From 59b1eba38d665ec439746957fd65445638635e43 Mon Sep 17 00:00:00 2001 From: anyakors Date: Wed, 6 Mar 2024 10:27:37 -0800 Subject: [PATCH 1/2] set up consistent python3.12 env --- build/lib/solo/__init__.py | 5 + build/lib/solo/hashsolo.py | 616 +++++++++++++++++++++++++++++++++++++ build/lib/solo/solo.py | 510 ++++++++++++++++++++++++++++++ build/lib/solo/utils.py | 74 +++++ prespecified.txt | 18 +- requirements.txt | 23 +- setup.py | 4 +- solo/solo.py | 18 +- testdata/2c.h5ad | Bin 133 -> 71183035 bytes 9 files changed, 1242 insertions(+), 26 deletions(-) create mode 100644 build/lib/solo/__init__.py create mode 100644 build/lib/solo/hashsolo.py create mode 100644 build/lib/solo/solo.py create mode 100644 build/lib/solo/utils.py diff --git a/build/lib/solo/__init__.py b/build/lib/solo/__init__.py new file mode 100644 index 0000000..e53900e --- /dev/null +++ b/build/lib/solo/__init__.py @@ -0,0 +1,5 @@ +__author__ = "David Kelley, Nick Bernstein" +__email__ = "nicholas@calicolabs.com" +__version__ = "0.1" + +from . import hashsolo, utils diff --git a/build/lib/solo/hashsolo.py b/build/lib/solo/hashsolo.py new file mode 100644 index 0000000..0a3f506 --- /dev/null +++ b/build/lib/solo/hashsolo.py @@ -0,0 +1,616 @@ +#!/usr/bin/env python +import os +import json +from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter + +from scipy.stats import norm +from itertools import product +import anndata +import numpy as np +import pandas as pd +import scanpy as sc + +from scipy.sparse import issparse +from sklearn.metrics import calinski_harabasz_score + +""" +HashSolo script provides a probabilistic cell hashing demultiplexing method +which generates a noise distribution and signal distribution for +each hashing barcode from empirically observed counts. These distributions +are updates from the global signal and noise barcode distributions, which +helps in the setting where not many cells are observed. Signal distributions +for a hashing barcode are estimated from samples where that hashing barcode +has the highest count. Noise distributions for a hashing barcode are estimated +from samples where that hashing barcode is one the k-2 lowest barcodes, where +k is the number of barcodes. A doublet should then have its two highest +barcode counts most likely coming from a signal distribution for those barcodes. +A singlet should have its highest barcode from a signal distribution, and its +second highest barcode from a noise distribution. A negative two highest +barcodes should come from noise distributions. We test each of these +hypotheses in a bayesian fashion, and select the most probable hypothesis. +""" + + +def _calculate_log_likelihoods(data, number_of_noise_barcodes): + """Calculate log likelihoods for each hypothesis, negative, singlet, doublet + + Parameters + ---------- + data : np.ndarray + cells by hashing counts matrix + number_of_noise_barcodes : int, + number of barcodes to used to calculated noise distribution + Returns + ------- + log_likelihoods_for_each_hypothesis : np.ndarray + a 2d np.array log likelihood of each hypothesis + all_indices + counter_to_barcode_combo + """ + + def gaussian_updates(data, mu_o, std_o): + """Update parameters of your gaussian + https://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf + Parameters + ---------- + data : np.array + 1-d array of counts + mu_o : float, + global mean for hashing count distribution + std_o : float, + global std for hashing count distribution + Returns + ------- + float + mean of gaussian + float + std of gaussian + """ + lam_o = 1 / (std_o ** 2) + n = len(data) + lam = 1 / np.var(data) if len(data) > 1 else lam_o + lam_n = lam_o + n * lam + mu_n = ( + (np.mean(data) * n * lam + mu_o * lam_o) / lam_n if len(data) > 0 else mu_o + ) + return mu_n, (1 / (lam_n / (n + 1))) ** (1 / 2) + + eps = 1e-15 + # probabilites for negative, singlet, doublets + log_likelihoods_for_each_hypothesis = np.zeros((data.shape[0], 3)) + + all_indices = np.empty(data.shape[0]) + num_of_barcodes = data.shape[1] + number_of_non_noise_barcodes = ( + num_of_barcodes - number_of_noise_barcodes + if number_of_noise_barcodes is not None + else 2 + ) + num_of_noise_barcodes = num_of_barcodes - number_of_non_noise_barcodes + + # assume log normal + data = np.log(data + 1) + data_arg = np.argsort(data, axis=1) + data_sort = np.sort(data, axis=1) + + # global signal and noise counts useful for when we have few cells + # barcodes with the highest number of counts are assumed to be a true signal + # barcodes with rank < k are considered to be noise + global_signal_counts = np.ravel(data_sort[:, -1]) + global_noise_counts = np.ravel(data_sort[:, :-number_of_non_noise_barcodes]) + global_mu_signal_o, global_sigma_signal_o = np.mean(global_signal_counts), np.std( + global_signal_counts + ) + global_mu_noise_o, global_sigma_noise_o = np.mean(global_noise_counts), np.std( + global_noise_counts + ) + + noise_params_dict = {} + signal_params_dict = {} + + # for each barcode get empirical noise and signal distribution parameterization + for x in np.arange(num_of_barcodes): + sample_barcodes = data[:, x] + sample_barcodes_noise_idx = np.where(data_arg[:, :num_of_noise_barcodes] == x)[ + 0 + ] + sample_barcodes_signal_idx = np.where(data_arg[:, -1] == x) + + # get noise and signal counts + noise_counts = sample_barcodes[sample_barcodes_noise_idx] + signal_counts = sample_barcodes[sample_barcodes_signal_idx] + + # get parameters of distribution, assuming lognormal do update from global values + noise_param = gaussian_updates( + noise_counts, global_mu_noise_o, global_sigma_noise_o + ) + signal_param = gaussian_updates( + signal_counts, global_mu_signal_o, global_sigma_signal_o + ) + noise_params_dict[x] = noise_param + signal_params_dict[x] = signal_param + + counter_to_barcode_combo = {} + counter = 0 + + # for each combination of noise and signal barcode calculate probiltiy of in silico and real cell hypotheses + for noise_sample_idx, signal_sample_idx in product( + np.arange(num_of_barcodes), np.arange(num_of_barcodes) + ): + signal_subset = data_arg[:, -1] == signal_sample_idx + noise_subset = data_arg[:, -2] == noise_sample_idx + subset = signal_subset & noise_subset + if sum(subset) == 0: + continue + + indices = np.where(subset)[0] + barcode_combo = "_".join([str(noise_sample_idx), str(signal_sample_idx)]) + all_indices[np.where(subset)[0]] = counter + counter_to_barcode_combo[counter] = barcode_combo + counter += 1 + noise_params = noise_params_dict[noise_sample_idx] + signal_params = signal_params_dict[signal_sample_idx] + + # calculate probabilties for each hypothesis for each cell + data_subset = data[subset] + log_signal_signal_probs = np.log( + norm.pdf( + data_subset[:, signal_sample_idx], + *signal_params[:-2], + loc=signal_params[-2], + scale=signal_params[-1] + ) + + eps + ) + signal_noise_params = signal_params_dict[noise_sample_idx] + log_noise_signal_probs = np.log( + norm.pdf( + data_subset[:, noise_sample_idx], + *signal_noise_params[:-2], + loc=signal_noise_params[-2], + scale=signal_noise_params[-1] + ) + + eps + ) + + log_noise_noise_probs = np.log( + norm.pdf( + data_subset[:, noise_sample_idx], + *noise_params[:-2], + loc=noise_params[-2], + scale=noise_params[-1] + ) + + eps + ) + log_signal_noise_probs = np.log( + norm.pdf( + data_subset[:, signal_sample_idx], + *noise_params[:-2], + loc=noise_params[-2], + scale=noise_params[-1] + ) + + eps + ) + + probs_of_negative = np.sum( + [log_noise_noise_probs, log_signal_noise_probs], axis=0 + ) + probs_of_singlet = np.sum( + [log_noise_noise_probs, log_signal_signal_probs], axis=0 + ) + probs_of_doublet = np.sum( + [log_noise_signal_probs, log_signal_signal_probs], axis=0 + ) + log_probs_list = [probs_of_negative, probs_of_singlet, probs_of_doublet] + + # each cell and each hypothesis probability + for prob_idx, log_prob in enumerate(log_probs_list): + log_likelihoods_for_each_hypothesis[indices, prob_idx] = log_prob + return log_likelihoods_for_each_hypothesis, all_indices, counter_to_barcode_combo + + +def _calculate_bayes_rule(data, priors, number_of_noise_barcodes): + """ + Calculate bayes rule from log likelihoods + + Parameters + ---------- + data : np.array + Anndata object filled only with hashing counts + priors : list, + a list of your prior for each hypothesis + first element is your prior for the negative hypothesis + second element is your prior for the singlet hypothesis + third element is your prior for the doublet hypothesis + We use [0.01, 0.8, 0.19] by default because we assume the barcodes + in your cell hashing matrix are those cells which have passed QC + in the transcriptome space, e.g. UMI counts, pct mito reads, etc. + number_of_noise_barcodes : int + number of barcodes to used to calculated noise distribution + Returns + ------- + bayes_dict_results : dict + 'most_likely_hypothesis' key is a 1d np.array of the most likely hypothesis + 'probs_hypotheses' key is a 2d np.array probability of each hypothesis + 'log_likelihoods_for_each_hypothesis' key is a 2d np.array log likelihood of each hypothesis + """ + priors = np.array(priors) + log_likelihoods_for_each_hypothesis, _, _ = _calculate_log_likelihoods( + data, number_of_noise_barcodes + ) + probs_hypotheses = ( + np.exp(log_likelihoods_for_each_hypothesis) + * priors + / np.sum( + np.multiply(np.exp(log_likelihoods_for_each_hypothesis), priors), axis=1 + )[:, None] + ) + most_likely_hypothesis = np.argmax(probs_hypotheses, axis=1) + return { + "most_likely_hypothesis": most_likely_hypothesis, + "probs_hypotheses": probs_hypotheses, + "log_likelihoods_for_each_hypothesis": log_likelihoods_for_each_hypothesis, + } + + +def _get_clusters(clustering_data: anndata.AnnData, resolutions: list): + """ + Principled cell clustering + Parameters + ---------- + cell_hashing_adata : anndata.AnnData + Anndata object filled only with hashing counts + resolutions : list + clustering resolutions for leiden + Returns + ------- + np.ndarray + leiden clustering results for each cell + """ + sc.pp.normalize_per_cell(clustering_data, counts_per_cell_after=1e4) + sc.pp.log1p(clustering_data) + sc.pp.highly_variable_genes( + clustering_data, min_mean=0.0125, max_mean=3, min_disp=0.5 + ) + clustering_data = clustering_data[:, clustering_data.var["highly_variable"]] + sc.pp.scale(clustering_data, max_value=10) + sc.tl.pca(clustering_data, svd_solver="arpack") + sc.pp.neighbors(clustering_data, n_neighbors=10, n_pcs=40) + sc.tl.umap(clustering_data) + best_ch_score = -np.inf + + for resolution in resolutions: + sc.tl.leiden(clustering_data, resolution=resolution) + + ch_score = calinski_harabasz_score( + clustering_data.X, clustering_data.obs["leiden"] + ) + + if ch_score > best_ch_score: + clustering_data.obs["best_leiden"] = clustering_data.obs["leiden"].values + best_ch_score = ch_score + return clustering_data.obs["best_leiden"].values + + +def hashsolo( + cell_hashing_adata: anndata.AnnData, + priors: list = [0.01, 0.8, 0.19], + pre_existing_clusters: str = None, + clustering_data: anndata.AnnData = None, + resolutions: list = [0.1, 0.25, 0.5, 0.75, 1], + number_of_noise_barcodes: int = None, + inplace: bool = True, +): + """Demultiplex cell hashing dataset using HashSolo method + + Parameters + ---------- + cell_hashing_adata : anndata.AnnData + Anndata object filled only with hashing counts + priors : list, + a list of your prior for each hypothesis + first element is your prior for the negative hypothesis + second element is your prior for the singlet hypothesis + third element is your prior for the doublet hypothesis + We use [0.01, 0.8, 0.19] by default because we assume the barcodes + in your cell hashing matrix are those cells which have passed QC + in the transcriptome space, e.g. UMI counts, pct mito reads, etc. + clustering_data : anndata.AnnData + transcriptional data for clustering + resolutions : list + clustering resolutions for leiden + pre_existing_clusters : str + column in cell_hashing_adata.obs for how to break up demultiplexing + inplace : bool + To do operation in place + + Returns + ------- + cell_hashing_adata : AnnData + if inplace is False returns AnnData with demultiplexing results + in .obs attribute otherwise does is in place + """ + if issparse(cell_hashing_adata.X): + cell_hashing_adata.X = np.array(cell_hashing_adata.X.todense()) + + if clustering_data is not None: + print( + "This may take awhile we are running clustering at {} different resolutions".format( + len(resolutions) + ) + ) + if not all(clustering_data.obs_names == cell_hashing_adata.obs_names): + raise ValueError( + "clustering_data and cell hashing cell_hashing_adata must have same index" + ) + cell_hashing_adata.obs["best_leiden"] = _get_clusters( + clustering_data, resolutions + ) + + data = cell_hashing_adata.X + num_of_cells = cell_hashing_adata.shape[0] + results = pd.DataFrame( + np.zeros((num_of_cells, 6)), + columns=[ + "most_likely_hypothesis", + "probs_hypotheses", + "cluster_feature", + "negative_hypothesis_probability", + "singlet_hypothesis_probability", + "doublet_hypothesis_probability", + ], + index=cell_hashing_adata.obs_names, + ) + if clustering_data is not None or pre_existing_clusters is not None: + cluster_features = ( + "best_leiden" if pre_existing_clusters is None else pre_existing_clusters + ) + unique_cluster_features = np.unique(cell_hashing_adata.obs[cluster_features]) + for cluster_feature in unique_cluster_features: + cluster_feature_bool_vector = ( + cell_hashing_adata.obs[cluster_features] == cluster_feature + ) + posterior_dict = _calculate_bayes_rule( + data[cluster_feature_bool_vector], priors, number_of_noise_barcodes + ) + results.loc[ + cluster_feature_bool_vector, "most_likely_hypothesis" + ] = posterior_dict["most_likely_hypothesis"] + results.loc[ + cluster_feature_bool_vector, "cluster_feature" + ] = cluster_feature + results.loc[ + cluster_feature_bool_vector, "negative_hypothesis_probability" + ] = posterior_dict["probs_hypotheses"][:, 0] + results.loc[ + cluster_feature_bool_vector, "singlet_hypothesis_probability" + ] = posterior_dict["probs_hypotheses"][:, 1] + results.loc[ + cluster_feature_bool_vector, "doublet_hypothesis_probability" + ] = posterior_dict["probs_hypotheses"][:, 2] + else: + posterior_dict = _calculate_bayes_rule(data, priors, number_of_noise_barcodes) + results.loc[:, "most_likely_hypothesis"] = posterior_dict[ + "most_likely_hypothesis" + ] + results.loc[:, "cluster_feature"] = 0 + results.loc[:, "negative_hypothesis_probability"] = posterior_dict[ + "probs_hypotheses" + ][:, 0] + results.loc[:, "singlet_hypothesis_probability"] = posterior_dict[ + "probs_hypotheses" + ][:, 1] + results.loc[:, "doublet_hypothesis_probability"] = posterior_dict[ + "probs_hypotheses" + ][:, 2] + + cell_hashing_adata.obs["most_likely_hypothesis"] = results.loc[ + cell_hashing_adata.obs_names, "most_likely_hypothesis" + ] + cell_hashing_adata.obs["cluster_feature"] = results.loc[ + cell_hashing_adata.obs_names, "cluster_feature" + ] + cell_hashing_adata.obs["negative_hypothesis_probability"] = results.loc[ + cell_hashing_adata.obs_names, "negative_hypothesis_probability" + ] + cell_hashing_adata.obs["singlet_hypothesis_probability"] = results.loc[ + cell_hashing_adata.obs_names, "singlet_hypothesis_probability" + ] + cell_hashing_adata.obs["doublet_hypothesis_probability"] = results.loc[ + cell_hashing_adata.obs_names, "doublet_hypothesis_probability" + ] + + cell_hashing_adata.obs["Classification"] = None + cell_hashing_adata.obs.loc[ + cell_hashing_adata.obs["most_likely_hypothesis"] == 2, "Classification" + ] = "Doublet" + cell_hashing_adata.obs.loc[ + cell_hashing_adata.obs["most_likely_hypothesis"] == 0, "Classification" + ] = "Negative" + all_sings = cell_hashing_adata.obs["most_likely_hypothesis"] == 1 + singlet_sample_index = np.argmax(cell_hashing_adata.X[all_sings], axis=1) + cell_hashing_adata.obs.loc[ + all_sings, "Classification" + ] = cell_hashing_adata.var_names[singlet_sample_index] + + return cell_hashing_adata if not inplace else None + + +def plot_qc_checks_cell_hashing( + cell_hashing_adata: anndata.AnnData, alpha: float = 0.05, fig_path: str = None +): + """Plot HashSolo demultiplexing results + + Parameters + ---------- + cell_hashing_adata : Anndata + Anndata object filled only with hashing counts + alpha : float + Tranparency of scatterplot points + fig_path : str + Path to save figure + Returns + ------- + """ + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + cell_hashing_demultiplexing = cell_hashing_adata.obs + cell_hashing_demultiplexing["log_counts"] = np.log( + np.sum(cell_hashing_adata.X, axis=1) + ) + number_of_clusters = ( + cell_hashing_demultiplexing["cluster_feature"].drop_duplicates().shape[0] + ) + fig, all_axes = plt.subplots( + number_of_clusters, 4, figsize=(40, 10 * number_of_clusters) + ) + counter = 0 + for cluster_feature, group in cell_hashing_demultiplexing.groupby( + "cluster_feature" + ): + if number_of_clusters > 1: + axes = all_axes[counter] + else: + axes = all_axes + + ax = axes[0] + ax.plot( + group["log_counts"], + group["negative_hypothesis_probability"], + "bo", + alpha=alpha, + ) + ax.set_title("Probability of negative hypothesis vs log hashing counts") + ax.set_ylabel("Probability of negative hypothesis") + ax.set_xlabel("Log hashing counts") + + ax = axes[1] + ax.plot( + group["log_counts"], + group["singlet_hypothesis_probability"], + "bo", + alpha=alpha, + ) + ax.set_title("Probability of singlet hypothesis vs log hashing counts") + ax.set_ylabel("Probability of singlet hypothesis") + ax.set_xlabel("Log hashing counts") + + ax = axes[2] + ax.plot( + group["log_counts"], + group["doublet_hypothesis_probability"], + "bo", + alpha=alpha, + ) + ax.set_title("Probability of doublet hypothesis vs log hashing counts") + ax.set_ylabel("Probability of doublet hypothesis") + ax.set_xlabel("Log hashing counts") + + ax = axes[3] + group["Classification"].value_counts().plot.bar(ax=ax) + ax.set_title("Count of each samples classification") + counter += 1 + plt.show() + if fig_path is not None: + fig.savefig(fig_path, dpi=300, format="pdf") + + +def main(): + usage = "hashsolo" + parser = ArgumentParser(usage, formatter_class=ArgumentDefaultsHelpFormatter) + + parser.add_argument( + dest="data_file", help="h5ad file containing cell hashing counts" + ) + parser.add_argument( + "-j", + dest="model_json_file", + default=None, + help="json file to pass optional arguments", + ) + parser.add_argument( + "-o", + dest="out_dir", + default="hashsolo_output", + help="Output directory for results", + ) + parser.add_argument( + "-c", + dest="clustering_data", + default=None, + help="h5ad file with count transcriptional data to\ + perform clustering on", + ) + parser.add_argument( + "-p", + dest="pre_existing_clusters", + default=None, + help="column in cell_hashing_data_file.obs to \ + specifying different cell types or clusters", + ) + parser.add_argument( + "-q", + dest="plot_name", + default="hashing_qc_plots.pdf", + help="name of plot to output", + ) + parser.add_argument( + "-n", + dest="number_of_noise_barcodes", + default=None, + help="Number of barcodes to use to create noise \ + distribution", + ) + + args = parser.parse_args() + + model_json_file = args.model_json_file + if model_json_file is not None: + # read parameters + with open(model_json_file) as model_json_open: + params = json.load(model_json_open) + else: + params = {} + data_file = args.data_file + data_ext = os.path.splitext(data_file)[-1] + if data_ext == ".h5ad": + cell_hashing_adata = anndata.read(data_file) + else: + print("Unrecognized file format") + + if args.clustering_data is not None: + clustering_data_file = args.clustering_data + clustering_data_ext = os.path.splitext(clustering_data_file)[-1] + if clustering_data_ext == ".h5ad": + clustering_data = anndata.read(clustering_data_file) + else: + print("Unrecognized file format for clustering data") + else: + clustering_data = None + + if not os.path.isdir(args.out_dir): + os.mkdir(args.out_dir) + + hashsolo( + cell_hashing_adata, + pre_existing_clusters=args.pre_existing_clusters, + number_of_noise_barcodes=args.number_of_noise_barcodes, + clustering_data=clustering_data, + **params + ) + cell_hashing_adata.write(os.path.join(args.out_dir, "hashsoloed.h5ad")) + plot_qc_checks_cell_hashing( + cell_hashing_adata, fig_path=os.path.join(args.out_dir, args.plot_name) + ) + + +############################################################################### +# __main__ +############################################################################### + + +if __name__ == "__main__": + main() diff --git a/build/lib/solo/solo.py b/build/lib/solo/solo.py new file mode 100644 index 0000000..0db3218 --- /dev/null +++ b/build/lib/solo/solo.py @@ -0,0 +1,510 @@ +#!/usr/bin/env python +import json +import os +import umap +import sys +from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter +import pkg_resources + +import numpy as np +from sklearn.metrics import * +from scipy.special import softmax +from scanpy import read_10x_mtx + +import torch +from lightning.pytorch.callbacks.early_stopping import EarlyStopping + +import scvi +from scvi.data import read_h5ad, read_loom +from scvi.model import SCVI +from scvi.external import SOLO + +from .utils import knn_smooth_pred_class + +""" +solo.py + +Simulate doublets, train a VAE, and then a classifier on top. +""" + + +############################################################################### +# main +############################################################################### + + +def main(): + usage = "solo" + parser = ArgumentParser(usage, formatter_class=ArgumentDefaultsHelpFormatter) + parser.add_argument( + "-j", + dest="model_json_file", + help="json file to pass VAE parameters", + required="--version" not in sys.argv, + ) + parser.add_argument( + "-d", + dest="data_path", + help="path to h5ad, loom, or 10x mtx dir cell by genes counts", + required="--version" not in sys.argv, + ) + parser.add_argument( + "--set-reproducible-seed", + dest="reproducible_seed", + default=None, + type=int, + help="Reproducible seed, give an int to set seed", + ) + parser.add_argument( + "--doublet-depth", + dest="doublet_depth", + default=2.0, + type=float, + help="Depth multiplier for a doublet relative to the \ + average of its constituents", + ) + parser.add_argument( + "-g", dest="gpu", default=True, action="store_true", help="Run on GPU" + ) + parser.add_argument( + "-a", + dest="anndata_output", + default=False, + action="store_true", + help="output modified anndata object with solo scores \ + Only works for anndata", + ) + parser.add_argument("-o", dest="out_dir", default="solo_out") + parser.add_argument( + "-r", + dest="doublet_ratio", + default=2, + type=int, + help="Ratio of doublets to true \ + cells", + ) + parser.add_argument( + "-s", + dest="seed", + default=None, + help="Path to previous solo output \ + directory. Seed VAE models with previously \ + trained solo model. Directory structure is assumed to \ + be the same as solo output directory structure. \ + should at least have a vae.pt a pickled object of \ + vae weights and a latent.npy an np.ndarray of the \ + latents of your cells.", + ) + parser.add_argument( + "-e", + dest="expected_number_of_doublets", + help="Experimentally expected number of doublets", + type=int, + default=None, + ) + parser.add_argument( + "-p", + dest="plot", + default=False, + action="store_true", + help="Plot outputs for solo", + ) + parser.add_argument( + "-recalibrate_scores", + dest="recalibrate_scores", + default=False, + action="store_true", + help="Recalibrate doublet scores (not recommended anymore)", + ) + parser.add_argument( + "--version", + dest="version", + default=False, + action="store_true", + help="Get version of solo-sc", + ) + + parser.add_argument( + "--lr_st", + dest="lr_st", + default=1e-3, + type=int, + help="Learning rate used for solo.train", + ) + parser.add_argument( + "--lr_vae", + dest="lr_vae", + default=1e-3, + type=int, + help="Learning rate used for vae", + ) + + args = parser.parse_args() + + if args.version: + version = pkg_resources.require("solo-sc")[0].version + print(f"Current version of solo-sc is {version}") + if args.model_json_file is None or args.data_path is None: + print("Json or data path not give exiting solo") + sys.exit() + + model_json_file = args.model_json_file + data_path = args.data_path + if args.gpu and not torch.cuda.is_available(): + args.gpu = torch.cuda.is_available() + print("Cuda is not available, switching to cpu running!") + + if not os.path.isdir(args.out_dir): + os.mkdir(args.out_dir) + + if args.reproducible_seed is not None: + scvi.settings.seed = args.reproducible_seed + else: + scvi.settings.seed = np.random.randint(10000) + + ################################################## + # data + + # read loom/anndata + data_ext = os.path.splitext(data_path)[-1] + if data_ext == ".loom": + scvi_data = read_loom(data_path) + elif data_ext == ".h5ad": + scvi_data = read_h5ad(data_path) + elif os.path.isdir(data_path): + scvi_data = read_10x_mtx(path=data_path) + cell_umi_depth = scvi_data.X.sum(axis=1) + fifth, ninetyfifth = np.percentile(cell_umi_depth, [5, 95]) + min_cell_umi_depth = np.min(cell_umi_depth) + max_cell_umi_depth = np.max(cell_umi_depth) + if fifth * 10 < ninetyfifth: + print( + """WARNING YOUR DATA HAS A WIDE RANGE OF CELL DEPTHS. + PLEASE MANUALLY REVIEW YOUR DATA""" + ) + print( + f"Min cell depth: {min_cell_umi_depth}, Max cell depth: {max_cell_umi_depth}" + ) + else: + msg = f"{data_path} is not a recognized format.\n" + msg += "must be one of {h5ad, loom, 10x mtx dir}" + raise TypeError(msg) + + num_cells, num_genes = scvi_data.X.shape + + # check for parameters + if not os.path.exists(model_json_file): + raise FileNotFoundError(f"{model_json_file} does not exist.") + # read parameters + with open(model_json_file, "r") as model_json_open: + params = json.load(model_json_open) + + # set VAE params + vae_params = {} + for par in ["n_hidden", "n_latent", "n_layers", "dropout_rate", "ignore_batch"]: + if par in params: + vae_params[par] = params[par] + + # training parameters + batch_key = params.get("batch_key", None) + batch_size = params.get("batch_size", 128) + valid_pct = params.get("valid_pct", 0.1) + check_val_every_n_epoch = params.get("check_val_every_n_epoch", 5) + learning_rate = params.get("learning_rate", args.lr_st) + stopping_params = {"patience": params.get("patience", 8), "min_delta": 0} + + # protect against single example batch + while num_cells % batch_size == 1: + batch_size = int(np.round(1.25 * batch_size)) + print("Increasing batch_size to %d to avoid single example batch." % batch_size) + + scvi.settings.batch_size = batch_size + ################################################## + # SCVI + #setup_anndata(scvi_data, batch_key=batch_key) + scvi.model.SCVI.setup_anndata(scvi_data, batch_key=batch_key) + vae = SCVI( + scvi_data, + gene_likelihood="nb", + log_variational=True, + **vae_params, + use_observed_lib_size=False, + ) + + if args.seed: + vae = vae.load(os.path.join(args.seed, "vae"), use_gpu=args.gpu) + else: + scvi_callbacks = [] + scvi_callbacks += [ + EarlyStopping( + monitor="reconstruction_loss_validation", mode="min", **stopping_params + ) + ] + plan_kwargs = { + "reduce_lr_on_plateau": True, + "lr_factor": 0.1, + "lr": args.lr_vae, + "lr_patience": 10, + "lr_threshold": 0, + "lr_min": 1e-4, + "lr_scheduler_metric": "reconstruction_loss_validation", + } + vae.train( + max_epochs=2000, + validation_size=valid_pct, + check_val_every_n_epoch=check_val_every_n_epoch, + plan_kwargs=plan_kwargs, + callbacks=scvi_callbacks, + ) + # save VAE + vae.save(os.path.join(args.out_dir, "vae")) + + latent = vae.get_latent_representation() + # save latent representation + np.save(os.path.join(args.out_dir, "latent.npy"), latent.astype("float32")) + + ################################################## + # classifier + + # model + # todo add doublet ratio + solo = SOLO.from_scvi_model(vae, doublet_ratio=args.doublet_ratio) + solo.train( + 2000, + lr=learning_rate, + train_size=0.9, + check_val_every_n_epoch=5, + early_stopping_patience=6, + ) + solo.train( + 2000, + lr=learning_rate * 0.1, + train_size=0.9, + check_val_every_n_epoch=1, + early_stopping_patience=30, + callbacks=[], + ) + solo.save(os.path.join(args.out_dir, "classifier")) + + logit_predictions = solo.predict(include_simulated_doublets=True) + + is_doublet_known = solo.adata.obs._solo_doub_sim == "doublet" + is_doublet_pred = logit_predictions.idxmin(axis=1) == "singlet" + + validation_is_doublet_known = is_doublet_known[solo.validation_indices] + validation_is_doublet_pred = is_doublet_pred[solo.validation_indices] + training_is_doublet_known = is_doublet_known[solo.train_indices] + training_is_doublet_pred = is_doublet_pred[solo.train_indices] + + valid_as = accuracy_score(validation_is_doublet_known, validation_is_doublet_pred) + valid_roc = roc_auc_score(validation_is_doublet_known, validation_is_doublet_pred) + valid_ap = average_precision_score( + validation_is_doublet_known, validation_is_doublet_pred + ) + + train_as = accuracy_score(training_is_doublet_known, training_is_doublet_pred) + train_roc = roc_auc_score(training_is_doublet_known, training_is_doublet_pred) + train_ap = average_precision_score( + training_is_doublet_known, training_is_doublet_pred + ) + + print(f"Training results") + print(f"AUROC: {train_roc}, Accuracy: {train_as}, Average precision: {train_ap}") + + print(f"Validation results") + print(f"AUROC: {valid_roc}, Accuracy: {valid_as}, Average precision: {valid_ap}") + + # write predictions + # softmax predictions + softmax_predictions = softmax(logit_predictions, axis=1) + if logit_predictions.columns[0]=='doublet': + doublet_score = softmax_predictions[:, 0] + else: + doublet_score = softmax_predictions[:, 1] + + np.save( + os.path.join(args.out_dir, "no_updates_softmax_scores.npy"), + doublet_score[:num_cells], + ) + np.savetxt( + os.path.join(args.out_dir, "no_updates_softmax_scores.csv"), + doublet_score[:num_cells], + delimiter=",", + ) + np.save( + os.path.join(args.out_dir, "no_updates_softmax_scores_sim.npy"), + doublet_score[num_cells:], + ) + + # logit predictions + logit_doublet_score = logit_predictions.loc[:, "doublet"] + np.save( + os.path.join(args.out_dir, "logit_scores.npy"), logit_doublet_score[:num_cells] + ) + np.savetxt( + os.path.join(args.out_dir, "logit_scores.csv"), + logit_doublet_score[:num_cells], + delimiter=",", + ) + np.save( + os.path.join(args.out_dir, "logit_scores_sim.npy"), + logit_doublet_score[num_cells:], + ) + + # update threshold as a function of Solo's estimate of the number of + # doublets + # essentially a log odds update + # TODO put in a function + # currently overshrinking softmaxes + diff = np.inf + counter_update = 0 + solo_scores = doublet_score[:num_cells] + logit_scores = logit_doublet_score[:num_cells] + d_s = args.doublet_ratio / (args.doublet_ratio + 1) + if args.recalibrate_scores: + while (diff > 0.01) | (counter_update < 5): + + # calculate log odds calibration for logits + d_o = np.mean(solo_scores) + c = np.log(d_o / (1 - d_o)) - np.log(d_s / (1 - d_s)) + + # update solo scores + solo_scores = 1 / (1 + np.exp(-(logit_scores + c))) + + # update while conditions + diff = np.abs(d_o - np.mean(solo_scores)) + counter_update += 1 + + np.save(os.path.join(args.out_dir, "softmax_scores.npy"), solo_scores) + np.savetxt( + os.path.join(args.out_dir, "softmax_scores.csv"), solo_scores, delimiter="," + ) + + if args.expected_number_of_doublets is not None: + k = len(solo_scores) - args.expected_number_of_doublets + if args.expected_number_of_doublets / len(solo_scores) > 0.5: + print( + """Make sure you actually expect more than half your cells + to be doublets. If not change your + -e parameter value""" + ) + assert k > 0 + idx = np.argpartition(solo_scores, k) + threshold = np.max(solo_scores[idx[:k]]) + is_solo_doublet = solo_scores > threshold + else: + is_solo_doublet = solo_scores > 0.5 + + np.save(os.path.join(args.out_dir, "is_doublet.npy"), is_solo_doublet[:num_cells]) + np.savetxt( + os.path.join(args.out_dir, "is_doublet.csv"), + is_solo_doublet[:num_cells], + delimiter=",", + ) + + np.save( + os.path.join(args.out_dir, "is_doublet_sim.npy"), is_solo_doublet[num_cells:] + ) + + np.save(os.path.join(args.out_dir, "preds.npy"), is_doublet_pred[:num_cells]) + np.savetxt( + os.path.join(args.out_dir, "preds.csv"), + is_doublet_pred[:num_cells], + delimiter=",", + ) + + smoothed_preds = knn_smooth_pred_class( + X=latent, pred_class=is_doublet_pred[:num_cells] + ) + np.save(os.path.join(args.out_dir, "smoothed_preds.npy"), smoothed_preds) + + if args.anndata_output and data_ext == ".h5ad": + scvi_data.obs["is_doublet"] = is_solo_doublet[:num_cells].astype(bool) + scvi_data.obs["logit_scores"] = logit_doublet_score[:num_cells].astype( + float + ) + scvi_data.obs["softmax_scores"] = solo_scores[:num_cells].astype(float) + scvi_data.write(os.path.join(args.out_dir, "soloed.h5ad")) + + if args.plot: + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + import seaborn as sns + + train_solo_scores = doublet_score[solo.train_indices] + validation_solo_scores = doublet_score[solo.validation_indices] + + train_fpr, train_tpr, _ = roc_curve( + training_is_doublet_known, train_solo_scores + ) + val_fpr, val_tpr, _ = roc_curve( + validation_is_doublet_known, validation_solo_scores + ) + + # plot ROC + plt.figure() + plt.plot(train_fpr, train_tpr, label="Train") + plt.plot(val_fpr, val_tpr, label="Validation") + plt.gca().set_xlabel("False positive rate") + plt.gca().set_ylabel("True positive rate") + plt.legend() + plt.savefig(os.path.join(args.out_dir, "roc.pdf")) + plt.close() + + train_precision, train_recall, _ = precision_recall_curve( + training_is_doublet_known, train_solo_scores + ) + val_precision, val_recall, _ = precision_recall_curve( + validation_is_doublet_known, validation_solo_scores + ) + # plot accuracy + plt.figure() + plt.plot(train_recall, train_precision, label="Train") + plt.plot(val_recall, val_precision, label="Validation") + plt.gca().set_xlabel("Recall") + plt.gca().set_ylabel("pytPrecision") + plt.legend() + plt.savefig(os.path.join(args.out_dir, "precision_recall.pdf")) + plt.close() + + # plot distributions + obs_indices = solo.validation_indices[solo.validation_indices < num_cells] + sim_indices = solo.validation_indices[solo.validation_indices > num_cells] + + plt.figure() + sns.displot(doublet_score[sim_indices], label="Simulated") + sns.displot(doublet_score[obs_indices], label="Observed") + plt.legend() + plt.savefig(os.path.join(args.out_dir, "sim_vs_obs_dist.pdf")) + plt.close() + + plt.figure() + sns.distplot(solo_scores[:num_cells], label="Observed (transformed)") + plt.legend() + plt.savefig(os.path.join(args.out_dir, "real_cells_dist.pdf")) + plt.close() + + scvi_umap = umap.UMAP(n_neighbors=16).fit_transform(latent) + fig, ax = plt.subplots(1, 1, figsize=(10, 10)) + ax.scatter( + scvi_umap[:, 0], + scvi_umap[:, 1], + c=doublet_score[:num_cells], + s=8, + cmap="GnBu", + ) + + ax.set_xlabel("UMAP 1") + ax.set_ylabel("UMAP 2") + fig.savefig(os.path.join(args.out_dir, "umap_solo_scores.pdf")) + + +############################################################################### +# __main__ +############################################################################### + + +if __name__ == "__main__": + main() diff --git a/build/lib/solo/utils.py b/build/lib/solo/utils.py new file mode 100644 index 0000000..9352561 --- /dev/null +++ b/build/lib/solo/utils.py @@ -0,0 +1,74 @@ +import numpy as np + +from scipy.stats import multinomial +from sklearn.neighbors import NearestNeighbors + + +def knn_smooth_pred_class( + X: np.ndarray, + pred_class: np.ndarray, + grouping: np.ndarray = None, + k: int = 15, +) -> np.ndarray: + """ + Smooths class predictions by taking the modal class from each cell's + nearest neighbors. + Parameters + ---------- + X : np.ndarray + [N, Features] embedding space for calculation of nearest neighbors. + pred_class : np.ndarray + [N,] array of unique class labels. + groupings : np.ndarray + [N,] unique grouping labels for i.e. clusters. + if provided, only considers nearest neighbors *within the cluster*. + k : int + number of nearest neighbors to use for smoothing. + Returns + ------- + smooth_pred_class : np.ndarray + [N,] unique class labels, smoothed by kNN. + Examples + -------- + >>> smooth_pred_class = knn_smooth_pred_class( + ... X = X, + ... pred_class = raw_predicted_classes, + ... grouping = louvain_cluster_groups, + ... k = 15,) + Notes + ----- + scNym classifiers do not incorporate neighborhood information. + By using a simple kNN smoothing heuristic, we can leverage neighborhood + information to improve classification performance, smoothing out cells + that have an outlier prediction relative to their local neighborhood. + """ + if grouping is None: + # do not use a grouping to restrict local neighborhood + # associations, create a universal pseudogroup `0`. + grouping = np.zeros(X.shape[0]) + + smooth_pred_class = np.zeros_like(pred_class) + for group in np.unique(grouping): + # identify only cells in the relevant group + group_idx = np.where(grouping == group)[0].astype("int") + X_group = X[grouping == group, :] + # if there are < k cells in the group, change `k` to the + # group size + if X_group.shape[0] < k: + k_use = X_group.shape[0] + else: + k_use = k + # compute a nearest neighbor graph and identify kNN + nns = NearestNeighbors( + n_neighbors=k_use, + ).fit(X_group) + dist, idx = nns.kneighbors(X_group) + + # for each cell in the group, assign a class as + # the majority class of the kNN + for i in range(X_group.shape[0]): + classes = pred_class[group_idx[idx[i, :]]] + uniq_classes, counts = np.unique(classes, return_counts=True) + maj_class = uniq_classes[int(np.argmax(counts))] + smooth_pred_class[group_idx[i]] = maj_class + return smooth_pred_class diff --git a/prespecified.txt b/prespecified.txt index fd0ce46..d45ddc7 100644 --- a/prespecified.txt +++ b/prespecified.txt @@ -1,9 +1,9 @@ -ConfigArgParse==0.14.0 -pandas==0.24.2 -seaborn==0.9.0 -tqdm==4.32.1 -scanpy==1.4.5.1 -scvi==0.6.0 -leidenalg==0.7.0 -torch==1.0.1 -numba==0.45.0 \ No newline at end of file +ConfigArgParse +pandas +seaborn +tqdm +scanpy +scvi +leidenalg +torch +numba diff --git a/requirements.txt b/requirements.txt index 5fc6360..7d05a24 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,15 @@ -ConfigArgParse -pandas -seaborn -tqdm -scvi-tools -leidenalg -scanpy -pytorch-lightning==1.3.1 +ConfigArgParse==1.7 +h5py==3.10.0 +leidenalg==0.10.2 +lightning==2.1.4 +lightning-utilities==0.10.1 +numba==0.59.0 +pandas==2.2.1 +pytorch-lightning==2.2.1 +scanpy==1.9.8 +scipy==1.12.0 +scvi-tools==1.1.2 +seaborn==0.13.2 +torch==2.2.1 +torchmetrics==1.3.1 +tqdm==4.66.2 \ No newline at end of file diff --git a/setup.py b/setup.py index 292ddb4..e57d62d 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import setup, find_packages if sys.version_info < (3,): - sys.exit("solo requires Python >= 3.7") + sys.exit("solo requires Python >= 3.12") try: from solo import __author__, __email__ @@ -22,7 +22,7 @@ author=__author__, author_email=__email__, license="Apache", - python_requires=">=3.7", + python_requires=">=3.12", install_requires=[ l.strip() for l in Path("requirements.txt").read_text("utf-8").splitlines() ], diff --git a/solo/solo.py b/solo/solo.py index ac022e4..0db3218 100755 --- a/solo/solo.py +++ b/solo/solo.py @@ -12,10 +12,10 @@ from scanpy import read_10x_mtx import torch -from pytorch_lightning.callbacks.early_stopping import EarlyStopping +from lightning.pytorch.callbacks.early_stopping import EarlyStopping import scvi -from scvi.data import read_h5ad, read_loom, setup_anndata +from scvi.data import read_h5ad, read_loom from scvi.model import SCVI from scvi.external import SOLO @@ -221,7 +221,8 @@ def main(): scvi.settings.batch_size = batch_size ################################################## # SCVI - setup_anndata(scvi_data, batch_key=batch_key) + #setup_anndata(scvi_data, batch_key=batch_key) + scvi.model.SCVI.setup_anndata(scvi_data, batch_key=batch_key) vae = SCVI( scvi_data, gene_likelihood="nb", @@ -316,7 +317,10 @@ def main(): # write predictions # softmax predictions softmax_predictions = softmax(logit_predictions, axis=1) - doublet_score = softmax_predictions.loc[:, "doublet"] + if logit_predictions.columns[0]=='doublet': + doublet_score = softmax_predictions[:, 0] + else: + doublet_score = softmax_predictions[:, 1] np.save( os.path.join(args.out_dir, "no_updates_softmax_scores.npy"), @@ -415,11 +419,11 @@ def main(): np.save(os.path.join(args.out_dir, "smoothed_preds.npy"), smoothed_preds) if args.anndata_output and data_ext == ".h5ad": - scvi_data.obs["is_doublet"] = is_solo_doublet[:num_cells].values.astype(bool) - scvi_data.obs["logit_scores"] = logit_doublet_score[:num_cells].values.astype( + scvi_data.obs["is_doublet"] = is_solo_doublet[:num_cells].astype(bool) + scvi_data.obs["logit_scores"] = logit_doublet_score[:num_cells].astype( float ) - scvi_data.obs["softmax_scores"] = solo_scores[:num_cells].values.astype(float) + scvi_data.obs["softmax_scores"] = solo_scores[:num_cells].astype(float) scvi_data.write(os.path.join(args.out_dir, "soloed.h5ad")) if args.plot: diff --git a/testdata/2c.h5ad b/testdata/2c.h5ad index 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