metaAnalysisDE {DExMA} | R Documentation |
It performs meta-analysis using eight different methods.
metaAnalysisDE( objectMA, typeMethod = c("FEM", "REM", "maxP", "minP", "Fisher", "Stouffer"), missAllow = 0.3, proportionData = 0.5 )
objectMA |
A list of list. Each list contains two elements. The first element is the expression matrix (genes in rows and sample in columns) and the second element is a vector of zeros and ones that represents the state of the diffenrent samples of the expression matrix. 0 represents one group (controls) and 1 represents the other group (cases). The result of the CreateobjectMA can be used too. |
typeMethod |
A character that indicates the method to be peformed. See "Details"for more information |
missAllow |
a number that indicates the maximun proportion of missing values allowed in a sample. If the sample has more proportion of missing values the sample will be eliminated. In the other case the missing values will be imputed using the K-NN algorithm. |
proportionData |
The minimum proportion of datasets in which a gene must be contained to be included. By default, the gene must be contained in at least half of the datasets |
The different meta-analysis method that can be applied are:
Effects sizes methods:
"FEM": Fixed Effects model
"REM": Random Effects model
P-value combination mehods
"Fisher": Fisher's methods
"Stouffer": Stouffer's method
"maxP": maximum p-value method (Wilkinson's method)
"minP": minimum p-value method (Tippet's method)
A dataframe with the meta-analysis results. Depending on the applied method, a different dataframe is obtained. For more information see the package vignette.
Juan Antonio Villatoro Garcia, juanantoniovillatorogarcia@gmail.com
Daniel Toro-Domínguez, Juan Antonio Villatoro-García, Jordi Martorell-Marugán, Yolanda Román-Montoya, Marta E Alarcón-Riquelme, Pedro Carmona-Sáez, A survey of gene expression meta-analysis: methods and applications, Briefings in Bioinformatics, 2020;, bbaa019, https://doi.org/10.1093/bib/bbaa019
Michael Dewey (2020). metap: meta-analysis of significance values.
data(DExMAExampleData) ResultsMA <- metaAnalysisDE(objectMA=maObject, typeMethod="REM", missAllow=0.3, proportionData=0.5) ResultsMA