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 inclusion/exclusion decision.

MetaQC: Quantitative Quality Assessment for Inclusion/Exclusion
Criteria of Genomic Meta-Analysis
doMC, doSMP, FactoMineR, samr, matrixStats, gdata, gtools

doMC, doSNOW, FactoMineR, samr, matrixStats, gdata, gtools

Meta analysis for SNP-set (Sequence) Kernel Association Test
Meta analysis for SNP-set (Sequence) Kernel Association
Test.

Meta analysis for SNP-set (Sequence) Kernel Association Test.

Meta Analysis for SNP-Set (Sequence) Kernel Association Test
Functions for Meta-analysis Burden test, SKAT and SKAT-O. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.

Functions for Meta-analysis Burden test, SKAT and SKAT-O by  Lee et al. (2013) <doi: 10.1016/j.ajhg.2013.05.010>. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.