# 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
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.
if (!requireNamespace("BiocManager", quietly = TRUE))
# How to run
See vignette for more detailed examples.
# simulate data with 500 samples, 3 replicates and 5 latent factors
# generating 50 features
samples <- 500
latent_factors <- 5
replicates <- 3
sim <- FeatSeekR::simData(samples=samples,latent_factors =latent_factors,
replicates = replicates)
data <- sim[]
reps <- sim[]
# select the top 5 features
res <- FeatSeek(data, replicates =reps, max_features=5)
# plot a heatmap of the top 5 selected features
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.