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
J Comput Biol 12:882-93. 2005..We discuss how the proposed model can be used to obtain improved measures of expression useful for the data analysts...
- Background adjustment for DNA microarrays using a database of microarray experimentsYunxia Sui
Department of Community Health, Brown University, Providence, Rhode Island 02912, USA
J Comput Biol 16:1501-15. 2009..An R package dbRMA implementing our method can be obtained from the authors...
- Empirical bayes analysis of sequencing-based transcriptional profiling without replicatesZhijin Wu
Center for Statistical Sciences and Department of Community Health, Box G 121S 7, Brown University, Providence RI 02912, USA
BMC Bioinformatics 11:564. 2010..However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses...
- The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shoresRafael A Irizarry
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
Nat Genet 41:178-86. 2009....
- DNA hypomethylation arises later in prostate cancer progression than CpG island hypermethylation and contributes to metastatic tumor heterogeneitySrinivasan Yegnasubramanian
Sidney Kimmel Comprehensive Cancer Center, School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
Cancer Res 68:8954-67. 2008....
- A benchmark for Affymetrix GeneChip expression measuresLeslie M Cope
Department of Mathematical Sciences, Johns Hopkins University, 104 Whitehead Hall, 3400 North Charles Street, Baltimore, MD 21218, USA
Bioinformatics 20:323-31. 2004..Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data...
- Suppression of radiation-induced testicular germ cell apoptosis by 2,5-hexanedione pretreatment. II. Gene array analysis reveals adaptive changes in cell cycle and cell death pathwaysSarah N Campion
Department of Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island 02912, USA
Toxicol Sci 117:457-65. 2010....
- Exploration, visualization, and preprocessing of high-dimensional dataZhijin Wu
Center for Statistical Sciences, Brown University, Providence, RI, USA
Methods Mol Biol 620:267-84. 2010..In this chapter we review the common techniques in exploring and visualizing high-dimensional data and introduce the basic preprocessing procedures...
- Alternative statistical parameter for high-throughput screening assay quality assessmentYunxia Sui
Department of Community Health, Brown University, Providence, RI 02903, USA
J Biomol Screen 12:229-34. 2007..Studying the power of identifying true "hits" gives a more direct interpretation of assay quality and may provide guidance in assay optimization on some occasions...
- Phylogenetic analysis of gene expressionCasey W Dunn
Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA Department of Biostatistics and Center for Statistical Sciences, Brown University, Providence, RI 02903, USA
Integr Comp Biol 53:847-56. 2013..These topics are relevant to high-throughput phenotypic data well beyond gene expression. ..
- Genetic variation in stearoyl-CoA desaturase 1 is associated with metabolic syndrome prevalence in Costa Rican adultsJian Gong
Department of Community Health, Brown University, Providence, RI, USA
J Nutr 141:2211-8. 2011..No gene-fatty acid interactive effects were observed. Our results suggest that genetic variation in the SCD1 gene may play a role in the development of MetS...
- Feature-level exploration of a published Affymetrix GeneChip control datasetRafael A Irizarry
Genome Biol 7:404. 2006..A comment on Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset by SE Choe, M Boutros, AM Michelson, GM Church and MS Halfon. Genome Biology 2005, 6:R16...