ARDSMAICR

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")

Getting started

library(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 1^st January 1967 to 1^st 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.