Mercurial > repos > jason-ellul > iterativepca

comparison R_functions/plotting_functions.R @ 0:cb54350e76ae draft default tip

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author jason-ellul
date Wed, 01 Jun 2016 03:24:56 -0400
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1 ## Plotting and grouping ##
2 # input data: some number of 2d observations. Each row represents a single observation,
3 # column 1 = variable 1, to be plotted on the x-axis,
4 # column 2 = variable 2, to be plotted on the y-axis
5 # groups: Integer vector with same number of entries as there are rows in the input data,
6 # representing which group each observation belongs to. Negative numbers are not plotted
7 # tags: the tag to put on the legend for each group
8 # plot_colors: colors to use for each group
9 # plot_symbols: symbols to use for each group
10 # plot_title: as name suggests
11 # plot_filename: if this is not null, graph is output to a png with the specified name
12 plot_by_groups = function(input_data, groups, tags, plot_colors, plot_symbols, plot_title, plot_filename=NULL) {
13 if(!is.null(plot_filename)) {
14 png(plot_filename)
15 }
16 # leave some extra room on the RHS for the legend
17 par(mar=c(5.1, 4.1, 4.1, 8.1))
18 x = as.numeric(input_data[, 1])
19 y = as.numeric(input_data[, 2])
20 gids = sort(unique(groups[which(groups >= 0)]))
21 n = length(gids)
22
23 # first set up the plot area to the correct dimensions
24 plot(x, y, col="white")
25
26 for (i in 1:n) {
27 gid = gids[i]
28 pts_x = x[which(groups == gid)]
29 pts_y = y[which(groups == gid)]
30 pts_color = plot_colors[i]
31 pts_symbol = plot_symbols[i]
32 points(pts_x, pts_y, col=pts_color, pch=pts_symbol)
33 }
34 legend(x="topright",
35 xpd=TRUE,
36 inset=c(-0.3, 0),
37 col=plot_colors,
38 pch=plot_symbols,
39 legend=tags,
40 text.col=plot_colors)
41 title(main=plot_title)
42 if(!is.null(plot_filename)) {
43 dev.off()
44 }
45 }
46
47 # Controls vs cases plot. Colour controls blue, cases red,
48 # Samples which are neither control nor case are black.
49 setup_cvc_plot = function(pca_data, control_tag, cases_tag) {
50 plot_info = list()
51 nsamples = length(pca_data$ids)
52 groups = rep(1, nsamples)
53 control_legend = paste0("CO: ", control_tag)
54 cases_legend = paste0("CA: ", cases_tag)
55 if (!is.null(control_tag)) {
56 groups[grep(control_tag, pca_data$ids)] = 2
57 }
58 if (!is.null(cases_tag)) {
59 groups[grep(cases_tag, pca_data$ids)] = 3
60 }
61 res = sort(unique(groups))
62 if (length(res) == 1) {
63 tags = c("UNKNOWN")
64 plot_colors = c("black")
65 } else if (length(res) == 3) {
66 tags = c("UNKNOWN", control_legend, cases_legend)
67 plot_colors = c("black", "blue", "red")
68 } else {
69 if (all(res == c(1, 2))) {
70 tags = c("UNKNOWN", control_legend)
71 plot_colors = c("black", "blue")
72 } else if (all(res == c(1, 3))) {
73 tags = c("UNKNOWN", cases_legend)
74 plot_colors = c("black", "red")
75 } else {
76 tags = c(control_legend, cases_legend)
77 plot_colors = c("blue", "red")
78 }
79 }
80 plot_info$groups = groups
81 plot_info$tags = tags
82 plot_info$plot_colors = plot_colors
83 plot_info$plot_symbols = rep(1, length(res))
84 plot_info$plot_title = "Control vs Cases Plot"
85 return(plot_info)
86 }
87
88 # outliers plot; colour outliers red, non-outliers green
89 setup_ol_plot = function(pca_data, outliers) {
90 plot_info = list()
91 nsamples = dim(pca_data$values)[1]
92 groups = 1:nsamples
93 groups[outliers] = 1
94 groups[setdiff(1:nsamples, outliers)] = 2
95 plot_info$groups = groups
96 plot_info$tags = c("outliers", "good data")
97 plot_info$plot_colors = c("red", "green")
98 plot_info$plot_symbols = c(1, 20)
99 plot_info$plot_title = "Outliers Plot"
100 return(plot_info)
101 }
102
103 # standard deviations plot; colour samples by s.dev
104 setup_sd_plot = function(pca_data) {
105 plot_info = list()
106 nsamples = dim(pca_data$values)[1]
107 pc1 = as.numeric(pca_data$values[, 1])
108 pc2 = as.numeric(pca_data$values[, 2])
109 pc1_sds = as.numeric(lapply(pc1, compute_numsds, pc1))
110 pc2_sds = as.numeric(lapply(pc2, compute_numsds, pc2))
111
112 groups = 1:nsamples
113 groups[get_sdset2d(pc1_sds, pc2_sds, 1)] = 1
114 groups[get_sdset2d(pc1_sds, pc2_sds, 2)] = 2
115 groups[get_sdset2d(pc1_sds, pc2_sds, 3)] = 3
116 groups[union(which(pc1_sds > 3), which(pc2_sds > 3))] = 4
117 plot_info$groups = groups
118 plot_info$tags = c("SD = 1", "SD = 2", "SD = 3", "SD > 3")
119 plot_info$plot_colors = rainbow(4)
120 plot_info$plot_symbols = rep(20, 4)
121 plot_info$plot_title = "Standard Deviations Plot"
122 return(plot_info)
123 }
124
125 # Plot samples, with coloured clusters. Rejected clusters use
126 # a cross symbol instead of a filled circle
127 setup_cluster_plot = function(pca_data, clusters, rc=NULL) {
128 plot_info = list()
129 groups = clusters
130 ids = sort(unique(groups))
131 n = length(ids)
132 tags = 1:n
133 for (i in 1:n) {
134 tags[i] = sprintf("cluster %s", ids[i])
135 }
136 outliers = which(groups == 0)
137 if (length(outliers) != 0) {
138 tags[1] = "outliers"
139 }
140 plot_colors = rainbow(n)
141 plot_symbols = rep(20, n)
142 if (length(outliers) != 0) {
143 plot_symbols[1] = 1
144 }
145 # labelling for rejected clusters
146 if(!is.null(rc)) {
147 for(i in 1:n) {
148 if((ids[i] != 0) && (ids[i] %in% as.numeric(rc))) {
149 tags[i] = "rej. clust."
150 plot_symbols[i] = 4
151 }
152 }
153 }
154 plot_info$groups = groups
155 plot_info$tags = tags
156 plot_info$plot_colors = plot_colors
157 plot_info$plot_symbols = plot_symbols
158 plot_info$plot_title = "Cluster Plot"
159 return(plot_info)
160 }
161
162 # Plot samples, colouring by ethnicity. Different ethnicities also
163 # have different symbols.
164 setup_ethnicity_plot = function(pca_data, ethnicity_data) {
165 plot_info = list()
166 nsamples = dim(pca_data$values)[1]
167 eth = 1:nsamples
168
169 for (i in 1:nsamples) {
170 sample_id = pca_data$ids[i]
171 eth[i] = as.character(ethnicity_data[sample_id, "population"])
172 if(is.na(eth[i])) {
173 eth[i] = "UNKNOWN"
174 }
175 }
176 n = length(unique(eth))
177 plot_info$groups = as.numeric(as.factor(eth))
178 plot_info$tags = sort(unique(eth))
179 plot_info$plot_colors = rainbow(n)
180 plot_info$plot_symbols = 1:n
181 plot_info$plot_title = "Ethnicity Plot"
182 return(plot_info)
183 }
184
185 draw_cutoffs = function(input_data, x, y, numsds) {
186 pcx = as.numeric(input_data[x, ])
187 pcy = as.numeric(input_data[y, ])
188
189 vlines = c(median(pcx) - numsds*sd(pcx),
190 median(pcx) + numsds*sd(pcx))
191 hlines = c(median(pcy) - numsds*sd(pcy),
192 median(pcy) + numsds*sd(pcy))
193 abline(v=vlines)
194 abline(h=hlines)
195 }
196
197 # Following helper functions are used in the 'setup_sd_plot' function
198 # given a list of standard deviations, work out which points are n standard deviations away
199 get_sdset2d = function(x1, x2, n) {
200 if (n == 1) {
201 ind = intersect(which(x1 == 1), which(x2 == 1))
202 } else {
203 lower = get_sdset2d(x1, x2, n - 1)
204 upper = union(which(x1 > n), which(x2 > n))
205 xset = union(lower, upper)
206 bigset = union(which(x1 == n), which(x2 == n))
207 ind = setdiff(bigset, xset)
208 }
209 return(ind)
210 }
211
212 # work out how many standard deviations away from the sample median a single point is
213 # accuracy of this decreases for outliers, as the error in the estimated sd is
214 # multiplied
215 compute_numsds = function(point, x) {
216 x_sd = sd(x)
217 sum = x_sd
218 m = median(x)
219 i = 1
220 while(abs(point - m) > sum) {
221 i = i + 1
222 sum = sum + x_sd
223 }
224 return(i)
225 }