""" Geneformer embedding extractor. Usage: from geneformer import EmbExtractor embex = EmbExtractor(model_type="CellClassifier", num_classes=3, emb_mode="cell", cell_emb_style="mean_pool", filter_data={"cell_type":["cardiomyocyte"]}, max_ncells=1000, max_ncells_to_plot=1000, emb_layer=-1, emb_label=["disease","cell_type"], labels_to_plot=["disease","cell_type"], forward_batch_size=100, nproc=16, summary_stat=None) embs = embex.extract_embs("path/to/model", "path/to/input_data", "path/to/output_directory", "output_prefix") embex.plot_embs(embs=embs, plot_style="heatmap", output_directory="path/to/output_directory", output_prefix="output_prefix") """ # imports import logging import anndata import matplotlib.pyplot as plt import numpy as np import pandas as pd import pickle from tdigest import TDigest import scanpy as sc import seaborn as sns import torch from collections import Counter from pathlib import Path from tqdm.auto import trange from transformers import BertForMaskedLM, BertForTokenClassification, BertForSequenceClassification from .tokenizer import TOKEN_DICTIONARY_FILE from .in_silico_perturber import downsample_and_sort, \ gen_attention_mask, \ get_model_input_size, \ load_and_filter, \ load_model, \ mean_nonpadding_embs, \ pad_tensor_list, \ quant_layers logger = logging.getLogger(__name__) # extract embeddings def get_embs(model, filtered_input_data, emb_mode, layer_to_quant, pad_token_id, forward_batch_size, summary_stat): model_input_size = get_model_input_size(model) total_batch_length = len(filtered_input_data) if summary_stat is None: embs_list = [] elif summary_stat is not None: # test embedding extraction for example cell and extract # emb dims example = filtered_input_data.select([i for i in range(1)]) example.set_format(type="torch") emb_dims = test_emb(model, example["input_ids"], layer_to_quant) # initiate tdigests for # of emb dims embs_tdigests = [TDigest() for _ in range(emb_dims)] for i in trange(0, total_batch_length, forward_batch_size): max_range = min(i+forward_batch_size, total_batch_length) minibatch = filtered_input_data.select([i for i in range(i, max_range)]) max_len = max(minibatch["length"]) original_lens = torch.tensor(minibatch["length"]).to("cuda") minibatch.set_format(type="torch") input_data_minibatch = minibatch["input_ids"] input_data_minibatch = pad_tensor_list(input_data_minibatch, max_len, pad_token_id, model_input_size) with torch.no_grad(): outputs = model( input_ids = input_data_minibatch.to("cuda"), attention_mask = gen_attention_mask(minibatch) ) embs_i = outputs.hidden_states[layer_to_quant] if emb_mode == "cell": mean_embs = mean_nonpadding_embs(embs_i, original_lens) if summary_stat is None: embs_list += [mean_embs] elif summary_stat is not None: # update tdigests with current batch for each emb dim # note: tdigest batch update known to be slow so updating serially [embs_tdigests[j].update(mean_embs[i,j].item()) for i in range(mean_embs.size(0)) for j in range(emb_dims)] del outputs del minibatch del input_data_minibatch del embs_i del mean_embs torch.cuda.empty_cache() if summary_stat is None: embs_stack = torch.cat(embs_list) # calculate summary stat embs from approximated tdigests elif summary_stat is not None: if summary_stat == "mean": summary_emb_list = [embs_tdigests[i].trimmed_mean(0,100) for i in range(emb_dims)] elif summary_stat == "median": summary_emb_list = [embs_tdigests[i].percentile(50) for i in range(emb_dims)] embs_stack = torch.tensor(summary_emb_list) return embs_stack def test_emb(model, example, layer_to_quant): with torch.no_grad(): outputs = model( input_ids = example.to("cuda") ) embs_test = outputs.hidden_states[layer_to_quant] return embs_test.size()[2] def label_embs(embs, downsampled_data, emb_labels): embs_df = pd.DataFrame(embs.cpu().numpy()) if emb_labels is not None: for label in emb_labels: emb_label = downsampled_data[label] embs_df[label] = emb_label return embs_df def plot_umap(embs_df, emb_dims, label, output_file, kwargs_dict): only_embs_df = embs_df.iloc[:,:emb_dims] only_embs_df.index = pd.RangeIndex(0, only_embs_df.shape[0], name=None).astype(str) only_embs_df.columns = pd.RangeIndex(0, only_embs_df.shape[1], name=None).astype(str) vars_dict = {"embs": only_embs_df.columns} obs_dict = {"cell_id": list(only_embs_df.index), f"{label}": list(embs_df[label])} adata = anndata.AnnData(X=only_embs_df, obs=obs_dict, var=vars_dict) sc.tl.pca(adata, svd_solver='arpack') sc.pp.neighbors(adata) sc.tl.umap(adata) sns.set(rc={'figure.figsize':(10,10)}, font_scale=2.3) sns.set_style("white") default_kwargs_dict = {"palette":"Set2", "size":200} if kwargs_dict is not None: default_kwargs_dict.update(kwargs_dict) sc.pl.umap(adata, color=label, save=output_file, **default_kwargs_dict) def gen_heatmap_class_colors(labels, df): pal = sns.cubehelix_palette(len(Counter(labels).keys()), light=0.9, dark=0.1, hue=1, reverse=True, start=1, rot=-2) lut = dict(zip(map(str, Counter(labels).keys()), pal)) colors = pd.Series(labels, index=df.index).map(lut) return colors def gen_heatmap_class_dict(classes, label_colors_series): class_color_dict_df = pd.DataFrame({"classes": classes, "color": label_colors_series}) class_color_dict_df = class_color_dict_df.drop_duplicates(subset=["classes"]) return dict(zip(class_color_dict_df["classes"],class_color_dict_df["color"])) def make_colorbar(embs_df, label): labels = list(embs_df[label]) cell_type_colors = gen_heatmap_class_colors(labels, embs_df) label_colors = pd.DataFrame(cell_type_colors, columns=[label]) for i,row in label_colors.iterrows(): colors=row[0] if len(colors)!=3 or any(np.isnan(colors)): print(i,colors) label_colors.isna().sum() # create dictionary for colors and classes label_color_dict = gen_heatmap_class_dict(labels, label_colors[label]) return label_colors, label_color_dict def plot_heatmap(embs_df, emb_dims, label, output_file, kwargs_dict): sns.set_style("white") sns.set(font_scale=2) plt.figure(figsize=(15, 15), dpi=150) label_colors, label_color_dict = make_colorbar(embs_df, label) default_kwargs_dict = {"row_cluster": True, "col_cluster": True, "row_colors": label_colors, "standard_scale": 1, "linewidths": 0, "xticklabels": False, "yticklabels": False, "figsize": (15,15), "center": 0, "cmap": "magma"} if kwargs_dict is not None: default_kwargs_dict.update(kwargs_dict) g = sns.clustermap(embs_df.iloc[:,0:emb_dims].apply(pd.to_numeric), **default_kwargs_dict) plt.setp(g.ax_row_colors.get_xmajorticklabels(), rotation=45, ha="right") for label_color in list(label_color_dict.keys()): g.ax_col_dendrogram.bar(0, 0, color=label_color_dict[label_color], label=label_color, linewidth=0) l1 = g.ax_col_dendrogram.legend(title=f"{label}", loc="lower center", ncol=4, bbox_to_anchor=(0.5, 1), facecolor="white") plt.savefig(output_file, bbox_inches='tight') class EmbExtractor: valid_option_dict = { "model_type": {"Pretrained","GeneClassifier","CellClassifier"}, "num_classes": {int}, "emb_mode": {"cell","gene"}, "cell_emb_style": {"mean_pool"}, "filter_data": {None, dict}, "max_ncells": {None, int}, "emb_layer": {-1, 0}, "emb_label": {None, list}, "labels_to_plot": {None, list}, "forward_batch_size": {int}, "nproc": {int}, "summary_stat": {None, "mean", "median"}, } def __init__( self, model_type="Pretrained", num_classes=0, emb_mode="cell", cell_emb_style="mean_pool", filter_data=None, max_ncells=1000, emb_layer=-1, emb_label=None, labels_to_plot=None, forward_batch_size=100, nproc=4, summary_stat=None, token_dictionary_file=TOKEN_DICTIONARY_FILE, ): """ Initialize embedding extractor. Parameters ---------- 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","gene"} Whether to output cell 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 extract embeddings from all input data. Otherwise, dictionary specifying .dataset column name and list of values to filter by. max_ncells : None, int Maximum number of cells to extract embeddings from. Default is 1000 cells randomly sampled from input data. If None, will extract embeddings from all cells. emb_layer : {-1, 0} Embedding layer to extract. The last layer is most specifically weighted to optimize the given learning objective. Generally, it is best to extract the 2nd to last layer to get a more general representation. -1: 2nd to last layer 0: last layer emb_label : None, list List of column name(s) in .dataset to add as labels to embedding output. labels_to_plot : None, list Cell labels to plot. Shown as color bar in heatmap. Shown as cell color in umap. Plotting umap requires labels to plot. forward_batch_size : int Batch size for forward pass. nproc : int Number of CPU processes to use. summary_stat : {None, "mean", "median"} If not None, outputs only approximated mean or median embedding of input data. Recommended if encountering memory constraints while generating goal embedding positions. Slower but more memory-efficient. token_dictionary_file : Path Path to pickle file containing token dictionary (Ensembl ID:token). """ 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.max_ncells = max_ncells self.emb_layer = emb_layer self.emb_label = emb_label self.labels_to_plot = labels_to_plot self.forward_batch_size = forward_batch_size self.nproc = nproc self.summary_stat = summary_stat 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("") def validate_options(self): # first disallow options under development if self.emb_mode == "gene": logger.error( "Extraction and plotting of gene-level embeddings currently under development. " \ "Current valid option for 'emb_mode': 'cell'" ) 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 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.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 extract_embs(self, model_directory, input_data_file, output_directory, output_prefix, output_torch_embs=False): """ Extract embeddings from 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 embedding data will be saved as csv output_prefix : str Prefix for output file output_torch_embs : bool Whether or not to also output the embeddings as a tensor. Note, if true, will output embeddings as both dataframe and tensor. """ filtered_input_data = load_and_filter(self.filter_data, self.nproc, input_data_file) downsampled_data = downsample_and_sort(filtered_input_data, self.max_ncells) model = load_model(self.model_type, self.num_classes, model_directory) layer_to_quant = quant_layers(model)+self.emb_layer embs = get_embs(model, downsampled_data, self.emb_mode, layer_to_quant, self.pad_token_id, self.forward_batch_size, self.summary_stat) if self.summary_stat is None: embs_df = label_embs(embs, downsampled_data, self.emb_label) elif self.summary_stat is not None: embs_df = pd.DataFrame(embs.cpu().numpy()).T # save embeddings to output_path output_path = (Path(output_directory) / output_prefix).with_suffix(".csv") embs_df.to_csv(output_path) if output_torch_embs == True: return embs_df, embs else: return embs_df def plot_embs(self, embs, plot_style, output_directory, output_prefix, max_ncells_to_plot=1000, kwargs_dict=None): """ Plot embeddings, coloring by provided labels. Parameters ---------- embs : pandas.core.frame.DataFrame Pandas dataframe containing embeddings output from extract_embs plot_style : str Style of plot: "heatmap" or "umap" output_directory : Path Path to directory where plots will be saved as pdf output_prefix : str Prefix for output file max_ncells_to_plot : None, int Maximum number of cells to plot. Default is 1000 cells randomly sampled from embeddings. If None, will plot embeddings from all cells. kwargs_dict : dict Dictionary of kwargs to pass to plotting function. """ if plot_style not in ["heatmap","umap"]: logger.error( "Invalid option for 'plot_style'. " \ "Valid options: {'heatmap','umap'}" ) raise if (plot_style == "umap") and (self.labels_to_plot is None): logger.error( "Plotting UMAP requires 'labels_to_plot'. " ) raise if max_ncells_to_plot > self.max_ncells: max_ncells_to_plot = self.max_ncells logger.warning( "max_ncells_to_plot must be <= max_ncells. " \ f"Changing max_ncells_to_plot to {self.max_ncells}.") if (max_ncells_to_plot is not None) \ and (max_ncells_to_plot < self.max_ncells): embs = embs.sample(max_ncells_to_plot, axis=0) if self.emb_label is None: label_len = 0 else: label_len = len(self.emb_label) emb_dims = embs.shape[1] - label_len if self.emb_label is None: emb_labels = None else: emb_labels = embs.columns[emb_dims:] if plot_style == "umap": for label in self.labels_to_plot: if label not in emb_labels: logger.warning( f"Label {label} from labels_to_plot " \ f"not present in provided embeddings dataframe.") continue output_prefix_label = "_" + output_prefix + f"_umap_{label}" output_file = (Path(output_directory) / output_prefix_label).with_suffix(".pdf") plot_umap(embs, emb_dims, label, output_prefix_label, kwargs_dict) if plot_style == "heatmap": for label in self.labels_to_plot: if label not in emb_labels: logger.warning( f"Label {label} from labels_to_plot " \ f"not present in provided embeddings dataframe.") continue output_prefix_label = output_prefix + f"_heatmap_{label}" output_file = (Path(output_directory) / output_prefix_label).with_suffix(".pdf") plot_heatmap(embs, emb_dims, label, output_file, kwargs_dict)