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"""
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)
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
import scanpy as sc
import seaborn as sns
import torch
from collections import Counter
from pathlib import Path
from tqdm.notebook 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__)
# average embedding position of goal cell states
def get_embs(model,
filtered_input_data,
emb_mode,
layer_to_quant,
pad_token_id,
forward_batch_size):
model_input_size = get_model_input_size(model)
total_batch_length = len(filtered_input_data)
if ((total_batch_length-1)/forward_batch_size).is_integer():
forward_batch_size = forward_batch_size-1
embs_list = []
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)
embs_list += [mean_embs]
del outputs
del minibatch
del input_data_minibatch
del embs_i
del mean_embs
torch.cuda.empty_cache()
embs_stack = torch.cat(embs_list)
return embs_stack
def label_embs(embs, downsampled_data, emb_labels):
embs_df = pd.DataFrame(embs.cpu())
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},
}
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,
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.
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.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("<pad>")
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):
"""
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
"""
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)
embs_df = label_embs(embs, downsampled_data, self.emb_label)
# save embeddings to output_path
output_path = (Path(output_directory) / output_prefix).with_suffix(".csv")
embs_df.to_csv(output_path)
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)
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