Running, on this dataset, these commands
library("pcaMethods")
df <- read.table('norm.tsv', header=TRUE, sep="\t", row.names=1)
imputed = llsImpute(t(norm), correlation = "pearson", allVariables = TRUE)
t(completeObs(imputed))
Gives me some very strange results, with numbers that are bigger than the sum of the non imputted data. E.g., df["A0A090I5T7",] returns
CS1 CS2 CS3 CS4 CS5 CS6
NA NA NA NA NA 0.6316226
and after imputation is
CS1 CS2 CS3 CS4 CS5 CS6
0.3368018 0.4863894 0.2958871 0.4730441 1686.0216401 0.6316226
What happened to sample CS5?
Running, on this dataset, these commands
Gives me some very strange results, with numbers that are bigger than the sum of the non imputted data. E.g.,
df["A0A090I5T7",]returnsand after imputation is
What happened to sample CS5?