mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-17 05:47:58 +08:00
223 lines
9.1 KiB
Python
223 lines
9.1 KiB
Python
import os
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import warnings
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import numpy as np
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from pandas import Series
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import scanpy.api as sc
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from scipy import stats
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from server.app.app import cache
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from server.app.driver.driver import CXGDriver
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class ScanpyEngine(CXGDriver):
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def __init__(self, data, graph_method="umap", diffexp_method="ttest"):
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self.data = self._load_data(data)
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self._validatate_data_types()
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self._add_mandatory_annotations()
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self.cell_count = self.data.shape[0]
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self.gene_count = self.data.shape[1]
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self.graph_method = graph_method
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self.diffexp_method = diffexp_method
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def _set_cell_names(self):
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self.data.obs["cell_name"] = list(self.data.obs.index)
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@classmethod
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def add_to_parser(cls, subparsers, invocation_function):
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scanpy_group = subparsers.add_parser("scanpy", help="run cellxgene using the scanpy engine")
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# TODO these choices should be generated from the actual available methods
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scanpy_group.add_argument("-l", "--layout", choices=["umap", "tsne"], default="umap",
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help="Algorithm to use for graph layout")
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scanpy_group.add_argument("-d", "--diffexp", choices=["ttest"], default="ttest",
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help="Algorithm to use to calculate differential expression")
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scanpy_group.add_argument("data_directory", metavar="dir", help="Directory containing data and schema file")
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scanpy_group.set_defaults(func=invocation_function)
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return scanpy_group
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@staticmethod
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def _load_data(data):
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return sc.read(os.path.join(data, "data.h5ad"))
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def _add_mandatory_annotations(self):
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# ensure gene
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self.data.var["name"] = list(self.data.var.index)
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self.data.var.index = Series(list(range(self.data.var.shape[0])), dtype="int32")
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# ensure cell name
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self.data.obs["name"] = list(self.data.obs.index)
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self.data.obs.index = Series(list(range(self.data.obs.shape[0])), dtype="int32")
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def _validatate_data_types(self):
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if self.data.X.dtype != "float32":
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warnings.warn(f"Scanpy data matrix is in {self.data.X.dtype} format not float32. "
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f"Precision may be truncated.")
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def cells(self):
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return list(self.data.obs.index)
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def genes(self):
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return self.data.var.index.tolist()
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# Can't seem to cache a view of a dataframe, need to investigate why
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def filter_cells(self, filter):
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"""
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Filter cells from data and return a subset of the data
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A filter is a dictionary where the key is a metadatata category
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Value is dictionary
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value_type: int, float, string
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variable_type: continuous, categorical
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query: filter value, for categorical [val1, val2], for continuous {min: x, max:y}
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Filters are combined with the and operator
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:param filter:
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:return: filtered dataframe
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"""
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cell_idx = np.ones((self.cell_count,), dtype=bool)
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for key, value in filter.items():
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if value["variable_type"] == "categorical":
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key_idx = np.in1d(getattr(self.data.obs, key), value["query"])
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cell_idx = np.logical_and(cell_idx, key_idx)
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else:
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min_ = value["query"]["min"]
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max_ = value["query"]["max"]
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if min_ is not None:
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key_idx = np.array((getattr(self.data.obs, key) >= min_).data)
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cell_idx = np.logical_and(cell_idx, key_idx)
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if max_ is not None:
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key_idx = np.array((getattr(self.data.obs, key) <= max_).data)
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cell_idx = np.logical_and(cell_idx, key_idx)
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return self.data[cell_idx, :]
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@cache.memoize()
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def metadata_ranges(self, df=None):
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metadata_ranges = {}
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if not df:
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df = self.data
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for field in self.schema:
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if self.schema[field]["variabletype"] == "categorical":
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group_by = field
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if group_by == "CellName":
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group_by = "cell_name"
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metadata_ranges[field] = {"options": df.obs.groupby(group_by).size().to_dict()}
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else:
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metadata_ranges[field] = {
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"range": {
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"min": df.obs[field].min(),
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"max": df.obs[field].max()
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}
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}
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return metadata_ranges
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@cache.memoize()
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def metadata(self, df, fields=None):
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"""
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Gets metadata key:value for each cells
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:param df: from filter_cells, dataframe
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:param fields: list of keys for metadata to return, returns all metadata values if not set.
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:return: list of metadata values
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"""
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metadata = df.obs.to_dict(orient="records")
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for idx in range(len(metadata)):
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metadata[idx]["CellName"] = metadata[idx].pop("cell_name", None)
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return metadata
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@cache.memoize()
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def create_graph(self, df):
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"""
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Computes a n-d layout for cells through dimensionality reduction.
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:param df: from filter_cells, dataframe
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:return: [cellid, x, y]
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"""
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getattr(sc.tl, self.graph_method)(df, random_state=123)
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graph = df.obsm["X_{graph_method}".format(graph_method=self.graph_method)]
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normalized_graph = (graph - graph.min()) / (graph.max() - graph.min())
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return np.hstack((df.obs["cell_name"].values.reshape(len(df.obs.index), 1), normalized_graph)).tolist()
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@cache.memoize()
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def diffexp(self, cell_list_1, cell_list_2, pval, num_genes):
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"""
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Computes the top differentially expressed genes between two clusters
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:param df1: from filter_cells, dataframe containing first set of cells
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:param df2: from filter_cells, dataframe containing second set of cells
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:return: top genes, stats and expression values for top genes
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"""
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cells_idx_1 = np.in1d(self.data.obs["cell_name"], cell_list_1)
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cells_idx_2 = np.in1d(self.data.obs["cell_name"], cell_list_2)
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expression_1 = self.data.X[cells_idx_1, :]
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expression_2 = self.data.X[cells_idx_2, :]
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diff_exp = stats.ttest_ind(expression_1, expression_2)
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# TODO break this up into functions
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set1 = np.logical_and(diff_exp.pvalue < pval, diff_exp.statistic > 0)
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set2 = np.logical_and(diff_exp.pvalue < pval, diff_exp.statistic < 0)
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stat1 = diff_exp.statistic[set1]
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stat2 = diff_exp.statistic[set2]
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sort_set1 = np.argsort(stat1)[::-1]
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sort_set2 = np.argsort(stat2)
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pval1 = diff_exp.pvalue[set1][sort_set1]
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pval2 = diff_exp.pvalue[set2][sort_set2]
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mean_ex1_set1 = np.mean(expression_1[:, set1], axis=0)[sort_set1]
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mean_ex2_set1 = np.mean(expression_2[:, set1], axis=0)[sort_set1]
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mean_ex1_set2 = np.mean(expression_1[:, set2], axis=0)[sort_set2]
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mean_ex2_set2 = np.mean(expression_2[:, set2], axis=0)[sort_set2]
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mean_diff1 = mean_ex1_set1 - mean_ex2_set1
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mean_diff2 = mean_ex1_set2 - mean_ex2_set2
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genes_cellset_1 = self.data.var_names[set1][sort_set1]
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genes_cellset_2 = self.data.var_names[set2][sort_set2]
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return {
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"celllist1": {
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"topgenes": genes_cellset_1.tolist()[:num_genes],
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"mean_expression_cellset1": mean_ex1_set1.tolist()[:num_genes],
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"mean_expression_cellset2": mean_ex2_set1.tolist()[:num_genes],
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"pval": pval1.tolist()[:num_genes],
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"ave_diff": mean_diff1.tolist()[:num_genes]
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},
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"celllist2": {
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"topgenes": genes_cellset_2.tolist()[:num_genes],
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"mean_expression_cellset1": mean_ex1_set2.tolist()[:num_genes],
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"mean_expression_cellset2": mean_ex2_set2.tolist()[:num_genes],
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"pval": pval2.tolist()[:num_genes],
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"ave_diff": mean_diff2.tolist()[:num_genes]
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},
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}
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@cache.memoize()
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def expression(self, cells=None, genes=None):
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"""
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Retrieves expression for each gene for cells in data frame
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:param df:
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:return: {
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"genes": list of genes,
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"cells": list of cells and expression list,
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"nonzero_gene_count": number of nonzero genes
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}
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"""
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if cells:
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cells_idx = np.in1d(self.data.obs["cell_name"], cells)
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else:
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cells_idx = np.ones((self.cell_count,), dtype=bool)
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if genes:
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genes_idx = np.in1d(self.data.var_names, genes)
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else:
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genes_idx = np.ones((self.gene_count,), dtype=bool)
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index = np.ix_(cells_idx, genes_idx)
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expression = self.data.X[index]
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if not genes:
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genes = self.data.var.index.tolist()
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if not cells:
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cells = self.data.obs["cell_name"].tolist()
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cell_data = []
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for idx, cell in enumerate(cells):
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cell_data.append({
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"cellname": cell,
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"e": list(expression[idx]),
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})
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return {
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"genes": genes,
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"cells": cell_data,
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"nonzero_gene_count": int(np.sum(expression.any(axis=0)))
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}
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