The ARDSMAICR package contains the results of a meta-analysis by information content (MAIC) of genome-wide studies of the host response to acute respiratory distress syndrome (ARDS). These data are accompanied by a range of helper functions useful for analysis.
Meta-Analysis by Information Content (MAIC)
MAIC was developed in the Baillie Lab, Roslin Institute, University of Edinburgh as a method for combining lists of genes arising from diverse experimental sources.
It has been used to study the host response to Influenza and SARS-CoV-2.
MAIC consistently out performs similar algorithms in the case of ranked and unranked data sources and in the presence of heterogeneity in the quality of studies. Further details can be found here.
The source code for MAIC is hosted here.
ARDSMAICR
Installation
You can install the latest version of ARDSMAICR from GitHub with:
if (!require(devtools)) install.packages("devtools")
devtools::install_github("baillielab/ARDSMAICR")ARDS MAIC results
The systematic review, data extraction, and MAIC are described in detail on the study website.
The package contains the output of the MAIC covering the period 1st January 1967 to 1st December 2022. Future releases will match regular updates of the systematic review.
These data are contained in data_genes. It has the following format:
| gene | study_1_id | study_2_id | study_n_id | maic_score | contributors |
|---|---|---|---|---|---|
| GENE_A | 0.000 | 1.234 | 0.000 | 1.234 | METHOD_1: study_2_id |
| GENE_B | 1.345 | 1.234 | 0.000 | 1.456 | METHOD_1: study_2_id, METHOD_2: study_id_1 |
Additional data are:
-
data_study- A summary of the studies identified by the systematic review, including their methods. -
data_contributions- Calculated study contributions to MAIC. -
data_biolitmine- The results of a BioLitMine search for the MeSH “Respiratory Distress Syndrome, Adult”. -
data_covidmaic- A ranked list of genes from a MAIC of SARS-CoV-2 studies.
Helper functions
Several groups of functions useful in the analysis of the MAIC results are included. They fall into the following broad families:
- Summary tables
- Helper functions - counts, genes, lists, methods and categories
- Gene prioritisation
- Information content and contribution
The majority of these functions can be applied to the standard MAIC output of any analysis.