Title: | Combinatorial and Statistical Analyses of Rare Events |
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Description: | A custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. <doi:10.1101/2021.10.01.462832>. |
Authors: | Vijay Kumar Pounraja [aut, cre]
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Maintainer: | Vijay Kumar Pounraja <[email protected]> |
License: | MIT + file LICENSE |
Version: | 1.1 |
Built: | 2025-02-12 04:25:37 UTC |
Source: | https://github.com/cran/RareComb |
This function takes a Boolean dataframe as input and analyzes the relationship between input and output variables for the combinations that that include at least a single output variable andmeet all the input criteria specified by the user.
analyze_in_out_simultaneity(boolean_input_mult_df, combo_length, min_output_count, max_output_count, min_indv_threshold, max_freq_threshold, input_format, output_format, pval_filter_threshold, adj_pval_type)
analyze_in_out_simultaneity(boolean_input_mult_df, combo_length, min_output_count, max_output_count, min_indv_threshold, max_freq_threshold, input_format, output_format, pval_filter_threshold, adj_pval_type)
boolean_input_mult_df |
An input Boolean dataframe with multiple input and outcome variables |
combo_length |
The length of the combinations specified by the user |
min_output_count |
Minimum number of output variables present in the combination |
max_output_count |
Maximum number of output variables present in the combination |
min_indv_threshold |
Minimum number of instances that support the combination |
max_freq_threshold |
Maximum fraction of the cohort size that could support a combination (i.e., filter out highly frequent events) |
input_format |
Optional | Naming convention used for input variables (Default = 'Input_') |
output_format |
Optional | Naming convention used for output variables (Default = 'Output_') |
pval_filter_threshold |
Optional | p-value cut-off to use to identify significant combinations (Default = 0.05) |
adj_pval_type |
Optional | Type of multiple testing corrections to use (Default = 'BH'; Alternative option = 'bonferroni') |
A dataframe with the list of multiple-testing adjusted statistically significant combinations along with quantitative measures (frequencies, p-values etc) that support the findings.
Vijay Kumar Pounraja
analyze_in_out_simultaneity(boolean_input_mult_df, 3, 1, 2, 5, 0.25, input_format = 'Input_', output_format = 'Output_', pval_filter_threshold = 0.05, adj_pval_type = 'BH')
analyze_in_out_simultaneity(boolean_input_mult_df, 3, 1, 2, 5, 0.25, input_format = 'Input_', output_format = 'Output_', pval_filter_threshold = 0.05, adj_pval_type = 'BH')
A synthetic dataset containing information about 5000 individuals (rows) and 1000 rare variants (columns).
boolean_input_df
boolean_input_df
A data frame with 5000 rows and 1002 variables:
Unique identifier of the samples
Presence and absense of rare variant 1
Presence and absense of rare variant 2
Presence and absense of rare variant 3
Presence and absense of rare variant 4
Presence and absense of rare variant 5
Presence and absense of rare variant 6
Presence and absense of rare variant 7
Presence and absense of rare variant 8
Presence and absense of rare variant 9
Presence and absense of rare variant 10
Presence and absense of rare variant 11
Presence and absense of rare variant 12
Presence and absense of rare variant 13
Presence and absense of rare variant 14
Presence and absense of rare variant 15
Presence and absense of rare variant 16
Presence and absense of rare variant 17
Presence and absense of rare variant 18
Presence and absense of rare variant 19
Presence and absense of rare variant 20
Presence and absense of rare variant 21
Presence and absense of rare variant 22
Presence and absense of rare variant 23
Presence and absense of rare variant 24
Presence and absense of rare variant 25
Presence and absense of rare variant 26
Presence and absense of rare variant 27
Presence and absense of rare variant 28
Presence and absense of rare variant 29
Presence and absense of rare variant 30
Presence and absense of rare variant 31
Presence and absense of rare variant 32
Presence and absense of rare variant 33
Presence and absense of rare variant 34
Presence and absense of rare variant 35
Presence and absense of rare variant 36
Presence and absense of rare variant 37
Presence and absense of rare variant 38
Presence and absense of rare variant 39
Presence and absense of rare variant 40
Presence and absense of rare variant 41
Presence and absense of rare variant 42
Presence and absense of rare variant 43
Presence and absense of rare variant 44
Presence and absense of rare variant 45
Presence and absense of rare variant 46
Presence and absense of rare variant 47
Presence and absense of rare variant 48
Presence and absense of rare variant 49
Presence and absense of rare variant 50
Presence and absense of rare variant 51
Presence and absense of rare variant 52
Presence and absense of rare variant 53
Presence and absense of rare variant 54
Presence and absense of rare variant 55
Presence and absense of rare variant 56
Presence and absense of rare variant 57
Presence and absense of rare variant 58
Presence and absense of rare variant 59
Presence and absense of rare variant 60
Presence and absense of rare variant 61
Presence and absense of rare variant 62
Presence and absense of rare variant 63
Presence and absense of rare variant 64
Presence and absense of rare variant 65
Presence and absense of rare variant 66
Presence and absense of rare variant 67
Presence and absense of rare variant 68
Presence and absense of rare variant 69
Presence and absense of rare variant 70
Presence and absense of rare variant 71
Presence and absense of rare variant 72
Presence and absense of rare variant 73
Presence and absense of rare variant 74
Presence and absense of rare variant 75
Presence and absense of rare variant 76
Presence and absense of rare variant 77
Presence and absense of rare variant 78
Presence and absense of rare variant 79
Presence and absense of rare variant 80
Presence and absense of rare variant 81
Presence and absense of rare variant 82
Presence and absense of rare variant 83
Presence and absense of rare variant 84
Presence and absense of rare variant 85
Presence and absense of rare variant 86
Presence and absense of rare variant 87
Presence and absense of rare variant 88
Presence and absense of rare variant 89
Presence and absense of rare variant 90
Presence and absense of rare variant 91
Presence and absense of rare variant 92
Presence and absense of rare variant 93
Presence and absense of rare variant 94
Presence and absense of rare variant 95
Presence and absense of rare variant 96
Presence and absense of rare variant 97
Presence and absense of rare variant 98
Presence and absense of rare variant 99
Presence and absense of rare variant 100
Presence and absense of rare variant 101
Presence and absense of rare variant 102
Presence and absense of rare variant 103
Presence and absense of rare variant 104
Presence and absense of rare variant 105
Presence and absense of rare variant 106
Presence and absense of rare variant 107
Presence and absense of rare variant 108
Presence and absense of rare variant 109
Presence and absense of rare variant 110
Presence and absense of rare variant 111
Presence and absense of rare variant 112
Presence and absense of rare variant 113
Presence and absense of rare variant 114
Presence and absense of rare variant 115
Presence and absense of rare variant 116
Presence and absense of rare variant 117
Presence and absense of rare variant 118
Presence and absense of rare variant 119
Presence and absense of rare variant 120
Presence and absense of rare variant 121
Presence and absense of rare variant 122
Presence and absense of rare variant 123
Presence and absense of rare variant 124
Presence and absense of rare variant 125
Presence and absense of rare variant 126
Presence and absense of rare variant 127
Presence and absense of rare variant 128
Presence and absense of rare variant 129
Presence and absense of rare variant 130
Presence and absense of rare variant 131
Presence and absense of rare variant 132
Presence and absense of rare variant 133
Presence and absense of rare variant 134
Presence and absense of rare variant 135
Presence and absense of rare variant 136
Presence and absense of rare variant 137
Presence and absense of rare variant 138
Presence and absense of rare variant 139
Presence and absense of rare variant 140
Presence and absense of rare variant 141
Presence and absense of rare variant 142
Presence and absense of rare variant 143
Presence and absense of rare variant 144
Presence and absense of rare variant 145
Presence and absense of rare variant 146
Presence and absense of rare variant 147
Presence and absense of rare variant 148
Presence and absense of rare variant 149
Presence and absense of rare variant 150
Presence and absense of rare variant 151
Presence and absense of rare variant 152
Presence and absense of rare variant 153
Presence and absense of rare variant 154
Presence and absense of rare variant 155
Presence and absense of rare variant 156
Presence and absense of rare variant 157
Presence and absense of rare variant 158
Presence and absense of rare variant 159
Presence and absense of rare variant 160
Presence and absense of rare variant 161
Presence and absense of rare variant 162
Presence and absense of rare variant 163
Presence and absense of rare variant 164
Presence and absense of rare variant 165
Presence and absense of rare variant 166
Presence and absense of rare variant 167
Presence and absense of rare variant 168
Presence and absense of rare variant 169
Presence and absense of rare variant 170
Presence and absense of rare variant 171
Presence and absense of rare variant 172
Presence and absense of rare variant 173
Presence and absense of rare variant 174
Presence and absense of rare variant 175
Presence and absense of rare variant 176
Presence and absense of rare variant 177
Presence and absense of rare variant 178
Presence and absense of rare variant 179
Presence and absense of rare variant 180
Presence and absense of rare variant 181
Presence and absense of rare variant 182
Presence and absense of rare variant 183
Presence and absense of rare variant 184
Presence and absense of rare variant 185
Presence and absense of rare variant 186
Presence and absense of rare variant 187
Presence and absense of rare variant 188
Presence and absense of rare variant 189
Presence and absense of rare variant 190
Presence and absense of rare variant 191
Presence and absense of rare variant 192
Presence and absense of rare variant 193
Presence and absense of rare variant 194
Presence and absense of rare variant 195
Presence and absense of rare variant 196
Presence and absense of rare variant 197
Presence and absense of rare variant 198
Presence and absense of rare variant 199
Presence and absense of rare variant 200
Presence and absense of rare variant 201
Presence and absense of rare variant 202
Presence and absense of rare variant 203
Presence and absense of rare variant 204
Presence and absense of rare variant 205
Presence and absense of rare variant 206
Presence and absense of rare variant 207
Presence and absense of rare variant 208
Presence and absense of rare variant 209
Presence and absense of rare variant 210
Presence and absense of rare variant 211
Presence and absense of rare variant 212
Presence and absense of rare variant 213
Presence and absense of rare variant 214
Presence and absense of rare variant 215
Presence and absense of rare variant 216
Presence and absense of rare variant 217
Presence and absense of rare variant 218
Presence and absense of rare variant 219
Presence and absense of rare variant 220
Presence and absense of rare variant 221
Presence and absense of rare variant 222
Presence and absense of rare variant 223
Presence and absense of rare variant 224
Presence and absense of rare variant 225
Presence and absense of rare variant 226
Presence and absense of rare variant 227
Presence and absense of rare variant 228
Presence and absense of rare variant 229
Presence and absense of rare variant 230
Presence and absense of rare variant 231
Presence and absense of rare variant 232
Presence and absense of rare variant 233
Presence and absense of rare variant 234
Presence and absense of rare variant 235
Presence and absense of rare variant 236
Presence and absense of rare variant 237
Presence and absense of rare variant 238
Presence and absense of rare variant 239
Presence and absense of rare variant 240
Presence and absense of rare variant 241
Presence and absense of rare variant 242
Presence and absense of rare variant 243
Presence and absense of rare variant 244
Presence and absense of rare variant 245
Presence and absense of rare variant 246
Presence and absense of rare variant 247
Presence and absense of rare variant 248
Presence and absense of rare variant 249
Presence and absense of rare variant 250
Presence and absense of rare variant 251
Presence and absense of rare variant 252
Presence and absense of rare variant 253
Presence and absense of rare variant 254
Presence and absense of rare variant 255
Presence and absense of rare variant 256
Presence and absense of rare variant 257
Presence and absense of rare variant 258
Presence and absense of rare variant 259
Presence and absense of rare variant 260
Presence and absense of rare variant 261
Presence and absense of rare variant 262
Presence and absense of rare variant 263
Presence and absense of rare variant 264
Presence and absense of rare variant 265
Presence and absense of rare variant 266
Presence and absense of rare variant 267
Presence and absense of rare variant 268
Presence and absense of rare variant 269
Presence and absense of rare variant 270
Presence and absense of rare variant 271
Presence and absense of rare variant 272
Presence and absense of rare variant 273
Presence and absense of rare variant 274
Presence and absense of rare variant 275
Presence and absense of rare variant 276
Presence and absense of rare variant 277
Presence and absense of rare variant 278
Presence and absense of rare variant 279
Presence and absense of rare variant 280
Presence and absense of rare variant 281
Presence and absense of rare variant 282
Presence and absense of rare variant 283
Presence and absense of rare variant 284
Presence and absense of rare variant 285
Presence and absense of rare variant 286
Presence and absense of rare variant 287
Presence and absense of rare variant 288
Presence and absense of rare variant 289
Presence and absense of rare variant 290
Presence and absense of rare variant 291
Presence and absense of rare variant 292
Presence and absense of rare variant 293
Presence and absense of rare variant 294
Presence and absense of rare variant 295
Presence and absense of rare variant 296
Presence and absense of rare variant 297
Presence and absense of rare variant 298
Presence and absense of rare variant 299
Presence and absense of rare variant 300
Presence and absense of rare variant 301
Presence and absense of rare variant 302
Presence and absense of rare variant 303
Presence and absense of rare variant 304
Presence and absense of rare variant 305
Presence and absense of rare variant 306
Presence and absense of rare variant 307
Presence and absense of rare variant 308
Presence and absense of rare variant 309
Presence and absense of rare variant 310
Presence and absense of rare variant 311
Presence and absense of rare variant 312
Presence and absense of rare variant 313
Presence and absense of rare variant 314
Presence and absense of rare variant 315
Presence and absense of rare variant 316
Presence and absense of rare variant 317
Presence and absense of rare variant 318
Presence and absense of rare variant 319
Presence and absense of rare variant 320
Presence and absense of rare variant 321
Presence and absense of rare variant 322
Presence and absense of rare variant 323
Presence and absense of rare variant 324
Presence and absense of rare variant 325
Presence and absense of rare variant 326
Presence and absense of rare variant 327
Presence and absense of rare variant 328
Presence and absense of rare variant 329
Presence and absense of rare variant 330
Presence and absense of rare variant 331
Presence and absense of rare variant 332
Presence and absense of rare variant 333
Presence and absense of rare variant 334
Presence and absense of rare variant 335
Presence and absense of rare variant 336
Presence and absense of rare variant 337
Presence and absense of rare variant 338
Presence and absense of rare variant 339
Presence and absense of rare variant 340
Presence and absense of rare variant 341
Presence and absense of rare variant 342
Presence and absense of rare variant 343
Presence and absense of rare variant 344
Presence and absense of rare variant 345
Presence and absense of rare variant 346
Presence and absense of rare variant 347
Presence and absense of rare variant 348
Presence and absense of rare variant 349
Presence and absense of rare variant 350
Presence and absense of rare variant 351
Presence and absense of rare variant 352
Presence and absense of rare variant 353
Presence and absense of rare variant 354
Presence and absense of rare variant 355
Presence and absense of rare variant 356
Presence and absense of rare variant 357
Presence and absense of rare variant 358
Presence and absense of rare variant 359
Presence and absense of rare variant 360
Presence and absense of rare variant 361
Presence and absense of rare variant 362
Presence and absense of rare variant 363
Presence and absense of rare variant 364
Presence and absense of rare variant 365
Presence and absense of rare variant 366
Presence and absense of rare variant 367
Presence and absense of rare variant 368
Presence and absense of rare variant 369
Presence and absense of rare variant 370
Presence and absense of rare variant 371
Presence and absense of rare variant 372
Presence and absense of rare variant 373
Presence and absense of rare variant 374
Presence and absense of rare variant 375
Presence and absense of rare variant 376
Presence and absense of rare variant 377
Presence and absense of rare variant 378
Presence and absense of rare variant 379
Presence and absense of rare variant 380
Presence and absense of rare variant 381
Presence and absense of rare variant 382
Presence and absense of rare variant 383
Presence and absense of rare variant 384
Presence and absense of rare variant 385
Presence and absense of rare variant 386
Presence and absense of rare variant 387
Presence and absense of rare variant 388
Presence and absense of rare variant 389
Presence and absense of rare variant 390
Presence and absense of rare variant 391
Presence and absense of rare variant 392
Presence and absense of rare variant 393
Presence and absense of rare variant 394
Presence and absense of rare variant 395
Presence and absense of rare variant 396
Presence and absense of rare variant 397
Presence and absense of rare variant 398
Presence and absense of rare variant 399
Presence and absense of rare variant 400
Presence and absense of rare variant 401
Presence and absense of rare variant 402
Presence and absense of rare variant 403
Presence and absense of rare variant 404
Presence and absense of rare variant 405
Presence and absense of rare variant 406
Presence and absense of rare variant 407
Presence and absense of rare variant 408
Presence and absense of rare variant 409
Presence and absense of rare variant 410
Presence and absense of rare variant 411
Presence and absense of rare variant 412
Presence and absense of rare variant 413
Presence and absense of rare variant 414
Presence and absense of rare variant 415
Presence and absense of rare variant 416
Presence and absense of rare variant 417
Presence and absense of rare variant 418
Presence and absense of rare variant 419
Presence and absense of rare variant 420
Presence and absense of rare variant 421
Presence and absense of rare variant 422
Presence and absense of rare variant 423
Presence and absense of rare variant 424
Presence and absense of rare variant 425
Presence and absense of rare variant 426
Presence and absense of rare variant 427
Presence and absense of rare variant 428
Presence and absense of rare variant 429
Presence and absense of rare variant 430
Presence and absense of rare variant 431
Presence and absense of rare variant 432
Presence and absense of rare variant 433
Presence and absense of rare variant 434
Presence and absense of rare variant 435
Presence and absense of rare variant 436
Presence and absense of rare variant 437
Presence and absense of rare variant 438
Presence and absense of rare variant 439
Presence and absense of rare variant 440
Presence and absense of rare variant 441
Presence and absense of rare variant 442
Presence and absense of rare variant 443
Presence and absense of rare variant 444
Presence and absense of rare variant 445
Presence and absense of rare variant 446
Presence and absense of rare variant 447
Presence and absense of rare variant 448
Presence and absense of rare variant 449
Presence and absense of rare variant 450
Presence and absense of rare variant 451
Presence and absense of rare variant 452
Presence and absense of rare variant 453
Presence and absense of rare variant 454
Presence and absense of rare variant 455
Presence and absense of rare variant 456
Presence and absense of rare variant 457
Presence and absense of rare variant 458
Presence and absense of rare variant 459
Presence and absense of rare variant 460
Presence and absense of rare variant 461
Presence and absense of rare variant 462
Presence and absense of rare variant 463
Presence and absense of rare variant 464
Presence and absense of rare variant 465
Presence and absense of rare variant 466
Presence and absense of rare variant 467
Presence and absense of rare variant 468
Presence and absense of rare variant 469
Presence and absense of rare variant 470
Presence and absense of rare variant 471
Presence and absense of rare variant 472
Presence and absense of rare variant 473
Presence and absense of rare variant 474
Presence and absense of rare variant 475
Presence and absense of rare variant 476
Presence and absense of rare variant 477
Presence and absense of rare variant 478
Presence and absense of rare variant 479
Presence and absense of rare variant 480
Presence and absense of rare variant 481
Presence and absense of rare variant 482
Presence and absense of rare variant 483
Presence and absense of rare variant 484
Presence and absense of rare variant 485
Presence and absense of rare variant 486
Presence and absense of rare variant 487
Presence and absense of rare variant 488
Presence and absense of rare variant 489
Presence and absense of rare variant 490
Presence and absense of rare variant 491
Presence and absense of rare variant 492
Presence and absense of rare variant 493
Presence and absense of rare variant 494
Presence and absense of rare variant 495
Presence and absense of rare variant 496
Presence and absense of rare variant 497
Presence and absense of rare variant 498
Presence and absense of rare variant 499
Presence and absense of rare variant 500
Disease outcome or phenotype
A synthetic dataset containing information about 5000 individuals (rows) and 1000 rare variants (columns) and 3 outcome variables.
boolean_input_mult_df
boolean_input_mult_df
A data frame with 5000 rows and 1004 variables:
Unique identifier of the samples
Presence and absense of rare variant 1
Presence and absense of rare variant 2
Presence and absense of rare variant 3
Presence and absense of rare variant 4
Presence and absense of rare variant 5
Presence and absense of rare variant 6
Presence and absense of rare variant 7
Presence and absense of rare variant 8
Presence and absense of rare variant 9
Presence and absense of rare variant 10
Presence and absense of rare variant 11
Presence and absense of rare variant 12
Presence and absense of rare variant 13
Presence and absense of rare variant 14
Presence and absense of rare variant 15
Presence and absense of rare variant 16
Presence and absense of rare variant 17
Presence and absense of rare variant 18
Presence and absense of rare variant 19
Presence and absense of rare variant 20
Presence and absense of rare variant 21
Presence and absense of rare variant 22
Presence and absense of rare variant 23
Presence and absense of rare variant 24
Presence and absense of rare variant 25
Presence and absense of rare variant 26
Presence and absense of rare variant 27
Presence and absense of rare variant 28
Presence and absense of rare variant 29
Presence and absense of rare variant 30
Presence and absense of rare variant 31
Presence and absense of rare variant 32
Presence and absense of rare variant 33
Presence and absense of rare variant 34
Presence and absense of rare variant 35
Presence and absense of rare variant 36
Presence and absense of rare variant 37
Presence and absense of rare variant 38
Presence and absense of rare variant 39
Presence and absense of rare variant 40
Presence and absense of rare variant 41
Presence and absense of rare variant 42
Presence and absense of rare variant 43
Presence and absense of rare variant 44
Presence and absense of rare variant 45
Presence and absense of rare variant 46
Presence and absense of rare variant 47
Presence and absense of rare variant 48
Presence and absense of rare variant 49
Presence and absense of rare variant 50
Presence and absense of rare variant 51
Presence and absense of rare variant 52
Presence and absense of rare variant 53
Presence and absense of rare variant 54
Presence and absense of rare variant 55
Presence and absense of rare variant 56
Presence and absense of rare variant 57
Presence and absense of rare variant 58
Presence and absense of rare variant 59
Presence and absense of rare variant 60
Presence and absense of rare variant 61
Presence and absense of rare variant 62
Presence and absense of rare variant 63
Presence and absense of rare variant 64
Presence and absense of rare variant 65
Presence and absense of rare variant 66
Presence and absense of rare variant 67
Presence and absense of rare variant 68
Presence and absense of rare variant 69
Presence and absense of rare variant 70
Presence and absense of rare variant 71
Presence and absense of rare variant 72
Presence and absense of rare variant 73
Presence and absense of rare variant 74
Presence and absense of rare variant 75
Presence and absense of rare variant 76
Presence and absense of rare variant 77
Presence and absense of rare variant 78
Presence and absense of rare variant 79
Presence and absense of rare variant 80
Presence and absense of rare variant 81
Presence and absense of rare variant 82
Presence and absense of rare variant 83
Presence and absense of rare variant 84
Presence and absense of rare variant 85
Presence and absense of rare variant 86
Presence and absense of rare variant 87
Presence and absense of rare variant 88
Presence and absense of rare variant 89
Presence and absense of rare variant 90
Presence and absense of rare variant 91
Presence and absense of rare variant 92
Presence and absense of rare variant 93
Presence and absense of rare variant 94
Presence and absense of rare variant 95
Presence and absense of rare variant 96
Presence and absense of rare variant 97
Presence and absense of rare variant 98
Presence and absense of rare variant 99
Presence and absense of rare variant 100
Presence and absense of rare variant 101
Presence and absense of rare variant 102
Presence and absense of rare variant 103
Presence and absense of rare variant 104
Presence and absense of rare variant 105
Presence and absense of rare variant 106
Presence and absense of rare variant 107
Presence and absense of rare variant 108
Presence and absense of rare variant 109
Presence and absense of rare variant 110
Presence and absense of rare variant 111
Presence and absense of rare variant 112
Presence and absense of rare variant 113
Presence and absense of rare variant 114
Presence and absense of rare variant 115
Presence and absense of rare variant 116
Presence and absense of rare variant 117
Presence and absense of rare variant 118
Presence and absense of rare variant 119
Presence and absense of rare variant 120
Presence and absense of rare variant 121
Presence and absense of rare variant 122
Presence and absense of rare variant 123
Presence and absense of rare variant 124
Presence and absense of rare variant 125
Presence and absense of rare variant 126
Presence and absense of rare variant 127
Presence and absense of rare variant 128
Presence and absense of rare variant 129
Presence and absense of rare variant 130
Presence and absense of rare variant 131
Presence and absense of rare variant 132
Presence and absense of rare variant 133
Presence and absense of rare variant 134
Presence and absense of rare variant 135
Presence and absense of rare variant 136
Presence and absense of rare variant 137
Presence and absense of rare variant 138
Presence and absense of rare variant 139
Presence and absense of rare variant 140
Presence and absense of rare variant 141
Presence and absense of rare variant 142
Presence and absense of rare variant 143
Presence and absense of rare variant 144
Presence and absense of rare variant 145
Presence and absense of rare variant 146
Presence and absense of rare variant 147
Presence and absense of rare variant 148
Presence and absense of rare variant 149
Presence and absense of rare variant 150
Presence and absense of rare variant 151
Presence and absense of rare variant 152
Presence and absense of rare variant 153
Presence and absense of rare variant 154
Presence and absense of rare variant 155
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Disease outcome or phenotype 1
Disease outcome or phenotype 2
Disease outcome or phenotype 3
This function takes a Boolean dataframe as input and quantifies the enrichment in the observed frequency of combinations that meet the criteria specified by the users compared to their corresponding expectation derived under the assumption of independence between the constituent elements of each combination. The function then reports the multiple-testing adjusted significant combinations in which enrichment is observed in cases but not in controls.
compare_enrichment(boolean_input_df, combo_length, min_indv_threshold, max_freq_threshold, input_format, output_format, pval_filter_threshold, adj_pval_type, min_power_threshold, sample_names_ind)
compare_enrichment(boolean_input_df, combo_length, min_indv_threshold, max_freq_threshold, input_format, output_format, pval_filter_threshold, adj_pval_type, min_power_threshold, sample_names_ind)
boolean_input_df |
An input Boolean dataframe with multiple input and a single binary outcome variable |
combo_length |
The length of the combinations specified by the user |
min_indv_threshold |
Minimum number of instances that support the combination |
max_freq_threshold |
Maximum fraction of the cohort size that could support a combination (i.e., filter out highly frequent events) |
input_format |
Optional | Naming convention used for input variables (Default = 'Input_') |
output_format |
Optional | Naming convention used for output variables (Default = 'Output_') |
pval_filter_threshold |
Optional | p-value cut-off to use to identify significant combinations in cases (Default = 0.05) |
adj_pval_type |
Optional | Type of multiple testing corrections to use (Default = 'BH'; Alternative option = 'bonferroni') |
min_power_threshold |
Optional | Minimum statistical power (at 5% sig.threshold) required for significant combinations to be returned in the results (Default = 0.7) |
sample_names_ind |
Optional | Indicator to specify if the output should includes row names that support each significant combination (Default = 'N'; Alternative option = 'Y') |
A dataframe with the list of multiple-testing adjusted statistically significant combinations along with quantitative measures (frequencies, p-values etc) that support the findings.
Vijay Kumar Pounraja
compare_enrichment(boolean_input_df, 3, 5, 0.25, input_format = 'Input_', output_format = 'Output_', adj_pval_type = 'bonferroni', sample_names_ind = 'N')
compare_enrichment(boolean_input_df, 3, 5, 0.25, input_format = 'Input_', output_format = 'Output_', adj_pval_type = 'bonferroni', sample_names_ind = 'N')
This function takes a Boolean dataframe as input and quantifies the enrichment in the observed frequency of combinations that meet the criteria specified by the users compared to their corresponding expectation derived under the assumption of independence between the constituent elements of each combination. The function then reports the multiple-testing adjusted significant combinations in which enrichment is observed in cases and depletion is observed in controls.
compare_enrichment_depletion(boolean_input_df, combo_length, min_indv_threshold, max_freq_threshold, input_format, output_format, pval_filter_threshold, adj_pval_type, min_power_threshold, sample_names_ind)
compare_enrichment_depletion(boolean_input_df, combo_length, min_indv_threshold, max_freq_threshold, input_format, output_format, pval_filter_threshold, adj_pval_type, min_power_threshold, sample_names_ind)
boolean_input_df |
An input Boolean dataframe with multiple input and a single binary outcome variable |
combo_length |
The length of the combinations specified by the user |
min_indv_threshold |
Minimum number of instances that support the combination |
max_freq_threshold |
Maximum fraction of the cohort size that could support a combination (i.e., filter out highly frequent events) |
input_format |
Optional | Naming convention used for input variables (Default = 'Input_') |
output_format |
Optional | Naming convention used for output variables (Default = 'Output_') |
pval_filter_threshold |
Optional | p-value cut-off to use to identify significant combinations in cases (Default = 0.05) |
adj_pval_type |
Optional | Type of multiple testing corrections to use (Default = 'BH'; Alternative option = 'bonferroni') |
min_power_threshold |
Optional | Minimum statistical power (at 5% sig.threshold) required for significant combinations to be returned in the results (Default = 0.7) |
sample_names_ind |
Optional | Indicator to specify if the output should includes row names that support each significant combination (Default = 'N'; Alternative option = 'Y') |
A dataframe with the list of multiple-testing adjusted statistically significant combinations along with quantitative measures (frequencies, p-values etc) that support the findings.
Vijay Kumar Pounraja
compare_enrichment_depletion(boolean_input_df, 3, 5, 0.25, input_format = 'Input_', output_format = 'Output_', adj_pval_type = 'bonferroni', sample_names_ind = 'N')
compare_enrichment_depletion(boolean_input_df, 3, 5, 0.25, input_format = 'Input_', output_format = 'Output_', adj_pval_type = 'bonferroni', sample_names_ind = 'N')
This function takes a Boolean dataframe as input and quantifies the enrichment in the observed frequency of combinations that include at least one of the input variables supplied by the user as well as meet other user-specified criteria compared to their corresponding expectation derived under the assumption of independence between the constituent elements of each combination. The function then reports the combinations in which enrichment is observed in cases but not in controls.
compare_enrichment_modifiers(boolean_input_df, combo_length, min_indv_threshold, max_freq_threshold, primary_input_entities, input_format, output_format, pval_filter_threshold, adj_pval_type, min_power_threshold, sample_names_ind)
compare_enrichment_modifiers(boolean_input_df, combo_length, min_indv_threshold, max_freq_threshold, primary_input_entities, input_format, output_format, pval_filter_threshold, adj_pval_type, min_power_threshold, sample_names_ind)
boolean_input_df |
An input Boolean dataframe with multiple input and a single binary outcome variable |
combo_length |
The length of the combinations specified by the user |
min_indv_threshold |
Minimum number of instances that support the combination |
max_freq_threshold |
Maximum fraction of the cohort size that could support a combination (i.e., filter out highly frequent events) |
primary_input_entities |
List of variables that MUST be part of the combinations identified by the method |
input_format |
Optional | Naming convention used for input variables (Default = 'Input_') |
output_format |
Optional | Naming convention used for output variables (Default = 'Output_') |
pval_filter_threshold |
Optional | p-value cut-off to use to identify significant combinations in cases (Default = 0.05) |
adj_pval_type |
Optional | Type of multiple testing corrections to use (Default = 'BH'; Alternative option = 'bonferroni') |
min_power_threshold |
Optional | Minimum statistical power (at 5% sig.threshold) required for significant combinations to be returned in the results (Default = 0.7) |
sample_names_ind |
Optional | Indicator to specify if the output should includes row names that support each significant combination (Default = 'N'; Alternative option = 'Y') |
A dataframe with the list of multiple-testing adjusted statistically significant combinations along with quantitative measures (frequencies, p-values etc) that support the findings.
Vijay Kumar Pounraja
compare_enrichment_modifiers(boolean_input_df, 2, 4, 0.25, input_format = 'Input_', output_format = 'Output_', primary_input_entities = input_list, adj_pval_type = 'bonferroni', sample_names_ind = 'N')
compare_enrichment_modifiers(boolean_input_df, 2, 4, 0.25, input_format = 'Input_', output_format = 'Output_', primary_input_entities = input_list, adj_pval_type = 'bonferroni', sample_names_ind = 'N')
This function takes a Boolean dataframe as input and compares the observed frequency of combinations that meet the criteria specified by the users with their corresponding expectation derived under the assumption of independence between the constituent elements of each combination
compare_expected_vs_observed(boolean_input_df, combo_length, min_indv_threshold, max_freq_threshold, input_format, pval_filter_threshold, adj_pval_type)
compare_expected_vs_observed(boolean_input_df, combo_length, min_indv_threshold, max_freq_threshold, input_format, pval_filter_threshold, adj_pval_type)
boolean_input_df |
An input Boolean dataframe with multiple input variables |
combo_length |
The length of the combinations specified by the user |
min_indv_threshold |
Minimum number of instances that support the combination |
max_freq_threshold |
Maximum fraction of the cohort size that could support a combination (i.e., filter out highly frequent events) |
input_format |
Optional | Naming convention used for input variables (Default = 'Input_') |
pval_filter_threshold |
Optional | p-value cut-off to use for multiple testing adjustment (Default = 0.05) |
adj_pval_type |
Optional | Type of multiple testing corrections to use (Default = 'BH'; Alternative option = 'bonferroni') |
A dataframe with the list of multiple-testing adjusted statistically significant combinations along with quantitative measures (frequencies, p-values etc) that support the findings.
Vijay Kumar Pounraja
compare_expected_vs_observed(boolean_input_df, 2, 10, 0.25, 0.05, input_format = 'Input_', adj_pval_type = 'BH')
compare_expected_vs_observed(boolean_input_df, 2, 10, 0.25, 0.05, input_format = 'Input_', adj_pval_type = 'BH')
Fetching the frequency of multiple individual elements that make up the combinations of varying length and hence varying variable names or to join two similar data frames using identical variable names necessitates this function that supplements and joins data based on the length of the combinations.
custom_left_join( left_df, right_df, combo_length = combo_length, diff_colnames = diff_colnames )
custom_left_join( left_df, right_df, combo_length = combo_length, diff_colnames = diff_colnames )
left_df |
The data frame with information about the combinations |
right_df |
The data frame with information either about the combinations or their constituent elements |
combo_length |
The length of the combinations specified by the user used to determine the number of successive joins to attempt |
diff_colnames |
Indicator that specifies if the joins are to be made based on same or different column names |
An output dataframe with the results of the join operation
Vijay Kumar Pounraja
A list of 50 random input variables
input_list
input_list
A list of 50 random input variables:
This function takes in a factorized Boolean matrix and generate frequent itemsets that meet all the user provided criteria provided by the calling function.
run_apriori_freqitems( apriori_input_df, combo_length, support_threshold, input_colname_list, confidence_threshold = confidence_threshold, include_output_ind = include_output_ind, output_colname_list = output_colname_list )
run_apriori_freqitems( apriori_input_df, combo_length, support_threshold, input_colname_list, confidence_threshold = confidence_threshold, include_output_ind = include_output_ind, output_colname_list = output_colname_list )
apriori_input_df |
An input factorized Boolean dataframe with multiple input and outcome variables |
combo_length |
The length of the combinations specified by the user |
support_threshold |
Minimum support value calculated based on the minimum absolute observed frequency threshold specified by the user |
input_colname_list |
A list of column names that identify the input variables |
confidence_threshold |
Minimum confidence threshold specified by the user |
include_output_ind |
Specifies if the outcome variables must also be made part of the analysis using the algorithm |
output_colname_list |
A list of column names that identify the outcome variables |
This is a function leveraged by few of the four main methods available to the users.
A list of frequent item sets that meet all the constraints supplied to the apriori algorithm
Vijay Kumar Pounraja
This function takes in a factorized Boolean matrix and generate rules that meet all the user provided criteria while restricting the RHS of the rule based on the list of variables allowed in RHS provided by the calling function.
run_apriori_rules( apriori_input_df, combo_length, support_threshold, input_colname_list, confidence_threshold = confidence_threshold, output_colname_list = output_colname_list )
run_apriori_rules( apriori_input_df, combo_length, support_threshold, input_colname_list, confidence_threshold = confidence_threshold, output_colname_list = output_colname_list )
apriori_input_df |
An input factorized Boolean dataframe with multiple input and outcome variables |
combo_length |
The length of the combinations specified by the user |
support_threshold |
Minimum support value calculated based on the minimum absolute observed frequency threshold specified by the user |
input_colname_list |
A list of column names that identify the input variables |
confidence_threshold |
Minimum confidence threshold specified by the user |
output_colname_list |
Optional | A list of column names that identify the outcome variables |
This is a function leveraged by few of the four main methods available to the users.
A list of rules that meet all the constraints supplied to the apriori algorithm
Vijay Kumar Pounraja
This function takes in a factorized Boolean matrix and generate rules that meet all the user provided criteria while allowing the outcome variables to be part of either LHS or RHS of the rules but restricting the input variables to the LHS of the rules.
run_apriori_rules_inout_simult( apriori_input_df, combo_length, support_threshold, input_colname_list, output_colname_list = output_colname_list )
run_apriori_rules_inout_simult( apriori_input_df, combo_length, support_threshold, input_colname_list, output_colname_list = output_colname_list )
apriori_input_df |
An input factorized Boolean dataframe with multiple input and outcome variables |
combo_length |
The length of the combinations specified by the user |
support_threshold |
Minimum support value calculated based on the minimum absolute observed frequency threshold specified by the user |
input_colname_list |
A list of column names that identify the input variables |
output_colname_list |
Optional | A list of column names that identify the outcome variables |
This is a function leveraged by few of the four main methods available to the users.
A list of rules that meet all the constraints supplied to the apriori algorithm
Vijay Kumar Pounraja
This function takes in a factorized Boolean matrix and generate rules that meet all the user provided criteria while restricting the RHS of the rule based on the list of variables allowed in RHS provided by the calling function.
run_apriori_rules_modifiers( apriori_input_df, combo_length, support_threshold, input_colname_list, output_colname_list = output_colname_list )
run_apriori_rules_modifiers( apriori_input_df, combo_length, support_threshold, input_colname_list, output_colname_list = output_colname_list )
apriori_input_df |
An input factorized Boolean dataframe with multiple input and outcome variables |
combo_length |
The length of the combinations specified by the user |
support_threshold |
Minimum support value calculated based on the minimum absolute observed frequency threshold specified by the user |
input_colname_list |
A list of column names that identify the input variables |
output_colname_list |
Optional | A list of column names that identify the outcome variables |
This is a function leveraged by few of the four main methods available to the users.
A list of rules that meet all the constraints supplied to the apriori algorithm
Vijay Kumar Pounraja
This function takes in a factorized Boolean matrix and generate frequent item sets that meet all the user provided criteria provided by the calling function. This function includes in it's output the identifiers of observations that support each significant combination.
run_apriori_w_sample_names( apriori_input_df, combo_length, support_threshold, input_colname_list, input_sample_list, confidence_threshold = confidence_threshold, include_output_ind = include_output_ind, output_colname_list = output_colname_list )
run_apriori_w_sample_names( apriori_input_df, combo_length, support_threshold, input_colname_list, input_sample_list, confidence_threshold = confidence_threshold, include_output_ind = include_output_ind, output_colname_list = output_colname_list )
apriori_input_df |
An input factorized Boolean dataframe with multiple input and outcome variables |
combo_length |
The length of the combinations specified by the user |
support_threshold |
Minimum support value calculated based on the minimum absolute observed frequency threshold specified by the user |
input_colname_list |
A list of column names that identify the input variables |
input_sample_list |
A list of row names that identify the samples/observations |
confidence_threshold |
Minimum confidence threshold specified by the user |
include_output_ind |
Specifies if the outcome variables must also be made part of the analysis using the algorithm |
output_colname_list |
A list of column names that identify the outcome variables |
This is a function leveraged by few of the four main methods available to the users.
A list of frequent item sets that meet all the constraints supplied to the apriori algorithm
Vijay Kumar Pounraja