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56 lines
2.1 KiB
R
56 lines
2.1 KiB
R
#' Rank genes based on how ubiquitous they are.
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#'
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#' This function will compute a weighted average across multiple metrics that
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#' define how ubiquitous a gene is based on its expression across samples.
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#'
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#' @param data The input data to use. This should either be the result of a
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#' previous call to this function or the return value of [analyze()].
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#' @param cross_sample_metric Name of the column that should be used as the
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#' metric measuring the expression across samples.
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#' @param cross_sample_weight Relative weight that should be assigned to the
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#' cross sample metric.
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#' @param level_metric Name of the column that should be used to represent
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#' overall expression levels.
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#' @param level_weight Relative weight that should be assigned to the level
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#' metric.
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#' @param variation_metric Name of the column that should be used as the metric
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#' representing variation in expression.
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#' @param variation_weight Relative weight that should be assigned to the
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#' variation metric.
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#'
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#' @return A `data.table` with gene data as well as the scores, ranks and
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#' percentiles for each gene.
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#'
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#' @export
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rank_genes <- function(data = ubigen::gtex_all,
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cross_sample_metric = "above_95",
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cross_sample_weight = 0.5,
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level_metric = "median_expression_normalized",
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level_weight = 0.25,
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variation_metric = "qcv_expression_normalized",
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variation_weight = -0.25) {
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data <- copy(data)
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total_weight <- abs(cross_sample_weight) +
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abs(level_weight) +
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abs(variation_weight)
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data[, score :=
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(cross_sample_weight * get(cross_sample_metric) +
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level_weight * get(level_metric) +
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variation_weight * get(variation_metric)) /
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total_weight]
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# Normalize scores to be between 0.0 and 1.0.
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data[, score := (score - min(score, na.rm = TRUE)) /
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(max(score, na.rm = TRUE) - min(score, na.rm = TRUE))]
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# Exclude genes that are not expressed at all.
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data <- data[!is.na(score)]
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setorder(data, -score)
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data[, rank := .I]
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data[, percentile := 1 - rank / max(rank)]
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data
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}
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