Imputation for Proteomics
Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.
Estimation of lower and upper bounds for missing values.
Estimation of a mixture model of MCAR and MNAR values in each column of a data matrix.
Function similar to the function apply(X,dim,function(x)sum(is.na(x))).
Function similar to the function apply(X,dim,function(x)sum(!is.na(x))).
Function similar to the function apply(X,dim,sd,na.rm=TRUE).
Function similar to the function apply(X,dim,sum,na.rm=TRUE).
Function to compute similarity measures between a vector and each row of a matrix.
Function allowing to create a vector indicating the membership of each sample to a condition.
Introduction to the IMP4P package
Imputing missing values using Principal Components Analysis.
Imputing missing values using Random Forest.
Imputing missing values by assuming that the distribution of complete values is Gaussian in each column of an input matrix. This algorithm is named "Imputation under a Gaussian Complete Data Assumption" (IGCDA).
Imputation of data sets containing peptide intensities with a multiple imputation strategy.
Imputation using a decision rule under an assumption of a mixture of MCAR and MNAR values.
Imputing missing values using a maximum likelihood estimation (MLE).
Imputation of peptides having no value in a biological condition (present in a condition / absent in another).
Imputation of peptides with a random value.
Imputing missing values using an adaptation of the LSimpute algorithm (Bo et al. (2004)) to experimental designs. This algorithm is named "Structured Least Squares Algorithm" (SLSA).
Multiple imputation from a matrix of probabilities of being MCAR for each missing value.
Estimating the MCAR mechanism in a sample.
Estimating the missing data mechanism in a sample.
Estimating the proportion of MCAR values in biological conditions using the method of Karpievitch (2009).
Estimating the proportion of MCAR values in a sample using a logit model.
Estimating the proportion of MCAR values in a sample using a probit model.
Estimation of a vector of probabilities that missing values are MCAR.
Estimation of a matrix of probabilities that missing values are MCAR.
Simulation of data sets by controlling the proportion of MCAR values and the distribution of MNAR values.
Function to generated values following a translated Beta distribution
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