... | ... |
@@ -59,14 +59,23 @@ export(simulateObservedMatrix) |
59 | 59 |
export(subsetCeldaList) |
60 | 60 |
export(topRank) |
61 | 61 |
export(violinPlot) |
62 |
+exportMethods(bestLogLikelihood) |
|
62 | 63 |
exportMethods(celdaHeatmap) |
64 |
+exportMethods(celdaPerplexity) |
|
63 | 65 |
exportMethods(celdaProbabilityMap) |
64 | 66 |
exportMethods(celdaTsne) |
65 | 67 |
exportMethods(celdaUmap) |
66 | 68 |
exportMethods(clusterProbability) |
69 |
+exportMethods(clusters) |
|
67 | 70 |
exportMethods(factorizeMatrix) |
68 | 71 |
exportMethods(featureModuleLookup) |
72 |
+exportMethods(logLikelihoodHistory) |
|
73 |
+exportMethods(matrixNames) |
|
74 |
+exportMethods(params) |
|
69 | 75 |
exportMethods(perplexity) |
76 |
+exportMethods(resList) |
|
77 |
+exportMethods(runParams) |
|
78 |
+exportMethods(sampleLabel) |
|
70 | 79 |
import(RColorBrewer) |
71 | 80 |
import(data.table) |
72 | 81 |
import(foreach) |
... | ... |
@@ -8,12 +8,20 @@ setClass("celdaModel", |
8 | 8 |
|
9 | 9 |
#' @title Get parameter values provided for celda model creation |
10 | 10 |
#' @description Retrieves the K/L, model priors (e.g. alpha, beta), random seed, and count matrix checksum parameters provided during the creation of the provided celda model. |
11 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
11 | 12 |
#' @return List. Contains the model-specific parameters for the provided celda model object depending on its class. |
12 | 13 |
#' @examples |
13 | 14 |
#' params(celda.CG.mod) |
14 | 15 |
#' @export |
15 | 16 |
setGeneric("params", |
16 | 17 |
function(celda.mod){ standardGeneric("params") }) |
18 |
+#' @title Get parameter values provided for celda model creation |
|
19 |
+#' @description Retrieves the K/L, model priors (e.g. alpha, beta), random seed, and count matrix checksum parameters provided during the creation of the provided celda model. |
|
20 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
21 |
+#' @return List. Contains the model-specific parameters for the provided celda model object depending on its class. |
|
22 |
+#' @examples |
|
23 |
+#' params(celda.CG.mod) |
|
24 |
+#' @export |
|
17 | 25 |
setMethod("params", |
18 | 26 |
signature=c(celda.mod="celdaModel"), |
19 | 27 |
function(celda.mod){ celda.mod@params }) |
... | ... |
@@ -21,12 +29,20 @@ setMethod("params", |
21 | 29 |
|
22 | 30 |
#' @title Get feature, cell and sample names from a celda model |
23 | 31 |
#' @description Retrieves the row, column, and sample names used to generate a celda model. |
32 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
24 | 33 |
#' @return List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated. |
25 | 34 |
#' @examples |
26 | 35 |
#' matrixNames(celda.CG.mod) |
27 | 36 |
#' @export |
28 | 37 |
setGeneric("matrixNames", |
29 | 38 |
function(celda.mod){ standardGeneric("matrixNames") }) |
39 |
+#' @title Get feature, cell and sample names from a celda model |
|
40 |
+#' @description Retrieves the row, column, and sample names used to generate a celda model. |
|
41 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
42 |
+#' @return List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated. |
|
43 |
+#' @examples |
|
44 |
+#' matrixNames(celda.CG.mod) |
|
45 |
+#' @export |
|
30 | 46 |
setMethod("matrixNames", |
31 | 47 |
signature=c(celda.mod="celdaModel"), |
32 | 48 |
function(celda.mod){ celda.mod@names }) |
... | ... |
@@ -34,13 +50,20 @@ setMethod("matrixNames", |
34 | 50 |
|
35 | 51 |
#' @title Get log-likelihood history |
36 | 52 |
#' @description Retrieves the complete log-likelihood from all iterations of Gibbs sampling used to generate a celda model. |
37 |
-#' |
|
53 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
38 | 54 |
#' @return Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model. |
39 | 55 |
#' @examples |
40 | 56 |
#' logLikelihoodHistory(celda.CG.mod) |
41 | 57 |
#' @export |
42 | 58 |
setGeneric("logLikelihoodHistory", |
43 | 59 |
function(celda.mod){ standardGeneric("logLikelihoodHistory") }) |
60 |
+#' @title Get log-likelihood history |
|
61 |
+#' @description Retrieves the complete log-likelihood from all iterations of Gibbs sampling used to generate a celda model. |
|
62 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
63 |
+#' @return Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model. |
|
64 |
+#' @examples |
|
65 |
+#' logLikelihoodHistory(celda.CG.mod) |
|
66 |
+#' @export |
|
44 | 67 |
setMethod("logLikelihoodHistory", |
45 | 68 |
signature=c(celda.mod="celdaModel"), |
46 | 69 |
function(celda.mod){ celda.mod@completeLogLik }) |
... | ... |
@@ -49,11 +72,18 @@ setMethod("logLikelihoodHistory", |
49 | 72 |
#' @title Get the log-likelihood |
50 | 73 |
#' @description Retrieves the final log-likelihood from all iterations of Gibbs sampling used to generate a celda model. |
51 | 74 |
#' @return Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model. |
75 |
+#' @param celda.mod A celda model object of class celda_C, celda_G, or celda_CG. |
|
52 | 76 |
#' @examples |
53 | 77 |
#' bestLogLikelihood(celda.CG.mod) |
54 | 78 |
#' @export |
55 | 79 |
setGeneric("bestLogLikelihood", |
56 | 80 |
function(celda.mod){ standardGeneric("bestLogLikelihood") }) |
81 |
+#' @title Get the log-likelihood |
|
82 |
+#' @description Retrieves the final log-likelihood from all iterations of Gibbs sampling used to generate a celda model. |
|
83 |
+#' @return Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model. |
|
84 |
+#' @examples |
|
85 |
+#' bestLogLikelihood(celda.CG.mod) |
|
86 |
+#' @export |
|
57 | 87 |
setMethod("bestLogLikelihood", |
58 | 88 |
signature=c(celda.mod="celdaModel"), |
59 | 89 |
function(celda.mod){ celda.mod@finalLogLik }) |
... | ... |
@@ -61,12 +91,20 @@ setMethod("bestLogLikelihood", |
61 | 91 |
|
62 | 92 |
#' @title Get clustering outcomes from a celda model |
63 | 93 |
#' @description Returns the z / y results corresponding to the cell / gene cluster labels determined by the provided celda model. |
94 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
64 | 95 |
#' @return List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models) |
65 | 96 |
#' @examples |
66 | 97 |
#' clusters(celda.CG.mod) |
67 | 98 |
#' @export |
68 | 99 |
setGeneric("clusters", |
69 | 100 |
function(celda.mod){ standardGeneric("clusters")}) |
101 |
+#' @title Get clustering outcomes from a celda model |
|
102 |
+#' @description Returns the z / y results corresponding to the cell / gene cluster labels determined by the provided celda model. |
|
103 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
104 |
+#' @return List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models) |
|
105 |
+#' @examples |
|
106 |
+#' clusters(celda.CG.mod) |
|
107 |
+#' @export |
|
70 | 108 |
setMethod("clusters", signature=c(celda.mod="celdaModel"), |
71 | 109 |
function(celda.mod){ |
72 | 110 |
return(celda.mod@clusters) |
... | ... |
@@ -80,12 +118,20 @@ setClass("celda_C", |
80 | 118 |
|
81 | 119 |
#' @title Get sample labels from a celda model |
82 | 120 |
#' @description Returns the sample labels for the count matrix provided for generation of a given celda model. |
121 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
83 | 122 |
#' @return Character. Contains the sample labels provided at model creation time, or those automatically generated by celda. |
84 | 123 |
#' @examples |
85 | 124 |
#' sampleLabel(celda.CG.mod) |
86 | 125 |
#' @export |
87 | 126 |
setGeneric("sampleLabel", |
88 | 127 |
function(celda.mod){ standardGeneric("sampleLabel") }) |
128 |
+#' @title Get sample labels from a celda model |
|
129 |
+#' @description Returns the sample labels for the count matrix provided for generation of a given celda model. |
|
130 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
131 |
+#' @return Character. Contains the sample labels provided at model creation time, or those automatically generated by celda. |
|
132 |
+#' @examples |
|
133 |
+#' sampleLabel(celda.CG.mod) |
|
134 |
+#' @export |
|
89 | 135 |
setMethod("sampleLabel", |
90 | 136 |
signature=c(celda.mod="celdaModel"), |
91 | 137 |
function(celda.mod){ celda.mod@sample.label }) |
... | ... |
@@ -106,12 +152,20 @@ setClass("celdaList", |
106 | 152 |
|
107 | 153 |
#' @title Get run parameters provided to `celdaGridSearch()` |
108 | 154 |
#' @description Returns details on the clustering parameters, model priors, and seeds provided to `celdaGridSearch()` when the provided celdaList was created. |
155 |
+#' @param celda.mod An object of class celdaList. |
|
109 | 156 |
#' @return Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList. |
110 | 157 |
#' @examples |
111 | 158 |
#' runParams(celda.CG.grid.search.res) |
112 | 159 |
#' @export |
113 | 160 |
setGeneric("runParams", |
114 | 161 |
function(celda.mod){ standardGeneric("runParams") }) |
162 |
+#' @title Get run parameters provided to `celdaGridSearch()` |
|
163 |
+#' @description Returns details on the clustering parameters, model priors, and seeds provided to `celdaGridSearch()` when the provided celdaList was created. |
|
164 |
+#' @param celda.mod An object of class celdaList. |
|
165 |
+#' @return Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList. |
|
166 |
+#' @examples |
|
167 |
+#' runParams(celda.CG.grid.search.res) |
|
168 |
+#' @export |
|
115 | 169 |
setMethod("runParams", |
116 | 170 |
signature=c(celda.mod="celdaList"), |
117 | 171 |
function(celda.mod){ celda.mod@run.params }) |
... | ... |
@@ -119,12 +173,20 @@ setMethod("runParams", |
119 | 173 |
|
120 | 174 |
#' @title Get final celda models from a celdaList |
121 | 175 |
#' @description Returns all models generated during a `celdaGridSearch()` run. |
176 |
+#' @param celda.mod An object of class celdaList. |
|
122 | 177 |
#' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list. |
123 | 178 |
#' @examples |
124 | 179 |
#' celda.CG.grid.models = resList(celda.CG.grid.search.res) |
125 | 180 |
#' @export |
126 | 181 |
setGeneric("resList", |
127 | 182 |
function(celda.mod){ standardGeneric("resList") }) |
183 |
+#' @title Get final celda models from a celdaList |
|
184 |
+#' @description Returns all models generated during a `celdaGridSearch()` run. |
|
185 |
+#' @param celda.mod An object of class celdaList. |
|
186 |
+#' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list. |
|
187 |
+#' @examples |
|
188 |
+#' celda.CG.grid.models = resList(celda.CG.grid.search.res) |
|
189 |
+#' @export |
|
128 | 190 |
setMethod("resList", |
129 | 191 |
signature=c(celda.mod="celdaList"), |
130 | 192 |
function(celda.mod){ celda.mod@res.list }) |
... | ... |
@@ -132,12 +194,19 @@ setMethod("resList", |
132 | 194 |
|
133 | 195 |
#' @title Get perplexity for every model in a celdaList |
134 | 196 |
#' @description Returns perplexity for each model in a celdaList as calculated by `perplexity().` |
197 |
+#' @param celda.mod A celda model object of class "celda_C", "celda_G", or "celda_CG". |
|
135 | 198 |
#' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list. |
136 | 199 |
#' @examples |
137 | 200 |
#' celda.CG.grid.model.perplexities = celdaPerplexity(celda.CG.grid.search.res) |
138 | 201 |
#' @export |
139 | 202 |
setGeneric("celdaPerplexity", |
140 | 203 |
function(celda.mod){ standardGeneric("celdaPerplexity") }) |
204 |
+#' @title Get perplexity for every model in a celdaList |
|
205 |
+#' @description Returns perplexity for each model in a celdaList as calculated by `perplexity().` |
|
206 |
+#' @return List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list. |
|
207 |
+#' @examples |
|
208 |
+#' celda.CG.grid.model.perplexities = celdaPerplexity(celda.CG.grid.search.res) |
|
209 |
+#' @export |
|
141 | 210 |
setMethod("celdaPerplexity", |
142 | 211 |
signature=c(celda.mod="celdaList"), |
143 | 212 |
function(celda.mod){ celda.mod@perplexity }) |
... | ... |
@@ -146,6 +215,8 @@ setMethod("celdaPerplexity", |
146 | 215 |
#' @title Append two celdaList objects |
147 | 216 |
#' @description Returns a single celdaList representing the combination of two provided celdaList objects. |
148 | 217 |
#' @return A celdaList object. This object contains all resList entries and runParam records from both lists. |
218 |
+#' @param list1 A celda_list object |
|
219 |
+#' @param list2 A celda_list object to be joined with list_1 |
|
149 | 220 |
#' @examples |
150 | 221 |
#' appended.list = appendCeldaList(celda.CG.grid.search.res, celda.CG.grid.search.res) |
151 | 222 |
#' @export |
... | ... |
@@ -172,7 +243,8 @@ appendCeldaList = function(list1, list2) { |
172 | 243 |
#' Render a stylable heatmap of count data based on celda clustering results. |
173 | 244 |
#' |
174 | 245 |
#' @param counts Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`. |
175 |
-#' @param celda.mod Celda object of class "celda_C", "celda_G", or "celda_CG". |
|
246 |
+#' @param celda.mod A celda model object of class "celda_C", "celda_G", or "celda_CG". |
|
247 |
+#' @param feature.ix Integer vector. Select features for display in heatmap. If NULL, no subsetting will be performed. Default NULL. |
|
176 | 248 |
#' @param ... Additional parameters. |
177 | 249 |
#' @examples |
178 | 250 |
#' celdaHeatmap(celda.CG.sim$counts, celda.CG.mod) |
... | ... |
@@ -212,13 +284,14 @@ logLikelihood = function(counts, model, ...) { |
212 | 284 |
#' @param counts Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`. |
213 | 285 |
#' @param celda.mod Celda model. Options available in `celda::available.models`. |
214 | 286 |
#' @param log Logical. If FALSE, then the normalized conditional probabilities will be returned. If TRUE, then the unnormalized log probabilities will be returned. Default FALSE. |
287 |
+#' @param ... Additional parameters. |
|
215 | 288 |
#' @examples |
216 | 289 |
#' cluster.prob = clusterProbability(celda.CG.sim$counts, celda.CG.mod) |
217 | 290 |
#' @return A numeric vector of the cluster assignment probabilties |
218 | 291 |
#' @export |
219 | 292 |
setGeneric("clusterProbability", |
220 | 293 |
signature="celda.mod", |
221 |
- function(counts, celda.mod, log=FALSE, modules=NULL, ...) { |
|
294 |
+ function(counts, celda.mod, log=FALSE, ...) { |
|
222 | 295 |
standardGeneric("clusterProbability") |
223 | 296 |
}) |
224 | 297 |
|
... | ... |
@@ -229,7 +302,7 @@ setGeneric("clusterProbability", |
229 | 302 |
#' cluster assignments fit the data being clustered. |
230 | 303 |
#' |
231 | 304 |
#' @param counts Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`. |
232 |
-#' @param celda.mod Celda object of class "celda_C", "celda_G" or "celda_CG". |
|
305 |
+#' @param celda.mod Celda model. Options available in `celda::available.models`. |
|
233 | 306 |
#' @param new.counts A new counts matrix used to calculate perplexity. If NULL, perplexity will be calculated for the 'counts' matrix. Default NULL. |
234 | 307 |
#' @return Numeric. The perplexity for the provided count data and model. |
235 | 308 |
#' @examples |
... | ... |
@@ -308,7 +381,6 @@ setGeneric("celdaProbabilityMap", |
308 | 381 |
#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500. |
309 | 382 |
#' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made. |
310 | 383 |
#' @param ... Additional parameters. |
311 |
-#' @param ... Additional parameters. |
|
312 | 384 |
#' @return Numeric Matrix of dimension `ncol(counts)` x 2, colums representing the "X" and "Y" coordinates in the data's t-SNE represetation. |
313 | 385 |
#' @examples |
314 | 386 |
#' tsne.res = celdaTsne(celda.CG.sim$counts, celda.CG.mod) |
... | ... |
@@ -331,10 +403,7 @@ setGeneric("celdaTsne", |
331 | 403 |
#' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > max.cells. Larger numbers of cells requires more memory. Default 25000. |
332 | 404 |
#' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. |
333 | 405 |
#' @param modules Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL. |
334 |
-#' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20. |
|
335 |
-#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500. |
|
336 |
-#' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made. |
|
337 |
-#' @param ... Additional parameters. |
|
406 |
+#' @param umap.config An object of class "umap.config" specifying parameters to the UMAP algorithm. |
|
338 | 407 |
#' @param ... Additional parameters. |
339 | 408 |
#' @return Numeric Matrix of dimension `ncol(counts)` x 2, colums representing the "X" and "Y" coordinates in the data's t-SNE represetation. |
340 | 409 |
#' @examples |
... | ... |
@@ -421,6 +421,7 @@ cC.calcLL = function(m.CP.by.S, n.G.by.CP, s, z, K, nS, nG, alpha, beta) { |
421 | 421 |
#' @description Calculates the log likelihood for user-provided cell population clusters using the `celda_C()` model. |
422 | 422 |
#' |
423 | 423 |
#' @param counts Integer matrix. Rows represent features and columns represent cells. |
424 |
+#' @param model An object of class celda_C. |
|
424 | 425 |
#' @param sample.label Vector or factor. Denotes the sample label for each cell (column) in the count matrix. |
425 | 426 |
#' @param z Numeric vector. Denotes cell population labels. |
426 | 427 |
#' @param K Integer. Number of cell populations. |
... | ... |
@@ -605,6 +606,7 @@ setMethod("celdaHeatmap", |
605 | 606 |
#' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > max.cells. Larger numbers of cells requires more memory. Default 25000. |
606 | 607 |
#' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. |
607 | 608 |
#' @param initial.dims Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20. |
609 |
+#' @param modules Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL. |
|
608 | 610 |
#' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20. |
609 | 611 |
#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500. |
610 | 612 |
#' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made. |
... | ... |
@@ -641,10 +643,8 @@ setMethod("celdaTsne", |
641 | 643 |
#' @param celda.mod Celda object of class `celda_C`. |
642 | 644 |
#' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > max.cells. Larger numbers of cells requires more memory. Default 25000. |
643 | 645 |
#' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. |
644 |
-#' @param initial.dims Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20. |
|
645 |
-#' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20. |
|
646 |
-#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500. |
|
647 |
-#' @param seed Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made. |
|
646 |
+#' @param modules Integer vector. Determines which features modules to use for UMAP. If NULL, all modules will be used. Default NULL. |
|
647 |
+#' @param umap.config An object of class "umap.config" specifying parameters to the UMAP algorithm. |
|
648 | 648 |
#' @param ... Additional parameters. |
649 | 649 |
#' @seealso `celda_C()` for clustering cells and `celdaHeatmap()` for displaying expression |
650 | 650 |
#' @examples |
... | ... |
@@ -731,6 +731,7 @@ setMethod("celdaHeatmap", |
731 | 731 |
#' @param celda.mod Celda object of class `celda_CG`. |
732 | 732 |
#' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > max.cells. Larger numbers of cells requires more memory. Default 25000. |
733 | 733 |
#' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. |
734 |
+#' @param initial.dims Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20. |
|
734 | 735 |
#' @param modules Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL. |
735 | 736 |
#' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20. |
736 | 737 |
#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500. |
... | ... |
@@ -589,6 +589,8 @@ setMethod("celdaHeatmap", |
589 | 589 |
#' @param counts Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`. |
590 | 590 |
#' @param celda.mod Celda object of class `celda_G`. |
591 | 591 |
#' @param max.cells Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(conts) > max.cells. Larger numbers of cells requires more memory. Default 10000. |
592 |
+#' @param min.cluster.size Integer. Do not subsample cell clusters below this threshold. Default 100. |
|
593 |
+#' @param initial.dims Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20. |
|
592 | 594 |
#' @param modules Integer vector. Determines which feature modules to use for tSNE. If NULL, all modules will be used. Default NULL. |
593 | 595 |
#' @param perplexity Numeric. Perplexity parameter for tSNE. Default 20. |
594 | 596 |
#' @param max.iter Integer. Maximum number of iterations in tSNE generation. Default 2500. |
... | ... |
@@ -6,6 +6,11 @@ |
6 | 6 |
\usage{ |
7 | 7 |
appendCeldaList(list1, list2) |
8 | 8 |
} |
9 |
+\arguments{ |
|
10 |
+\item{list1}{A celda_list object} |
|
11 |
+ |
|
12 |
+\item{list2}{A celda_list object to be joined with list_1} |
|
13 |
+} |
|
9 | 14 |
\value{ |
10 | 15 |
A celdaList object. This object contains all resList entries and runParam records from both lists. |
11 | 16 |
} |
12 | 17 |
new file mode 100644 |
... | ... |
@@ -0,0 +1,18 @@ |
1 |
+% Generated by roxygen2: do not edit by hand |
|
2 |
+% Please edit documentation in R/all_generics.R |
|
3 |
+\docType{methods} |
|
4 |
+\name{bestLogLikelihood,celdaModel-method} |
|
5 |
+\alias{bestLogLikelihood,celdaModel-method} |
|
6 |
+\title{Get the log-likelihood} |
|
7 |
+\usage{ |
|
8 |
+\S4method{bestLogLikelihood}{celdaModel}(celda.mod) |
|
9 |
+} |
|
10 |
+\value{ |
|
11 |
+Numeric. The log-likelihood at the final step of Gibbs sampling used to generate the model. |
|
12 |
+} |
|
13 |
+\description{ |
|
14 |
+Retrieves the final log-likelihood from all iterations of Gibbs sampling used to generate a celda model. |
|
15 |
+} |
|
16 |
+\examples{ |
|
17 |
+bestLogLikelihood(celda.CG.mod) |
|
18 |
+} |
... | ... |
@@ -6,7 +6,8 @@ |
6 | 6 |
\usage{ |
7 | 7 |
celdaGridSearch(counts, model, params.test, params.fixed = NULL, |
8 | 8 |
max.iter = 200, nchains = 3, cores = 1, best.only = TRUE, |
9 |
- seed = 12345, verbose = TRUE, logfile.prefix = "Celda") |
|
9 |
+ seed = 12345, perplexity = TRUE, verbose = TRUE, |
|
10 |
+ logfile.prefix = "Celda") |
|
10 | 11 |
} |
11 | 12 |
\arguments{ |
12 | 13 |
\item{counts}{Integer matrix. Rows represent features and columns represent cells.} |
... | ... |
@@ -27,6 +28,8 @@ celdaGridSearch(counts, model, params.test, params.fixed = NULL, |
27 | 28 |
|
28 | 29 |
\item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.} |
29 | 30 |
|
31 |
+\item{perplexity}{Logical. Whether to calculate perplexity for each model. If FALSE, then perplexity can be calculated later with `resamplePerplexity()`. Default TRUE.} |
|
32 |
+ |
|
30 | 33 |
\item{verbose}{Logical. Whether to print log messages during celda chain execution. Default TRUE.} |
31 | 34 |
|
32 | 35 |
\item{logfile.prefix}{Character. Prefix for log files from worker threads and main process. Default "Celda".} |
... | ... |
@@ -9,7 +9,9 @@ celdaHeatmap(counts, celda.mod, feature.ix, ...) |
9 | 9 |
\arguments{ |
10 | 10 |
\item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.} |
11 | 11 |
|
12 |
-\item{celda.mod}{Celda object of class "celda_C", "celda_G", or "celda_CG".} |
|
12 |
+\item{celda.mod}{A celda model object of class "celda_C", "celda_G", or "celda_CG".} |
|
13 |
+ |
|
14 |
+\item{feature.ix}{Integer vector. Select features for display in heatmap. If NULL, no subsetting will be performed. Default NULL.} |
|
13 | 15 |
|
14 | 16 |
\item{...}{Additional parameters.} |
15 | 17 |
} |
16 | 18 |
new file mode 100644 |
... | ... |
@@ -0,0 +1,18 @@ |
1 |
+% Generated by roxygen2: do not edit by hand |
|
2 |
+% Please edit documentation in R/all_generics.R |
|
3 |
+\docType{methods} |
|
4 |
+\name{celdaPerplexity,celdaList-method} |
|
5 |
+\alias{celdaPerplexity,celdaList-method} |
|
6 |
+\title{Get perplexity for every model in a celdaList} |
|
7 |
+\usage{ |
|
8 |
+\S4method{celdaPerplexity}{celdaList}(celda.mod) |
|
9 |
+} |
|
10 |
+\value{ |
|
11 |
+List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list. |
|
12 |
+} |
|
13 |
+\description{ |
|
14 |
+Returns perplexity for each model in a celdaList as calculated by `perplexity().` |
|
15 |
+} |
|
16 |
+\examples{ |
|
17 |
+celda.CG.grid.model.perplexities = celdaPerplexity(celda.CG.grid.search.res) |
|
18 |
+} |
... | ... |
@@ -6,6 +6,9 @@ |
6 | 6 |
\usage{ |
7 | 7 |
celdaPerplexity(celda.mod) |
8 | 8 |
} |
9 |
+\arguments{ |
|
10 |
+\item{celda.mod}{A celda model object of class "celda_C", "celda_G", or "celda_CG".} |
|
11 |
+} |
|
9 | 12 |
\value{ |
10 | 13 |
List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list. |
11 | 14 |
} |
... | ... |
@@ -20,6 +20,8 @@ |
20 | 20 |
|
21 | 21 |
\item{initial.dims}{Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.} |
22 | 22 |
|
23 |
+\item{modules}{Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.} |
|
24 |
+ |
|
23 | 25 |
\item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.} |
24 | 26 |
|
25 | 27 |
\item{max.iter}{Integer. Maximum number of iterations in tSNE generation. Default 2500.} |
... | ... |
@@ -18,6 +18,8 @@ |
18 | 18 |
|
19 | 19 |
\item{min.cluster.size}{Integer. Do not subsample cell clusters below this threshold. Default 100.} |
20 | 20 |
|
21 |
+\item{initial.dims}{Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.} |
|
22 |
+ |
|
21 | 23 |
\item{modules}{Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.} |
22 | 24 |
|
23 | 25 |
\item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.} |
... | ... |
@@ -16,6 +16,10 @@ |
16 | 16 |
|
17 | 17 |
\item{max.cells}{Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(conts) > max.cells. Larger numbers of cells requires more memory. Default 10000.} |
18 | 18 |
|
19 |
+\item{min.cluster.size}{Integer. Do not subsample cell clusters below this threshold. Default 100.} |
|
20 |
+ |
|
21 |
+\item{initial.dims}{Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.} |
|
22 |
+ |
|
19 | 23 |
\item{modules}{Integer vector. Determines which feature modules to use for tSNE. If NULL, all modules will be used. Default NULL.} |
20 | 24 |
|
21 | 25 |
\item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.} |
... | ... |
@@ -25,8 +25,6 @@ celdaTsne(counts, celda.mod, max.cells = 25000, min.cluster.size = 100, |
25 | 25 |
|
26 | 26 |
\item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.} |
27 | 27 |
|
28 |
-\item{...}{Additional parameters.} |
|
29 |
- |
|
30 | 28 |
\item{...}{Additional parameters.} |
31 | 29 |
} |
32 | 30 |
\value{ |
... | ... |
@@ -18,13 +18,9 @@ |
18 | 18 |
|
19 | 19 |
\item{min.cluster.size}{Integer. Do not subsample cell clusters below this threshold. Default 100.} |
20 | 20 |
|
21 |
-\item{initial.dims}{Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.} |
|
21 |
+\item{modules}{Integer vector. Determines which features modules to use for UMAP. If NULL, all modules will be used. Default NULL.} |
|
22 | 22 |
|
23 |
-\item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.} |
|
24 |
- |
|
25 |
-\item{max.iter}{Integer. Maximum number of iterations in tSNE generation. Default 2500.} |
|
26 |
- |
|
27 |
-\item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.} |
|
23 |
+\item{umap.config}{An object of class "umap.config" specifying parameters to the UMAP algorithm.} |
|
28 | 24 |
|
29 | 25 |
\item{...}{Additional parameters.} |
30 | 26 |
} |
... | ... |
@@ -18,13 +18,7 @@ celdaUmap(counts, celda.mod, max.cells = 25000, min.cluster.size = 100, |
18 | 18 |
|
19 | 19 |
\item{modules}{Integer vector. Determines which features modules to use for tSNE. If NULL, all modules will be used. Default NULL.} |
20 | 20 |
|
21 |
-\item{perplexity}{Numeric. Perplexity parameter for tSNE. Default 20.} |
|
22 |
- |
|
23 |
-\item{max.iter}{Integer. Maximum number of iterations in tSNE generation. Default 2500.} |
|
24 |
- |
|
25 |
-\item{seed}{Integer. Passed to `set.seed()`. Default 12345. If NULL, no calls to `set.seed()` are made.} |
|
26 |
- |
|
27 |
-\item{...}{Additional parameters.} |
|
21 |
+\item{umap.config}{An object of class "umap.config" specifying parameters to the UMAP algorithm.} |
|
28 | 22 |
|
29 | 23 |
\item{...}{Additional parameters.} |
30 | 24 |
} |
... | ... |
@@ -6,9 +6,9 @@ |
6 | 6 |
\usage{ |
7 | 7 |
celda_G(counts, L, beta = 1, delta = 1, gamma = 1, stop.iter = 10, |
8 | 8 |
max.iter = 200, split.on.iter = 10, split.on.last = TRUE, |
9 |
- seed = 12345, nchains = 3, y.initialize = c("split", "random"), |
|
10 |
- count.checksum = NULL, y.init = NULL, logfile = NULL, |
|
11 |
- verbose = TRUE) |
|
9 |
+ seed = 12345, nchains = 3, y.initialize = c("split", "random", |
|
10 |
+ "predefined"), count.checksum = NULL, y.init = NULL, |
|
11 |
+ logfile = NULL, verbose = TRUE) |
|
12 | 12 |
} |
13 | 13 |
\arguments{ |
14 | 14 |
\item{counts}{Integer matrix. Rows represent features and columns represent cells.} |
... | ... |
@@ -6,7 +6,7 @@ |
6 | 6 |
\title{Conditional probabilities for cells in subpopulations from a Celda_C model} |
7 | 7 |
\usage{ |
8 | 8 |
\S4method{clusterProbability}{celda_C}(counts, celda.mod, log = FALSE, |
9 |
- modules = NULL, ...) |
|
9 |
+ ...) |
|
10 | 10 |
} |
11 | 11 |
\arguments{ |
12 | 12 |
\item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.} |
... | ... |
@@ -6,7 +6,7 @@ |
6 | 6 |
\title{Conditional probabilities for cells and features from a Celda_CG model} |
7 | 7 |
\usage{ |
8 | 8 |
\S4method{clusterProbability}{celda_CG}(counts, celda.mod, log = FALSE, |
9 |
- modules = NULL, ...) |
|
9 |
+ ...) |
|
10 | 10 |
} |
11 | 11 |
\arguments{ |
12 | 12 |
\item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.} |
... | ... |
@@ -6,7 +6,7 @@ |
6 | 6 |
\title{Conditional probabilities for features in modules from a Celda_G model} |
7 | 7 |
\usage{ |
8 | 8 |
\S4method{clusterProbability}{celda_G}(counts, celda.mod, log = FALSE, |
9 |
- modules = NULL, ...) |
|
9 |
+ ...) |
|
10 | 10 |
} |
11 | 11 |
\arguments{ |
12 | 12 |
\item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.} |
... | ... |
@@ -4,7 +4,7 @@ |
4 | 4 |
\alias{clusterProbability} |
5 | 5 |
\title{Get the probability of the cluster assignments generated during a celda run.} |
6 | 6 |
\usage{ |
7 |
-clusterProbability(counts, celda.mod, log = FALSE, modules = NULL, ...) |
|
7 |
+clusterProbability(counts, celda.mod, log = FALSE, ...) |
|
8 | 8 |
} |
9 | 9 |
\arguments{ |
10 | 10 |
\item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.} |
... | ... |
@@ -12,6 +12,8 @@ clusterProbability(counts, celda.mod, log = FALSE, modules = NULL, ...) |
12 | 12 |
\item{celda.mod}{Celda model. Options available in `celda::available.models`.} |
13 | 13 |
|
14 | 14 |
\item{log}{Logical. If FALSE, then the normalized conditional probabilities will be returned. If TRUE, then the unnormalized log probabilities will be returned. Default FALSE.} |
15 |
+ |
|
16 |
+\item{...}{Additional parameters.} |
|
15 | 17 |
} |
16 | 18 |
\value{ |
17 | 19 |
A numeric vector of the cluster assignment probabilties |
18 | 20 |
new file mode 100644 |
... | ... |
@@ -0,0 +1,21 @@ |
1 |
+% Generated by roxygen2: do not edit by hand |
|
2 |
+% Please edit documentation in R/all_generics.R |
|
3 |
+\docType{methods} |
|
4 |
+\name{clusters,celdaModel-method} |
|
5 |
+\alias{clusters,celdaModel-method} |
|
6 |
+\title{Get clustering outcomes from a celda model} |
|
7 |
+\usage{ |
|
8 |
+\S4method{clusters}{celdaModel}(celda.mod) |
|
9 |
+} |
|
10 |
+\arguments{ |
|
11 |
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.} |
|
12 |
+} |
|
13 |
+\value{ |
|
14 |
+List. Contains z (for celda_C and celda_CG models) and/or y (for celda_G and celda_CG models) |
|
15 |
+} |
|
16 |
+\description{ |
|
17 |
+Returns the z / y results corresponding to the cell / gene cluster labels determined by the provided celda model. |
|
18 |
+} |
|
19 |
+\examples{ |
|
20 |
+clusters(celda.CG.mod) |
|
21 |
+} |
... | ... |
@@ -9,6 +9,8 @@ logLikelihood.celda_C(counts, model, sample.label, z, K, alpha, beta) |
9 | 9 |
\arguments{ |
10 | 10 |
\item{counts}{Integer matrix. Rows represent features and columns represent cells.} |
11 | 11 |
|
12 |
+\item{model}{An object of class celda_C.} |
|
13 |
+ |
|
12 | 14 |
\item{sample.label}{Vector or factor. Denotes the sample label for each cell (column) in the count matrix.} |
13 | 15 |
|
14 | 16 |
\item{z}{Numeric vector. Denotes cell population labels.} |
15 | 17 |
new file mode 100644 |
... | ... |
@@ -0,0 +1,21 @@ |
1 |
+% Generated by roxygen2: do not edit by hand |
|
2 |
+% Please edit documentation in R/all_generics.R |
|
3 |
+\docType{methods} |
|
4 |
+\name{logLikelihoodHistory,celdaModel-method} |
|
5 |
+\alias{logLikelihoodHistory,celdaModel-method} |
|
6 |
+\title{Get log-likelihood history} |
|
7 |
+\usage{ |
|
8 |
+\S4method{logLikelihoodHistory}{celdaModel}(celda.mod) |
|
9 |
+} |
|
10 |
+\arguments{ |
|
11 |
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.} |
|
12 |
+} |
|
13 |
+\value{ |
|
14 |
+Numeric. The log-likelihood at each step of Gibbs sampling used to generate the model. |
|
15 |
+} |
|
16 |
+\description{ |
|
17 |
+Retrieves the complete log-likelihood from all iterations of Gibbs sampling used to generate a celda model. |
|
18 |
+} |
|
19 |
+\examples{ |
|
20 |
+logLikelihoodHistory(celda.CG.mod) |
|
21 |
+} |
12 | 15 |
new file mode 100644 |
... | ... |
@@ -0,0 +1,21 @@ |
1 |
+% Generated by roxygen2: do not edit by hand |
|
2 |
+% Please edit documentation in R/all_generics.R |
|
3 |
+\docType{methods} |
|
4 |
+\name{matrixNames,celdaModel-method} |
|
5 |
+\alias{matrixNames,celdaModel-method} |
|
6 |
+\title{Get feature, cell and sample names from a celda model} |
|
7 |
+\usage{ |
|
8 |
+\S4method{matrixNames}{celdaModel}(celda.mod) |
|
9 |
+} |
|
10 |
+\arguments{ |
|
11 |
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.} |
|
12 |
+} |
|
13 |
+\value{ |
|
14 |
+List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated. |
|
15 |
+} |
|
16 |
+\description{ |
|
17 |
+Retrieves the row, column, and sample names used to generate a celda model. |
|
18 |
+} |
|
19 |
+\examples{ |
|
20 |
+matrixNames(celda.CG.mod) |
|
21 |
+} |
... | ... |
@@ -6,6 +6,9 @@ |
6 | 6 |
\usage{ |
7 | 7 |
matrixNames(celda.mod) |
8 | 8 |
} |
9 |
+\arguments{ |
|
10 |
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.} |
|
11 |
+} |
|
9 | 12 |
\value{ |
10 | 13 |
List. Contains row, column, and sample character vectors corresponding to the values provided when the celda model was generated. |
11 | 14 |
} |
12 | 15 |
new file mode 100644 |
... | ... |
@@ -0,0 +1,21 @@ |
1 |
+% Generated by roxygen2: do not edit by hand |
|
2 |
+% Please edit documentation in R/all_generics.R |
|
3 |
+\docType{methods} |
|
4 |
+\name{params,celdaModel-method} |
|
5 |
+\alias{params,celdaModel-method} |
|
6 |
+\title{Get parameter values provided for celda model creation} |
|
7 |
+\usage{ |
|
8 |
+\S4method{params}{celdaModel}(celda.mod) |
|
9 |
+} |
|
10 |
+\arguments{ |
|
11 |
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.} |
|
12 |
+} |
|
13 |
+\value{ |
|
14 |
+List. Contains the model-specific parameters for the provided celda model object depending on its class. |
|
15 |
+} |
|
16 |
+\description{ |
|
17 |
+Retrieves the K/L, model priors (e.g. alpha, beta), random seed, and count matrix checksum parameters provided during the creation of the provided celda model. |
|
18 |
+} |
|
19 |
+\examples{ |
|
20 |
+params(celda.CG.mod) |
|
21 |
+} |
... | ... |
@@ -9,7 +9,7 @@ perplexity(counts, celda.mod, new.counts = NULL) |
9 | 9 |
\arguments{ |
10 | 10 |
\item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.} |
11 | 11 |
|
12 |
-\item{celda.mod}{Celda object of class "celda_C", "celda_G" or "celda_CG".} |
|
12 |
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.} |
|
13 | 13 |
|
14 | 14 |
\item{new.counts}{A new counts matrix used to calculate perplexity. If NULL, perplexity will be calculated for the 'counts' matrix. Default NULL.} |
15 | 15 |
} |
16 | 16 |
new file mode 100644 |
... | ... |
@@ -0,0 +1,21 @@ |
1 |
+% Generated by roxygen2: do not edit by hand |
|
2 |
+% Please edit documentation in R/all_generics.R |
|
3 |
+\docType{methods} |
|
4 |
+\name{resList,celdaList-method} |
|
5 |
+\alias{resList,celdaList-method} |
|
6 |
+\title{Get final celda models from a celdaList} |
|
7 |
+\usage{ |
|
8 |
+\S4method{resList}{celdaList}(celda.mod) |
|
9 |
+} |
|
10 |
+\arguments{ |
|
11 |
+\item{celda.mod}{An object of class celdaList.} |
|
12 |
+} |
|
13 |
+\value{ |
|
14 |
+List. Contains one celdaModel object for each of the parameters specified in the `runParams()` of the provided celda list. |
|
15 |
+} |
|
16 |
+\description{ |
|
17 |
+Returns all models generated during a `celdaGridSearch()` run. |
|
18 |
+} |
|
19 |
+\examples{ |
|
20 |
+celda.CG.grid.models = resList(celda.CG.grid.search.res) |
|
21 |
+} |
12 | 15 |
new file mode 100644 |
... | ... |
@@ -0,0 +1,21 @@ |
1 |
+% Generated by roxygen2: do not edit by hand |
|
2 |
+% Please edit documentation in R/all_generics.R |
|
3 |
+\docType{methods} |
|
4 |
+\name{runParams,celdaList-method} |
|
5 |
+\alias{runParams,celdaList-method} |
|
6 |
+\title{Get run parameters provided to `celdaGridSearch()`} |
|
7 |
+\usage{ |
|
8 |
+\S4method{runParams}{celdaList}(celda.mod) |
|
9 |
+} |
|
10 |
+\arguments{ |
|
11 |
+\item{celda.mod}{An object of class celdaList.} |
|
12 |
+} |
|
13 |
+\value{ |
|
14 |
+Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList. |
|
15 |
+} |
|
16 |
+\description{ |
|
17 |
+Returns details on the clustering parameters, model priors, and seeds provided to `celdaGridSearch()` when the provided celdaList was created. |
|
18 |
+} |
|
19 |
+\examples{ |
|
20 |
+runParams(celda.CG.grid.search.res) |
|
21 |
+} |
... | ... |
@@ -6,6 +6,9 @@ |
6 | 6 |
\usage{ |
7 | 7 |
runParams(celda.mod) |
8 | 8 |
} |
9 |
+\arguments{ |
|
10 |
+\item{celda.mod}{An object of class celdaList.} |
|
11 |
+} |
|
9 | 12 |
\value{ |
10 | 13 |
Data Frame. Contains details on the various K/L parameters, chain parameters, and final log-likelihoods derived for each model in the provided celdaList. |
11 | 14 |
} |
12 | 15 |
new file mode 100644 |
... | ... |
@@ -0,0 +1,21 @@ |
1 |
+% Generated by roxygen2: do not edit by hand |
|
2 |
+% Please edit documentation in R/all_generics.R |
|
3 |
+\docType{methods} |
|
4 |
+\name{sampleLabel,celdaModel-method} |
|
5 |
+\alias{sampleLabel,celdaModel-method} |
|
6 |
+\title{Get sample labels from a celda model} |
|
7 |
+\usage{ |
|
8 |
+\S4method{sampleLabel}{celdaModel}(celda.mod) |
|
9 |
+} |
|
10 |
+\arguments{ |
|
11 |
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.} |
|
12 |
+} |
|
13 |
+\value{ |
|
14 |
+Character. Contains the sample labels provided at model creation time, or those automatically generated by celda. |
|
15 |
+} |
|
16 |
+\description{ |
|
17 |
+Returns the sample labels for the count matrix provided for generation of a given celda model. |
|
18 |
+} |
|
19 |
+\examples{ |
|
20 |
+sampleLabel(celda.CG.mod) |
|
21 |
+} |
... | ... |
@@ -6,6 +6,9 @@ |
6 | 6 |
\usage{ |
7 | 7 |
sampleLabel(celda.mod) |
8 | 8 |
} |
9 |
+\arguments{ |
|
10 |
+\item{celda.mod}{Celda model. Options available in `celda::available.models`.} |
|
11 |
+} |
|
9 | 12 |
\value{ |
10 | 13 |
Character. Contains the sample labels provided at model creation time, or those automatically generated by celda. |
11 | 14 |
} |