Perform LSimpute_gene as described by Bo et al. (2004)
impute_LS_gene(
ds,
k = 10,
eps = 1e-06,
min_common_obs = 5,
return_r_max = FALSE,
verbose = FALSE
)An object of the same class as ds with imputed missing values.
If return_r_max = TRUE, a list with the imputed dataset and r_max.
A data frame or matrix with missing values.
Number of most correlated genes used for the imputation of a gene.
Used in the calculation of the weights (Bo et al. (2004) used
eps = 1e-6).
A row can only take part in the imputation of another
row, if both rows share at least min_common_obs columns with no missing
values.
Logical; normally, this should be FALSE. TRUE is
used inside of impute_LS_adaptive() to speed up some computations.
Should messages be given for special cases (see details)?
This function performs LSimpute_gene as described by Bo et al. (2004).The function assumes that the genes are the rows of ds.
Bo et al. (2004) seem to have chosen min_common_obs = 5. However, they did
not document this behavior. This value emerged from inspecting
imputation results from the original jar-file, which is provided by Bo et
al. (2004).
If there are less than min_common_obs observed values in a row and at least
one observed value, the mean of the observed row values is imputed. If no
value is observed in a row, the observed column means are imputed for the
missing row values. This is the only known difference between this function
and the original one from Bo et al. (2004). The original function would not
impute such a row and return a dataset with missing values in this row. There
is one more case that needs a special treatment: If no suitable row can be
found to impute a row, the mean of the observed values is imputed, too. If
verbose = TRUE, a message will be given for the encountered instances of
the described special cases. If verbose = FALSE, the function will deal
with theses cases silently.
Bo, T. H., Dysvik, B., & Jonassen, I. (2004). LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic acids research, 32(3), e34
Other LSimpute functions:
impute_LS_adaptive(),
impute_LS_array(),
impute_LS_combined()
set.seed(123)
ds_mis <- delete_MCAR(mvtnorm::rmvnorm(100, rep(0, 10)), 0.1)
ds_imp <- impute_LS_gene(ds_mis)
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