Mercurial > repos > eschen42 > w4mcorcov

annotate w4mcorcov_calc.R @ 0:50a07adddfbd draft

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planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
author eschen42
date Mon, 09 Oct 2017 15:46:13 -0400
parents
children e25fd8a13665
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50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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1 # center with 'colMeans()' - ref: http://gastonsanchez.com/visually-enforced/how-to/2014/01/15/Center-data-in-R/
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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2 center_colmeans <- function(x) {
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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3 xcenter = colMeans(x)
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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4 x - rep(xcenter, rep.int(nrow(x), ncol(x)))
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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5 }
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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6
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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7 #### OPLS-DA
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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8 algoC <- "nipals"
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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9
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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10 do_detail_plot <- function(x_dataMatrix, x_predictor, x_is_match, x_algorithm, x_prefix, x_show_labels, x_progress = print, x_env) {
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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11 off <- function(x) if (x_show_labels) x else 0
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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12 salience_lookup <- x_env$salience_lookup
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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13 salient_rcv_lookup <- x_env$salient_rcv_lookup
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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14 # x_progress("head(salience_df): ", head(salience_df))
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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15 # x_progress("head(salience): ", head(salience))
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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16 if (x_is_match && ncol(x_dataMatrix) > 0 && length(unique(x_predictor))> 1) {
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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17 my_oplsda <- opls(
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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18 x = x_dataMatrix
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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19 , y = x_predictor
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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20 , algoC = x_algorithm
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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21 , predI = 1
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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22 , orthoI = if (ncol(x_dataMatrix) > 1) 1 else 0
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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23 , printL = FALSE
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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24 , plotL = FALSE
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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25 )
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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26 my_oplsda_suppLs_y_levels <- levels(as.factor(my_oplsda@suppLs$y))
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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27 fctr_lvl_1 <- my_oplsda_suppLs_y_levels[1]
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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28 fctr_lvl_2 <- my_oplsda_suppLs_y_levels[2]
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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29 my_cor_vs_cov <- cor_vs_cov(
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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30 matrix_x = x_dataMatrix
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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31 , ropls_x = my_oplsda
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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32 )
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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33 with(
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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34 my_cor_vs_cov
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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35 , {
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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36 min_x <- min(covariance)
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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37 max_x <- max(covariance)
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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38 lim_x <- max(sapply(X=c(min_x, max_x), FUN=abs))
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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39 covariance <- covariance / lim_x
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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40 lim_x <- 1.2
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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41 main_label <- sprintf("%s for levels %s versus %s", x_prefix, fctr_lvl_1, fctr_lvl_2)
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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42 # print("main_label")
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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43 # print(main_label)
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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44 main_cex <- min(1.0, 46.0/nchar(main_label))
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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45 # " It is generally accepted that a variable should be selected if vj>1, [27–29], but a proper threshold between 0.83 and 1.21 can yield more relevant variables according to [28]." (Mehmood 2012 doi:10.1016/j.chemolab.2004.12.011)
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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46 vipco <- pmax(0, pmin(1,(vip4p-0.83)/(1.21-0.83)))
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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47 alpha <- 0.1 + 0.4 * vipco
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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48 red <- as.numeric(correlation < 0) * vipco
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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49 blue <- as.numeric(correlation > 0) * vipco
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50 minus_cor <- -correlation
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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51 minus_cov <- -covariance
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52 # x_progress("head(names(minus_cor)): ", head(names(minus_cor)))
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53 cex <- salience_lookup(feature = names(minus_cor))
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54 # x_progress("head(cex.1): ", head(cex))
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55 # cex <- 0.25 + (0.75 * cex / max(cex))
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56 cex <- 0.25 + (1.25 * cex / max(cex))
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57 # x_progress("head(cex.2): ", head(cex))
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58 plot(
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59 y = minus_cor
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60 , x = minus_cov
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61 , type="p"
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62 , xlim=c(-lim_x, lim_x + off(0.1))
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63 , ylim=c(-1.0 - off(0.1), 1.0)
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64 , xlab = sprintf("relative covariance(feature,t1)")
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65 , ylab = sprintf("correlation(feature,t1)")
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66 , main = main_label
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67 , cex.main = main_cex
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68 , cex = cex
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69 , pch = 16
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70 , col = rgb(blue = blue, red = red, green = 0, alpha = alpha)
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71 )
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72 low_x <- -0.7 * lim_x
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73 high_x <- 0.7 * lim_x
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74 text(x = low_x, y = -0.15, labels = fctr_lvl_1)
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75 text(x = high_x, y = 0.15, labels = fctr_lvl_2)
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76 if (x_show_labels) {
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77 text(
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78 y = minus_cor - 0.013
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79 , x = minus_cov + 0.020
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80 , cex = 0.3
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81 , labels = tsv1$featureID
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82 , col = rgb(blue = blue, red = red, green = 0, alpha = 0.2 + 0.8 * alpha)
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83 , srt = -30 # slant 30 degrees downward
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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84 , adj = 0 # left-justified
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85 )
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86 }
50a07adddfbd planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 52e588e19fe93d83d221710bb75559c5700ba637
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87 }
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88 )
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89 typeVc <- c("correlation", # 1
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90 "outlier", # 2
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91 "overview", # 3
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92 "permutation", # 4
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93 "predict-train", # 5
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94 "predict-test", # 6
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95 "summary", # 7 = c(2,3,4,9)
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96 "x-loading", # 8
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97 "x-score", # 9
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98 "x-variance", # 10
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99 "xy-score", # 11
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100 "xy-weight" # 12
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101 ) # [c(3,8,9)] # [c(4,3,8,9)]
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102 if ( length(my_oplsda@orthoVipVn) > 0 ) {
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103 my_typevc <- typeVc[c(9,3,8)]
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104 } else {
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105 my_typevc <- c("(dummy)","overview","(dummy)")
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106 }
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107 for (my_type in my_typevc) {
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108 if (my_type %in% typeVc) {
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109 # print(sprintf("plotting type %s", my_type))
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110 plot(
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111 x = my_oplsda
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112 , typeVc = my_type
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113 , parCexN = 0.4
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114 , parDevNewL = FALSE
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115 , parLayL = TRUE
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116 , parEllipsesL = TRUE
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117 )
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118 } else {
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119 # print("plotting dummy graph")
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120 plot(x=1, y=1, xaxt="n", yaxt="n", xlab="", ylab="", type="n")
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121 text(x=1, y=1, labels="no orthogonal projection is possible")
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122 }
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123 }
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124 return (my_cor_vs_cov)
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125 } else {
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126 # x_progress(sprintf("x_is_match = %s, ncol(x_dataMatrix) = %d, length(unique(x_predictor)) = %d",x_is_match, ncol(x_dataMatrix), length(unique(x_predictor))))
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127 return (NULL)
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128 }
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129 }
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130
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131 # S-PLOT and OPLS reference: Wiklund_2008 doi:10.1021/ac0713510
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132 corcov_calc <- function(calc_env, failure_action = stop, progress_action = function(x){}, corcov_tsv_action = NULL) {
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133 if ( ! is.environment(calc_env) ) {
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134 failure_action("corcov_calc: fatal error - 'calc_env' is not an environment")
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135 return ( FALSE )
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136 }
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137 if ( is.null(corcov_tsv_action) || ! is.function(corcov_tsv_action) ) {
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138 failure_action("corcov_calc: fatal error - 'corcov_tsv_action' is not a function")
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139 return ( FALSE )
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140 }
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141
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142 # extract parameters from the environment
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143 vrbl_metadata <- calc_env$vrbl_metadata
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144 vrbl_metadata_names <- vrbl_metadata[,1]
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145 smpl_metadata <- calc_env$smpl_metadata
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146 data_matrix <- calc_env$data_matrix
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147 pairSigFeatOnly <- calc_env$pairSigFeatOnly
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148 facC <- calc_env$facC
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149 tesC <- calc_env$tesC
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150 # extract the levels from the environment
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151 originalLevCSV <- levCSV <- calc_env$levCSV
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152 # matchingC is one of { "none", "wildcard", "regex" }
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153 matchingC <- calc_env$matchingC
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154 labelFeatures <- calc_env$labelFeatures
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155
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156 # arg/env checking
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157 if (!(facC %in% names(smpl_metadata))) {
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158 failure_action(sprintf("bad parameter! Factor name '%s' not found in sampleMetadata", facC))
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159 return ( FALSE )
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160 }
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161
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162 # calculate salience_df as data.frame(feature, max_level, max_median, max_rcv, mean_median, salience, salient_rcv)
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163 salience_df <- calc_env$salience_df <- w4msalience(
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164 data_matrix = data_matrix
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165 , sample_class = smpl_metadata[,facC]
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166 , failure_action = failure_action
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167 )
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168 salience <- salience_df$salience
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169 names(salience) <- salience_df$feature
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170 salience_lookup <- calc_env$salience_lookup <- function(feature) unname(salience[feature])
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171 salient_rcv <- salience_df$salient_rcv
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172 names(salient_rcv) <- salience_df$feature
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173 salient_rcv_lookup <- calc_env$salient_rcv_lookup <- function(feature) unname(salient_rcv[feature])
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174 salient_level <- salience_df$max_level
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175 names(salient_level) <- salience_df$feature
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176 salient_level_lookup <- calc_env$salient_level_lookup <- function(feature) unname(salient_level[feature])
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177
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178 # transform wildcards to regexen
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179 if (matchingC == "wildcard") {
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180 # strsplit(x = "hello,wild,world", split = ",")
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181 levCSV <- gsub("[.]", "[.]", levCSV)
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182 levCSV <- strsplit(x = levCSV, split = ",")
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183 levCSV <- sapply(levCSV, utils::glob2rx, trim.tail = FALSE)
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184 levCSV <- paste(levCSV, collapse = ",")
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185 }
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186 # function to determine whether level is a member of levCSV
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187 isLevelSelected <- function(lvl) {
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188 matchFun <- if (matchingC != "none") grepl else `==`
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189 return(
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190 Reduce(
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191 f = "||"
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192 , x = sapply(
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193 X = strsplit(
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194 x = levCSV
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195 , split = ","
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196 , fixed = TRUE
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197 )[[1]]
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198 , FUN = matchFun
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199 , lvl
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200 )
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201 )
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202 )
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203 }
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204
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205 # transpose matrix because ropls matrix is the transpose of XCMS matrix
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206 # Wiklund_2008 centers and pareto-scales data before OPLS-DA S-plot
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207 # center
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208 cdm <- center_colmeans(t(data_matrix))
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209 # pareto-scale
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210 my_scale <- sqrt(apply(cdm, 2, sd, na.rm=TRUE))
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211 scdm <- sweep(cdm, 2, my_scale, "/")
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212
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213 # pattern to match column names like k10_kruskal_k4.k3_sig
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214 col_pattern <- sprintf('^%s_%s_(.*)[.](.*)_sig$', facC, tesC)
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215 # column name like k10_kruskal_sig
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216 intersample_sig_col <- sprintf('%s_%s_sig', facC, tesC)
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217 # get the facC column from sampleMetadata, dropping to one dimension
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218 smpl_metadata_facC <- smpl_metadata[,facC]
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219
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220 # allocate a slot in the environment for the contrast_list, each element of which will be a data.frame with this structure:
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221 # - feature ID
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222 # - value1
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223 # - value2
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224 # - Wiklund_2008 correlation
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225 # - Wiklund_2008 covariance
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226 # - Wiklund_2008 VIP
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227 calc_env$contrast_list <- list()
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228
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229
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230 did_plot <- FALSE
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231 if (tesC != "none") {
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232 # for each column name, extract the parts of the name matched by 'col_pattern', if any
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233 the_colnames <- colnames(vrbl_metadata)
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234 if (!Reduce(f = "||", x = grepl(tesC, the_colnames))) {
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235 failure_action(sprintf("bad parameter! variableMetadata must contain results of W4M Univariate test '%s'.", tesC))
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236 return ( FALSE )
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237 }
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238 col_matches <- lapply(
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239 X = the_colnames,
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240 FUN = function(x) {
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241 regmatches( x, regexec(col_pattern, x) )[[1]]
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242 }
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243 )
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244 # process columns matching the pattern
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245 for (i in 1:length(col_matches)) {
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246 # for each potential match of the pattern
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247 col_match <- col_matches[[i]]
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248 if (length(col_match) > 0) {
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249 # it's an actual match; extract the pieces, e.g., k10_kruskal_k4.k3_sig
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250 vrbl_metadata_col <- col_match[1] # ^^^^^^^^^^^^^^^^^^^^^ # Column name
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251 fctr_lvl_1 <- col_match[2] # ^^ # Factor-level 1
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252 fctr_lvl_2 <- col_match[3] # ^^ # Factor-level 2
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253 # only process this column if both factors are members of lvlCSV
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254 is_match <- isLevelSelected(fctr_lvl_1) && isLevelSelected(fctr_lvl_2)
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255 progress_action(sprintf("calculating/plotting contrast of %s vs. %s", fctr_lvl_1, fctr_lvl_2))
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256 # TODO delete next line displaying currently-processed column
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257 # cat(sprintf("%s %s %s %s\n", vrbl_metadata_col, fctr_lvl_1, fctr_lvl_2, is_match))
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258 # choose only samples with one of the two factors for this column
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259 chosen_samples <- smpl_metadata_facC %in% c(fctr_lvl_1, fctr_lvl_2)
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260 predictor <- smpl_metadata_facC[chosen_samples]
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261 # extract only the significantly-varying features and the chosen samples
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262 fully_significant <- 1 == vrbl_metadata[,vrbl_metadata_col] * vrbl_metadata[,intersample_sig_col]
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263 overall_significant <- 1 == vrbl_metadata[,intersample_sig_col]
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264 col_selector <- vrbl_metadata_names[ if ( pairSigFeatOnly ) fully_significant else overall_significant ]
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265 my_matrix <- scdm[ chosen_samples, col_selector, drop = FALSE ]
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266 my_cor_cov <- do_detail_plot(
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267 x_dataMatrix = my_matrix
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268 , x_predictor = predictor
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269 , x_is_match = is_match
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270 , x_algorithm = algoC
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271 , x_prefix = if (pairSigFeatOnly) "Significantly contrasting features" else "Significant features"
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272 , x_show_labels = labelFeatures
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273 , x_progress = progress_action
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274 , x_env = calc_env
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275 )
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276 if ( is.null(my_cor_cov) ) {
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277 progress_action("NOTHING TO PLOT.")
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278 } else {
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279 tsv <- my_cor_cov$tsv1
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280 tsv$salientLevel <- salient_level_lookup(tsv$featureID)
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281 tsv$salientRCV <- salient_rcv_lookup(tsv$featureID)
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282 tsv$salience <- salience_lookup(tsv$featureID)
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283 tsv["level1Level2Sig"] <- vrbl_metadata[ match(tsv$featureID, vrbl_metadata_names), vrbl_metadata_col ]
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284 corcov_tsv_action(tsv)
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285 did_plot <- TRUE
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286 }
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287 }
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288 }
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289 } else { # tesC == "none"
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290 utils::combn(
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291 x = unique(sort(smpl_metadata_facC))
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292 , m = 2
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293 , FUN = function(x) {
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294 fctr_lvl_1 <- x[1]
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295 fctr_lvl_2 <- x[2]
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296 progress_action(sprintf("calculating/plotting contrast of %s vs. %s", fctr_lvl_1, fctr_lvl_2))
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297 chosen_samples <- smpl_metadata_facC %in% c(fctr_lvl_1, fctr_lvl_2)
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298 if (length(unique(chosen_samples)) < 1) {
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299 progress_action("NOTHING TO PLOT...")
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300 } else {
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301 predictor <- smpl_metadata_facC[chosen_samples]
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302 my_matrix <- scdm[ chosen_samples, , drop = FALSE ]
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303 # only process this column if both factors are members of lvlCSV
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304 is_match <- isLevelSelected(fctr_lvl_1) && isLevelSelected(fctr_lvl_2)
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305 my_cor_cov <- do_detail_plot(
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306 x_dataMatrix = my_matrix
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307 , x_predictor = predictor
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308 , x_is_match = is_match
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309 , x_algorithm = algoC
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310 , x_prefix = "Features"
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311 , x_show_labels = labelFeatures
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312 , x_progress = progress_action
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313 , x_env = calc_env
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314 )
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315 if ( is.null(my_cor_cov) ) {
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316 progress_action("NOTHING TO PLOT")
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317 } else {
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318 tsv <- my_cor_cov$tsv1
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319 tsv$salientLevel <- salient_level_lookup(tsv$featureID)
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320 tsv$salientRCV <- salient_rcv_lookup(tsv$featureID)
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321 tsv$salience <- salience_lookup(tsv$featureID)
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322 corcov_tsv_action(tsv)
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323 did_plot <<- TRUE
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324 }
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325 }
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326 #print("baz")
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327 "dummy" # need to return a value; otherwise combn fails with an error
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328 }
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329 )
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330 }
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331 if (!did_plot) {
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332 failure_action(sprintf("bad parameter! sampleMetadata must have at least two levels of factor '%s' matching '%s'", facC, originalLevCSV))
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333 return ( FALSE )
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334 }
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335 return ( TRUE )
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336 }
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337
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338 # Calculate data for correlation-versus-covariance plot
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339 # Adapted from:
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340 # Wiklund_2008 doi:10.1021/ac0713510
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341 # Galindo_Prieto_2014 doi:10.1002/cem.2627
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342 # https://github.com/HegemanLab/extra_tools/blob/master/generic_PCA.R
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343 cor_vs_cov <- function(matrix_x, ropls_x) {
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344 x_class <- class(ropls_x)
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345 if ( !( as.character(x_class) == "opls" ) ) { # || !( attr(class(x_class),"package") == "ropls" ) )
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346 stop( "cor_vs_cov: Expected ropls_x to be of class ropls::opls but instead it was of class ", as.character(x_class) )
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347 }
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348 result <- list()
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349 # suppLs$algoC - Character: algorithm used - "svd" for singular value decomposition; "nipals" for NIPALS
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350 if ( ropls_x@suppLs$algoC == "nipals") {
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351 # Equations (1) and (2) from *Supplement to* Wiklund 2008, doi:10.1021/ac0713510
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352 mag <- function(one_dimensional) sqrt(sum(one_dimensional * one_dimensional))
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353 mag_xi <- sapply(X = 1:ncol(matrix_x), FUN = function(x) mag(matrix_x[,x]))
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354 score_matrix <- ropls_x@scoreMN
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355 score_matrix_transposed <- t(score_matrix)
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356 score_matrix_magnitude <- mag(score_matrix)
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357 result$covariance <- score_matrix_transposed %*% matrix_x / ( score_matrix_magnitude * score_matrix_magnitude )
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358 result$correlation <- score_matrix_transposed %*% matrix_x / ( score_matrix_magnitude * mag_xi )
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359 } else {
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360 # WARNING - untested code - I don't have test data to exercise this branch
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361 # Equations (1) and (2) from Wiklund 2008, doi:10.1021/ac0713510
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362 # scoreMN - Numerical matrix of x scores (T; dimensions: nrow(x) x predI) X = TP' + E; Y = TC' + F
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363 score_matrix <- ropls_x@scoreMN
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364 score_matrix_transposed <- t(score_matrix)
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365 cov_divisor <- nrow(matrix_x) - 1
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366 result$covariance <- sapply(
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367 X = 1:ncol(matrix_x)
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368 , FUN = function(x) score_matrix_transposed %*% matrix_x[,x] / cov_divisor
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369 )
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370 score_sd <- sapply(
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371 X = 1:ncol(score_matrix)
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372 , FUN = function(x) sd(score_matrix[,x])
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373 )
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374 # xSdVn - Numerical vector: variable standard deviations of the 'x' matrix
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375 xSdVn <- ropls_x@xSdVn
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376 result$correlation <- sapply(
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377 X = 1:ncol(matrix_x)
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378 , FUN = function(x) {
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379 ( score_matrix_transposed / score_sd ) %*% ( matrix_x[,x] / (xSdVn[x] * cov_divisor) )
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380 }
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381 )
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382 }
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383 result$correlation <- result$correlation[1,,drop = TRUE]
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384 result$covariance <- result$covariance[1,,drop = TRUE]
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385
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386 # Variant 4 of Variable Influence on Projection for OPLS from Galindo_Prieto_2014
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387 # Length = number of features; labels = feature identifiers. (The same is true for $correlation and $covariance.)
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388 result$vip4p <- as.numeric(ropls_x@vipVn)
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389 result$vip4o <- as.numeric(ropls_x@orthoVipVn)
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390 # get the level names
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391 level_names <- sort(levels(as.factor(ropls_x@suppLs$y)))
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392 feature_count <- length(ropls_x@vipVn)
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393 result$level1 <- rep.int(x = level_names[1], times = feature_count)
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394 result$level2 <- rep.int(x = level_names[2], times = feature_count)
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395 # print(sprintf("sd(covariance) = %f; sd(correlation) = %f", sd(result$covariance), sd(result$correlation)))
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396 superresult <- list()
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397 if (length(result$vip4o) == 0) result$vip4o <- NA
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398 superresult$tsv1 <- data.frame(
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399 featureID = names(ropls_x@vipVn)
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400 , factorLevel1 = result$level1
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401 , factorLevel2 = result$level2
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402 , correlation = result$correlation
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403 , covariance = result$covariance
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404 , vip4p = result$vip4p
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405 , vip4o = result$vip4o
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406 , row.names = NULL
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407 )
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408 rownames(superresult$tsv1) <- superresult$tsv1$featureID
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409 superresult$covariance <- result$covariance
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410 superresult$correlation <- result$correlation
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411 superresult$vip4p <- result$vip4p
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412 superresult$vip4o <- result$vip4o
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413 superresult$details <- result
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414 # #print(superresult$tsv1)
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415 result$superresult <- superresult
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416 # Include thise in case future consumers of this routine want to use it in currently unanticipated ways
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417 result$oplsda <- ropls_x
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418 result$predictor <- ropls_x@suppLs$y # in case future consumers of this routine want to use it in currently unanticipated ways
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419 return (superresult)
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420 }
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421
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422