File size: 40,823 Bytes
efec1c4 2181aa4 efec1c4 feeecd0 efec1c4 feeecd0 efec1c4 feeecd0 efec1c4 feeecd0 efec1c4 feeecd0 efec1c4 feeecd0 efec1c4 feeecd0 efec1c4 019165f efec1c4 b36d210 efec1c4 feeecd0 efec1c4 bb217cf efec1c4 feeecd0 efec1c4 3d06203 efec1c4 5fcf2b8 efec1c4 bb217cf efec1c4 5fcf2b8 efec1c4 feeecd0 efec1c4 feeecd0 efec1c4 feeecd0 efec1c4 8c2fae7 efec1c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 |
"""
Geneformer in silico perturber.
Usage:
from geneformer import InSilicoPerturber
isp = InSilicoPerturber(perturb_type="delete",
perturb_rank_shift=None,
genes_to_perturb="all",
combos=0,
anchor_gene=None,
model_type="Pretrained",
num_classes=0,
emb_mode="cell",
cell_emb_style="mean_pool",
filter_data={"cell_type":["cardiomyocyte"]},
cell_states_to_model={"disease":(["dcm"],["ctrl"],["hcm"])},
max_ncells=None,
emb_layer=-1,
forward_batch_size=100,
nproc=4,
save_raw_data=False)
isp.perturb_data("path/to/model",
"path/to/input_data",
"path/to/output_directory",
"output_prefix")
"""
# imports
import itertools as it
import logging
import pickle
import seaborn as sns; sns.set()
import torch
from collections import defaultdict
from datasets import Dataset, load_from_disk
from tqdm.notebook import trange
from transformers import BertForMaskedLM, BertForTokenClassification, BertForSequenceClassification
from .tokenizer import TOKEN_DICTIONARY_FILE
logger = logging.getLogger(__name__)
def quant_layers(model):
layer_nums = []
for name, parameter in model.named_parameters():
if "layer" in name:
layer_nums += [int(name.split("layer.")[1].split(".")[0])]
return int(max(layer_nums))+1
def flatten_list(megalist):
return [item for sublist in megalist for item in sublist]
def forward_pass_single_cell(model, example_cell, layer_to_quant):
example_cell.set_format(type="torch")
input_data = example_cell["input_ids"]
with torch.no_grad():
outputs = model(
input_ids = input_data.to("cuda")
)
emb = torch.squeeze(outputs.hidden_states[layer_to_quant])
del outputs
return emb
def perturb_emb_by_index(emb, indices):
mask = torch.ones(emb.numel(), dtype=torch.bool)
mask[indices] = False
return emb[mask]
def delete_index(example):
indexes = example["perturb_index"]
if len(indexes)>1:
indexes = flatten_list(indexes)
for index in sorted(indexes, reverse=True):
del example["input_ids"][index]
return example
def overexpress_index(example):
indexes = example["perturb_index"]
if len(indexes)>1:
indexes = flatten_list(indexes)
for index in sorted(indexes, reverse=True):
example["input_ids"].insert(0, example["input_ids"].pop(index))
return example
def make_perturbation_batch(example_cell,
perturb_type,
tokens_to_perturb,
anchor_token,
combo_lvl,
num_proc):
if tokens_to_perturb == "all":
if perturb_type in ["overexpress","activate"]:
range_start = 1
elif perturb_type in ["delete","inhibit"]:
range_start = 0
indices_to_perturb = [[i] for i in range(range_start,example_cell["length"][0])]
elif combo_lvl>0 and (anchor_token is not None):
example_input_ids = example_cell["input_ids "][0]
anchor_index = example_input_ids.index(anchor_token[0])
indices_to_perturb = [sorted([anchor_index,i]) if i!=anchor_index else None for i in range(example_cell["length"][0])]
indices_to_perturb = [item for item in indices_to_perturb if item is not None]
else:
example_input_ids = example_cell["input_ids"][0]
indices_to_perturb = [[example_input_ids.index(token)] if token in example_input_ids else None for token in tokens_to_perturb]
indices_to_perturb = [item for item in indices_to_perturb if item is not None]
# create all permutations of combo_lvl of modifiers from tokens_to_perturb
if combo_lvl>0 and (anchor_token is None):
if tokens_to_perturb != "all":
if len(tokens_to_perturb) == combo_lvl+1:
indices_to_perturb = [list(x) for x in it.combinations(indices_to_perturb, combo_lvl+1)]
else:
all_indices = [[i] for i in range(example_cell["length"][0])]
all_indices = [index for index in all_indices if index not in indices_to_perturb]
indices_to_perturb = [[[j for i in indices_to_perturb for j in i], x] for x in all_indices]
length = len(indices_to_perturb)
perturbation_dataset = Dataset.from_dict({"input_ids": example_cell["input_ids"]*length, "perturb_index": indices_to_perturb})
if length<400:
num_proc_i = 1
else:
num_proc_i = num_proc
if perturb_type == "delete":
perturbation_dataset = perturbation_dataset.map(delete_index, num_proc=num_proc_i)
elif perturb_type == "overexpress":
perturbation_dataset = perturbation_dataset.map(overexpress_index, num_proc=num_proc_i)
return perturbation_dataset, indices_to_perturb
# original cell emb removing the respective perturbed gene emb
def make_comparison_batch(original_emb, indices_to_perturb):
all_embs_list = []
for indices in indices_to_perturb:
emb_list = []
start = 0
if len(indices)>1 and isinstance(indices[0],list):
indices = flatten_list(indices)
for i in sorted(indices):
emb_list += [original_emb[start:i]]
start = i+1
emb_list += [original_emb[start:]]
all_embs_list += [torch.cat(emb_list)]
return torch.stack(all_embs_list)
# perturbed cell emb removing the activated/overexpressed/inhibited gene emb
# so that only non-perturbed gene embeddings are compared to each other
# in original or perturbed context
def make_perturbed_remainder_batch(emb_batch, indices_to_remove):
if type(indices_to_remove) == int:
indices_to_keep = [i for i in range(emb_batch.size()[1])]
indices_to_keep.pop(indices_to_remove)
perturbed_remainder_batch = torch.stack([emb[indices_to_keep,:] for emb in emb_batch])
elif type(indices_to_remove) == list:
perturbed_remainder_batch = torch.stack([make_comparison_batch(emb_batch[i],indices_to_remove[i]) for i in range(len(emb_batch))])
return perturbed_remainder_batch
# average embedding position of goal cell states
def get_cell_state_avg_embs(model,
filtered_input_data,
cell_states_to_model,
layer_to_quant,
token_dictionary,
forward_batch_size,
num_proc):
possible_states = [value[0]+value[1]+value[2] for value in cell_states_to_model.values()][0]
state_embs_dict = dict()
for possible_state in possible_states:
state_embs_list = []
def filter_states(example):
return example[list(cell_states_to_model.keys())[0]] in [possible_state]
filtered_input_data_state = filtered_input_data.filter(filter_states, num_proc=num_proc)
total_batch_length = len(filtered_input_data_state)
if ((total_batch_length-1)/forward_batch_size).is_integer():
forward_batch_size = forward_batch_size-1
max_len = max(filtered_input_data_state["length"])
for i in range(0, total_batch_length, forward_batch_size):
max_range = min(i+forward_batch_size, total_batch_length)
state_minibatch = filtered_input_data_state.select([i for i in range(i, max_range)])
state_minibatch.set_format(type="torch")
input_data_minibatch = state_minibatch["input_ids"]
input_data_minibatch = pad_tensor_list(input_data_minibatch, max_len, token_dictionary)
with torch.no_grad():
outputs = model(
input_ids = input_data_minibatch.to("cuda")
)
state_embs_i = outputs.hidden_states[layer_to_quant]
state_embs_list += [state_embs_i]
del outputs
del state_minibatch
del input_data_minibatch
del state_embs_i
torch.cuda.empty_cache()
state_embs_stack = torch.cat(state_embs_list)
avg_state_emb = torch.mean(state_embs_stack,dim=[0,1],keepdim=True)
state_embs_dict[possible_state] = avg_state_emb
return state_embs_dict
# quantify cosine similarity of perturbed vs original or alternate states
def quant_cos_sims(model,
perturb_type,
perturbation_batch,
forward_batch_size,
layer_to_quant,
original_emb,
indices_to_perturb,
cell_states_to_model,
state_embs_dict):
cos = torch.nn.CosineSimilarity(dim=2)
total_batch_length = len(perturbation_batch)
if ((total_batch_length-1)/forward_batch_size).is_integer():
forward_batch_size = forward_batch_size-1
if cell_states_to_model is None:
comparison_batch = make_comparison_batch(original_emb, indices_to_perturb)
cos_sims = []
else:
possible_states = [value[0]+value[1]+value[2] for value in cell_states_to_model.values()][0]
cos_sims_vs_alt_dict = dict(zip(possible_states,[[] for i in range(len(possible_states))]))
for i in range(0, total_batch_length, forward_batch_size):
max_range = min(i+forward_batch_size, total_batch_length)
perturbation_minibatch = perturbation_batch.select([i for i in range(i, max_range)])
perturbation_minibatch.set_format(type="torch")
input_data_minibatch = perturbation_minibatch["input_ids"]
with torch.no_grad():
outputs = model(
input_ids = input_data_minibatch.to("cuda")
)
del input_data_minibatch
del perturbation_minibatch
# cosine similarity between original emb and batch items
if len(indices_to_perturb)>1:
minibatch_emb = torch.squeeze(outputs.hidden_states[layer_to_quant])
else:
minibatch_emb = outputs.hidden_states[layer_to_quant]
if cell_states_to_model is None:
minibatch_comparison = comparison_batch[i:max_range]
if perturb_type == "overexpress":
index_to_remove = 0
minibatch_emb = make_perturbed_remainder_batch(minibatch_emb, index_to_remove)
# elif (perturb_type == "inhibit") or (perturb_type == "activate"):
# index_to_remove = placeholder
# minibatch_emb = make_perturbed_remainder_batch(minibatch_emb, index_to_remove)
cos_sims += [cos(minibatch_emb, minibatch_comparison).to("cpu")]
elif cell_states_to_model is not None:
for state in possible_states:
cos_sims_vs_alt_dict[state] += cos_sim_shift(original_emb, minibatch_emb, state_embs_dict[state])
del outputs
del minibatch_emb
if cell_states_to_model is None:
del minibatch_comparison
torch.cuda.empty_cache()
if cell_states_to_model is None:
cos_sims_stack = torch.cat(cos_sims)
return cos_sims_stack
else:
for state in possible_states:
cos_sims_vs_alt_dict[state] = torch.cat(cos_sims_vs_alt_dict[state])
return cos_sims_vs_alt_dict
# calculate cos sim shift of perturbation with respect to origin and alternative cell
def cos_sim_shift(original_emb, minibatch_emb, alt_emb):
cos = torch.nn.CosineSimilarity(dim=2)
original_emb = torch.mean(original_emb,dim=0,keepdim=True)[None, :]
origin_v_end = cos(original_emb,alt_emb)
perturb_v_end = cos(torch.mean(minibatch_emb,dim=1,keepdim=True),alt_emb)
return [(perturb_v_end-origin_v_end).to("cpu")]
# pad list of tensors and convert to tensor
def pad_tensor_list(tensor_list, dynamic_or_constant, token_dictionary):
pad_token_id = token_dictionary.get("<pad>")
# Determine maximum tensor length
if dynamic_or_constant == "dynamic":
max_len = max([tensor.squeeze().numel() for tensor in tensor_list])
elif type(dynamic_or_constant) == int:
max_len = dynamic_or_constant
else:
logger.warning(
"If padding style is constant, must provide integer value. " \
"Setting padding to max input size 2048.")
# pad all tensors to maximum length
tensor_list = [torch.nn.functional.pad(tensor, pad=(0,
max_len - tensor.numel()),
mode='constant',
value=pad_token_id) for tensor in tensor_list]
# return stacked tensors
return torch.stack(tensor_list)
class InSilicoPerturber:
valid_option_dict = {
"perturb_type": {"delete","overexpress","inhibit","activate"},
"perturb_rank_shift": {None, 1, 2, 3},
"genes_to_perturb": {"all", list},
"combos": {0, 1, 2},
"anchor_gene": {None, str},
"model_type": {"Pretrained","GeneClassifier","CellClassifier"},
"num_classes": {int},
"emb_mode": {"cell","cell_and_gene"},
"cell_emb_style": {"mean_pool"},
"filter_data": {None, dict},
"cell_states_to_model": {None, dict},
"max_ncells": {None, int},
"emb_layer": {-1, 0},
"forward_batch_size": {int},
"nproc": {int},
"save_raw_data": {False, True},
}
def __init__(
self,
perturb_type="delete",
perturb_rank_shift=None,
genes_to_perturb="all",
combos=0,
anchor_gene=None,
model_type="Pretrained",
num_classes=0,
emb_mode="cell",
cell_emb_style="mean_pool",
filter_data=None,
cell_states_to_model=None,
max_ncells=None,
emb_layer=-1,
forward_batch_size=100,
nproc=4,
save_raw_data=False,
token_dictionary_file=TOKEN_DICTIONARY_FILE,
):
"""
Initialize in silico perturber.
Parameters
----------
perturb_type : {"delete","overexpress","inhibit","activate"}
Type of perturbation.
"delete": delete gene from rank value encoding
"overexpress": move gene to front of rank value encoding
"inhibit": move gene to lower quartile of rank value encoding
"activate": move gene to higher quartile of rank value encoding
perturb_rank_shift : None, {1,2,3}
Number of quartiles by which to shift rank of gene.
For example, if perturb_type="activate" and perturb_rank_shift=1:
genes in 4th quartile will move to middle of 3rd quartile.
genes in 3rd quartile will move to middle of 2nd quartile.
genes in 2nd quartile will move to middle of 1st quartile.
genes in 1st quartile will move to front of rank value encoding.
For example, if perturb_type="inhibit" and perturb_rank_shift=2:
genes in 1st quartile will move to middle of 3rd quartile.
genes in 2nd quartile will move to middle of 4th quartile.
genes in 3rd or 4th quartile will move to bottom of rank value encoding.
genes_to_perturb : "all", list
Default is perturbing each gene detected in each cell in the dataset.
Otherwise, may provide a list of ENSEMBL IDs of genes to perturb.
combos : {0,1,2}
Whether to perturb genes individually (0), in pairs (1), or in triplets (2).
anchor_gene : None, str
ENSEMBL ID of gene to use as anchor in combination perturbations.
For example, if combos=1 and anchor_gene="ENSG00000148400":
anchor gene will be perturbed in combination with each other gene.
model_type : {"Pretrained","GeneClassifier","CellClassifier"}
Whether model is the pretrained Geneformer or a fine-tuned gene or cell classifier.
num_classes : int
If model is a gene or cell classifier, specify number of classes it was trained to classify.
For the pretrained Geneformer model, number of classes is 0 as it is not a classifier.
emb_mode : {"cell","cell_and_gene"}
Whether to output impact of perturbation on cell and/or gene embeddings.
cell_emb_style : "mean_pool"
Method for summarizing cell embeddings.
Currently only option is mean pooling of gene embeddings for given cell.
filter_data : None, dict
Default is to use all input data for in silico perturbation study.
Otherwise, dictionary specifying .dataset column name and list of values to filter by.
cell_states_to_model: None, dict
Cell states to model if testing perturbations that achieve goal state change.
Single-item dictionary with key being cell attribute (e.g. "disease").
Value is tuple of three lists indicating start state, goal end state, and alternate possible end states.
If no alternate possible end states, third list should be empty (i.e. the third list should be []).
max_ncells : None, int
Maximum number of cells to test.
If None, will test all cells.
emb_layer : {-1, 0}
Embedding layer to use for quantification.
-1: 2nd to last layer (recommended for pretrained Geneformer)
0: last layer (recommended for cell classifier fine-tuned for disease state)
forward_batch_size : int
Batch size for forward pass.
nproc : int
Number of CPU processes to use.
save_raw_data: {False,True}
Whether to save raw perturbation data for each gene/cell.
token_dictionary_file : Path
Path to pickle file containing token dictionary (Ensembl ID:token).
"""
self.perturb_type = perturb_type
self.perturb_rank_shift = perturb_rank_shift
self.genes_to_perturb = genes_to_perturb
self.combos = combos
self.anchor_gene = anchor_gene
self.model_type = model_type
self.num_classes = num_classes
self.emb_mode = emb_mode
self.cell_emb_style = cell_emb_style
self.filter_data = filter_data
self.cell_states_to_model = cell_states_to_model
self.max_ncells = max_ncells
self.emb_layer = emb_layer
self.forward_batch_size = forward_batch_size
self.nproc = nproc
self.save_raw_data = save_raw_data
self.validate_options()
# load token dictionary (Ensembl IDs:token)
with open(token_dictionary_file, "rb") as f:
self.gene_token_dict = pickle.load(f)
if anchor_gene is None:
self.anchor_token = None
else:
self.anchor_token = [self.gene_token_dict[self.anchor_gene]]
if genes_to_perturb == "all":
self.tokens_to_perturb = "all"
else:
self.tokens_to_perturb = [self.gene_token_dict[gene] for gene in self.genes_to_perturb]
def validate_options(self):
# first disallow options under development
if self.perturb_type in ["inhibit", "activate"]:
logger.error(
f"In silico inhibition and activation currently under developemnt. " \
f"Current valid options for 'perturb_type': 'delete' or 'overexpress'"
)
raise
# confirm arguments are within valid options and compatible with each other
for attr_name,valid_options in self.valid_option_dict.items():
attr_value = self.__dict__[attr_name]
if type(attr_value) not in {list, dict}:
if attr_value in valid_options:
continue
if attr_name in ["anchor_gene"]:
if type(attr_name) in {str}:
continue
valid_type = False
for option in valid_options:
if (option in [int,list,dict]) and isinstance(attr_value, option):
valid_type = True
break
if valid_type:
continue
logger.error(
f"Invalid option for {attr_name}. " \
f"Valid options for {attr_name}: {valid_options}"
)
raise
if self.perturb_type in ["delete","overexpress"]:
if self.perturb_rank_shift is not None:
if self.perturb_type == "delete":
logger.warning(
"perturb_rank_shift set to None. " \
"If perturb type is delete then gene is deleted entirely " \
"rather than shifted by quartile")
elif self.perturb_type == "overexpress":
logger.warning(
"perturb_rank_shift set to None. " \
"If perturb type is overexpress then gene is moved to front " \
"of rank value encoding rather than shifted by quartile")
self.perturb_rank_shift = None
if (self.anchor_gene is not None) and (self.emb_mode == "cell_and_gene"):
self.emb_mode = "cell"
logger.warning(
"emb_mode set to 'cell'. " \
"Currently, analysis with anchor gene " \
"only outputs effect on cell embeddings.")
if self.cell_states_to_model is not None:
if len(self.cell_states_to_model.items()) == 1:
for key,value in self.cell_states_to_model.items():
if (len(value) == 3) and isinstance(value, tuple):
if isinstance(value[0],list) and isinstance(value[1],list) and isinstance(value[2],list):
if len(value[0]) == 1 and len(value[1]) == 1:
all_values = value[0]+value[1]+value[2]
if len(all_values) == len(set(all_values)):
continue
else:
logger.error(
"Cell states to model must be a single-item dictionary with " \
"key being cell attribute (e.g. 'disease') and value being " \
"tuple of three lists indicating start state, goal end state, and alternate possible end states. " \
"Values should all be unique. " \
"For example: {'disease':(['dcm'],['ctrl'],['hcm'])}")
raise
if self.anchor_gene is not None:
self.anchor_gene = None
logger.warning(
"anchor_gene set to None. " \
"Currently, anchor gene not available " \
"when modeling multiple cell states.")
if self.perturb_type in ["inhibit","activate"]:
if self.perturb_rank_shift is None:
logger.error(
"If perturb type is inhibit or activate then " \
"quartile to shift by must be specified.")
raise
if self.filter_data is not None:
for key,value in self.filter_data.items():
if type(value) != list:
self.filter_data[key] = [value]
logger.warning(
"Values in filter_data dict must be lists. " \
f"Changing {key} value to list ([{value}]).")
def perturb_data(self,
model_directory,
input_data_file,
output_directory,
output_prefix):
"""
Perturb genes in input data and save as results in output_directory.
Parameters
----------
model_directory : Path
Path to directory containing model
input_data_file : Path
Path to directory containing .dataset inputs
output_directory : Path
Path to directory where perturbation data will be saved as .csv
output_prefix : str
Prefix for output .dataset
"""
filtered_input_data = self.load_and_filter(input_data_file)
model = self.load_model(model_directory)
layer_to_quant = quant_layers(model)+self.emb_layer
if self.cell_states_to_model is None:
state_embs_dict = None
else:
# get dictionary of average cell state embeddings for comparison
state_embs_dict = get_cell_state_avg_embs(model,
filtered_input_data,
self.cell_states_to_model,
layer_to_quant,
self.gene_token_dict,
self.forward_batch_size,
self.nproc)
# filter for start state cells
start_state = list(self.cell_states_to_model.values())[0][0][0]
def filter_for_origin(example):
return example[list(self.cell_states_to_model.keys())[0]] in [start_state]
filtered_input_data = filtered_input_data.filter(filter_for_origin, num_proc=self.nproc)
self.in_silico_perturb(model,
filtered_input_data,
layer_to_quant,
state_embs_dict,
output_directory,
output_prefix)
# if self.save_raw_data is False:
# # delete intermediate dictionaries
# output_dir = os.listdir(output_directory)
# for output_file in output_dir:
# if output_file.endswith("_raw.pickle"):
# os.remove(os.path.join(output_directory, output_file))
# load data and filter by defined criteria
def load_and_filter(self, input_data_file):
data = load_from_disk(input_data_file)
if self.filter_data is not None:
for key,value in self.filter_data.items():
def filter_data_by_criteria(example):
return example[key] in value
data = data.filter(filter_data_by_criteria, num_proc=self.nproc)
if len(data) == 0:
logger.error(
"No cells remain after filtering. Check filtering criteria.")
raise
data_shuffled = data.shuffle(seed=42)
num_cells = len(data_shuffled)
# if max number of cells is defined, then subsample to this max number
if self.max_ncells != None:
num_cells = min(self.max_ncells,num_cells)
data_subset = data_shuffled.select([i for i in range(num_cells)])
# sort dataset with largest cell first to encounter any memory errors earlier
data_sorted = data_subset.sort("length",reverse=True)
return data_sorted
# load model to GPU
def load_model(self, model_directory):
if self.model_type == "Pretrained":
model = BertForMaskedLM.from_pretrained(model_directory,
output_hidden_states=True,
output_attentions=False)
elif self.model_type == "GeneClassifier":
model = BertForTokenClassification.from_pretrained(model_directory,
num_labels=self.num_classes,
output_hidden_states=True,
output_attentions=False)
elif self.model_type == "CellClassifier":
model = BertForSequenceClassification.from_pretrained(model_directory,
num_labels=self.num_classes,
output_hidden_states=True,
output_attentions=False)
# put the model in eval mode for fwd pass
model.eval()
model = model.to("cuda:0")
return model
# determine effect of perturbation on other genes
def in_silico_perturb(self,
model,
filtered_input_data,
layer_to_quant,
state_embs_dict,
output_directory,
output_prefix):
output_path_prefix = f"{output_directory}in_silico_{self.perturb_type}_{output_prefix}_dict_1Kbatch"
# filter dataset for cells that have tokens to be perturbed
if self.anchor_token is not None:
def if_has_tokens_to_perturb(example):
return (len(set(example["input_ids"]).intersection(self.anchor_token))==len(self.anchor_token))
filtered_input_data = filtered_input_data.filter(if_has_tokens_to_perturb, num_proc=self.nproc)
logger.info(f"# cells with anchor gene: {len(filtered_input_data)}")
if self.tokens_to_perturb != "all":
def if_has_tokens_to_perturb(example):
return (len(set(example["input_ids"]).intersection(self.tokens_to_perturb))>self.combos)
filtered_input_data = filtered_input_data.filter(if_has_tokens_to_perturb, num_proc=self.nproc)
cos_sims_dict = defaultdict(list)
pickle_batch = -1
for i in trange(len(filtered_input_data)):
example_cell = filtered_input_data.select([i])
original_emb = forward_pass_single_cell(model, example_cell, layer_to_quant)
gene_list = torch.squeeze(example_cell["input_ids"])
# reset to original type to prevent downstream issues due to forward_pass_single_cell modifying as torch format in place
example_cell = filtered_input_data.select([i])
if self.anchor_token is None:
for combo_lvl in range(self.combos+1):
perturbation_batch, indices_to_perturb = make_perturbation_batch(example_cell,
self.perturb_type,
self.tokens_to_perturb,
self.anchor_token,
combo_lvl,
self.nproc)
cos_sims_data = quant_cos_sims(model,
self.perturb_type,
perturbation_batch,
self.forward_batch_size,
layer_to_quant,
original_emb,
indices_to_perturb,
self.cell_states_to_model,
state_embs_dict)
if self.cell_states_to_model is None:
# update cos sims dict
# key is tuple of (perturbed_gene, affected_gene)
# or (perturbed_gene, "cell_emb") for avg cell emb change
cos_sims_data = cos_sims_data.to("cuda")
for j in range(cos_sims_data.shape[0]):
if self.genes_to_perturb != "all":
j_index = torch.tensor(indices_to_perturb[j])
if j_index.shape[0]>1:
j_index = torch.squeeze(j_index)
else:
j_index = torch.tensor([j])
perturbed_gene = torch.index_select(gene_list, 0, j_index)
if perturbed_gene.shape[0]==1:
perturbed_gene = perturbed_gene.item()
elif perturbed_gene.shape[0]>1:
perturbed_gene = tuple(perturbed_gene.tolist())
cell_cos_sim = torch.mean(cos_sims_data[j]).item()
cos_sims_dict[(perturbed_gene, "cell_emb")] += [cell_cos_sim]
# not_j_index = list(set(i for i in range(gene_list.shape[0])).difference(j_index))
# gene_list_j = torch.index_select(gene_list, 0, j_index)
if self.emb_mode == "cell_and_gene":
for k in range(cos_sims_data.shape[1]):
cos_sim_value = cos_sims_data[j][k]
affected_gene = gene_list[k].item()
cos_sims_dict[(perturbed_gene, affected_gene)] += [cos_sim_value.item()]
else:
# update cos sims dict
# key is tuple of (perturbed_gene, "cell_emb")
# value is list of tuples of cos sims for cell_states_to_model
origin_state_key = [value[0] for value in self.cell_states_to_model.values()][0][0]
cos_sims_origin = cos_sims_data[origin_state_key]
for j in range(cos_sims_origin.shape[0]):
if (self.genes_to_perturb != "all") or (combo_lvl>0):
j_index = torch.tensor(indices_to_perturb[j])
if j_index.shape[0]>1:
j_index = torch.squeeze(j_index)
else:
j_index = torch.tensor([j])
perturbed_gene = torch.index_select(gene_list, 0, j_index)
if perturbed_gene.shape[0]==1:
perturbed_gene = perturbed_gene.item()
elif perturbed_gene.shape[0]>1:
perturbed_gene = tuple(perturbed_gene.tolist())
data_list = []
for data in list(cos_sims_data.values()):
data_item = data.to("cuda")
cell_data = torch.mean(data_item[j]).item()
data_list += [cell_data]
cos_sims_dict[(perturbed_gene, "cell_emb")] += [tuple(data_list)]
elif self.anchor_token is not None:
perturbation_batch, indices_to_perturb = make_perturbation_batch(example_cell,
self.perturb_type,
self.tokens_to_perturb,
None, # first run without anchor token to test individual gene perturbations
0,
self.nproc)
cos_sims_data = quant_cos_sims(model,
self.perturb_type,
perturbation_batch,
self.forward_batch_size,
layer_to_quant,
original_emb,
indices_to_perturb,
self.cell_states_to_model,
state_embs_dict)
cos_sims_data = cos_sims_data.to("cuda")
combo_perturbation_batch, combo_indices_to_perturb = make_perturbation_batch(example_cell,
self.perturb_type,
self.tokens_to_perturb,
self.anchor_token,
1,
self.nproc)
combo_cos_sims_data = quant_cos_sims(model,
self.perturb_type,
combo_perturbation_batch,
self.forward_batch_size,
layer_to_quant,
original_emb,
combo_indices_to_perturb,
self.cell_states_to_model,
state_embs_dict)
combo_cos_sims_data = combo_cos_sims_data.to("cuda")
# update cos sims dict
# key is tuple of (perturbed_gene, "cell_emb") for avg cell emb change
anchor_index = example_cell["input_ids"][0].index(self.anchor_token[0])
anchor_cell_cos_sim = torch.mean(cos_sims_data[anchor_index]).item()
non_anchor_indices = [k for k in range(cos_sims_data.shape[0]) if k != anchor_index]
cos_sims_data = cos_sims_data[non_anchor_indices,:]
for j in range(cos_sims_data.shape[0]):
if j<anchor_index:
j_index = torch.tensor([j])
else:
j_index = torch.tensor([j+1])
perturbed_gene = torch.index_select(gene_list, 0, j_index)
perturbed_gene = perturbed_gene.item()
cell_cos_sim = torch.mean(cos_sims_data[j]).item()
combo_cos_sim = torch.mean(combo_cos_sims_data[j]).item()
cos_sims_dict[(perturbed_gene, "cell_emb")] += [(anchor_cell_cos_sim, # cos sim anchor gene alone
cell_cos_sim, # cos sim deleted gene alone
combo_cos_sim)] # cos sim anchor gene + deleted gene
# save dict to disk every 100 cells
if (i/100).is_integer():
with open(f"{output_path_prefix}{pickle_batch}_raw.pickle", "wb") as fp:
pickle.dump(cos_sims_dict, fp)
# reset and clear memory every 1000 cells
if (i/1000).is_integer():
pickle_batch = pickle_batch+1
# clear memory
del perturbed_gene
del cos_sims_data
if self.cell_states_to_model is None:
del cell_cos_sim
if self.cell_states_to_model is not None:
del cell_data
del data_list
elif self.anchor_token is None:
if self.emb_mode == "cell_and_gene":
del affected_gene
del cos_sim_value
else:
del combo_cos_sim
del combo_cos_sims_data
# reset dict
del cos_sims_dict
cos_sims_dict = defaultdict(list)
torch.cuda.empty_cache()
# save remainder cells
with open(f"{output_path_prefix}{pickle_batch}_raw.pickle", "wb") as fp:
pickle.dump(cos_sims_dict, fp)
|