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Cross-Platform Meta-Analyis =========================== `crossmeta` streamlines the cross-platform meta-analysis of microarray data. For the analysis, you will need a list of Affymetrix, Illumina, and/or Agilent GSE numbers from [GEO]( All 21 species in the current [homologene]( build are supported. See [vignette]( for detailed usage. `crossmeta` is available through [Bioconductor]( To install the latest release from github: ```R install.packages('remotes') remotes::install_github('alexvpickering/crossmeta') ``` Basic Workflow -------------- ```R # studies from GEO gse_names <- c("GSE9601", "GSE15069") # get raw data for specified studies get_raw(gse_names) # load and annotate raw data esets <- load_raw(gse_names) # perform differential expression analysis anals <- diff_expr(esets) # perform meta-analysis es <- es_meta(anals) ``` Approach -------- A high quality meta-analysis is achieved by addressing the key issues in conducting a meta-analysis of microarray data (1): ##### Uses raw data * Different labs process their raw data differently. These differences in data processing may lead to flawed conclusions upon meta-analysis. * `crossmeta` starts with raw data, and uses a consistent processing pipeline for all studies. ##### Maps probes to human genes * Related genes from different species are often referenced with different symbols. These differences can make it challenging to compare similar microarray experiments in diverse species. * `crossmeta` maps probes to human gene symbols using homology relationships established by [HomoloGene]( ##### Resolves many-to-many mappings * Single probes can measure multiple genes. To incorporate these measurements appropriately, they are replaced with a new record for each gene. * Multiple probes can measure the same gene. Averaging these measurements is inappropriate because measurement scales will vary with probe affinity. `crossmeta` selects the measurement with the highest inter-quartile range as it is the least likely to occur by chance. ##### Models nuisance variables * In addition to variables of interest, there are sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. These factors can either hide true effects or introduce spurious ones. * `crossmeta` discovers and accounts for these nuissance variables using surrogate variable analysis. ##### Simplifies model specification * Correctly specifying a model and contrast matrix can be challenging. * `crossmeta` uses an attractive user interface that allows you to simply select the samples you want to compare. * Paired samples (eg. the same subject before and after treatment) can also be selected using the same interface. ##### Meta-analyzes genes with missing data * Differences in microarray platforms often lead to thousands of genes that are not measured in all studies. Most existing meta-analysis software requires that these genes with missing data are discarded. * `crossmeta` extends the effect size meta-analysis method in `GeneMeta` to allow for genes that were not measured in all studies. Keep your data, find more insights. ----------------- *(1) Ramasamy A, Mondry A, Holmes CC, Altman DG (2008) Key Issues in Conducting a* *Meta-Analysis of Gene Expression Microarray Datasets. PLoS Med 5(9): e184.* doi:10.1371/journal.pmed.0050184