| ... | ... |
@@ -5,124 +5,177 @@ |
| 5 | 5 |
#' chart_plot(C,D) |
| 6 | 6 |
#' @export feature_boxplot |
| 7 | 7 |
feature_boxplot = function(label_outliers=TRUE,feature_to_plot, |
| 8 |
- factor_name,show_counts=TRUE,...) {
|
|
| 9 |
- out=struct::new_struct('feature_boxplot',
|
|
| 10 |
- label_outliers=label_outliers, |
|
| 11 |
- feature_to_plot=feature_to_plot, |
|
| 12 |
- factor_name=factor_name, |
|
| 13 |
- show_counts=show_counts, |
|
| 14 |
- ...) |
|
| 15 |
- return(out) |
|
| 8 |
+ factor_name,show_counts=TRUE,style='boxplot',jitter=FALSE,fill=FALSE,...) {
|
|
| 9 |
+ out=struct::new_struct('feature_boxplot',
|
|
| 10 |
+ label_outliers=label_outliers, |
|
| 11 |
+ feature_to_plot=feature_to_plot, |
|
| 12 |
+ factor_name=factor_name, |
|
| 13 |
+ show_counts=show_counts, |
|
| 14 |
+ style=style, |
|
| 15 |
+ jitter=jitter, |
|
| 16 |
+ fill=fill, |
|
| 17 |
+ ...) |
|
| 18 |
+ return(out) |
|
| 16 | 19 |
} |
| 17 | 20 |
|
| 18 | 21 |
|
| 19 | 22 |
.feature_boxplot<-setClass( |
| 20 |
- "feature_boxplot", |
|
| 21 |
- contains=c('chart','stato'),
|
|
| 22 |
- slots=c( |
|
| 23 |
- # INPUTS |
|
| 24 |
- label_outliers='entity', |
|
| 25 |
- feature_to_plot='entity', |
|
| 26 |
- factor_name='entity', |
|
| 27 |
- show_counts='entity' |
|
| 28 |
- ), |
|
| 29 |
- prototype = list(name='Feature boxplot', |
|
| 30 |
- description='A boxplot to visualise the distribution of values within a feature.', |
|
| 31 |
- type="boxlot", |
|
| 32 |
- stato_id='STATO:0000243', |
|
| 33 |
- .params=c('label_outliers','feature_to_plot','factor_name','show_counts'),
|
|
| 34 |
- |
|
| 35 |
- label_outliers=entity(name='Label outliers', |
|
| 36 |
- value=TRUE, |
|
| 37 |
- type='logical', |
|
| 38 |
- description=c( |
|
| 39 |
- 'TRUE' = 'The index for outlier samples is included on the plot.', |
|
| 40 |
- 'FALSE' = 'No labels are displayed.' |
|
| 41 |
- ) |
|
| 42 |
- ), |
|
| 43 |
- feature_to_plot=entity(name='Feature to plot', |
|
| 44 |
- value='V1', |
|
| 45 |
- type=c('character','numeric','integer'),
|
|
| 46 |
- description='The column name of the plotted feature.' |
|
| 47 |
- ), |
|
| 48 |
- factor_name=ents$factor_name, |
|
| 49 |
- show_counts=ents$show_counts |
|
| 50 |
- ) |
|
| 23 |
+ "feature_boxplot", |
|
| 24 |
+ contains=c('chart'),
|
|
| 25 |
+ slots=c( |
|
| 26 |
+ # INPUTS |
|
| 27 |
+ label_outliers='entity', |
|
| 28 |
+ feature_to_plot='entity', |
|
| 29 |
+ factor_name='entity', |
|
| 30 |
+ show_counts='entity', |
|
| 31 |
+ style='enum', |
|
| 32 |
+ jitter='entity', |
|
| 33 |
+ fill='entity' |
|
| 34 |
+ ), |
|
| 35 |
+ prototype = list(name='Feature boxplot', |
|
| 36 |
+ description='A boxplot to visualise the distribution of values within a feature.', |
|
| 37 |
+ type="boxlot", |
|
| 38 |
+ .params=c('label_outliers','feature_to_plot','factor_name','show_counts','style','jitter','fill'),
|
|
| 39 |
+ |
|
| 40 |
+ label_outliers=entity(name='Label outliers', |
|
| 41 |
+ value=TRUE, |
|
| 42 |
+ type='logical', |
|
| 43 |
+ description=c( |
|
| 44 |
+ 'TRUE' = 'The index for outlier samples is included on the plot.', |
|
| 45 |
+ 'FALSE' = 'No labels are displayed.' |
|
| 46 |
+ ) |
|
| 47 |
+ ), |
|
| 48 |
+ feature_to_plot=entity(name='Feature to plot', |
|
| 49 |
+ value='V1', |
|
| 50 |
+ type=c('character','numeric','integer'),
|
|
| 51 |
+ description='The column name of the plotted feature.' |
|
| 52 |
+ ), |
|
| 53 |
+ factor_name=ents$factor_name, |
|
| 54 |
+ show_counts=ents$show_counts, |
|
| 55 |
+ style=enum( |
|
| 56 |
+ name='Plot style', |
|
| 57 |
+ description=c( |
|
| 58 |
+ 'boxplot' = 'boxplot style', |
|
| 59 |
+ 'violin' = 'violon plot style' |
|
| 60 |
+ ), |
|
| 61 |
+ allowed=c('boxplot','violin'),
|
|
| 62 |
+ value='boxplot', |
|
| 63 |
+ max_length=1, |
|
| 64 |
+ type='character' |
|
| 65 |
+ ), |
|
| 66 |
+ jitter=entity( |
|
| 67 |
+ name = 'Jitter', |
|
| 68 |
+ description = 'Include points plotted with added jitter.', |
|
| 69 |
+ value=FALSE, |
|
| 70 |
+ type='logical', |
|
| 71 |
+ max_length=1 |
|
| 72 |
+ ), |
|
| 73 |
+ fill=entity( |
|
| 74 |
+ name = 'Fill', |
|
| 75 |
+ description = 'Block fill the boxes or violins with the group colour.', |
|
| 76 |
+ value=FALSE, |
|
| 77 |
+ type='logical', |
|
| 78 |
+ max_length=1 |
|
| 79 |
+ ) |
|
| 80 |
+ ) |
|
| 51 | 81 |
) |
| 52 | 82 |
|
| 53 | 83 |
#' @export |
| 54 | 84 |
#' @template chart_plot |
| 55 | 85 |
setMethod(f="chart_plot", |
| 56 |
- signature=c("feature_boxplot",'DatasetExperiment'),
|
|
| 57 |
- definition=function(obj,dobj) |
|
| 58 |
- {
|
|
| 59 |
- # get options |
|
| 60 |
- opt=param_list(obj) |
|
| 61 |
- # get data |
|
| 62 |
- Xt=dobj$data |
|
| 63 |
- # column name |
|
| 64 |
- if (is.numeric(opt$feature_to_plot)) {
|
|
| 65 |
- varn=colnames(Xt)[opt$feature_to_plot] |
|
| 66 |
- } else {
|
|
| 67 |
- varn=opt$feature_to_plot |
|
| 68 |
- } |
|
| 69 |
- # get requested column |
|
| 70 |
- Xt=Xt[[opt$feature_to_plot]] |
|
| 71 |
- # meta data |
|
| 72 |
- SM=dobj$sample_meta |
|
| 73 |
- SM=SM[[obj$factor_name]] |
|
| 74 |
- |
|
| 75 |
- # remove NA |
|
| 76 |
- SM=SM[!is.na(Xt)] |
|
| 77 |
- Xt=Xt[!is.na(Xt)] |
|
| 78 |
- |
|
| 79 |
- # count number of values |
|
| 80 |
- L=levels(SM) |
|
| 81 |
- count=numeric(length(L)) |
|
| 82 |
- for (i in 1:length(L)) {
|
|
| 83 |
- count[i]=sum(SM==L[i]) |
|
| 84 |
- } |
|
| 85 |
- |
|
| 86 |
- # get color pallete using pmp |
|
| 87 |
- clrs= createClassAndColors(class = SM) |
|
| 88 |
- SM=clrs$class |
|
| 89 |
- |
|
| 90 |
- #prep the plot |
|
| 91 |
- temp=data.frame(x=SM,y=Xt) |
|
| 92 |
- p<-ggplot(temp, aes_(x=~x,y=~y,color=~x)) + |
|
| 93 |
- geom_boxplot() + |
|
| 94 |
- xlab(opt$factor) + |
|
| 95 |
- ylab('') +
|
|
| 96 |
- ggtitle(varn) + |
|
| 97 |
- scale_colour_manual(values=clrs$manual_colors,name=opt$factor_name) + |
|
| 98 |
- theme_Publication(base_size = 12) + |
|
| 99 |
- theme(legend.position="none") |
|
| 100 |
- |
|
| 101 |
- if (opt$show_counts) {
|
|
| 102 |
- newlabels=as.character(count) |
|
| 103 |
- newlabels=paste0(as.character(L),'\n(n = ',newlabels,')') |
|
| 104 |
- p=p+scale_x_discrete(labels=newlabels) |
|
| 105 |
- } |
|
| 106 |
- |
|
| 107 |
- if (opt$label_outliers) {
|
|
| 108 |
- outliers=numeric() |
|
| 109 |
- for (l in L) {
|
|
| 86 |
+ signature=c("feature_boxplot",'DatasetExperiment'),
|
|
| 87 |
+ definition=function(obj,dobj) |
|
| 88 |
+ {
|
|
| 89 |
+ # get options |
|
| 90 |
+ opt=param_list(obj) |
|
| 91 |
+ # get data |
|
| 92 |
+ Xt=dobj$data |
|
| 93 |
+ # column name |
|
| 94 |
+ if (is.numeric(opt$feature_to_plot)) {
|
|
| 95 |
+ varn=colnames(Xt)[opt$feature_to_plot] |
|
| 96 |
+ } else {
|
|
| 97 |
+ varn=opt$feature_to_plot |
|
| 98 |
+ } |
|
| 99 |
+ # get requested column |
|
| 100 |
+ Xt=Xt[[opt$feature_to_plot]] |
|
| 101 |
+ # meta data |
|
| 102 |
+ SM=dobj$sample_meta |
|
| 103 |
+ SM=SM[[obj$factor_name]] |
|
| 104 |
+ names(SM)=rownames(dobj) |
|
| 105 |
+ |
|
| 106 |
+ # remove NA |
|
| 107 |
+ SM=SM[!is.na(Xt)] |
|
| 108 |
+ Xt=Xt[!is.na(Xt)] |
|
| 109 |
+ |
|
| 110 |
+ # get color pallete using pmp |
|
| 111 |
+ clrs= createClassAndColors(class = SM) |
|
| 112 |
+ SM=clrs$class |
|
| 113 |
+ |
|
| 114 |
+ # count number of values |
|
| 115 |
+ L=levels(SM) |
|
| 116 |
+ count=numeric(length(L)) |
|
| 117 |
+ for (i in 1:length(L)) {
|
|
| 118 |
+ count[i]=sum(SM==L[i]) |
|
| 119 |
+ } |
|
| 120 |
+ |
|
| 121 |
+ #prep the plot |
|
| 122 |
+ temp=data.frame(x=SM,y=Xt,row.names=names(SM)) |
|
| 123 |
+ |
|
| 124 |
+ if (obj$fill) {
|
|
| 125 |
+ A = aes_string(x='x',y='y',color='x',fill='x') |
|
| 126 |
+ } else {
|
|
| 127 |
+ A = aes_string(x='x',y='y',color='x') |
|
| 128 |
+ } |
|
| 129 |
+ |
|
| 130 |
+ |
|
| 131 |
+ p<-ggplot(temp, A) |
|
| 132 |
+ |
|
| 133 |
+ if (obj$style=='boxplot') {
|
|
| 134 |
+ p=p+geom_boxplot(alpha=0.3) |
|
| 135 |
+ } else {
|
|
| 136 |
+ p=p+geom_violin(alpha=0.3, draw_quantiles = c(0.25, 0.5, 0.75), |
|
| 137 |
+ scale = "width", adjust = .5) |
|
| 138 |
+ } |
|
| 139 |
+ |
|
| 140 |
+ if (obj$jitter) {
|
|
| 141 |
+ p=p+geom_jitter(show.legend = F, width = 0.1) |
|
| 142 |
+ } |
|
| 143 |
+ p=p+xlab(opt$factor) + |
|
| 144 |
+ ylab('') +
|
|
| 145 |
+ ggtitle(varn) + |
|
| 146 |
+ scale_colour_manual(values=clrs$manual_colors,name=opt$factor_name) |
|
| 147 |
+ |
|
| 148 |
+ p=p+scale_fill_manual(values=clrs$manual_colors,name=opt$factor_name) |
|
| 149 |
+ |
|
| 150 |
+ p=p+theme_Publication(base_size = 12) + |
|
| 151 |
+ theme(legend.position="none") |
|
| 152 |
+ |
|
| 153 |
+ if (opt$show_counts) {
|
|
| 154 |
+ newlabels=as.character(count) |
|
| 155 |
+ newlabels=paste0(as.character(L),'\n(n = ',newlabels,')') |
|
| 156 |
+ p=p+scale_x_discrete(labels=newlabels) |
|
| 157 |
+ } |
|
| 158 |
+ |
|
| 159 |
+ if (opt$label_outliers) {
|
|
| 160 |
+ outliers=numeric() |
|
| 161 |
+ for (l in L) {
|
|
| 110 | 162 |
IN=which(SM==l) |
| 111 | 163 |
outliers=c(outliers,IN[which( Xt[IN]>(quantile(Xt[IN], 0.75) + 1.5*IQR(Xt[IN]))) ] ) |
| 112 | 164 |
outliers=c(outliers,IN[which( Xt[IN]<(quantile(Xt[IN], 0.25) - 1.5*IQR(Xt[IN]))) ] ) |
| 165 |
+ } |
|
| 166 |
+ outlier_df=temp[outliers,] |
|
| 167 |
+ outlier_df$out_label=paste0(' ',rownames(temp))[outliers]
|
|
| 168 |
+ p=p+geom_text(data=outlier_df,aes_(group=~x,color=~x,label=~out_label),hjust='left') |
|
| 113 | 169 |
} |
| 114 |
- outlier_df=temp[outliers,] |
|
| 115 |
- outlier_df$out_label=paste0(' ',rownames(dobj$data))[outliers]
|
|
| 116 |
- p=p+geom_text(data=outlier_df,aes_(group=~x,color=~x,label=~out_label),hjust='left') |
|
| 117 |
- } |
|
| 118 |
- |
|
| 119 |
- return(p) |
|
| 120 |
- |
|
| 121 |
- } |
|
| 170 |
+ |
|
| 171 |
+ return(p) |
|
| 172 |
+ |
|
| 173 |
+ } |
|
| 122 | 174 |
) |
| 123 | 175 |
|
| 124 | 176 |
|
| 125 | 177 |
|
| 178 |
+ |
|
| 126 | 179 |
###################################### |
| 127 | 180 |
###################################### |
| 128 | 181 |
|
| ... | ... |
@@ -138,64 +191,64 @@ setMethod(f="chart_plot", |
| 138 | 191 |
#' @return struct object |
| 139 | 192 |
#' @export mv_histogram |
| 140 | 193 |
mv_histogram = function(label_outliers=TRUE,by_sample=TRUE,...) {
|
| 141 |
- out=struct::new_struct('mv_histogram',
|
|
| 142 |
- label_outliers=label_outliers, |
|
| 143 |
- by_sample=by_sample, |
|
| 144 |
- ...) |
|
| 145 |
- return(out) |
|
| 194 |
+ out=struct::new_struct('mv_histogram',
|
|
| 195 |
+ label_outliers=label_outliers, |
|
| 196 |
+ by_sample=by_sample, |
|
| 197 |
+ ...) |
|
| 198 |
+ return(out) |
|
| 146 | 199 |
} |
| 147 | 200 |
|
| 148 | 201 |
|
| 149 | 202 |
.mv_histogram<-setClass( |
| 150 |
- "mv_histogram", |
|
| 151 |
- contains='chart', |
|
| 152 |
- slots=c( |
|
| 153 |
- # INPUTS |
|
| 154 |
- label_outliers='entity', |
|
| 155 |
- by_sample='entity' |
|
| 156 |
- ), |
|
| 157 |
- prototype = list(name='Missing value histogram', |
|
| 158 |
- description='A histogram of the numbers of missing values per sample/feature', |
|
| 159 |
- type="histogram", |
|
| 160 |
- .params=c('label_outliers','by_sample'),
|
|
| 161 |
- |
|
| 162 |
- by_sample=ents$by_sample, |
|
| 163 |
- label_outliers=ents$label_outliers |
|
| 164 |
- ) |
|
| 203 |
+ "mv_histogram", |
|
| 204 |
+ contains='chart', |
|
| 205 |
+ slots=c( |
|
| 206 |
+ # INPUTS |
|
| 207 |
+ label_outliers='entity', |
|
| 208 |
+ by_sample='entity' |
|
| 209 |
+ ), |
|
| 210 |
+ prototype = list(name='Missing value histogram', |
|
| 211 |
+ description='A histogram of the numbers of missing values per sample/feature', |
|
| 212 |
+ type="histogram", |
|
| 213 |
+ .params=c('label_outliers','by_sample'),
|
|
| 214 |
+ |
|
| 215 |
+ by_sample=ents$by_sample, |
|
| 216 |
+ label_outliers=ents$label_outliers |
|
| 217 |
+ ) |
|
| 165 | 218 |
) |
| 166 | 219 |
|
| 167 | 220 |
#' @export |
| 168 | 221 |
#' @template chart_plot |
| 169 | 222 |
setMethod(f="chart_plot", |
| 170 |
- signature=c("mv_histogram",'DatasetExperiment'),
|
|
| 171 |
- definition=function(obj,dobj) |
|
| 172 |
- {
|
|
| 173 |
- # get options |
|
| 174 |
- opt=param_list(obj) |
|
| 175 |
- # get data |
|
| 176 |
- Xt=dobj$data |
|
| 177 |
- # meta data |
|
| 178 |
- SM=dobj$sample_meta[ ,1] |
|
| 179 |
- |
|
| 180 |
- if (opt$by_sample) {
|
|
| 181 |
- # count NS per sample |
|
| 182 |
- count=apply(Xt,1,function(x) {sum(is.na(x))/length(x)*100})
|
|
| 183 |
- txt='Missing values per sample' |
|
| 184 |
- } else {
|
|
| 185 |
- # count NS per feature |
|
| 186 |
- count=apply(Xt,2,function(x) {sum(is.na(x))/length(x)*100})
|
|
| 187 |
- txt='Missing values per feature' |
|
| 188 |
- } |
|
| 189 |
- |
|
| 190 |
- A=data.frame(x=count) |
|
| 191 |
- p=ggplot (data=A, aes_(x=~x)) + geom_histogram()+ |
|
| 192 |
- xlab ("missing values, %")+ ggtitle(txt)+
|
|
| 193 |
- xlim (0,100)+ |
|
| 194 |
- scale_colour_Publication()+ theme_Publication(base_size = 12) |
|
| 195 |
- |
|
| 196 |
- return(p) |
|
| 197 |
- |
|
| 198 |
- } |
|
| 223 |
+ signature=c("mv_histogram",'DatasetExperiment'),
|
|
| 224 |
+ definition=function(obj,dobj) |
|
| 225 |
+ {
|
|
| 226 |
+ # get options |
|
| 227 |
+ opt=param_list(obj) |
|
| 228 |
+ # get data |
|
| 229 |
+ Xt=dobj$data |
|
| 230 |
+ # meta data |
|
| 231 |
+ SM=dobj$sample_meta[ ,1] |
|
| 232 |
+ |
|
| 233 |
+ if (opt$by_sample) {
|
|
| 234 |
+ # count NS per sample |
|
| 235 |
+ count=apply(Xt,1,function(x) {sum(is.na(x))/length(x)*100})
|
|
| 236 |
+ txt='Missing values per sample' |
|
| 237 |
+ } else {
|
|
| 238 |
+ # count NS per feature |
|
| 239 |
+ count=apply(Xt,2,function(x) {sum(is.na(x))/length(x)*100})
|
|
| 240 |
+ txt='Missing values per feature' |
|
| 241 |
+ } |
|
| 242 |
+ |
|
| 243 |
+ A=data.frame(x=count) |
|
| 244 |
+ p=ggplot (data=A, aes_(x=~x)) + geom_histogram()+ |
|
| 245 |
+ xlab ("missing values, %")+ ggtitle(txt)+
|
|
| 246 |
+ xlim (0,100)+ |
|
| 247 |
+ scale_colour_Publication()+ theme_Publication(base_size = 12) |
|
| 248 |
+ |
|
| 249 |
+ return(p) |
|
| 250 |
+ |
|
| 251 |
+ } |
|
| 199 | 252 |
) |
| 200 | 253 |
|
| 201 | 254 |
|
| ... | ... |
@@ -212,131 +265,131 @@ setMethod(f="chart_plot", |
| 212 | 265 |
#' @import struct |
| 213 | 266 |
#' @export mv_boxplot |
| 214 | 267 |
mv_boxplot = function(label_outliers=TRUE,by_sample=TRUE,factor_name,show_counts=TRUE,...) {
|
| 215 |
- out=struct::new_struct('mv_boxplot',
|
|
| 216 |
- label_outliers=label_outliers, |
|
| 217 |
- by_sample=by_sample, |
|
| 218 |
- factor_name=factor_name, |
|
| 219 |
- show_counts=show_counts, |
|
| 220 |
- ...) |
|
| 221 |
- return(out) |
|
| 268 |
+ out=struct::new_struct('mv_boxplot',
|
|
| 269 |
+ label_outliers=label_outliers, |
|
| 270 |
+ by_sample=by_sample, |
|
| 271 |
+ factor_name=factor_name, |
|
| 272 |
+ show_counts=show_counts, |
|
| 273 |
+ ...) |
|
| 274 |
+ return(out) |
|
| 222 | 275 |
} |
| 223 | 276 |
|
| 224 | 277 |
|
| 225 | 278 |
.mv_boxplot<-setClass( |
| 226 |
- "mv_boxplot", |
|
| 227 |
- contains='chart', |
|
| 228 |
- slots=c( |
|
| 229 |
- # INPUTS |
|
| 230 |
- label_outliers='entity', |
|
| 231 |
- by_sample='entity', |
|
| 232 |
- factor_name='entity', |
|
| 233 |
- show_counts='entity' |
|
| 234 |
- ), |
|
| 235 |
- prototype = list(name='Missing value boxplots', |
|
| 236 |
- description='Boxplots of the number of missing values per sample/feature.', |
|
| 237 |
- type="boxplot", |
|
| 238 |
- .params=c('label_outliers','by_sample','factor_name','show_counts'),
|
|
| 239 |
- |
|
| 240 |
- by_sample=ents$by_sample, |
|
| 241 |
- label_outliers=ents$label_outliers, |
|
| 242 |
- factor_name=ents$factor_name, |
|
| 243 |
- show_counts=ents$show_counts |
|
| 244 |
- ) |
|
| 279 |
+ "mv_boxplot", |
|
| 280 |
+ contains='chart', |
|
| 281 |
+ slots=c( |
|
| 282 |
+ # INPUTS |
|
| 283 |
+ label_outliers='entity', |
|
| 284 |
+ by_sample='entity', |
|
| 285 |
+ factor_name='entity', |
|
| 286 |
+ show_counts='entity' |
|
| 287 |
+ ), |
|
| 288 |
+ prototype = list(name='Missing value boxplots', |
|
| 289 |
+ description='Boxplots of the number of missing values per sample/feature.', |
|
| 290 |
+ type="boxplot", |
|
| 291 |
+ .params=c('label_outliers','by_sample','factor_name','show_counts'),
|
|
| 292 |
+ |
|
| 293 |
+ by_sample=ents$by_sample, |
|
| 294 |
+ label_outliers=ents$label_outliers, |
|
| 295 |
+ factor_name=ents$factor_name, |
|
| 296 |
+ show_counts=ents$show_counts |
|
| 297 |
+ ) |
|
| 245 | 298 |
) |
| 246 | 299 |
|
| 247 | 300 |
#' @export |
| 248 | 301 |
#' @template chart_plot |
| 249 | 302 |
setMethod(f="chart_plot", |
| 250 |
- signature=c("mv_boxplot",'DatasetExperiment'),
|
|
| 251 |
- definition=function(obj,dobj) {
|
|
| 252 |
- # get options |
|
| 253 |
- opt=param_list(obj) |
|
| 254 |
- # get data |
|
| 255 |
- Xt=dobj$data |
|
| 256 |
- # meta data |
|
| 257 |
- SM=dobj$sample_meta[ ,obj$factor_name] |
|
| 258 |
- |
|
| 259 |
- L=levels(SM) |
|
| 260 |
- |
|
| 261 |
- if (opt$by_sample) {
|
|
| 262 |
- # count NS per sample |
|
| 263 |
- count=apply(Xt,1,function(x) {sum(is.na(x))/length(x)*100})
|
|
| 264 |
- result=matrix(0,nrow=nrow(Xt),ncol=2) |
|
| 265 |
- result[,1]=count |
|
| 266 |
- result[,2]=SM |
|
| 267 |
- if (sum(result[,1])==0) {
|
|
| 303 |
+ signature=c("mv_boxplot",'DatasetExperiment'),
|
|
| 304 |
+ definition=function(obj,dobj) {
|
|
| 305 |
+ # get options |
|
| 306 |
+ opt=param_list(obj) |
|
| 307 |
+ # get data |
|
| 308 |
+ Xt=dobj$data |
|
| 309 |
+ # meta data |
|
| 310 |
+ SM=dobj$sample_meta[ ,obj$factor_name] |
|
| 311 |
+ |
|
| 312 |
+ L=levels(SM) |
|
| 313 |
+ |
|
| 314 |
+ if (opt$by_sample) {
|
|
| 315 |
+ # count NS per sample |
|
| 316 |
+ count=apply(Xt,1,function(x) {sum(is.na(x))/length(x)*100})
|
|
| 317 |
+ result=matrix(0,nrow=nrow(Xt),ncol=2) |
|
| 318 |
+ result[,1]=count |
|
| 319 |
+ result[,2]=SM |
|
| 320 |
+ if (sum(result[,1])==0) {
|
|
| 268 | 321 |
warning('No missing values were detected')
|
| 269 |
- } |
|
| 270 |
- txt='Missing values per sample' |
|
| 271 |
- # get color pallete using pmp |
|
| 272 |
- clrs= createClassAndColors(class = SM) |
|
| 273 |
- A=data.frame(x=clrs$class,y=result[,1]) |
|
| 274 |
- } else {
|
|
| 275 |
- for (i in 1:length(L)) {
|
|
| 322 |
+ } |
|
| 323 |
+ txt='Missing values per sample' |
|
| 324 |
+ # get color pallete using pmp |
|
| 325 |
+ clrs= createClassAndColors(class = SM) |
|
| 326 |
+ A=data.frame(x=clrs$class,y=result[,1]) |
|
| 327 |
+ } else {
|
|
| 328 |
+ for (i in 1:length(L)) {
|
|
| 276 | 329 |
# count NS per feature per group |
| 277 | 330 |
count=apply(Xt[SM==L[i],,drop=FALSE],2,function(x) {sum(is.na(x))/sum(SM==L[i])*100})
|
| 278 | 331 |
temp=data.frame(y=count,x=L[i]) |
| 279 | 332 |
if (i==1) {
|
| 280 |
- result=temp |
|
| 333 |
+ result=temp |
|
| 281 | 334 |
} else {
|
| 282 |
- result=rbind(result,temp) |
|
| 335 |
+ result=rbind(result,temp) |
|
| 283 | 336 |
} |
| 284 |
- } |
|
| 285 |
- if (sum(result$y)==0) {
|
|
| 337 |
+ } |
|
| 338 |
+ if (sum(result$y)==0) {
|
|
| 286 | 339 |
warning('No missing values were detected')
|
| 340 |
+ } |
|
| 341 |
+ txt='Missing values per feature' |
|
| 342 |
+ # get color pallete using pmp |
|
| 343 |
+ clrs= createClassAndColors(class = as.factor(result$x)) |
|
| 344 |
+ A=data.frame(x=clrs$class,y=result$y) |
|
| 287 | 345 |
} |
| 288 |
- txt='Missing values per feature' |
|
| 289 |
- # get color pallete using pmp |
|
| 290 |
- clrs= createClassAndColors(class = as.factor(result$x)) |
|
| 291 |
- A=data.frame(x=clrs$class,y=result$y) |
|
| 292 |
- } |
|
| 293 |
- |
|
| 294 |
- |
|
| 295 |
- p=ggplot (data=A, aes_(x=~x,y=~y,color=~x)) + |
|
| 296 |
- geom_boxplot() + |
|
| 297 |
- ggtitle(txt) + |
|
| 298 |
- xlab(opt$factor) + |
|
| 299 |
- ylim (0,100)+ |
|
| 300 |
- scale_colour_manual(values=clrs$manual_colors,name=opt$factor_name) + |
|
| 301 |
- theme_Publication(base_size = 12) + |
|
| 302 |
- ylab ("missing values, %") +
|
|
| 303 |
- coord_flip()+ |
|
| 304 |
- theme(legend.position="none") |
|
| 305 |
- |
|
| 306 |
- if (opt$show_counts) {
|
|
| 307 |
- L=levels(A$x) |
|
| 308 |
- num=numeric(length(L)) |
|
| 309 |
- for (i in 1:length(L)) {
|
|
| 346 |
+ |
|
| 347 |
+ |
|
| 348 |
+ p=ggplot (data=A, aes_(x=~x,y=~y,color=~x)) + |
|
| 349 |
+ geom_boxplot() + |
|
| 350 |
+ ggtitle(txt) + |
|
| 351 |
+ xlab(opt$factor) + |
|
| 352 |
+ ylim (0,100)+ |
|
| 353 |
+ scale_colour_manual(values=clrs$manual_colors,name=opt$factor_name) + |
|
| 354 |
+ theme_Publication(base_size = 12) + |
|
| 355 |
+ ylab ("missing values, %") +
|
|
| 356 |
+ coord_flip()+ |
|
| 357 |
+ theme(legend.position="none") |
|
| 358 |
+ |
|
| 359 |
+ if (opt$show_counts) {
|
|
| 360 |
+ L=levels(A$x) |
|
| 361 |
+ num=numeric(length(L)) |
|
| 362 |
+ for (i in 1:length(L)) {
|
|
| 310 | 363 |
num[i]=sum(A$x==L[i]) |
| 364 |
+ } |
|
| 365 |
+ newlabels=as.character(num) |
|
| 366 |
+ newlabels=paste0(as.character(L),'\n(n = ',newlabels,')') |
|
| 367 |
+ p=p+scale_x_discrete(labels=newlabels) |
|
| 311 | 368 |
} |
| 312 |
- newlabels=as.character(num) |
|
| 313 |
- newlabels=paste0(as.character(L),'\n(n = ',newlabels,')') |
|
| 314 |
- p=p+scale_x_discrete(labels=newlabels) |
|
| 315 |
- } |
|
| 316 |
- |
|
| 317 |
- if (opt$label_outliers) {
|
|
| 318 |
- outliers=numeric() |
|
| 319 |
- for (l in L) {
|
|
| 369 |
+ |
|
| 370 |
+ if (opt$label_outliers) {
|
|
| 371 |
+ outliers=numeric() |
|
| 372 |
+ for (l in L) {
|
|
| 320 | 373 |
IN=which(A$x==l) |
| 321 | 374 |
outliers=c(outliers,IN[which( A$y[IN]>(quantile(A$y[IN], 0.75) + 1.5*IQR(A$y[IN]))) ] ) |
| 322 | 375 |
outliers=c(outliers,IN[which( A$y[IN]<(quantile(A$y[IN], 0.25) - 1.5*IQR(A$y[IN]))) ] ) |
| 323 |
- } |
|
| 324 |
- outlier_df=A[outliers,] |
|
| 325 |
- |
|
| 326 |
- if (length(outliers)>0){
|
|
| 376 |
+ } |
|
| 377 |
+ outlier_df=A[outliers,] |
|
| 378 |
+ |
|
| 379 |
+ if (length(outliers)>0){
|
|
| 327 | 380 |
if (opt$by_sample) {
|
| 328 |
- outlier_df$out_label=paste0(' ',rownames(dobj$data))[outliers]
|
|
| 381 |
+ outlier_df$out_label=paste0(' ',rownames(dobj$data))[outliers]
|
|
| 329 | 382 |
} else |
| 330 | 383 |
{
|
| 331 |
- outlier_df$out_label=paste0(' ',rep(colnames(dobj$data),length(L))[outliers])
|
|
| 384 |
+ outlier_df$out_label=paste0(' ',rep(colnames(dobj$data),length(L))[outliers])
|
|
| 332 | 385 |
} |
| 333 | 386 |
p=p+geom_text(data=outlier_df,aes_(group=~x,color=~x,label=~out_label,angle =~ 90),hjust='left') |
| 387 |
+ } |
|
| 388 |
+ |
|
| 334 | 389 |
} |
| 335 | 390 |
|
| 336 |
- } |
|
| 337 |
- |
|
| 338 |
- return(p) |
|
| 339 |
- } |
|
| 391 |
+ return(p) |
|
| 392 |
+ } |
|
| 340 | 393 |
) |
| 341 | 394 |
|
| 342 | 395 |
#' @eval get_description('DatasetExperiment_dist')
|
| ... | ... |
@@ -347,74 +400,74 @@ setMethod(f="chart_plot", |
| 347 | 400 |
#' @import struct |
| 348 | 401 |
#' @export DatasetExperiment_dist |
| 349 | 402 |
DatasetExperiment_dist = function(factor_name,per_class=TRUE,...) {
|
| 350 |
- out=struct::new_struct('DatasetExperiment_dist',
|
|
| 351 |
- factor_name=factor_name, |
|
| 352 |
- per_class=per_class, |
|
| 353 |
- ...) |
|
| 354 |
- return(out) |
|
| 403 |
+ out=struct::new_struct('DatasetExperiment_dist',
|
|
| 404 |
+ factor_name=factor_name, |
|
| 405 |
+ per_class=per_class, |
|
| 406 |
+ ...) |
|
| 407 |
+ return(out) |
|
| 355 | 408 |
} |
| 356 | 409 |
|
| 357 | 410 |
.DatasetExperiment_dist<-setClass( |
| 358 |
- "DatasetExperiment_dist", |
|
| 359 |
- contains='chart', |
|
| 360 |
- slots=c( |
|
| 361 |
- # INPUTS |
|
| 362 |
- factor_name='entity', |
|
| 363 |
- per_class='entity' |
|
| 364 |
- ), |
|
| 365 |
- prototype = list(name='Feature distribution histogram', |
|
| 366 |
- description=paste0('A histogram to visualise the distribution of ',
|
|
| 367 |
- 'values within features.'), |
|
| 368 |
- type="histogram", |
|
| 369 |
- .params=c('factor_name','per_class'),
|
|
| 370 |
- |
|
| 371 |
- factor_name=ents$factor_name, |
|
| 372 |
- per_class=entity(name='Plot per class', |
|
| 373 |
- value=TRUE, |
|
| 374 |
- type='logical', |
|
| 375 |
- description=c( |
|
| 376 |
- "TRUE" = 'The distributions are plotted for each class.', |
|
| 377 |
- "FALSE" = 'The distribution is plotted for all samples') |
|
| 378 |
- ) |
|
| 379 |
- ) |
|
| 411 |
+ "DatasetExperiment_dist", |
|
| 412 |
+ contains='chart', |
|
| 413 |
+ slots=c( |
|
| 414 |
+ # INPUTS |
|
| 415 |
+ factor_name='entity', |
|
| 416 |
+ per_class='entity' |
|
| 417 |
+ ), |
|
| 418 |
+ prototype = list(name='Feature distribution histogram', |
|
| 419 |
+ description=paste0('A histogram to visualise the distribution of ',
|
|
| 420 |
+ 'values within features.'), |
|
| 421 |
+ type="histogram", |
|
| 422 |
+ .params=c('factor_name','per_class'),
|
|
| 423 |
+ |
|
| 424 |
+ factor_name=ents$factor_name, |
|
| 425 |
+ per_class=entity(name='Plot per class', |
|
| 426 |
+ value=TRUE, |
|
| 427 |
+ type='logical', |
|
| 428 |
+ description=c( |
|
| 429 |
+ "TRUE" = 'The distributions are plotted for each class.', |
|
| 430 |
+ "FALSE" = 'The distribution is plotted for all samples') |
|
| 431 |
+ ) |
|
| 432 |
+ ) |
|
| 380 | 433 |
) |
| 381 | 434 |
|
| 382 | 435 |
#' @export |
| 383 | 436 |
#' @template chart_plot |
| 384 | 437 |
setMethod(f="chart_plot", |
| 385 |
- signature=c("DatasetExperiment_dist",'DatasetExperiment'),
|
|
| 386 |
- definition=function(obj,dobj) |
|
| 387 |
- {
|
|
| 388 |
- opt=param_list(obj) |
|
| 389 |
- X=as.matrix(dobj$data) |
|
| 390 |
- S=dobj$sample_meta[[opt$factor_name]] |
|
| 391 |
- |
|
| 392 |
- if (opt$per_class) {
|
|
| 393 |
- L=levels(S) |
|
| 394 |
- for (k in L) {
|
|
| 438 |
+ signature=c("DatasetExperiment_dist",'DatasetExperiment'),
|
|
| 439 |
+ definition=function(obj,dobj) |
|
| 440 |
+ {
|
|
| 441 |
+ opt=param_list(obj) |
|
| 442 |
+ X=as.matrix(dobj$data) |
|
| 443 |
+ S=dobj$sample_meta[[opt$factor_name]] |
|
| 444 |
+ |
|
| 445 |
+ if (opt$per_class) {
|
|
| 446 |
+ L=levels(S) |
|
| 447 |
+ for (k in L) {
|
|
| 395 | 448 |
M=X[S==k,,drop=FALSE] |
| 396 | 449 |
if (k==L[1]) {
|
| 397 |
- temp=data.frame(values=as.vector(M),group=k) |
|
| 450 |
+ temp=data.frame(values=as.vector(M),group=k) |
|
| 398 | 451 |
} else {
|
| 399 |
- temp=rbind(temp,data.frame(values=as.vector(M),group=k)) |
|
| 452 |
+ temp=rbind(temp,data.frame(values=as.vector(M),group=k)) |
|
| 400 | 453 |
} |
| 401 |
- } |
|
| 402 |
- out=ggplot(data=temp, aes_(x=~values,color=~group))+ |
|
| 454 |
+ } |
|
| 455 |
+ out=ggplot(data=temp, aes_(x=~values,color=~group))+ |
|
| 403 | 456 |
geom_freqpoly(bins=100) |
| 404 |
- } else {
|
|
| 405 |
- temp=data.frame(values=as.vector(X),group='Sample') |
|
| 406 |
- out=ggplot(data=temp, aes_(x=~values,color=~group)) + |
|
| 457 |
+ } else {
|
|
| 458 |
+ temp=data.frame(values=as.vector(X),group='Sample') |
|
| 459 |
+ out=ggplot(data=temp, aes_(x=~values,color=~group)) + |
|
| 407 | 460 |
geom_freqpoly(bins=100,color='black') |
| 408 |
- } |
|
| 409 |
- out = out + |
|
| 410 |
- xlab('Values') +
|
|
| 411 |
- ylab('Density') +
|
|
| 412 |
- scale_colour_Publication(name=opt$factor_name)+ |
|
| 413 |
- theme_Publication(base_size = 12) |
|
| 414 |
- |
|
| 415 |
- return(out) |
|
| 416 |
- |
|
| 417 |
- } |
|
| 461 |
+ } |
|
| 462 |
+ out = out + |
|
| 463 |
+ xlab('Values') +
|
|
| 464 |
+ ylab('Density') +
|
|
| 465 |
+ scale_colour_Publication(name=opt$factor_name)+ |
|
| 466 |
+ theme_Publication(base_size = 12) |
|
| 467 |
+ |
|
| 468 |
+ return(out) |
|
| 469 |
+ |
|
| 470 |
+ } |
|
| 418 | 471 |
) |
| 419 | 472 |
|
| 420 | 473 |
#' @eval get_description('DatasetExperiment_boxplot')
|
| ... | ... |
@@ -425,107 +478,107 @@ setMethod(f="chart_plot", |
| 425 | 478 |
#' @return struct object |
| 426 | 479 |
#' @export DatasetExperiment_boxplot |
| 427 | 480 |
DatasetExperiment_boxplot = function(factor_name,by_sample=TRUE,per_class=TRUE,number=50,...) {
|
| 428 |
- out=struct::new_struct('DatasetExperiment_boxplot',
|
|
| 429 |
- factor_name=factor_name, |
|
| 430 |
- by_sample=by_sample, |
|
| 431 |
- per_class=per_class, |
|
| 432 |
- number=number, |
|
| 433 |
- ...) |
|
| 434 |
- return(out) |
|
| 481 |
+ out=struct::new_struct('DatasetExperiment_boxplot',
|
|
| 482 |
+ factor_name=factor_name, |
|
| 483 |
+ by_sample=by_sample, |
|
| 484 |
+ per_class=per_class, |
|
| 485 |
+ number=number, |
|
| 486 |
+ ...) |
|
| 487 |
+ return(out) |
|
| 435 | 488 |
} |
| 436 | 489 |
|
| 437 | 490 |
|
| 438 | 491 |
.DatasetExperiment_boxplot<-setClass( |
| 439 |
- "DatasetExperiment_boxplot", |
|
| 440 |
- contains='chart', |
|
| 441 |
- slots=c( |
|
| 442 |
- # INPUTS |
|
| 443 |
- factor_name='entity', |
|
| 444 |
- by_sample='entity', |
|
| 445 |
- per_class='entity', |
|
| 446 |
- number='entity' |
|
| 447 |
- ), |
|
| 448 |
- prototype = list(name='Feature distribution histogram', |
|
| 449 |
- description=paste0('A boxplot to visualise the distribution of ',
|
|
| 450 |
- 'values within a subset of features.'), |
|
| 451 |
- type="boxplot", |
|
| 452 |
- .params=c('factor_name','by_sample','per_class','number'),
|
|
| 453 |
- |
|
| 454 |
- factor_name=ents$factor_name, |
|
| 455 |
- by_sample=entity(name='Plot by sample', |
|
| 456 |
- value=TRUE, |
|
| 457 |
- type='logical', |
|
| 458 |
- description=c( |
|
| 459 |
- 'TRUE' = 'The data is plotted across features for a subset of samples.', |
|
| 460 |
- 'FALSE' = 'The data is plotted across samples for a subset of features.') |
|
| 461 |
- ), |
|
| 462 |
- per_class=entity(name='Plot per class', |
|
| 463 |
- value=TRUE, |
|
| 464 |
- type='logical', |
|
| 465 |
- description=c( |
|
| 466 |
- "TRUE" = 'The data is plotted for each class.', |
|
| 467 |
- "FALSE" = 'The data is plotted for all samples') |
|
| 468 |
- ), |
|
| 469 |
- number=entity(name='Number of features/samples', |
|
| 470 |
- value=50, |
|
| 471 |
- type=c('numeric','integer'),
|
|
| 472 |
- description='The number of features/samples plotted.', |
|
| 473 |
- max_length=1 |
|
| 474 |
- ) |
|
| 475 |
- ) |
|
| 492 |
+ "DatasetExperiment_boxplot", |
|
| 493 |
+ contains='chart', |
|
| 494 |
+ slots=c( |
|
| 495 |
+ # INPUTS |
|
| 496 |
+ factor_name='entity', |
|
| 497 |
+ by_sample='entity', |
|
| 498 |
+ per_class='entity', |
|
| 499 |
+ number='entity' |
|
| 500 |
+ ), |
|
| 501 |
+ prototype = list(name='Feature distribution histogram', |
|
| 502 |
+ description=paste0('A boxplot to visualise the distribution of ',
|
|
| 503 |
+ 'values within a subset of features.'), |
|
| 504 |
+ type="boxplot", |
|
| 505 |
+ .params=c('factor_name','by_sample','per_class','number'),
|
|
| 506 |
+ |
|
| 507 |
+ factor_name=ents$factor_name, |
|
| 508 |
+ by_sample=entity(name='Plot by sample', |
|
| 509 |
+ value=TRUE, |
|
| 510 |
+ type='logical', |
|
| 511 |
+ description=c( |
|
| 512 |
+ 'TRUE' = 'The data is plotted across features for a subset of samples.', |
|
| 513 |
+ 'FALSE' = 'The data is plotted across samples for a subset of features.') |
|
| 514 |
+ ), |
|
| 515 |
+ per_class=entity(name='Plot per class', |
|
| 516 |
+ value=TRUE, |
|
| 517 |
+ type='logical', |
|
| 518 |
+ description=c( |
|
| 519 |
+ "TRUE" = 'The data is plotted for each class.', |
|
| 520 |
+ "FALSE" = 'The data is plotted for all samples') |
|
| 521 |
+ ), |
|
| 522 |
+ number=entity(name='Number of features/samples', |
|
| 523 |
+ value=50, |
|
| 524 |
+ type=c('numeric','integer'),
|
|
| 525 |
+ description='The number of features/samples plotted.', |
|
| 526 |
+ max_length=1 |
|
| 527 |
+ ) |
|
| 528 |
+ ) |
|
| 476 | 529 |
) |
| 477 | 530 |
|
| 478 | 531 |
#' @export |
| 479 | 532 |
#' @template chart_plot |
| 480 | 533 |
setMethod(f="chart_plot", |
| 481 |
- signature=c("DatasetExperiment_boxplot",'DatasetExperiment'),
|
|
| 482 |
- definition=function(obj,dobj) |
|
| 483 |
- {
|
|
| 484 |
- opt=param_list(obj) |
|
| 485 |
- X=dobj$data |
|
| 486 |
- SM=dobj$sample_meta |
|
| 487 |
- if (!opt$by_sample) {
|
|
| 488 |
- #s=sample(ncol(X),min(c(ncol(X),opt$number)),replace=FALSE) |
|
| 489 |
- s=seq(from=1,to=ncol(X),length.out = min(c(opt$number,ncol(X)))) |
|
| 490 |
- ylabel='Features' |
|
| 491 |
- } else {
|
|
| 492 |
- #s=sample(nrow(X),min(c(nrow(X),opt$number)),replace=FALSE) |
|
| 493 |
- s=seq(from=1,to=nrow(X),length.out = min(c(opt$number,nrow(X)))) |
|
| 494 |
- X=as.data.frame(t(X)) |
|
| 495 |
- colnames(X)=rownames(dobj$data) |
|
| 496 |
- ylabel='Samples' |
|
| 497 |
- } |
|
| 498 |
- s=unique(floor(s)) |
|
| 499 |
- |
|
| 500 |
- for (i in s) {
|
|
| 501 |
- if (!opt$by_sample){
|
|
| 534 |
+ signature=c("DatasetExperiment_boxplot",'DatasetExperiment'),
|
|
| 535 |
+ definition=function(obj,dobj) |
|
| 536 |
+ {
|
|
| 537 |
+ opt=param_list(obj) |
|
| 538 |
+ X=dobj$data |
|
| 539 |
+ SM=dobj$sample_meta |
|
| 540 |
+ if (!opt$by_sample) {
|
|
| 541 |
+ #s=sample(ncol(X),min(c(ncol(X),opt$number)),replace=FALSE) |
|
| 542 |
+ s=seq(from=1,to=ncol(X),length.out = min(c(opt$number,ncol(X)))) |
|
| 543 |
+ ylabel='Features' |
|
| 544 |
+ } else {
|
|
| 545 |
+ #s=sample(nrow(X),min(c(nrow(X),opt$number)),replace=FALSE) |
|
| 546 |
+ s=seq(from=1,to=nrow(X),length.out = min(c(opt$number,nrow(X)))) |
|
| 547 |
+ X=as.data.frame(t(X)) |
|
| 548 |
+ colnames(X)=rownames(dobj$data) |
|
| 549 |
+ ylabel='Samples' |
|
| 550 |
+ } |
|
| 551 |
+ s=unique(floor(s)) |
|
| 552 |
+ |
|
| 553 |
+ for (i in s) {
|
|
| 554 |
+ if (!opt$by_sample){
|
|
| 502 | 555 |
sm=SM[[obj$factor_name]] |
| 503 | 556 |
fn=row |
| 504 |
- } else {
|
|
| 557 |
+ } else {
|
|
| 505 | 558 |
sm=SM[[obj$factor_name]][i] |
| 506 |
- } |
|
| 507 |
- if (!opt$per_class) {
|
|
| 559 |
+ } |
|
| 560 |
+ if (!opt$per_class) {
|
|
| 508 | 561 |
sm='Sample' |
| 509 |
- } |
|
| 510 |
- if (i==s[1]) {
|
|
| 562 |
+ } |
|
| 563 |
+ if (i==s[1]) {
|
|
| 511 | 564 |
temp=data.frame(value=X[,i],feature=colnames(X)[i],group=sm) |
| 512 |
- } else {
|
|
| 565 |
+ } else {
|
|
| 513 | 566 |
temp=rbind(temp,data.frame(value=X[,i],feature=colnames(X)[i],group=sm)) |
| 567 |
+ } |
|
| 514 | 568 |
} |
| 515 |
- } |
|
| 516 |
- out=ggplot(data=temp, aes_(x=~feature,y=~value,color=~group)) + |
|
| 517 |
- geom_boxplot()+ |
|
| 518 |
- xlab(ylabel) + |
|
| 519 |
- ylab('Values') +
|
|
| 520 |
- scale_colour_Publication(name=opt$factor_name)+ |
|
| 521 |
- theme_Publication(base_size = 12)+ |
|
| 522 |
- coord_flip() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) |
|
| 523 |
- if (!opt$per_class) {
|
|
| 524 |
- out=out+theme(legend.position="none") |
|
| 525 |
- } |
|
| 526 |
- return(out) |
|
| 527 |
- |
|
| 528 |
- } |
|
| 569 |
+ out=ggplot(data=temp, aes_(x=~feature,y=~value,color=~group)) + |
|
| 570 |
+ geom_boxplot()+ |
|
| 571 |
+ xlab(ylabel) + |
|
| 572 |
+ ylab('Values') +
|
|
| 573 |
+ scale_colour_Publication(name=opt$factor_name)+ |
|
| 574 |
+ theme_Publication(base_size = 12)+ |
|
| 575 |
+ coord_flip() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) |
|
| 576 |
+ if (!opt$per_class) {
|
|
| 577 |
+ out=out+theme(legend.position="none") |
|
| 578 |
+ } |
|
| 579 |
+ return(out) |
|
| 580 |
+ |
|
| 581 |
+ } |
|
| 529 | 582 |
) |
| 530 | 583 |
|
| 531 | 584 |
|
| ... | ... |
@@ -538,25 +591,25 @@ setMethod(f="chart_plot", |
| 538 | 591 |
#' @import struct |
| 539 | 592 |
#' @export compare_dist |
| 540 | 593 |
compare_dist = function(factor_name,...) {
|
| 541 |
- out=struct::new_struct('compare_dist',
|
|
| 542 |
- factor_name=factor_name, |
|
| 543 |
- ...) |
|
| 544 |
- return(out) |
|
| 594 |
+ out=struct::new_struct('compare_dist',
|
|
| 595 |
+ factor_name=factor_name, |
|
| 596 |
+ ...) |
|
| 597 |
+ return(out) |
|
| 545 | 598 |
} |
| 546 | 599 |
|
| 547 | 600 |
.compare_dist<-setClass( |
| 548 |
- "compare_dist", |
|
| 549 |
- contains=c('chart','stato'),
|
|
| 550 |
- slots=c(factor_name='entity'), |
|
| 551 |
- prototype = list(name='Compare distributions', |
|
| 552 |
- description=paste0('Histograms and boxplots computed across samples ',
|
|
| 553 |
- 'and features are used to visually compare two datasets e.g. before ', |
|
| 554 |
- 'and after filtering and/or normalisation.'), |
|
| 555 |
- type="mixed", |
|
| 556 |
- stato_id='STATO:0000161', |
|
| 557 |
- .params=c('factor_name'),
|
|
| 558 |
- factor_name=ents$factor_name |
|
| 559 |
- ) |
|
| 601 |
+ "compare_dist", |
|
| 602 |
+ contains=c('chart','stato'),
|
|
| 603 |
+ slots=c(factor_name='entity'), |
|
| 604 |
+ prototype = list(name='Compare distributions', |
|
| 605 |
+ description=paste0('Histograms and boxplots computed across samples ',
|
|
| 606 |
+ 'and features are used to visually compare two datasets e.g. before ', |
|
| 607 |
+ 'and after filtering and/or normalisation.'), |
|
| 608 |
+ type="mixed", |
|
| 609 |
+ stato_id='STATO:0000161', |
|
| 610 |
+ .params=c('factor_name'),
|
|
| 611 |
+ factor_name=ents$factor_name |
|
| 612 |
+ ) |
|
| 560 | 613 |
) |
| 561 | 614 |
|
| 562 | 615 |
#' @export |
| ... | ... |
@@ -564,79 +617,79 @@ compare_dist = function(factor_name,...) {
|
| 564 | 617 |
#' @template chart_plot |
| 565 | 618 |
#' @param eobj a second DatasetExperiment object to compare with the first |
| 566 | 619 |
setMethod(f="chart_plot", |
| 567 |
- signature=c("compare_dist",'DatasetExperiment'),
|
|
| 568 |
- definition=function(obj,dobj,eobj) |
|
| 569 |
- {
|
|
| 570 |
- |
|
| 571 |
- # match features across datasets |
|
| 572 |
- inboth=intersect(colnames(dobj),colnames(eobj)) |
|
| 573 |
- dobj=dobj[,inboth] |
|
| 574 |
- eobj=eobj[,inboth] |
|
| 575 |
- |
|
| 576 |
- C=DatasetExperiment_boxplot(by_sample=FALSE,per_class=FALSE,number=30,factor_name=obj$factor_name) |
|
| 577 |
- |
|
| 578 |
- C1=chart_plot(C,dobj)+ |
|
| 579 |
- labs(tag='c)')+ |
|
| 580 |
- theme(axis.text.x = element_text(angle = 90, hjust = 1))+ggtitle(NULL,'Before processing') |
|
| 581 |
- |
|
| 582 |
- C2=chart_plot(C,eobj)+ |
|
| 583 |
- labs(tag='d)')+ |
|
| 584 |
- theme(axis.text.x = element_text(angle = 90, hjust = 1))+ggtitle(NULL,'After processing') |
|
| 585 |
- |
|
| 586 |
- C=DatasetExperiment_dist(factor_name=obj$factor_name,per_class=TRUE) |
|
| 587 |
- |
|
| 588 |
- C3=chart_plot(C,dobj)+ |
|
| 589 |
- labs(tag='a)')+ |
|
| 590 |
- theme(legend.position="none",axis.text.x = element_text(angle = 90, hjust = 1))+ggtitle(NULL,'Before processing') |
|
| 591 |
- |
|
| 592 |
- C4=chart_plot(C,eobj)+ |
|
| 593 |
- labs(tag='b)')+ |
|
| 594 |
- theme(legend.position="none",axis.text.x = element_text(angle = 90, hjust = 1))+ggtitle(NULL,'After processing') |
|
| 595 |
- |
|
| 596 |
- rC1=ggplot_build(C1)$layout$panel_scales_y[[1]]$range$range |
|
| 597 |
- rC2=ggplot_build(C2)$layout$panel_scales_y[[1]]$range$range |
|
| 598 |
- rC3=ggplot_build(C3)$layout$panel_scales_x[[1]]$range$range |
|
| 599 |
- rC4=ggplot_build(C4)$layout$panel_scales_x[[1]]$range$range |
|
| 600 |
- rC1=max(abs(rC1)) |
|
| 601 |
- rC2=max(abs(rC2)) |
|
| 602 |
- rC3=max(abs(rC3)) |
|
| 603 |
- rC4=max(abs(rC4)) |
|
| 604 |
- |
|
| 605 |
- C1=C1+ylim(c(-rC1,rC1)) |
|
| 606 |
- C3=C3+xlim(c(-rC1,rC1)) |
|
| 607 |
- C2=C2+ylim(c(-rC2,rC2)) |
|
| 608 |
- C4=C4+xlim(c(-rC2,rC2)) |
|
| 609 |
- |
|
| 610 |
- |
|
| 611 |
- gA <- ggplotGrob(C3) |
|
| 612 |
- gB <- ggplotGrob(C4) |
|
| 613 |
- gC <- ggplotGrob(C1) |
|
| 614 |
- gD <- ggplotGrob(C2) |
|
| 615 |
- |
|
| 616 |
- L=matrix(nrow=4,ncol=1) |
|
| 617 |
- L=as.matrix(rbind(c(1,2),c(3,4),c(3,4),c(3,4))) |
|
| 618 |
- |
|
| 619 |
- temp=gtable_cbind(gA,gB) |
|
| 620 |
- heights1=temp$heights |
|
| 621 |
- gA$heights=heights1 |
|
| 622 |
- gB$heights=heights1 |
|
| 623 |
- |
|
| 624 |
- temp=gtable_cbind(gC,gD) |
|
| 625 |
- heights2=temp$heights |
|
| 626 |
- gC$heights=heights2 |
|
| 627 |
- gD$heights=heights2 |
|
| 628 |
- |
|
| 629 |
- maxWidth = grid::unit.pmax(gA$widths[2:5], gB$widths[2:5],gC$widths[2:5],gD$widths[2:5]) |
|
| 630 |
- gA$widths[2:5] <- as.list(maxWidth) |
|
| 631 |
- gB$widths[2:5] <- as.list(maxWidth) |
|
| 632 |
- gC$widths[2:5] <- as.list(maxWidth) |
|
| 633 |
- gD$widths[2:5] <- as.list(maxWidth) |
|
| 634 |
- |
|
| 635 |
- p=grid.arrange(grobs=list(gA,gB,gC,gD),layout_matrix=L) |
|
| 636 |
- |
|
| 637 |
- return(p) |
|
| 638 |
- |
|
| 639 |
- } |
|
| 620 |
+ signature=c("compare_dist",'DatasetExperiment'),
|
|
| 621 |
+ definition=function(obj,dobj,eobj) |
|
| 622 |
+ {
|
|
| 623 |
+ |
|
| 624 |
+ # match features across datasets |
|
| 625 |
+ inboth=intersect(colnames(dobj),colnames(eobj)) |
|
| 626 |
+ dobj=dobj[,inboth] |
|
| 627 |
+ eobj=eobj[,inboth] |
|
| 628 |
+ |
|
| 629 |
+ C=DatasetExperiment_boxplot(by_sample=FALSE,per_class=FALSE,number=30,factor_name=obj$factor_name) |
|
| 630 |
+ |
|
| 631 |
+ C1=chart_plot(C,dobj)+ |
|
| 632 |
+ labs(tag='c)')+ |
|
| 633 |
+ theme(axis.text.x = element_text(angle = 90, hjust = 1))+ggtitle(NULL,'Before processing') |
|
| 634 |
+ |
|
| 635 |
+ C2=chart_plot(C,eobj)+ |
|
| 636 |
+ labs(tag='d)')+ |
|
| 637 |
+ theme(axis.text.x = element_text(angle = 90, hjust = 1))+ggtitle(NULL,'After processing') |
|
| 638 |
+ |
|
| 639 |
+ C=DatasetExperiment_dist(factor_name=obj$factor_name,per_class=TRUE) |
|
| 640 |
+ |
|
| 641 |
+ C3=chart_plot(C,dobj)+ |
|
| 642 |
+ labs(tag='a)')+ |
|
| 643 |
+ theme(legend.position="none",axis.text.x = element_text(angle = 90, hjust = 1))+ggtitle(NULL,'Before processing') |
|
| 644 |
+ |
|
| 645 |
+ C4=chart_plot(C,eobj)+ |
|
| 646 |
+ labs(tag='b)')+ |
|
| 647 |
+ theme(legend.position="none",axis.text.x = element_text(angle = 90, hjust = 1))+ggtitle(NULL,'After processing') |
|
| 648 |
+ |
|
| 649 |
+ rC1=ggplot_build(C1)$layout$panel_scales_y[[1]]$range$range |
|
| 650 |
+ rC2=ggplot_build(C2)$layout$panel_scales_y[[1]]$range$range |
|
| 651 |
+ rC3=ggplot_build(C3)$layout$panel_scales_x[[1]]$range$range |
|
| 652 |
+ rC4=ggplot_build(C4)$layout$panel_scales_x[[1]]$range$range |
|
| 653 |
+ rC1=max(abs(rC1)) |
|
| 654 |
+ rC2=max(abs(rC2)) |
|
| 655 |
+ rC3=max(abs(rC3)) |
|
| 656 |
+ rC4=max(abs(rC4)) |
|
| 657 |
+ |
|
| 658 |
+ C1=C1+ylim(c(-rC1,rC1)) |
|
| 659 |
+ C3=C3+xlim(c(-rC1,rC1)) |
|
| 660 |
+ C2=C2+ylim(c(-rC2,rC2)) |
|
| 661 |
+ C4=C4+xlim(c(-rC2,rC2)) |
|
| 662 |
+ |
|
| 663 |
+ |
|
| 664 |
+ gA <- ggplotGrob(C3) |
|
| 665 |
+ gB <- ggplotGrob(C4) |
|
| 666 |
+ gC <- ggplotGrob(C1) |
|
| 667 |
+ gD <- ggplotGrob(C2) |
|
| 668 |
+ |
|
| 669 |
+ L=matrix(nrow=4,ncol=1) |
|
| 670 |
+ L=as.matrix(rbind(c(1,2),c(3,4),c(3,4),c(3,4))) |
|
| 671 |
+ |
|
| 672 |
+ temp=gtable_cbind(gA,gB) |
|
| 673 |
+ heights1=temp$heights |
|
| 674 |
+ gA$heights=heights1 |
|
| 675 |
+ gB$heights=heights1 |
|
| 676 |
+ |
|
| 677 |
+ temp=gtable_cbind(gC,gD) |
|
| 678 |
+ heights2=temp$heights |
|
| 679 |
+ gC$heights=heights2 |
|
| 680 |
+ gD$heights=heights2 |
|
| 681 |
+ |
|
| 682 |
+ maxWidth = grid::unit.pmax(gA$widths[2:5], gB$widths[2:5],gC$widths[2:5],gD$widths[2:5]) |
|
| 683 |
+ gA$widths[2:5] <- as.list(maxWidth) |
|
| 684 |
+ gB$widths[2:5] <- as.list(maxWidth) |
|
| 685 |
+ gC$widths[2:5] <- as.list(maxWidth) |
|
| 686 |
+ gD$widths[2:5] <- as.list(maxWidth) |
|
| 687 |
+ |
|
| 688 |
+ p=grid.arrange(grobs=list(gA,gB,gC,gD),layout_matrix=L) |
|
| 689 |
+ |
|
| 690 |
+ return(p) |
|
| 691 |
+ |
|
| 692 |
+ } |
|
| 640 | 693 |
) |
| 641 | 694 |
|
| 642 | 695 |
|
| ... | ... |
@@ -650,54 +703,54 @@ setMethod(f="chart_plot", |
| 650 | 703 |
#' C = DatasetExperiment_heatmap() |
| 651 | 704 |
#' chart_plot(C,D) |
| 652 | 705 |
DatasetExperiment_heatmap = function(na_colour='#FF00E4',...) {
|
| 653 |
- out=struct::new_struct('DatasetExperiment_heatmap',
|
|
| 654 |
- na_colour=na_colour,...) |
|
| 655 |
- return(out) |
|
| 706 |
+ out=struct::new_struct('DatasetExperiment_heatmap',
|
|
| 707 |
+ na_colour=na_colour,...) |
|
| 708 |
+ return(out) |
|
| 656 | 709 |
} |
| 657 | 710 |
|
| 658 | 711 |
|
| 659 | 712 |
.DatasetExperiment_heatmap<-setClass( |
| 660 |
- "DatasetExperiment_heatmap", |
|
| 661 |
- contains=c('chart'),
|
|
| 662 |
- slots=c( |
|
| 663 |
- # INPUTS |
|
| 664 |
- na_colour='entity' |
|
| 665 |
- ), |
|
| 666 |
- prototype = list(name='DatasetExperiment heatmap', |
|
| 667 |
- description='A heatmap to visualise the measured values in a data matrix.', |
|
| 668 |
- type="scatter", |
|
| 669 |
- libraries='reshape2', |
|
| 670 |
- .params=c('na_colour'),
|
|
| 671 |
- |
|
| 672 |
- na_colour=entity(name='Missing value colour', |
|
| 673 |
- value='#FF00E4', |
|
| 674 |
- type='character', |
|
| 675 |
- description='The hex colour code used to plot missing values.', |
|
| 676 |
- max_length=1 |
|
| 677 |
- ) |
|
| 678 |
- ) |
|
| 713 |
+ "DatasetExperiment_heatmap", |
|
| 714 |
+ contains=c('chart'),
|
|
| 715 |
+ slots=c( |
|
| 716 |
+ # INPUTS |
|
| 717 |
+ na_colour='entity' |
|
| 718 |
+ ), |
|
| 719 |
+ prototype = list(name='DatasetExperiment heatmap', |
|
| 720 |
+ description='A heatmap to visualise the measured values in a data matrix.', |
|
| 721 |
+ type="scatter", |
|
| 722 |
+ libraries='reshape2', |
|
| 723 |
+ .params=c('na_colour'),
|
|
| 724 |
+ |
|
| 725 |
+ na_colour=entity(name='Missing value colour', |
|
| 726 |
+ value='#FF00E4', |
|
| 727 |
+ type='character', |
|
| 728 |
+ description='The hex colour code used to plot missing values.', |
|
| 729 |
+ max_length=1 |
|
| 730 |
+ ) |
|
| 731 |
+ ) |
|
| 679 | 732 |
) |
| 680 | 733 |
|
| 681 | 734 |
#' @export |
| 682 | 735 |
#' @template chart_plot |
| 683 | 736 |
setMethod(f="chart_plot", |
| 684 |
- signature=c("DatasetExperiment_heatmap",'DatasetExperiment'),
|
|
| 685 |
- definition=function(obj,dobj) |
|
| 686 |
- {
|
|
| 687 |
- X=reshape2::melt(as.matrix(dobj$data)) |
|
| 688 |
- colnames(X)=c('Sample','Feature','Peak area')
|
|
| 689 |
- p=ggplot(data=X,aes(x=`Feature`,y=`Sample`,fill=`Peak area`)) + geom_raster() + |
|
| 690 |
- scale_colour_Publication()+ |
|
| 691 |
- theme_Publication(base_size = 12)+ |
|
| 692 |
- scale_fill_viridis_c(na.value=obj$na_colour)+ |
|
| 693 |
- theme(axis.text.x=element_blank(), |
|
| 694 |
- axis.ticks.x=element_blank(), |
|
| 695 |
- axis.text.y=element_blank(), |
|
| 696 |
- axis.ticks.y=element_blank() |
|
| 697 |
- ) |
|
| 698 |
- |
|
| 699 |
- |
|
| 700 |
- return(p) |
|
| 701 |
- |
|
| 702 |
- } |
|
| 737 |
+ signature=c("DatasetExperiment_heatmap",'DatasetExperiment'),
|
|
| 738 |
+ definition=function(obj,dobj) |
|
| 739 |
+ {
|
|
| 740 |
+ X=reshape2::melt(as.matrix(dobj$data)) |
|
| 741 |
+ colnames(X)=c('Sample','Feature','Peak area')
|
|
| 742 |
+ p=ggplot(data=X,aes(x=`Feature`,y=`Sample`,fill=`Peak area`)) + geom_raster() + |
|
| 743 |
+ scale_colour_Publication()+ |
|
| 744 |
+ theme_Publication(base_size = 12)+ |
|
| 745 |
+ scale_fill_viridis_c(na.value=obj$na_colour)+ |
|
| 746 |
+ theme(axis.text.x=element_blank(), |
|
| 747 |
+ axis.ticks.x=element_blank(), |
|
| 748 |
+ axis.text.y=element_blank(), |
|
| 749 |
+ axis.ticks.y=element_blank() |
|
| 750 |
+ ) |
|
| 751 |
+ |
|
| 752 |
+ |
|
| 753 |
+ return(p) |
|
| 754 |
+ |
|
| 755 |
+ } |
|
| 703 | 756 |
) |
