missGenesImput {DExMA}R Documentation

Imputation of unmeasured genes

Description

missGenesImput uses k-nearest neighbors in the space of samples to impute the unmeasured genes of the different datasets.

Usage

missGenesImput(objectMA, k = 7)

Arguments

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 different 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.

k

Number of neighbors to be used in the imputation (default=7).

Value

The same objectMA with missing genes imputed

Author(s)

Juan Antonio Villatoro Garcia, juanantoniovillatorogarcia@gmail.com

References

Christopher A Mancuso, Jacob L Canfield, Deepak Singla, Arjun Krishnan, A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes, Nucleic Acids Research, Volume 48, Issue 21, 2 December 2020, Page e125, https://doi.org/10.1093/nar/gkaa881

Alberto Franzin, Francesco Sambo, Barbara di Camillo. bnstruct: an R package for Bayesian Network structure learning in the presence of missing data. Bioinformatics, 2017; 33 (8): 1250-1252, Oxford University Press, https://doi.org/10.1093/bioinformatics/btw807

See Also

createObjectMA, metaAnalysisDE

Examples

data(DExMAExampleData)
missGenesImput(maObject)

[Package DExMA version 1.2.1 Index]