Perform feature selection or dimension reduction to remove noise variables.
prepare_data( data, scale = TRUE, type = c("conventional", "robust", "tsne"), min.var = 1 )
data matrix with rows as samples and columns as variables
logical; should the data be centered and scaled?
if we use "conventional" measures (default), then the mean and standard deviation are used for centering and scaling, respectively. If "robust" measures are specified, the median and median absolute deviation (MAD) are used. Alternatively, we can apply "tsne" for dimension reduction.
minimum variability measure threshold used to filter the
feature space for only highly variable features. Only features with a
minimum variability measure across all samples greater than
be used. If
type = "conventional", the standard deviation is the measure
used, and if
type = "robust", the MAD is the measure used.
dataset prepared for usage in
We can apply a basic filtering method of feature selection that removes variables with low signal and (optionally) scales before consensus clustering. Or, we can use t-SNE dimension reduction to transform the data to just two variables. This lower-dimensional embedding allows algorithms such as hierarchical clustering to achieve greater performance.