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43 lines
1.1 KiB
R
43 lines
1.1 KiB
R
# This script uses the results (See results.csv) and computes a score for each
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# gene. This is the data that will be used in the package.
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library(data.table)
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library(here)
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i_am("scripts/input.R")
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data <- fread(here("scripts", "output", "results.csv"))
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data[, `:=`(
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gene = stringr::str_split(gene, "\\.") |> purrr::map_chr(1),
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mean_expression_normalized = mean_expression / max(mean_expression),
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sd_expression_normalized = sd_expression / max(sd_expression)
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)]
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data[, score := 0.5 * above_95 +
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0.25 * mean_expression_normalized +
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-0.25 * sd_expression_normalized]
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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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# Remove duplicates. This will keep the best row for each duplicated gene.
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data <- unique(data, by = "gene")
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data[, `:=`(
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hgnc_name = gprofiler2::gconvert(
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gene,
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target = "HGNC",
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mthreshold = 1,
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filter_na = FALSE
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)$target,
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rank = .I
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)]
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fwrite(data, file = here("scripts", "output", "genes.csv"))
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