Identify time intervals of differentially abundant metagenomics features in longitudinal studies <doi:10.1145/3107411.3107429>.
Ahmed Metwally, Yang Dai, Patricia Finn, David Perkins

Identify time intervals of differentially abundant metagenomics features in longitudinal studies.
gss, plyr, caTools, ggplot2, parallel, doParallel,
metagenomeSeq, DESeq2, edgeR

Ahmed A. Metwally, Yang Dai, Patricia W. Finn, David L. Perkins
Identify time intervals of differentially abundant metagenomic features in longitudinal studies (Metwally AA, et al., Microbiome, 2018 <doi:10.1186/s40168-018-0402-y>).
gss, plyr, zoo, pracma, ggplot2, parallel, doParallel,
metagenomeSeq, DESeq2, edgeR

MetaPCA: Meta-analysis in the Dimension Reduction of Microarray
data
MetaPCA implements simultaneous dimension reduction using
PCA when multiple studies are combined. We propose two basic
ideas to find a common PC subspace by eigenvalue maximization
approach and angle minimization approach, and we extend the
concept to incorporate Robust PCA and Sparse PCA in the
meta-analysis realm.
MASS, GEOquery, pcaPP, affy, hgu133plus2.db, doMC, doSMP,
ellipse, impute
MetaPCA.R functions.R DropDupGenes.R requireAll.R PlotPC2D.R
PreprocessMetaAnalysis.R

MetaPCA: Meta-analysis in the Dimension Reduction of Genomic
data

Don Kang <donkang75@gmail.com> and George Tseng
<ctseng@pitt.edu>

Perform the Meta-Analysis for Pathway Enrichment analysis
(MAPE).
This R package was implemented to perform the
Meta-analysis for Pathway Enrichment (MAPE) methods introduced
by Shen and Tseng (2010). It includes functions to
automatically perform MAPE_G (integrating multiple studies at
gene level), MAPE_P (integrating multiple studies at pathway
level) and MAPE_I (a hybrid method integrating MAEP_G and
MAPE_P methods). In the simulation and real data analyses in
the paper, MAPE_G and MAPE_P have complementary advantages and
detection power depending on the data structure. In general,
the integrative form of MAPE_I is recommended to use. In the
case that MAPE_G (or MAPE_P) detects almost none pathway, the
integrative MAPE_I does not improve performance and MAPE_P (or
MAPE_G) should be used. Reference: Shen, Kui, and George C
Tseng. Meta-analysis for pathway enrichment analysis when
combining multiple microarray studies.Bioinformatics (Oxford,
England) 26, no. 10 (April 2010): 1316-1323.
doi:10.1093/bioinformatics/btq148.
http://www.ncbi.nlm.nih.gov/pubmed/20410053.