# 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]](#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 ```{r, eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("FeatSeekR") ``` # How to run See vignette for more detailed examples. ```{r} set.seed(111) # 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[[1]] reps <- sim[[2]] # select the top 5 features res <- FeatSeek(data, replicates =reps, max_features=5) # plot a heatmap of the top 5 selected features FeatSeekR::plotSelectedFeatures(data, res) ``` ## References <a id="1">[1]</a> 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. ```