missGenesImput: Imputation of unmeasured genes

In Juananvg/DExMA: Differential Expression Meta-Analysis

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

A list formed by two elements:

• First element (objectMA) the same objectMA with missign genes imputed

• Second element (imputIndicators) a list with 4 different objects:

  • imputValuesSample: Number of missing values imputed per sample

  • imputPercentageSample: Percentage of missing values imputed per sample

  • imputValuesGene: Number of missing values imputed per gene

  • imputPercentageGene: Percentage of missing values imputed per gene

Author(s)

Juan Antonio Villatoro Garcia, [e-mail]

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)

Juananvg/DExMA documentation built on Dec. 5, 2023, 1:12 p.m.