Numerical Imputation¶
Some methods e.g. PCA expect the data to have no missing values. But we often have some missing values which can be of following type -
-
For phenotypic data, if the value of a trait for a taxon is not known/recorded.
-
For genotypic data, if the value of some attribute is missing for a SNP.
Two methods are implemented to impute the missing values of data (both phenotypic and genotypic).
-
Imputation by Mean - here the missing value is replaced by mean of the values for corresponding attribute.
-
Imputation by k-nearest-neighbors - If data is missing for a taxon for one of the traits, the algorithm finds other taxa (neighbors) that are most like it for the non-missing traits. It uses the average of the neighbors to impute the missing data. Similarly if the value for some attribute for a SNP is missing, we compute k other SNPs which are most similar to it. The default value of k is 5, which can be changed. Also there are three different choices for distance measure to be for computing nearest neighbors.
-
Euclidean
- Manhattan
- Cosine
The Euclidean distance is chosen as default.
Running Numerical Imputation from the command line¶
Use the ImputationPlugin
Usage: ImputationPlugin