Newer
Older
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
#' The Params virtual class
#'
#' Virtual S4 class that all other Params classes inherit from.
#'
#' @section Parameters:
#'
#' The Params class defines the following parameters:
#'
#' \describe{
#' \item{\code{[nGenes]}}{The number of genes to simulate.}
#' \item{\code{[nCells]}}{The number of cells to simulate.}
#' \item{\code{seed}}{Seed to use for generating random numbers.}
#' }
#'
#' The parameters shown in brackets can be estimated from real data.
#'
#' @name Params
#' @rdname Params
#' @aliases Params-class
setClass("Params",
contains = "VIRTUAL",
slots = c(nGenes = "numeric",
nCells = "numeric",
seed = "numeric"),
prototype = prototype(nGenes = 10000, nCells = 100,
seed = sample(1:1e6, 1)))
#' The SimpleParams class
#'
#' S4 class that holds parameters for the simple simulation.
#'
#' @section Parameters:
#'
#' The simple simulation uses the following parameters:
#'
#' \describe{
#' \item{\code{nGenes}}{The number of genes to simulate.}
#' \item{\code{nCells}}{The number of cells to simulate.}
#' \item{\code{[seed]}}{Seed to use for generating random numbers.}
#' \item{\code{mean.shape}}{The shape parameter for the mean gamma
#' distribution.}
#' \item{\code{mean.rate}}{The rate parameter for the mean gamma
#' distribution.}
#' \item{\code{[count.disp]}}{The dispersion parameter for the counts
#' negative binomial distribution.}
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{simpleEstimate}}. For details of the simple simulation
#' see \code{\link{simpleSimulate}}.
#'
#' @name SimpleParams
#' @rdname SimpleParams
#' @aliases SimpleParams-class
#' @exportClass SimpleParams
setClass("SimpleParams",
contains = "Params",
slots = c(mean.shape = "numeric",
mean.rate = "numeric",
count.disp = "numeric"),
prototype = prototype(mean.shape = 0.4, mean.rate = 0.3,
count.disp = 0.1))
#' The SplatParams class
#'
#' S4 class that holds parameters for the Splatter simulation.
#'
#' @section Parameters:
#'
#' The Splatter simulation requires the following parameters:
#'
#' \describe{
#' \item{\code{nGenes}}{The number of genes to simulate.}
#' \item{\code{nCells}}{The number of cells to simulate.}
#' \item{\code{[nGroups]}}{The number of groups or paths to simulate.}
#' \item{\code{[groupCells]}}{Vector giving the number of cells in each
#' simulation group/path.}
#' \item{\code{[seed]}}{Seed to use for generating random numbers.}
#' \item{\emph{Mean parameters}}{
#' \describe{
#' \item{\code{mean.shape}}{Shape parameter for the mean gamma
#' distribution.}
#' \item{\code{mean.rate}}{Rate parameter for the mean gamma
#' distribution.}
#' }
#' }
#' \item{\emph{Library size parameters}}{
#' \describe{
#' \item{\code{lib.loc}}{Location (meanlog) parameter for the
#' library size log-normal distribution.}
#' \item{\code{lib.scale}}{Scale (sdlog) parameter for the library
#' size log-normal distribution.}
#' }
#' }
#' \item{\emph{Expression outlier parameters}}{
#' \describe{
#' \item{\code{out.prob}}{Probability that a gene is an expression
#' outlier.}
#' \item{\code{out.facLoc}}{Location (meanlog) parameter for the
#' expression outlier factor log-normal distribution.}
#' \item{\code{out.facScale}}{Scale (sdlog) parameter for the
#' expression outlier factor log-normal distribution.}
#' }
#' }
#' \item{\emph{Differential expression parameters}}{
#' \describe{
#' \item{\code{[de.prob]}}{Probability that a gene is differentially
#' expressed in a group. Can be a vector.}
#' \item{\code{[de.loProb]}}{Probability that a differentially
#' expressed gene is down-regulated. Can be a vector.}
#' \item{\code{[de.facLoc]}}{Location (meanlog) parameter for the
#' differential expression factor log-normal distribution. Can be a
#' vector.}
#' \item{\code{[de.facScale]}}{Scale (sdlog) parameter for the
#' differential expression factor log-normal distribution. Can be a
#' vector.}
#' }
#' }
#' \item{\emph{Biological Coefficient of Variation parameters}}{
#' \describe{
#' \item{\code{bcv.common}}{Underlying common dispersion across all
#' genes.}
#' \item{\code{bcv.df}}{Degrees of Freedom for the BCV inverse
#' chi-squared distribution.}
#' }
#' }
#' \item{\emph{Dropout parameters}}{
#' \describe{
#' \item{\code{dropout.present}}{Logical. Whether to simulate
#' dropout.}
#' \item{\code{dropout.mid}}{Midpoint parameter for the dropout
#' logistic function.}
#' \item{\code{dropout.shape}}{Shape parameter for the dropout
#' logistic function.}
#' }
#' }
#' \item{\emph{Differentiation path parameters}}{
#' \describe{
#' \item{\code{[path.from]}}{Vector giving the originating point of
#' each path. This allows path structure such as a cell type which
#' differentiates into an intermediate cell type that then
#' differentiates into two mature cell types. A path structure of
#' this form would have a "from" parameter of c(0, 1, 1) (where 0 is
#' the origin). If no vector is given all paths will start at the
#' origin.}
#' \item{\code{[path.length]}}{Vector giving the number of steps to
#' simulate along each path. If a single value is given it will be
#' applied to all paths.}
#' \item{\code{[path.skew]}}{Vector giving the skew of each path.
#' Values closer to 1 will give more cells towards the starting
#' population, values closer to 0 will give more cells towards the
#' final population. If a single value is given it will be applied
#' to all paths.}
#' \item{\code{[path.nonlinearProb]}}{Probability that a gene
#' follows a non-linear path along the differentiation path. This
#' allows more complex gene patterns such as a gene being equally
#' expressed at the beginning an end of a path but lowly expressed
#' in the middle.}
#' \item{\code{[path.sigmaFac]}}{Sigma factor for non-linear gene
#' paths. A higher value will result in more extreme non-linear
#' variations along a path.}
#' }
#' }
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{splatEstimate}}. For details of the Splatter simulation
#' see \code{\link{splatSimulate}}.
#'
#' @name SplatParams
#' @rdname SplatParams
#' @aliases SplatParams-class
#' @exportClass SplatParams
setClass("SplatParams",
contains = "Params",
slots = c(nGroups = "numeric",
groupCells = "numeric",
mean.shape = "numeric",
mean.rate = "numeric",
lib.loc = "numeric",
lib.scale = "numeric",
out.prob = "numeric",
out.facLoc = "numeric",
out.facScale = "numeric",
de.prob = "numeric",
de.downProb = "numeric",
de.facLoc = "numeric",
de.facScale = "numeric",
bcv.common = "numeric",
bcv.df = "numeric",
dropout.present = "logical",
dropout.mid = "numeric",
dropout.shape = "numeric",
path.from = "numeric",
path.length = "numeric",
path.skew = "numeric",
path.nonlinearProb = "numeric",
path.sigmaFac = "numeric"),
prototype = prototype(nGroups = 1,
groupCells = 100,
mean.rate = 0.3,
mean.shape = 0.6,
lib.loc = 11,
lib.scale = 0.2,
out.prob = 0.05,
out.facLoc = 4,
de.prob = 0.1,
de.downProb = 0.5,
de.facLoc = 4,
de.facScale = 1,
bcv.common = 0.1,
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
dropout.mid = 0,
dropout.shape = -1,
path.from = 0,
path.length = 100,
path.skew = 0.5,
path.nonlinearProb = 0.1,
path.sigmaFac = 0.8))
#' The LunParams class
#'
#' S4 class that holds parameters for the Lun simulation.
#'
#' @section Parameters:
#'
#' The Lun simulation uses the following parameters:
#'
#' \describe{
#' \item{\code{nGenes}}{The number of genes to simulate.}
#' \item{\code{nCells}}{The number of cells to simulate.}
#' \item{\code{[nGroups]}}{The number of groups to simulate.}
#' \item{\code{[groupCells]}}{Vector giving the number of cells in each
#' simulation group/path.}
#' \item{\code{[seed]}}{Seed to use for generating random numbers.}
#' \item{\emph{Mean parameters}}{
#' \describe{
#' \item{\code{[mean.shape]}}{Shape parameter for the mean gamma
#' distribution.}
#' \item{\code{[mean.rate]}}{Rate parameter for the mean gamma
#' distribution.}
#' }
#' }
#' \item{\emph{Counts parameters}}{
#' \describe{
#' \item{\code{[count.disp]}}{The dispersion parameter for the
#' counts negative binomial distribution.}
#' }
#' }
#' \item{\emph{Differential expression parameters}}{
#' \describe{
#' \item{\code{[de.nGenes]}}{The number of genes that are
#' differentially expressed in each group}
#' \item{\code{[de.upProp]}}{The proportion of differentially
#' expressed genes that are up-regulated in each group}
#' \item{\code{[de.upFC]}}{The fold change for up-regulated genes}
#' \item{\code{[de.downFC]}}{The fold change for down-regulated
#' genes}
#' }
#' }
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{lunEstimate}}. For details of the Lun simulation see
#' \code{\link{lunSimulate}}.
#'
#' @name LunParams
#' @rdname LunParams
#' @aliases LunParams-class
#' @exportClass LunParams
setClass("LunParams",
contains = "SimpleParams",
slots = c(nGroups = "numeric",
groupCells = "numeric",
de.nGenes = "numeric",
de.upProp = "numeric",
de.upFC = "numeric",
de.downFC = "numeric"),
prototype = prototype(nGroups = 1, groupCells = 100, mean.shape = 2,
mean.rate = 2, de.nGenes = 1000, de.upProp = 0.5,
de.upFC = 5, de.downFC = 0))
#' The Lun2Params class
#'
#' S4 class that holds parameters for the Lun simulation.
#'
#' @section Parameters:
#'
#' The Lun2 simulation uses the following parameters:
#'
#' \describe{
#' \item{\code{nGenes}}{The number of genes to simulate.}
#' \item{\code{nCells}}{The number of cells to simulate.}
#' \item{\code{[seed]}}{Seed to use for generating random numbers.}
#' \item{\emph{Gene parameters}}{
#' \describe{
#' \item{\code{gene.params}}{A \code{data.frame} containing gene
#' parameters with two coloumns: \code{Mean} (mean expression for
#' each gene) and \code{Disp} (dispersion for each gene).}
#' \item{\code{zi.params}}{A \code{data.frame} containing
#' zero-inflated gene parameters with three coloumns: \code{Mean}
#' (mean expression for each gene), \code{Disp} (dispersion for
#' each, gene), and \code{Prop} (zero proportion for each gene).}
#' }
#' }
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
#' \item{\code{[nPlates]}}{The number of plates to simulate.}
#' \item{\emph{Plate parameters}}{
#' \describe{
#' \item{\code{plate.ingroup}}{Character vecotor giving the plates
#' considered to be part of the "ingroup".}
#' \item{\code{plate.mod}}{Plate effect modifier factor. The plate
#' effect variance is divided by this value.}
#' \item{\code{plate.var}}{Plate effect variance.}
#' }
#' }
#' \item{\emph{Cell parameters}}{
#' \describe{
#' \item{\code{cell.plates}}{Factor giving the plate that each cell
#' comes from.}
#' \item{\code{cell.libSizes}}{Library size for each cell.}
#' \item{\code{cell.libMod}}{Modifier factor for library sizes.
#' The library sizes are multiplied by this value.}
#' }
#' }
#' \item{\emph{Differential expression parameters}}{
#' \describe{
#' \item{\code{de.nGenes}}{Number of differentially expressed
#' genes.}
#' \item{\code{de.fc}}{Fold change for differentially expressed
#' genes.}
#' }
#' }
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{lun2Estimate}}. For details of the Lun2 simulation see
#' \code{\link{lun2Simulate}}.
#'
#' @name Lun2Params
#' @rdname Lun2Params
#' @aliases Lun2Params-class
#' @exportClass Lun2Params
setClass("Lun2Params",
contains = "Params",
slots = c(nPlates = "numeric",
plate.ingroup = "character",
plate.mod = "numeric",
plate.var = "numeric",
gene.params = "data.frame",
zi.params = "data.frame",
cell.plates = "numeric",
cell.libSizes = "numeric",
cell.libMod = "numeric",
de.nGenes = "numeric",
de.fc = "numeric"),
prototype = prototype(nPlates = 1,
cell.plates = factor(rep(1, 100)),
plate.ingroup = "1",
plate.mod = 1,
plate.var = 14,
gene.params = data.frame(Mean = rep(3.2, 10000),
Disp = rep(0.03, 10000)
),
zi.params = data.frame(Mean = rep(1.6, 10000),
Disp = rep(0.1, 10000),
Prop = rep(2.3e-6, 10000)
),
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
cell.libSizes = rep(70000, 100),
cell.libMod = 1,
de.nGenes = 0,
de.fc = 3))
#' The SCDDParams class
#'
#' S4 class that holds parameters for the scDD simulation.
#'
#' @section Parameters:
#'
#' The SCDD simulation uses the following parameters:
#'
#' \describe{
#' \item{\code{[nGenes]}}{The number of genes to simulate (not used).}
#' \item{\code{nCells}}{The number of cells to simulate in each condition.}
#' \item{\code{[seed]}}{Seed to use for generating random numbers.}
#' \item{\code{SCdat}}{
#' \code{\link[SummarizedExperiment]{SummarizedExperiment}} containing real
#' data.}
#' \item{\code{[nDE]}}{Number of DE genes to simulate.}
#' \item{\code{[nDP]}}{Number of DP genes to simulate.}
#' \item{\code{[nDM]}}{Number of DM genes to simulate.}
#' \item{\code{[nDB]}}{Number of DB genes to simulate.}
#' \item{\code{[nEE]}}{Number of EE genes to simulate.}
#' \item{\code{[nEP]}}{Number of EP genes to simulate.}
#' \item{\code{[sd.range]}}{Interval for fold change standard deviations.}
#' \item{\code{[modeFC]}}{Values for DP, DM and DB mode fold changes.}
#' \item{\code{[varInflation]}}{Variance inflation factors for each
#' condition. If all equal to 1 will be set to \code{NULL} (default)}
#' \item{\code{[condition]}}{String giving the column that represents
#' biological group of interest}
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{scDDEstimate}}. See \code{\link[scDD]{simulateSet}} for more
#' details of the parameters. For details of the Splatter implementation of the
#' scDD simulation see \code{\link{scDDSimulate}}.
#'
#' @name SCDDParams
#' @rdname SCDDParams
#' @aliases SCDDParams-class
#' @exportClass SCDDParams
setClass("SCDDParams",
contains = "Params",
slots = c(SCdat = "SummarizedExperiment",
nDE = "numeric",
nDP = "numeric",
nDM = "numeric",
nDB = "numeric",
nEE = "numeric",
nEP = "numeric",
sd.range = "numeric",
modeFC = "numeric",
varInflation = "numeric",
condition = "character"),
prototype = prototype(SCdat =
SummarizedExperiment::SummarizedExperiment(),
nCells = 100,
nDE = 250,
nDP = 250,
nDM = 250,
nDB = 250,
nEE = 5000,
nEP = 4000,
sd.range = c(1, 3),
modeFC = c(2, 3, 4),
varInflation = c(1, 1),