FeatSeekR - Package for unsupervised feature selection

A fundamental step in many analyses of high-dimensional data is dimension reduction. Feature selection is one approach to dimension reduction whose strengths include interpretability, conceptual simplicity, transferability and modularity. Here, we introduce the FeatSeekR package, which selects features based on the consistency of their signal across replicates and their non-redundancy. It takes a 2 dimensional array (features x samples) of replicated measurements and returns a SummarizedExperiment object storing the selected and features ranked by reproducibility. This work was motivated by [1] who devised a special case of our current method to use it on microscopy data, but did not implement it as R package.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("FeatSeekR")

How to run

See vignette for more detailed examples.

library(FeatSeekR)
set.seed(111)
# simulate data with 500 conditions, 3 replicates (1500 samples) and 5 latent factors 
# generating 50 features
conditions <- 500
latent_factors <- 5
replicates <- 3

data <- FeatSeekR::simData(conditions=conditions,n_latent_factors =latent_factors,
  replicates = replicates)

# select the top 5 features
res <- FeatSeek(data, max_features=5)

# plot a heatmap of the top 5 selected features 
FeatSeekR::plotSelectedFeatures(res)

References

[1] Fischer, B., Sandmann, T., Horn, T., Billmann, M., Chaudhary, V., Huber, W. and Boutros, M., 2015. A map of directional genetic interactions in a metazoan cell. Elife, 4, p.e05464.