""" 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={"state_key": "disease", "start_state": "dcm", "goal_state": "nf", "alt_states": ["hcm", "other1", "other2"]}, max_ncells=None, emb_layer=-1, forward_batch_size=100, nproc=4) isp.perturb_data("path/to/model", "path/to/input_data", "path/to/output_directory", "output_prefix") """ # imports import itertools as it import logging import numpy as np import pickle import re 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__) # load data and filter by defined criteria def load_and_filter(filter_data, nproc, input_data_file): data = load_from_disk(input_data_file) if filter_data is not None: for key,value in filter_data.items(): def filter_data_by_criteria(example): return example[key] in value data = data.filter(filter_data_by_criteria, num_proc=nproc) if len(data) == 0: logger.error( "No cells remain after filtering. Check filtering criteria.") raise data_shuffled = data.shuffle(seed=42) return data_shuffled # load model to GPU def load_model(model_type, num_classes, model_directory): if model_type == "Pretrained": model = BertForMaskedLM.from_pretrained(model_directory, output_hidden_states=True, output_attentions=False) elif model_type == "GeneClassifier": model = BertForTokenClassification.from_pretrained(model_directory, num_labels=num_classes, output_hidden_states=True, output_attentions=False) elif model_type == "CellClassifier": model = BertForSequenceClassification.from_pretrained(model_directory, num_labels=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 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 get_model_input_size(model): return int(re.split("\(|,",str(model.bert.embeddings.position_embeddings))[1]) def flatten_list(megalist): return [item for sublist in megalist for item in sublist] def measure_length(example): example["length"] = len(example["input_ids"]) return example def downsample_and_sort(data_shuffled, max_ncells): num_cells = len(data_shuffled) # if max number of cells is defined, then subsample to this max number if max_ncells != None: num_cells = min(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 def get_possible_states(cell_states_to_model): possible_states = [] for key in ["start_state","goal_state"]: possible_states += [cell_states_to_model[key]] possible_states += cell_states_to_model.get("alt_states",[]) return possible_states 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_indices(example): indices = example["perturb_index"] if any(isinstance(el, list) for el in indices): indices = flatten_list(indices) for index in sorted(indices, reverse=True): del example["input_ids"][index] return example # for genes_to_perturb = "all" where only genes within cell are overexpressed def overexpress_indices(example): indices = example["perturb_index"] if any(isinstance(el, list) for el in indices): indices = flatten_list(indices) for index in sorted(indices, reverse=True): example["input_ids"].insert(0, example["input_ids"].pop(index)) return example # for genes_to_perturb = list of genes to overexpress that are not necessarily expressed in cell def overexpress_tokens(example): # -100 indicates tokens to overexpress are not present in rank value encoding if example["perturb_index"] != [-100]: example = delete_indices(example) [example["input_ids"].insert(0, token) for token in example["tokens_to_perturb"][::-1]] return example def remove_indices_from_emb(emb, indices_to_remove, gene_dim): # indices_to_remove is list of indices to remove indices_to_keep = [i for i in range(emb.size()[gene_dim]) if i not in indices_to_remove] num_dims = emb.dim() emb_slice = [slice(None) if dim != gene_dim else indices_to_keep for dim in range(num_dims)] sliced_emb = emb[emb_slice] return sliced_emb def remove_indices_from_emb_batch(emb_batch, list_of_indices_to_remove, gene_dim): output_batch = torch.stack([ remove_indices_from_emb(emb_batch[i, :, :], idx, gene_dim-1) for i, idx in enumerate(list_of_indices_to_remove) ]) return output_batch 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_indices, num_proc=num_proc_i) elif perturb_type == "overexpress": perturbation_dataset = perturbation_dataset.map(overexpress_indices, num_proc=num_proc_i) return perturbation_dataset, indices_to_perturb # 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_comparison_batch(original_emb_batch, indices_to_perturb, perturb_group): all_embs_list = [] # if making comparison batch for multiple perturbations in single cell if perturb_group == False: original_emb_list = [original_emb_batch]*len(indices_to_perturb) # if making comparison batch for single perturbation in multiple cells elif perturb_group == True: original_emb_list = original_emb_batch for i in range(len(original_emb_list)): original_emb = original_emb_list[i] indices = indices_to_perturb[i] if indices == [-100]: all_embs_list += [original_emb[:]] continue emb_list = [] start = 0 if any(isinstance(el, list) for el in indices): 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)] len_set = set([emb.size()[0] for emb in all_embs_list]) if len(len_set) > 1: max_len = max(len_set) all_embs_list = [pad_2d_tensor(emb, None, max_len, 0) for emb in all_embs_list] return torch.stack(all_embs_list) # average embedding position of goal cell states def get_cell_state_avg_embs(model, filtered_input_data, cell_states_to_model, layer_to_quant, pad_token_id, forward_batch_size, num_proc): model_input_size = get_model_input_size(model) possible_states = get_possible_states(cell_states_to_model) state_embs_dict = dict() for possible_state in possible_states: state_embs_list = [] original_lens = [] def filter_states(example): state_key = cell_states_to_model["state_key"] return example[state_key] 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"] original_lens += state_minibatch["length"] input_data_minibatch = pad_tensor_list(input_data_minibatch, max_len, pad_token_id, model_input_size) attention_mask = gen_attention_mask(state_minibatch, max_len) with torch.no_grad(): outputs = model( input_ids = input_data_minibatch.to("cuda"), attention_mask = attention_mask ) 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 attention_mask del state_embs_i torch.cuda.empty_cache() state_embs = torch.cat(state_embs_list) avg_state_emb = mean_nonpadding_embs(state_embs, torch.Tensor(original_lens).to("cuda")) avg_state_emb = torch.mean(avg_state_emb, dim=0, 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, tokens_to_perturb, indices_to_perturb, perturb_group, cell_states_to_model, state_embs_dict, pad_token_id, model_input_size, nproc): 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: if perturb_group == False: # (if perturb_group is True, original_emb is filtered_input_data) comparison_batch = make_comparison_batch(original_emb, indices_to_perturb, perturb_group) cos_sims = [] else: possible_states = get_possible_states(cell_states_to_model) cos_sims_vs_alt_dict = dict(zip(possible_states,[[] for i in range(len(possible_states))])) # measure length of each element in perturbation_batch perturbation_batch = perturbation_batch.map( measure_length, num_proc=nproc ) 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)]) # determine if need to pad or truncate batch minibatch_length_set = set(perturbation_minibatch["length"]) minibatch_lengths = perturbation_minibatch["length"] if (len(minibatch_length_set) > 1) or (max(minibatch_length_set) > model_input_size): needs_pad_or_trunc = True else: needs_pad_or_trunc = False max_len = max(minibatch_length_set) if needs_pad_or_trunc == True: max_len = min(max(minibatch_length_set),model_input_size) def pad_or_trunc_example(example): example["input_ids"] = pad_or_truncate_encoding(example["input_ids"], pad_token_id, max_len) return example perturbation_minibatch = perturbation_minibatch.map(pad_or_trunc_example, num_proc=nproc) perturbation_minibatch.set_format(type="torch") input_data_minibatch = perturbation_minibatch["input_ids"] attention_mask = gen_attention_mask(perturbation_minibatch, max_len) # extract embeddings for perturbation minibatch with torch.no_grad(): outputs = model( input_ids = input_data_minibatch.to("cuda"), attention_mask = attention_mask ) del input_data_minibatch del perturbation_minibatch del attention_mask 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 perturb_type == "overexpress": # remove overexpressed genes to quantify effect on remaining genes if perturb_group == False: overexpressed_to_remove = 1 if perturb_group == True: overexpressed_to_remove = len(tokens_to_perturb) minibatch_emb = minibatch_emb[:,overexpressed_to_remove:,:] # if quantifying single perturbation in multiple different cells, pad original batch and extract embs if perturb_group == True: # pad minibatch of original batch to extract embeddings # truncate to the (model input size - # tokens to overexpress) to ensure comparability # since max input size of perturb batch will be reduced by # tokens to overexpress original_minibatch = original_emb.select([i for i in range(i, max_range)]) original_minibatch_lengths = original_minibatch["length"] original_minibatch_length_set = set(original_minibatch["length"]) if perturb_type == "overexpress": new_max_len = model_input_size - len(tokens_to_perturb) else: new_max_len = model_input_size if (len(original_minibatch_length_set) > 1) or (max(original_minibatch_length_set) > new_max_len): original_max_len = min(max(original_minibatch_length_set),new_max_len) def pad_or_trunc_example(example): example["input_ids"] = pad_or_truncate_encoding(example["input_ids"], pad_token_id, original_max_len) return example original_minibatch = original_minibatch.map(pad_or_trunc_example, num_proc=nproc) original_minibatch.set_format(type="torch") original_input_data_minibatch = original_minibatch["input_ids"] attention_mask = gen_attention_mask(original_minibatch, original_max_len) # extract embeddings for original minibatch with torch.no_grad(): original_outputs = model( input_ids = original_input_data_minibatch.to("cuda"), attention_mask = attention_mask ) del original_input_data_minibatch del original_minibatch del attention_mask if len(indices_to_perturb)>1: original_minibatch_emb = torch.squeeze(original_outputs.hidden_states[layer_to_quant]) else: original_minibatch_emb = original_outputs.hidden_states[layer_to_quant] # embedding dimension of the genes gene_dim = 1 # exclude overexpression due to case when genes are not expressed but being overexpressed if perturb_type != "overexpress": original_minibatch_emb = remove_indices_from_emb_batch(original_minibatch_emb, indices_to_perturb, gene_dim) # cosine similarity between original emb and batch items if cell_states_to_model is None: if perturb_group == False: minibatch_comparison = comparison_batch[i:max_range] elif perturb_group == True: minibatch_comparison = make_comparison_batch(original_minibatch_emb, indices_to_perturb, perturb_group) cos_sims += [cos(minibatch_emb, minibatch_comparison).to("cpu")] elif cell_states_to_model is not None: for state in possible_states: if perturb_group == False: cos_sims_vs_alt_dict[state] += cos_sim_shift(original_emb, minibatch_emb, state_embs_dict[state], perturb_group) elif perturb_group == True: cos_sims_vs_alt_dict[state] += cos_sim_shift(original_minibatch_emb, minibatch_emb, state_embs_dict[state], perturb_group, torch.tensor(original_minibatch_lengths, device="cuda"), torch.tensor(minibatch_lengths, device="cuda")) 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, end_emb, perturb_group, original_minibatch_lengths = None, minibatch_lengths = None): cos = torch.nn.CosineSimilarity(dim=2) if not perturb_group: original_emb = torch.mean(original_emb,dim=0,keepdim=True) original_emb = original_emb[None, :] origin_v_end = torch.squeeze(cos(original_emb, end_emb)) #test else: if original_emb.size() != minibatch_emb.size(): logger.error( f"Embeddings are not the same dimensions. " \ f"original_emb is {original_emb.size()}. " \ f"minibatch_emb is {minibatch_emb.size()}. " ) raise if original_minibatch_lengths is not None: original_emb = mean_nonpadding_embs(original_emb, original_minibatch_lengths) # else: # original_emb = torch.mean(original_emb,dim=1,keepdim=True) end_emb = torch.unsqueeze(end_emb, 1) origin_v_end = cos(original_emb, end_emb) origin_v_end = torch.squeeze(origin_v_end) if minibatch_lengths is not None: perturb_emb = mean_nonpadding_embs(minibatch_emb, minibatch_lengths) else: perturb_emb = torch.mean(minibatch_emb,dim=1,keepdim=True) perturb_v_end = cos(perturb_emb, end_emb) perturb_v_end = torch.squeeze(perturb_v_end) return [(perturb_v_end-origin_v_end).to("cpu")] def pad_list(input_ids, pad_token_id, max_len): input_ids = np.pad(input_ids, (0, max_len-len(input_ids)), mode='constant', constant_values=pad_token_id) return input_ids def pad_tensor(tensor, pad_token_id, max_len): tensor = torch.nn.functional.pad(tensor, pad=(0, max_len - tensor.numel()), mode='constant', value=pad_token_id) return tensor def pad_2d_tensor(tensor, pad_token_id, max_len, dim): if dim == 0: pad = (0, 0, 0, max_len - tensor.size()[dim]) elif dim == 1: pad = (0, max_len - tensor.size()[dim], 0, 0) tensor = torch.nn.functional.pad(tensor, pad=pad, mode='constant', value=pad_token_id) return tensor def pad_or_truncate_encoding(encoding, pad_token_id, max_len): if isinstance(encoding, torch.Tensor): encoding_len = tensor.size()[0] elif isinstance(encoding, list): encoding_len = len(encoding) if encoding_len > max_len: encoding = encoding[0:max_len] elif encoding_len < max_len: if isinstance(encoding, torch.Tensor): encoding = pad_tensor(encoding, pad_token_id, max_len) elif isinstance(encoding, list): encoding = pad_list(encoding, pad_token_id, max_len) return encoding # pad list of tensors and convert to tensor def pad_tensor_list(tensor_list, dynamic_or_constant, pad_token_id, model_input_size): # 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: max_len = model_input_size logger.warning( "If padding style is constant, must provide integer value. " \ f"Setting padding to max input size {model_input_size}.") # pad all tensors to maximum length tensor_list = [pad_tensor(tensor, pad_token_id, max_len) for tensor in tensor_list] # return stacked tensors return torch.stack(tensor_list) def gen_attention_mask(minibatch_encoding, max_len = None): if max_len == None: max_len = max(minibatch_encoding["length"]) original_lens = minibatch_encoding["length"] attention_mask = [[1]*original_len +[0]*(max_len - original_len) if original_len <= max_len else [1]*max_len for original_len in original_lens] return torch.tensor(attention_mask).to("cuda") # get cell embeddings excluding padding def mean_nonpadding_embs(embs, original_lens): # mask based on padding lengths mask = torch.arange(embs.size(1)).unsqueeze(0).to("cuda") < original_lens.unsqueeze(1) # extend mask dimensions to match the embeddings tensor mask = mask.unsqueeze(2).expand_as(embs) # use the mask to zero out the embeddings in padded areas masked_embs = embs * mask.float() # sum and divide by the lengths to get the mean of non-padding embs mean_embs = masked_embs.sum(1) / original_lens.view(-1, 1).float() return mean_embs 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}, "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}, "cell_inds_to_perturb": {"all", dict}, "emb_layer": {-1, 0}, "forward_batch_size": {int}, "nproc": {int}, } 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, cell_inds_to_perturb="all", emb_layer=-1, forward_batch_size=100, nproc=4, 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. If gene list is provided, then perturber will only test perturbing them all together (rather than testing each possible combination of the provided genes). combos : {0,1} Whether to perturb genes individually (0) or in pairs (1). 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. Four-item dictionary with keys: state_key, start_state, goal_state, and alt_states state_key: key specifying name of column in .dataset that defines the start/goal states start_state: value in the state_key column that specifies the start state goal_state: value in the state_key column taht specifies the goal end state alt_states: list of values in the state_key column that specify the alternate end states For example: {"state_key": "disease", "start_state": "dcm", "goal_state": "nf", "alt_states": ["hcm", "other1", "other2"]} max_ncells : None, int Maximum number of cells to test. If None, will test all cells. cell_inds_to_perturb : "all", list Default is perturbing each cell in the dataset. Otherwise, may provide a dict of indices of cells to perturb with keys start_ind and end_ind. start_ind: the first index to perturb. end_ind: the last index to perturb (exclusive). Indices will be selected *after* the filter_data criteria and sorting. Useful for splitting extremely large datasets across separate GPUs. 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. 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 if self.genes_to_perturb == "all": self.perturb_group = False else: self.perturb_group = True if (self.anchor_gene != None) or (self.combos != 0): self.anchor_gene = None self.combos = 0 logger.warning( "anchor_gene set to None and combos set to 0. " \ "If providing list of genes to perturb, " \ "list of genes_to_perturb will be perturbed together, "\ "without anchor gene or combinations.") 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.cell_inds_to_perturb = cell_inds_to_perturb self.emb_layer = emb_layer self.forward_batch_size = forward_batch_size self.nproc = nproc self.validate_options() # load token dictionary (Ensembl IDs:token) with open(token_dictionary_file, "rb") as f: self.gene_token_dict = pickle.load(f) self.pad_token_id = self.gene_token_dict.get("") if self.anchor_gene is None: self.anchor_token = None else: try: self.anchor_token = [self.gene_token_dict[self.anchor_gene]] except KeyError: logger.error( f"Anchor gene {self.anchor_gene} not in token dictionary." ) raise if self.genes_to_perturb == "all": self.tokens_to_perturb = "all" else: missing_genes = [gene for gene in self.genes_to_perturb if gene not in self.gene_token_dict.keys()] if len(missing_genes) == len(self.genes_to_perturb): logger.error( "None of the provided genes to perturb are in token dictionary." ) raise elif len(missing_genes)>0: logger.warning( f"Genes to perturb {missing_genes} are not in token dictionary.") self.tokens_to_perturb = [self.gene_token_dict.get(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( "In silico inhibition and activation currently under development. " \ "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: logger.warning( "The single value dictionary for cell_states_to_model will be " \ "replaced with a dictionary with named keys for start, goal, and alternate states. " \ "Please specify state_key, start_state, goal_state, and alt_states " \ "in the cell_states_to_model dictionary for future use. " \ "For example, cell_states_to_model={" \ "'state_key': 'disease', " \ "'start_state': 'dcm', " \ "'goal_state': 'nf', " \ "'alt_states': ['hcm', 'other1', 'other2']}" ) 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 # reformat to the new named key format state_values = flatten_list(list(self.cell_states_to_model.values())) self.cell_states_to_model = { "state_key": list(self.cell_states_to_model.keys())[0], "start_state": state_values[0][0], "goal_state": state_values[1][0], "alt_states": state_values[2:][0] } elif set(self.cell_states_to_model.keys()) == {"state_key", "start_state", "goal_state", "alt_states"}: if (self.cell_states_to_model["state_key"] is None) \ or (self.cell_states_to_model["start_state"] is None) \ or (self.cell_states_to_model["goal_state"] is None): logger.error( "Please specify 'state_key', 'start_state', and 'goal_state' in cell_states_to_model.") raise if self.cell_states_to_model["start_state"] == self.cell_states_to_model["goal_state"]: logger.error( "All states must be unique.") raise if self.cell_states_to_model["alt_states"] is not None: if type(self.cell_states_to_model["alt_states"]) is not list: logger.error( "self.cell_states_to_model['alt_states'] must be a list (even if it is one element)." ) raise if len(self.cell_states_to_model["alt_states"])!= len(set(self.cell_states_to_model["alt_states"])): logger.error( "All states must be unique.") raise else: logger.error( "cell_states_to_model must only have the following four keys: " \ "'state_key', 'start_state', 'goal_state', 'alt_states'." \ "For example, cell_states_to_model={" \ "'state_key': 'disease', " \ "'start_state': 'dcm', " \ "'goal_state': 'nf', " \ "'alt_states': ['hcm', 'other1', 'other2']}" ) 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}]).") if self.cell_inds_to_perturb != "all": if set(self.cell_inds_to_perturb.keys()) != {"start", "end"}: logger.error( "If cell_inds_to_perturb is a dictionary, keys must be 'start' and 'end'." ) raise if self.cell_inds_to_perturb["start"] < 0 or self.cell_inds_to_perturb["end"] < 0: logger.error( 'cell_inds_to_perturb must be positive.' ) raise 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 batched pickle files output_prefix : str Prefix for output files """ filtered_input_data = load_and_filter(self.filter_data, self.nproc, input_data_file) model = load_model(self.model_type, self.num_classes, model_directory) layer_to_quant = quant_layers(model)+self.emb_layer if self.cell_states_to_model is None: state_embs_dict = None else: # confirm that all states are valid to prevent futile filtering state_name = self.cell_states_to_model["state_key"] state_values = filtered_input_data[state_name] for value in get_possible_states(self.cell_states_to_model): if value not in state_values: logger.error( f"{value} is not present in the dataset's {state_name} attribute.") raise # get dictionary of average cell state embeddings for comparison downsampled_data = downsample_and_sort(filtered_input_data, self.max_ncells) state_embs_dict = get_cell_state_avg_embs(model, downsampled_data, self.cell_states_to_model, layer_to_quant, self.pad_token_id, self.forward_batch_size, self.nproc) # filter for start state cells start_state = self.cell_states_to_model["start_state"] def filter_for_origin(example): return example[state_name] 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) # 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" model_input_size = get_model_input_size(model) # 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) if len(filtered_input_data) == 0: logger.error( "No cells in dataset contain anchor gene.") raise else: logger.info(f"# cells with anchor gene: {len(filtered_input_data)}") if (self.tokens_to_perturb != "all") and (self.perturb_type != "overexpress"): # minimum # genes needed for perturbation test min_genes = len(self.tokens_to_perturb) def if_has_tokens_to_perturb(example): return (len(set(example["input_ids"]).intersection(self.tokens_to_perturb))>=min_genes) filtered_input_data = filtered_input_data.filter(if_has_tokens_to_perturb, num_proc=self.nproc) if len(filtered_input_data) == 0: logger.error( "No cells in dataset contain all genes to perturb as a group.") raise cos_sims_dict = defaultdict(list) pickle_batch = -1 filtered_input_data = downsample_and_sort(filtered_input_data, self.max_ncells) if self.cell_inds_to_perturb != "all": if self.cell_inds_to_perturb["start"] >= len(filtered_input_data): logger.error("cell_inds_to_perturb['start'] is larger than the filtered dataset.") raise if self.cell_inds_to_perturb["end"] > len(filtered_input_data): logger.warning("cell_inds_to_perturb['end'] is larger than the filtered dataset. \ Setting to the end of the filtered dataset.") self.cell_inds_to_perturb["end"] = len(filtered_input_data) filtered_input_data = filtered_input_data.select([i for i in range(self.cell_inds_to_perturb["start"], self.cell_inds_to_perturb["end"])]) # make perturbation batch w/ single perturbation in multiple cells if self.perturb_group == True: def make_group_perturbation_batch(example): example_input_ids = example["input_ids"] example["tokens_to_perturb"] = self.tokens_to_perturb indices_to_perturb = [example_input_ids.index(token) if token in example_input_ids else None for token in self.tokens_to_perturb] indices_to_perturb = [item for item in indices_to_perturb if item is not None] if len(indices_to_perturb) > 0: example["perturb_index"] = indices_to_perturb else: # -100 indicates tokens to overexpress are not present in rank value encoding example["perturb_index"] = [-100] if self.perturb_type == "delete": example = delete_indices(example) elif self.perturb_type == "overexpress": example = overexpress_tokens(example) return example perturbation_batch = filtered_input_data.map(make_group_perturbation_batch, num_proc=self.nproc) indices_to_perturb = perturbation_batch["perturb_index"] cos_sims_data = quant_cos_sims(model, self.perturb_type, perturbation_batch, self.forward_batch_size, layer_to_quant, filtered_input_data, self.tokens_to_perturb, indices_to_perturb, self.perturb_group, self.cell_states_to_model, state_embs_dict, self.pad_token_id, model_input_size, self.nproc) perturbed_genes = tuple(self.tokens_to_perturb) original_lengths = filtered_input_data["length"] if self.cell_states_to_model is None: # update cos sims dict # key is tuple of (perturbed_gene, affected_gene) # or (perturbed_genes, "cell_emb") for avg cell emb change cos_sims_data = cos_sims_data.to("cuda") max_padded_len = cos_sims_data.shape[1] for j in range(cos_sims_data.shape[0]): # remove padding before mean pooling cell embedding original_length = original_lengths[j] gene_list = filtered_input_data[j]["input_ids"] indices_removed = indices_to_perturb[j] padding_to_remove = max_padded_len - (original_length \ - len(self.tokens_to_perturb) \ - len(indices_removed)) nonpadding_cos_sims_data = cos_sims_data[j][:-padding_to_remove] cell_cos_sim = torch.mean(nonpadding_cos_sims_data).item() cos_sims_dict[(perturbed_genes, "cell_emb")] += [cell_cos_sim] if self.emb_mode == "cell_and_gene": for k in range(cos_sims_data.shape[1]): cos_sim_value = nonpadding_cos_sims_data[k] affected_gene = gene_list[k].item() cos_sims_dict[(perturbed_genes, affected_gene)] += [cos_sim_value.item()] else: # update cos sims dict # key is tuple of (perturbed_genes, "cell_emb") # value is list of tuples of cos sims for cell_states_to_model origin_state_key = self.cell_states_to_model["start_state"] cos_sims_origin = cos_sims_data[origin_state_key] for j in range(cos_sims_origin.shape[0]): data_list = [] for data in list(cos_sims_data.values()): data_item = data.to("cuda") data_list += [data_item[j].item()] cos_sims_dict[(perturbed_genes, "cell_emb")] += [tuple(data_list)] with open(f"{output_path_prefix}_raw.pickle", "wb") as fp: pickle.dump(cos_sims_dict, fp) # make perturbation batch w/ multiple perturbations in single cell if self.perturb_group == False: 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, self.tokens_to_perturb, indices_to_perturb, self.perturb_group, self.cell_states_to_model, state_embs_dict, self.pad_token_id, model_input_size, self.nproc) 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.tokens_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 = self.cell_states_to_model["start_state"] cos_sims_origin = cos_sims_data[origin_state_key] for j in range(cos_sims_origin.shape[0]): if (self.tokens_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, self.tokens_to_perturb, indices_to_perturb, self.perturb_group, self.cell_states_to_model, state_embs_dict, self.pad_token_id, model_input_size, self.nproc) 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, self.tokens_to_perturb, combo_indices_to_perturb, self.perturb_group, self.cell_states_to_model, state_embs_dict, self.pad_token_id, model_input_size, self.nproc) 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