-
Notifications
You must be signed in to change notification settings - Fork 16
/
edata_summary.R
476 lines (437 loc) · 16.7 KB
/
edata_summary.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
#' Creates a list of six Data Frames, one for each summarizing metric
#'
#' This function takes in an omicsData object and returns a summary of the
#' e_data component. The six summarizing metrics include the mean, standard
#' deviation, median, percent observed, minimum, and maximum.
#'
#' @param omicsData object of the class 'lipidData', 'metabData', 'pepData',
#' 'proData', or 'nmrData' created by \code{\link{as.lipidData}},
#' \code{\link{as.metabData}}, \code{\link{as.pepData}},
#' \code{\link{as.proData}}, \code{\link{as.nmrData}}, respectively.
#' @param by character string indicating whether summarizing metrics will be
#' applied by 'sample' or by 'molecule'. Defaults to 'sample'.
#' @param groupvar a character vector with no more than two variable names that
#' should be used to determine group membership of samples. The variable name
#' must match a column name from \code{f_data}. Defaults to NULL, in which
#' case group_DF attribute will be used.
#'
#' @details If groupvar is NULL and group_designation has not been applied to
#' omicsData, then the metrics will be applied to each column of e_data (when
#' by = 'sample) or to each row of e_data (when by = 'molecule'). When
#' groupvar is provided, it must match a column name from \code{f_data}, this
#' column of f_data is used to group e_data in order to apply the metrics.
#'
#' @return A list of six data frames, of class 'dataRes' (data Result), which
#' are the results of applying the metrics (mean, standard deviation, median,
#' percent observed, minimum and maximum) to omicsData$e_data.
#'
#' @export
#'
#' @examplesIf requireNamespace("pmartRdata", quietly = TRUE)
#' library(pmartRdata)
#'
#' mylipid <- edata_transform(omicsData = lipid_pos_object, data_scale = "log2")
#' mylipid <- group_designation(omicsData = mylipid, main_effects = "Virus")
#' result <- edata_summary(omicsData = mylipid, by = "sample", groupvar = NULL)
#'
edata_summary <- function(omicsData, by = 'sample', groupvar = NULL) {
# some checks
if (!inherits(omicsData, c(
'pepData', 'proData', 'lipidData',
'metabData', 'nmrData', 'seqData'
)))
stop("omicsData must be an object of class pepData, proData, lipidData, metabData, nmrData, or seqData")
if (!isTRUE(by %in% c('sample', 'molecule')))
stop("by must be either sample or molecule")
if (isTRUE(groupvar == attr(omicsData, "cnames")$fdata_cname))
stop("The sample ID column in f_data cannot be used as a grouping column. Specify by = 'sample' to see a by-sample summary of the data")
if (!all(groupvar %in% names(omicsData$f_data))) {
# Why do you think you can group by variables that do not exist?!
stop("The variable(s) in groupvar are not present in f_data.")
}
# pull cnames attr from omicsData
edata = omicsData$e_data
fdata = omicsData$f_data
edata_cname = attr(omicsData, "cnames")$edata_cname
fdata_cname = attr(omicsData, "cnames")$fdata_cname
edata_cname_id = which(names(edata) == edata_cname)
fdata_cname_id = which(names(fdata) == fdata_cname)
groupDF = attr(omicsData, "group_DF")
# all groupvars must be present in fdata
if (any((groupvar %in% names(fdata)) == F)) {
stop(paste0(
"The following variables were not present in f_data: '",
groupvar[which((groupvar %in% names(fdata)) == F)],
"'"
))
}
if (length(groupvar) > 2) stop("No more than two groupvar can be provided")
if (by == 'sample') {
# check that groupvar is NULL, groupvar is only used when by == 'molecule'
if (!is.null(groupvar)) stop("groupvar is only used when by == 'molecule'")
avg = as.data.frame(apply(
edata[, -edata_cname_id], 2,
function(x) {
if (all(is.na(x))) {
mean(x)
} else {
mean(x, na.rm = TRUE)
}
}
))
avg = cbind(names(edata[, -edata_cname_id]), avg)
names(avg) <- c("sample", "mean")
rownames(avg) <- NULL
sd = as.data.frame(apply(edata[, -edata_cname_id], 2, sd, na.rm = TRUE))
sd = cbind(names(edata[, -edata_cname_id]), sd)
names(sd) <- c("sample", "sd")
rownames(sd) <- NULL
mds = as.data.frame(apply(edata[, -edata_cname_id], 2, median, na.rm = TRUE))
mds = cbind(names(edata[, -edata_cname_id]), mds)
names(mds) <- c("sample", "median")
rownames(mds) <- NULL
if (inherits(omicsData, "seqData")) {
pct_obs = as.data.frame(apply(
edata[, -edata_cname_id], 2,
function(x) {
sum(x != 0) / length(x)
}
))
pct_obs = cbind(names(edata[, -edata_cname_id]), pct_obs)
names(pct_obs) <- c("sample", "pct_nonzero_obs")
rownames(pct_obs) <- NULL
} else {
pct_obs = as.data.frame(apply(
edata[, -edata_cname_id], 2,
function(x) {
sum(!is.na(x)) / length(x)
}
))
pct_obs = cbind(names(edata[, -edata_cname_id]), pct_obs)
names(pct_obs) <- c("sample", "pct_obs")
rownames(pct_obs) <- NULL
}
min = as.data.frame(apply(edata[, -edata_cname_id], 2, min, na.rm = TRUE))
min = cbind(names(edata[, -edata_cname_id]), min)
names(min) <- c("sample", "min")
rownames(min) <- NULL
max = as.data.frame(apply(edata[, -edata_cname_id], 2, max, na.rm = TRUE))
max = cbind(names(edata[, -edata_cname_id]), max)
names(max) <- c("sample", "max")
rownames(max) <- NULL
res_list = list(
mean = avg,
sd = sd,
median = mds,
pct_obs = pct_obs,
min = min,
max = max
)
class(res_list) <- "dataRes"
attr(res_list, "by") <- by
attr(res_list, "groupvar") <- groupvar
attr(res_list, "cnames") <- list(
"edata_cname" = edata_cname,
"fdata_cname" = fdata_cname
)
attr(res_list, "data_scale") <- get_data_scale(omicsData)
}
if (by == "molecule") {
if (is.null(groupvar)) {
if (is.null(groupDF)) {
# when groupvar is NULL and group_designation is NULL, we calculate
# metric for each row
avg = apply(
edata[, -edata_cname_id], 1,
function(x) {
if (all(is.na(x))) {
mean(x)
} else {
mean(x, na.rm = TRUE)
}
}
)
avg = data.frame(
molecule = edata[, edata_cname_id],
mean = avg,
stringsAsFactors = F
)
names(avg)[1] <- edata_cname
sd = apply(edata[, -edata_cname_id], 1, sd, na.rm = TRUE)
sd = data.frame(
molecule = edata[, edata_cname_id],
sd = sd,
stringsAsFactors = F
)
names(sd)[1] <- edata_cname
mds = apply(edata[, -edata_cname_id], 1, median, na.rm = TRUE)
mds = data.frame(
molecule = edata[, edata_cname_id],
median = mds,
stringsAsFactors = F
)
names(mds)[1] <- edata_cname
pct_obs = apply(
edata[, -edata_cname_id], 1,
function(x) {
sum(!is.na(x)) / length(x)
}
)
pct_obs = data.frame(
molecule = edata[, edata_cname_id],
pct_obs = pct_obs,
stringsAsFactors = F
)
names(pct_obs)[1] <- edata_cname
min = apply(edata[, -edata_cname_id], 1, min, na.rm = TRUE)
min = data.frame(
molecule = edata[, edata_cname_id],
min = min,
stringsAsFactors = F
)
names(min)[1] <- edata_cname
max = apply(edata[, -edata_cname_id], 1, max, na.rm = TRUE)
max = data.frame(
molecule = edata[, edata_cname_id],
max = max,
stringsAsFactors = F
)
names(max)[1] <- edata_cname
res_list = list(
mean = avg,
sd = sd,
median = mds,
pct_obs = pct_obs,
min = min,
max = max
)
class(res_list) <- "dataRes"
attr(res_list, "by") <- by
attr(res_list, "groupvar") <- groupvar
attr(res_list, "cnames") <- list(
"edata_cname" = edata_cname,
"fdata_cname" = fdata_cname
)
attr(res_list, "group_DF") <- groupDF
attr(res_list, "data_scale") <- get_data_scale(omicsData)
} else if (!is.null(groupDF)) {
# when groupvar is NULL but group_designation has been run
# check that there are atleast 2 samples in each group and remove groups
# that have less than two samples per group
n_per_grp = as.data.frame(groupDF %>%
dplyr::group_by(Group) %>%
dplyr::summarise(count = dplyr::n()))
remove_group = as.character(
n_per_grp[which(n_per_grp$count < 2), "Group"]
)
if (length(remove_group) > 0) {
n_per_grp = n_per_grp[-which(n_per_grp$count < 2), ]
groupDF = groupDF[-which(groupDF$Group %in% remove_group), ]
}
# rearranging edata
edata_melt <- tidyr::pivot_longer(
edata, -!!edata_cname,
cols_vary = "slowest",
names_to = fdata_cname,
values_to = "value"
)
edata_melt <- merge.data.frame(edata_melt, groupDF, by = fdata_cname)
edata_melt <- edata_melt[, -which(names(edata_melt) == fdata_cname)]
# checking that n_per_grp group order matches that of edata_melt
n_per_grp = n_per_grp[match(
unique(edata_melt$Group),
n_per_grp$Group
), ]
dcast_fn <- function(fun) {
edata_melt %>%
dplyr::group_by(
!!dplyr::sym(edata_cname), Group
) %>%
dplyr::summarise(
value = fun(value),
.groups = "drop"
) %>%
tidyr::pivot_wider(
id_cols = !!edata_cname,
names_from = Group,
names_vary = "slowest",
values_from = value
) %>%
data.frame
}
avg <- dcast_fn(\(x) mean(x, na.rm = !all(is.na(x))))
std_dev <- dcast_fn(\(x) sd(x, na.rm = TRUE))
mds <- dcast_fn(\(x) median(x, na.rm = !all(is.na(x))))
pct_obs <- dcast_fn(\(x) sum(!is.na(x)) / length(x))
mins <- dcast_fn(\(x) min(x, na.rm = !all(is.na(x))))
maxs <- dcast_fn(\(x) max(x, na.rm = !all(is.na(x))))
res_list = list(
n_per_grp = n_per_grp,
mean = avg,
sd = std_dev,
median = mds,
pct_obs = pct_obs,
min = mins,
max = maxs
)
class(res_list) <- "dataRes"
attr(res_list, "by") <- by
attr(res_list, "groupvar") <- groupvar
attr(res_list, "cnames") <- list(
"edata_cname" = edata_cname,
"fdata_cname" = fdata_cname
)
attr(res_list, "group_DF") <- groupDF
attr(res_list, "data_scale") <- get_data_scale(omicsData)
}
} else if (length(groupvar) == 1) {
#### case where groupvar is provided and has length 1####
temp_fdata = fdata[, c(fdata_cname_id, which(names(fdata) == groupvar))]
names(temp_fdata)[2] <- "Group"
# check that there are atleast 2 samples in each group and remove groups
# that have less than two samples per group
n_per_grp = as.data.frame(temp_fdata %>%
dplyr::group_by(Group) %>%
dplyr::summarise(count = dplyr::n()))
remove_group = as.character(n_per_grp[which(n_per_grp$count < 2), "Group"])
if (length(remove_group) > 0) {
n_per_grp = n_per_grp[-which(n_per_grp$count < 2), ]
temp_fdata = temp_fdata[-which(temp_fdata$Group %in% remove_group), ]
}
# check to see if grouping structure was 1 sample per group
if (nrow(temp_fdata) == 0) stop("The grouping variable must assign more than 1 sample to at least one group level")
# rearranging edata
edata_melt <- tidyr::pivot_longer(
omicsData$e_data, -!!edata_cname,
cols_vary = "slowest",
names_to = fdata_cname,
values_to = "value"
)
edata_melt = merge.data.frame(edata_melt, temp_fdata, by = fdata_cname)
edata_melt = edata_melt[, -which(names(edata_melt) == fdata_cname)]
# checking that n_per_grp group order matches that of edata_melt
n_per_grp = n_per_grp[match(unique(edata_melt$Group), n_per_grp$Group), ]
dcast_fn <- function(fun) {
edata_melt %>%
dplyr::group_by(
!!dplyr::sym(edata_cname), Group
) %>%
dplyr::summarise(
value = fun(value),
.groups = "drop"
) %>%
tidyr::pivot_wider(
id_cols = !!edata_cname,
names_from = Group,
names_vary = "slowest",
values_from = value
) %>%
data.frame
}
avg <- dcast_fn(\(x) mean(x, na.rm = !all(is.na(x))))
std_dev <- dcast_fn(\(x) sd(x, na.rm = TRUE))
mds <- dcast_fn(\(x) median(x, na.rm = !all(is.na(x))))
pct_obs <- dcast_fn(\(x) sum(!is.na(x)) / length(x))
mins <- dcast_fn(\(x) min(x, na.rm = !all(is.na(x))))
maxs <- dcast_fn(\(x) max(x, na.rm = !all(is.na(x))))
res_list = list(
n_per_grp = n_per_grp,
mean = avg,
sd = std_dev,
median = mds,
pct_obs = pct_obs,
min = mins,
max = maxs
)
class(res_list) <- "dataRes"
attr(res_list, "by") <- by
attr(res_list, "groupvar") <- groupvar
attr(res_list, "cnames") <- list(
"edata_cname" = edata_cname,
"fdata_cname" = fdata_cname
)
attr(res_list, "data_scale") <- get_data_scale(omicsData)
} else if (length(groupvar) == 2) {
#### case where length of groupvar is 2####
group_vars = fdata[names(fdata) %in% groupvar]
group_vars <- apply(group_vars, 2, as.character)
# create a group variable and paste grouvar levels together for samples #
# samples with a value of NA for either groupvar will have a Group value
# of NA #
Group = rep(NA, nrow(fdata))
# identify samples that will have a Group membership that is not missing #
nonna.group = (!is.na(group_vars[, 1]) & !is.na(group_vars[, 2]))
Group[nonna.group] = paste(as.character(group_vars[nonna.group, 1]),
as.character(group_vars[nonna.group, 2]),
sep = "_"
)
# create output formatted with first column being fdata_cname and second
# column group id #
output = data.frame(Sample.ID = fdata[, fdata_cname], Group = Group)
names(output)[1] = fdata_cname
# check that there are atleast 2 samples in each group and remove groups
# that have less than two samples per group
n_per_grp = as.data.frame(output %>%
dplyr::group_by(Group) %>%
dplyr::summarise(count = dplyr::n()))
remove_group = as.character(n_per_grp[which(n_per_grp$count < 2), "Group"])
if (length(remove_group) > 0) {
n_per_grp = n_per_grp[-which(n_per_grp$count < 2), ]
output = output[-which(output$Group %in% remove_group), ]
}
if (nrow(output) == 0) stop("The grouping structure must assign more than 1 sample to at least one group level")
# groupvar was provided, rearranging edata
edata_melt <- tidyr::pivot_longer(
omicsData$e_data, -!!edata_cname,
cols_vary = "slowest",
names_to = fdata_cname,
values_to = "value"
)
edata_melt = merge.data.frame(edata_melt, output, by = fdata_cname)
edata_melt = edata_melt[, -which(names(edata_melt) == fdata_cname)]
# checking that n_per_grp group order matches that of edata_melt
n_per_grp = n_per_grp[match(unique(edata_melt$Group), n_per_grp$Group), ]
dcast_fn <- function(fun) {
edata_melt %>%
dplyr::group_by(
!!dplyr::sym(edata_cname), Group
) %>%
dplyr::summarise(
value = fun(value),
.groups = "drop"
) %>%
tidyr::pivot_wider(
id_cols = !!edata_cname,
names_from = Group,
names_vary = "slowest",
values_from = value
) %>%
data.frame
}
avg <- dcast_fn(\(x) mean(x, na.rm = !all(is.na(x))))
std_dev <- dcast_fn(\(x) sd(x, na.rm = TRUE))
mds <- dcast_fn(\(x) median(x, na.rm = !all(is.na(x))))
pct_obs <- dcast_fn(\(x) sum(!is.na(x)) / length(x))
mins <- dcast_fn(\(x) min(x, na.rm = !all(is.na(x))))
maxs <- dcast_fn(\(x) max(x, na.rm = !all(is.na(x))))
res_list = list(
n_per_grp = n_per_grp,
mean = avg,
sd = std_dev,
median = mds,
pct_obs = pct_obs,
min = mins,
max = maxs
)
class(res_list) <- "dataRes"
attr(res_list, "by") <- by
attr(res_list, "groupvar") <- groupvar
attr(res_list, "cnames") <- list(
"edata_cname" = edata_cname,
"fdata_cname" = fdata_cname
)
attr(res_list, "data_scale") <- get_data_scale(omicsData)
}
}
return(res_list)
}