Multivariate methods in metabolomics–from pre-processing to dimension reduction and statistical analysis
This article presents some of the multivariate methods used in metabolomics, and addresses
many of the data types and associated analyses of current instrumentation and applications …
many of the data types and associated analyses of current instrumentation and applications …
GSVA: gene set variation analysis for microarray and RNA-seq data
Background Gene set enrichment (GSE) analysis is a popular framework for condensing
information from gene expression profiles into a pathway or signature summary. The …
information from gene expression profiles into a pathway or signature summary. The …
Camera: a competitive gene set test accounting for inter-gene correlation
Competitive gene set tests are commonly used in molecular pathway analysis to test for
enrichment of a particular gene annotation category amongst the differential expression …
enrichment of a particular gene annotation category amongst the differential expression …
Missing heritability in Parkinson's disease: the emerging role of non-coding genetic variation
Parkinson's disease (PD) is a neurodegenerative disorder caused by a complex interplay of
genetic and environmental factors. For the stratification of PD patients and the development …
genetic and environmental factors. For the stratification of PD patients and the development …
Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn
Permutation tests are amongst the most commonly used statistical tools in modern genomic
research, a process by which p-values are attached to a test statistic by randomly permuting …
research, a process by which p-values are attached to a test statistic by randomly permuting …
ROAST: rotation gene set tests for complex microarray experiments
Motivation: A gene set test is a differential expression analysis in which a P-value is
assigned to a set of genes as a unit. Gene set tests are valuable for increasing statistical …
assigned to a set of genes as a unit. Gene set tests are valuable for increasing statistical …
Differential expression analysis for pathways
Life science technologies generate a deluge of data that hold the keys to unlocking the
secrets of important biological functions and disease mechanisms. We present DEAP …
secrets of important biological functions and disease mechanisms. We present DEAP …
[HTML][HTML] Transcriptome profiling reveals matrisome alteration as a key feature of ovarian cancer progression
Background: Ovarian cancer is the most lethal gynecologic malignancy. There is a lack of
comprehensive investigation of disease initiation and progression, including gene …
comprehensive investigation of disease initiation and progression, including gene …
FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis
Motivation Gene set enrichment analyses (GSEAs) are widely used in genomic research to
identify underlying biological mechanisms (defined by the gene sets), such as Gene …
identify underlying biological mechanisms (defined by the gene sets), such as Gene …
Network-based biomarkers enhance classical approaches to prognostic gene expression signatures
Background Classical approaches to predicting patient clinical outcome via gene
expression information are primarily based on differential expression of unrelated genes …
expression information are primarily based on differential expression of unrelated genes …