R/prefilterSAM.R
prefilterSAM.RdThis function performs pre-filtering of genes based on Significance Analysis of Microarrays (SAM). It runs a bootstrapping procedure to select significant genes based on the False Discovery Rate (FDR) and allows filtering by the percentage of times a gene is selected across multiple iterations.
prefilterSAM(
seData,
groupsVector,
FDRfilter = 0.05,
iter = 100,
percentageFilter = 80
)SummarizedExperiment object with the normalized expression data and the phenotypic data in colData.
A binary vector indicating group assignment the samples.
A numeric value indicating the FDR threshold for selecting significant genes. Default is 0.05.
The number of iterations for bootstrapping. Default is 100.
A numeric value indicating the percentage of iterations a gene must appear in to be considered significant. Default is 80.
An ordered vector with the names of differentially expressed genes between the categories of the grouping vector. A list of DE genes ordered by SAM d.value and filtered by percentageFilter.
This function implements SAM (Schwender H., 2022) robust diferential expression analysis based on bootstrap . It helps to remove noisy genes reducing the computational complexity of further analysis. The function uses a bootstrapping approach, where in each iteration a random sample is drawn from the input data (with replacement), and the SAM algorithm is applied to select significant genes. The genes that appear as significant in a specified percentage of iterations (controlled by `percentageFilter`) are retained.
schwender2025siggenesasuri martinezromero2018asuri BuenoFortes2023asuri
data(seBRCA)
# Bootstrapped differential expression based on SAM.
# Parameters: FDR = 0.05, iter = 100, percentage filter = 80 %
# CAUTION: if the data have a high number of genes this function will take
# several minutes to compute.
groupsVector <- SummarizedExperiment::colData(seBRCA)$ER.IHC
set.seed(5)
DE_list_genes <- prefilterSAM(seBRCA, groupsVector)
#> 2025-07-08 12:32:50.024929
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#> 2025-07-08 12:34:11.791351