get_gene_weights {ssPATHS} | R Documentation |
This method performs linear discriminant analysis on a reference dataset using a pre-defined set of genes related to a pathway of interest.
get_gene_weights(expression_se, gene_ids, unidirectional)
expression_se |
This is an SummarizedExperiment object of the reference samples. Rows are
genes and columns are samples. The colData component must contain a
|
gene_ids |
This is a vector of strings, where each element is a |
unidirectional |
This is a boolean, |
A list containing the gene weights and estimated scores of the reference samples.
proj_vector_df |
A dataframe containing the gene weights and gene ids |
dca_proj |
A dataframe containing the sample scores and sample ids. |
Natalie R. Davidson
Steven C.H. Hoi, W. Liu, M.R. Lyu and W.Y. Ma (2006). Learning Distance Metrics with Contextual Constraints for Image Retrieval. Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR2006).
data(tcga_expr_df) # transform from data.frame to SummarizedExperiment tcga_se <- SummarizedExperiment(t(tcga_expr_df[ , -(1:4)]), colData=tcga_expr_df[ , 2:4]) colnames(tcga_se) <- tcga_expr_df$tcga_id colData(tcga_se)$sample_id <- tcga_expr_df$tcga_id # get related genes, for us hypoxia hypoxia_gene_ids <- get_hypoxia_genes() hypoxia_gene_ids <- intersect(hypoxia_gene_ids, rownames(tcga_se)) # setup labels for classification colData(tcga_se)$Y <- ifelse(colData(tcga_se)$is_normal, 0, 1) # now we can get the gene weightings res <- get_gene_weights(tcga_se, hypoxia_gene_ids, unidirectional=TRUE) gene_weights_test <- res[[1]] sample_scores <- res[[2]]