## Mercurial > repos > eschen42 > w4mcorcov

### view w4mcorcov_salience.R @ 0:23f9fad4edfc draft

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planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit bd26542b811de06c1a877337a2840a9f899c2b94

author | eschen42 |
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date | Mon, 16 Oct 2017 14:56:52 -0400 |

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children | 06c51af11531 |

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w4msalience <- function( data_matrix # a matrix of intensities; features as rows, and samples as columns , sample_class # a vector of sample class-levels; length(sample_class) == ncol(data_matrix) , failure_action = stop ) { library(stats) # begin sanity checks if ( !is.vector(sample_class) || !( is.character(sample_class) || is.factor(sample_class) ) ) { failure_action("w4msalience: Expected sample_class to be a vector of characters of factor-levels") return (NULL) } if ( !is.matrix(data_matrix) && !is.data.frame(data_matrix) ) { failure_action("w4msalience: Expected data_matrix to be a matrix (or data.frame) of numeric") return (NULL) } # transpose data_matrix so that columns are the features t_data_matrix <- t(data_matrix) if ( !is.matrix(t_data_matrix) || !is.numeric(t_data_matrix) ) { failure_action("w4msalience: Expected data_matrix to be a matrix (or data.frame) of numeric") return (NULL) } n_features <- ncol(t_data_matrix) n_features_plus_1 <- 1 + n_features features <- colnames(t_data_matrix) n_samples <- nrow(t_data_matrix) if ( length(sample_class) != n_samples ) { strF(data_matrix) strF(sample_class) failure_action(sprintf("w4msalience: The data_matrix has %d samples but sample_class has %d", n_samples, length(sample_class))) return (NULL) } # end sanity checks # "For each feature, 'select sample_class, median(intensity) from feature group by sample_class'." # The first column(s) of the result of aggregate has the classifier value(s) specified in the 'by' list. medianOfFeatureBySampleClassLevel <- aggregate( x = as.data.frame(t_data_matrix) , by = list(sample_class) , FUN = "median" ) # "For each feature, 'select sample_class, max(intensity) from feature group by sample_class'." maxOfFeatureBySampleClassLevel <- aggregate( x = as.data.frame(t_data_matrix) , by = list(sample_class) , FUN = "max" ) # "For each feature, 'select sample_class, rcv(intensity) from feature group by sample_class'." # cv is less robust; deviation from normality degrades performance # cv(x) == sd(x) / mean(x) # rcv is a "robust" coefficient of variation, expressed as a proportion # rcv(x) == mad(x) / median(x) madOfFeatureBySampleClassLevel <- aggregate( x = as.data.frame(t_data_matrix) , by = list(sample_class) , FUN = "mad" ) rcvOfFeatureBySampleClassLevel <- as.matrix( madOfFeatureBySampleClassLevel[,2:n_features_plus_1] / medianOfFeatureBySampleClassLevel[,2:n_features_plus_1] ) rcvOfFeatureBySampleClassLevel[is.nan(rcvOfFeatureBySampleClassLevel)] <- max(9999,max(rcvOfFeatureBySampleClassLevel, na.rm = TRUE)) # "For each feature, 'select max(median_feature_intensity) from feature'." maxApplyMedianOfFeatureBySampleClassLevel <- sapply( X = 1:n_features , FUN = function(i) { match( max(maxOfFeatureBySampleClassLevel[, i + 1]) , maxOfFeatureBySampleClassLevel[, i + 1] ) } ) # "For each feature, 'select mean(median_feature_intensity) from feature'." meanApplyMedianOfFeatureBySampleClassLevel <- sapply( X = 1:n_features , FUN = function(i) mean(medianOfFeatureBySampleClassLevel[, i + 1]) ) # Compute the 'salience' for each feature, i.e., how salient the intensity of a feature # is for one particular class-level relative to the intensity across all class-levels. salience_df <- data.frame( # the feature name feature = features # the name (or factor-level) of the class-level with the highest median intensity for the feature , max_level = medianOfFeatureBySampleClassLevel[maxApplyMedianOfFeatureBySampleClassLevel,1] # the median intensity for the feature and the level max_level , max_median = sapply( X = 1:n_features , FUN = function(i) { maxOfFeatureBySampleClassLevel[maxApplyMedianOfFeatureBySampleClassLevel[i], 1 + i] } ) # the coefficient of variation (expressed as a proportion) for the intensity for the feature and the level max_level , max_rcv = sapply( X = 1:n_features , FUN = function(i) { rcvOfFeatureBySampleClassLevel[maxApplyMedianOfFeatureBySampleClassLevel[i], i] } ) # the mean of the medians of intensity for all class-levels for the feature , mean_median = meanApplyMedianOfFeatureBySampleClassLevel # don't coerce strings to factors (this is a parameter for the data.frame constructor, not a column of the data.frame) , stringsAsFactors = FALSE ) # raw salience is the ratio of the most-prominent level to the mean of all levels for the feature salience_df$salience <- sapply( X = 1:nrow(salience_df) , FUN = function(i) with(salience_df[i,], if (mean_median > 0) { max_median / mean_median } else { 0 } ) ) # "robust coefficient of variation, i.e., mad(feature-intensity for class-level max_level) / median(feature-intensity for class-level max_level) salience_df$salient_rcv <- salience_df$max_rcv return (salience_df) }