3TierMA is a three-tiered meta-analysis approach for studying the shared genetics of co-ocurring disease conditions in patients from their gene expression profiles.
Metalysis is meant for revealing higher level insights from multiple gene expression data sets. In particular, if you have up- and down-regulated gene sets from several different conditions and want to know what might be common to those different gene sets, you can use the Metalysis program.
“cis-Metalysis” is an extension to Metalysis specifically designed to use motif target sets as annotation sets. It takes gene target predictions of the transcription factor motifs and then uses the Metalysis framework to identify meta associations between a motif and set of conditions. Because of the general consensus that condition-specific expression of a gene may be determine by combinations of transcription factors, cis-Metalysis also searches for motif combinations associated with expression.
Qiita (canonically pronounced cheetah) is the QIIME database effort to enable rapid analysis of microbial ecology datasets. The Qiita repository is responsible for defining the data model and the Python API for interacting with a Qiita database.
B-LORE (Bayesian LOgistic REgression) is a command line tool that creates summary statistics from multiple logistic regression on GWAS data, and combines the summary statistics from multiple studies in a meta-analysis. It can also incorporate functional information about the SNPs from other external sources. Several genetic regions, or loci are preselected for analysis with B-LORE.
METRADISC (METa-analysis of RAnked DISCovery datasets), a generalized meta-analysis method for combining information across discovery-oriented datasets and for testing between-study heterogeneity for each biological variable of interest. The method is based on non-parametric Monte Carlo permutation testing.
METRADISC-XL is a software for METa-analysis of microarrays datasets and heterogeneity testing.
HEGESMA (HEterogeneity and GEnome Search Meta Analysis)is a comprehensive software for performing genome scan meta-analysis, a quantitative method to identify genetic regions (bins) with consistently increased linkage score across multiple genome scans, and for testing the heterogeneity of the results of each bin across scans. The program provides as an output the average of ranks and three heterogeneity statistics, as well as corresponding significance levels. Statistical inferences are based on Monte Carlo permutation tests. The program allows both unweighted and weighted analysis, with the weights for each study as specified by the user. Furthermore, the program performs heterogeneity analyses restricted to the bins with similar average ranks.
MetaPCA is a software of dimension reductioin by PCA, sparse PCA and robust PCA in meta-analysis. The software implements simultaneous dimension reduction using PCA when multiple studies are combined.
MetaQTL is a Java package designed to perform the integration of data from the field of gene mapping experiments (e.g molecular markers, QTL, candidate genes, etc…). This package consists in a modular library and several programs written in pure Java. These programs can perform various tasks, including formatting, analyzing and visualizing data or results produced by MetaQTL.
A-MADMAN (Annotation-based MicroArray DataMeta ANalysis tool) is an open source web application and gene chip analysis automation framework for annotation-based meta-analysis of data from public repositories (NCBI GEO).
Haplotype meta-analysis is a STATA programs for meta-analysis of haplotype association studies.It use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability.