## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----eval=FALSE--------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("MAI") ## ----------------------------------------------------------------------------- # Load the MAI package library(MAI) # Load the example data with missing values data("untargeted_LCMS_data") # Set a seed for reproducibility ## Estimating pattern of missingness involves imposing MCAR/MAR into the data ## these are done at random and as such may slightly change the results of the ## estimated parameters. set.seed(137690) # Impute the data using BPCA for predicted MCAR value imputation and # use Single imputation for predicted MNAR value imputation Results = MAI(data_miss = untargeted_LCMS_data, # The data with missing values MCAR_algorithm = "BPCA", # The MCAR algorithm to use MNAR_algorithm = "Single", # The MNAR algorithm to use assay_ix = 1, # If SE, designates the assay to impute forest_list_args = list( # random forest arguments for training ntree = 300, proximity = FALSE ), verbose = TRUE # allows console message output ) # Get MAI imputations Results[["Imputed_data"]][1:5, 1:5] # show only 5x5 ## ----------------------------------------------------------------------------- # Get the estimated mixed missingness parameters Results[["Estimated_Params"]] ## ----------------------------------------------------------------------------- # Load the SummarizedExperiment package suppressMessages( library(SummarizedExperiment) ) # Load the example data with missing values data("untargeted_LCMS_data") # Turn the data to a SummarizedExperiment se = SummarizedExperiment(untargeted_LCMS_data) # Set a seed for reproducibility ## Estimating pattern of missingness involves imposing MCAR/MAR into the data ## these are done at random and as such may slightly change the results of the ## estimated parameters. set.seed(137690) # Impute the data using BPCA for predicted MCAR value imputation and # use Single imputation for predicted MNAR value imputation Results = MAI(se, # The data with missing values MCAR_algorithm = "BPCA", # The MCAR algorithm to use MNAR_algorithm= "Single", # The MNAR algorithm to use assay_ix = 1, # If SE, designates the assay to impute forest_list_args = list( # random forest arguments for training ntree = 300, proximity = FALSE ), verbose = TRUE # allows console message output ) # Get MAI imputations assay(Results)[1:5, 1:5] # show only 5x5 ## ----------------------------------------------------------------------------- # Get the estimated mixed missingness parameters metadata(Results)[["meta_assay_1"]][["Estimated_Params"]] ## ----------------------------------------------------------------------------- sessionInfo()