Packaged: 2019-12-17 20:11:48 UTC; ahmedmetwally

Title: MetaPCA: Meta-analysis in the Dimension Reduction of Microarray
Description: 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
Suggests: MASS, GEOquery, pcaPP, affy, hgu133plus2.db, doMC, doSMP,
Collate: MetaPCA.R functions.R DropDupGenes.R requireAll.R PlotPC2D.R

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

Author: Don Kang <donkang75@gmail.com> and George Tseng

Title: Perform the Meta-Analysis for Pathway Enrichment analysis
Description: 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,

Title: Perform the Meta-Analysis for Pathway Enrichment Analysis (MAPE)
Description: Perform the Meta-analysis for Pathway Enrichment (MAPE)
methods introduced by Shen and Tseng (2010). It includes functions to

Package: MetaQC
Type: Package
Title: MetaQC: Objective Quality Control and Inclusion/Exclusion
Criteria for Genomic Meta-Analysis
Version: 0.1.10-1
Author: Don Kang <donkang75@gmail.com> and George Tseng
<ctseng@pitt.edu>
Maintainer: Don Kang <donkang75@gmail.com>
Description: MetaQC implements our proposed quantitative quality
control measures: (1) internal homogeneity of co-expression
structure among studies (internal quality control; IQC); (2)
external consistency of co-expression structure correlating
with pathway database (external quality control; EQC); (3)
accuracy of differentially expressed gene detection (accuracy
quality control; AQCg) or pathway identification (AQCp); (4)
consistency of differential expression ranking in genes
(consistency quality control; CQCg) or pathways (CQCp). (See
the reference for detailed explanation.) For each quality
control index, the p-values from statistical hypothesis testing
are minus log transformed and PCA biplots were applied to
assist visualization and decision. Results generate systematic
suggestions to exclude problematic studies in microarray
meta-analysis and potentially can be extended to GWAS or other
types of genomic meta-analysis. The identified problematic
studies can be scrutinized to identify technical and biological
causes (e.g. sample size, platform, tissue collection,
preprocessing etc) of their bad quality or irreproducibility
for final inclustion/exclusion decision.
Depends: R (>= 2.10.0), proto, foreach, iterators
Suggests: doMC, doSNOW, FactoMineR, matrixStats, gdata, gtools,
survival
License: GPL-2
URL: https://github.com/donkang75/MetaQC
LazyLoad: yes
Collate: MetaQC.R requireAll.R functions.R runQC.R cleanup.R
Date: 2012-04-25
Packaged: 2012-04-26 18:44:54 UTC; ddkang
Repository: CRAN
Date/Publication: 2012-04-27 06:13:30