get_new_samp_score: Get a pathway score for an unseen sample

In nrosed/ssPATHS: ssPATHS: Single Sample PATHway Score

Description

Using the gene weights learned from the reference cohort, we apply the weightings to new samples to estimate their pathway activity.

Usage

get_new_samp_score(gene_weights, expression_se, run_normalization = TRUE)

Arguments

gene_weights	This is a data.frame containing gene ids and gene weights, output by get_gene_weights. The gene ids must be in the column ids of expression_matr.
expression_se	This is an SummarizedExperiment object of the reference samples. Rows are genes and columns are samples. The colData component must contain columns Y and sample_id. The former indicates whether this is a positive or negative sample and the latter is the unique sample id. All genes in the SummarizedExperiment object are assumed to be genes in the pathway of interest.
run_normalization	Boolean value. If TRUE, the data will be log-transformed, centered and scaled. This is recommended since this is done to the reference set when learning the gene weights.

Value

A data.frame containing the sample id, sample score, and associated Y value if it was included in expression_se.

Author(s)

Natalie R. Davidson

Examples

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 the genes of interest, here hypoxia genes
hypoxia_gene_ids <- get_hypoxia_genes()
hypoxia_gene_ids <- intersect(hypoxia_gene_ids, rownames(tcga_se))
hypoxia_se <- tcga_se[hypoxia_gene_ids,]

# label the samples for classification
colData(hypoxia_se)$Y <- ifelse(colData(hypoxia_se)$is_normal, 0, 1)

# now we can get the gene weightings
res <- get_gene_weights(hypoxia_se)
gene_weights <- res[[1]]
sample_scores <- res[[2]]

# get the new data so we can apply our score to it
data(new_samp_df)
new_samp_se <- SummarizedExperiment(t(new_samp_df[ , -(1)]),
                                    colData=new_samp_df[ , 1, drop=FALSE])
colnames(colData(new_samp_se)) <- "sample_id"

new_score_df_calculated <- get_new_samp_score(gene_weights, new_samp_se)

nrosed/ssPATHS documentation built on April 24, 2020, 6:22 p.m.