get_new_samp_score {ssPATHS} | R Documentation

## Get a pathway score for an unseen sample

### 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)

[Package ssPATHS version 1.0.0 Index]