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changed name for new option, because its by scores, not just correlation
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NEWS.md

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# vsualizationQualityControl 0.4.11
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* Added a new argument `only_low` to `determine_outliers` to only look at the low end of the score distribution for outliers, as sometimes `boxplot.stats` will pick up outliers at the high end as well.
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* Added a new argument `only_high` to `determine_outliers` to only look at the high end of the score distribution for outliers, as sometimes `boxplot.stats` will pick up outliers at the low end as well.
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# visualizationQualityControl 0.4.10
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R/correlations.R

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@@ -812,7 +812,7 @@ outlier_fraction <- function(data, sample_classes = NULL, n_trim = 3,
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#' @param outlier_fraction outlier fractions
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#' @param cor_weight how much weight for the correlation score?
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#' @param frac_weight how much weight for the outlier fraction?
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#' @param only_low should only things at the low end of score be removed?
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#' @param only_high should only things at the low end of score be removed?
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#'
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#' @details For outlier sample detection, one should
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#' first generate median correlations using
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#' @export
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#' @return data.frame
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determine_outliers = function(median_correlations = NULL, outlier_fraction = NULL,
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cor_weight = 1, frac_weight = 1, only_low = TRUE){
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cor_weight = 1, frac_weight = 1, only_high = TRUE){
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if (!is.null(median_correlations) && !is.null(outlier_fraction)) {
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full_data = dplyr::left_join(median_correlations, outlier_fraction, by = "sample_id", suffix = c(".cor", ".frac"))
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full_data$outlier = FALSE
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full_data$outlier[full_data$sample_id %in% all_out] = TRUE
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if (only_low) {
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if (only_high) {
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split_data = split(full_data, full_data$sample_class)
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full_data = purrr::map(split_data, \(in_data){
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mean_score = mean(in_data$score)

docs/articles/quality_control.html

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docs/pkgdown.yml

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articles:
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pca_testing: pca_testing.html
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quality_control: quality_control.html
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last_built: 2023-07-13T00:03Z
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last_built: 2023-07-13T01:05Z
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docs/reference/determine_outliers.html

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docs/search.json

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man/determine_outliers.Rd

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