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48 lines
1.7 KiB
R
48 lines
1.7 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 cross_sample_metric Metric to use for calculating the number of
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#' samples a gene is expressed in. One of `above_95`, `above_median` or
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#' `above_zero`.
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#' @param cross_sample_weight Weighting of the cross sample metric within the
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#' final score.
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#' @param mean_expression_weight Weighting of the gene's mean expression within
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#' the final score.
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#' @param sd_expression_weight Weighting of the standard deviation of the
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#' gene's expression within the final score.
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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(cross_sample_metric = "above_95",
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cross_sample_weight = 0.5,
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mean_expression_weight = 0.25,
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sd_expression_weight = -0.25) {
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total_weight <- cross_sample_weight +
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mean_expression_weight +
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sd_expression_weight
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data <- copy(ubigen::genes)
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data[, score :=
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(cross_sample_weight * get(cross_sample_metric) +
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mean_expression_weight * mean_expression_normalized +
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sd_expression_weight * sd_expression_normalized) /
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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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# These are genes that are not expressed at all.
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data[is.na(score), score := 0.0]
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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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