mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-17 22:08:00 +08:00
Correct tabs to spaces
This commit is contained in:
Charlotte Weaver
@@ -2,77 +2,77 @@ from abc import ABCMeta, abstractmethod
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class CXGDriver(metaclass=ABCMeta):
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def __init__(self, data, schema=None, graph_method=None, diffexp_method=None):
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self.data = self._load_data(data)
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def __init__(self, data, schema=None, graph_method=None, diffexp_method=None):
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self.data = self._load_data(data)
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@staticmethod
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@abstractmethod
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def _load_data(data):
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pass
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@staticmethod
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@abstractmethod
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def _load_data(data):
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pass
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@abstractmethod
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def _load_or_infer_schema(data):
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pass
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@abstractmethod
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def _load_or_infer_schema(data):
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pass
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@abstractmethod
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def _set_cell_ids(self):
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pass
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@abstractmethod
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def _set_cell_ids(self):
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pass
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@abstractmethod
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def cells(self):
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pass
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@abstractmethod
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def cells(self):
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pass
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@abstractmethod
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def cellids(self):
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pass
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@abstractmethod
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def cellids(self):
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pass
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@abstractmethod
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def genes(self):
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pass
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@abstractmethod
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def genes(self):
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pass
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@abstractmethod
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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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:param filter:
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:return: filtered dataframe
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"""
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pass
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@abstractmethod
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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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:param filter:
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:return: filtered dataframe
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"""
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pass
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# Should this return the order of metadata fields as the first value?
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@abstractmethod
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def metadata(self, df, fields=None):
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"""
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Generator for metadata. Gets the metadata values cell by cell and returns all value
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or only certain values if names is not None
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# Should this return the order of metadata fields as the first value?
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@abstractmethod
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def metadata(self, df, fields=None):
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"""
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Generator for metadata. Gets the metadata values cell by cell and returns all value
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or only certain values if names is not None
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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: Iterator for cellid + list of cells metadata values ex. [cell-id, val1, val2, val3]
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"""
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pass
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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: Iterator for cellid + list of cells metadata values ex. [cell-id, val1, val2, val3]
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"""
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pass
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@abstractmethod
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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: Iterator for [cellid-1, pos1, pos2], [cellid-2, pos1, pos2]
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"""
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pass
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@abstractmethod
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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: Iterator for [cellid-1, pos1, pos2], [cellid-2, pos1, pos2]
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"""
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pass
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@abstractmethod
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def diffexp(self, df1, df2):
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"""
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Computes the top differentially expressed genes between two clusters
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@abstractmethod
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def diffexp(self, df1, df2):
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"""
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Computes the top differentially expressed genes between two clusters
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:param df1: First set of cells
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:param df2: Second set of cells
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:return: Up in the air: I recommend [gene name, mean_expression_cells1,
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mean_expression_cells2, average_difference, statistic_value]
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"""
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pass
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:param df1: First set of cells
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:param df2: Second set of cells
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:return: Up in the air: I recommend [gene name, mean_expression_cells1,
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mean_expression_cells2, average_difference, statistic_value]
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"""
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pass
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@abstractmethod
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def expression(self, df):
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pass
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@abstractmethod
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def expression(self, df):
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pass
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@@ -10,175 +10,174 @@ from ..driver.driver import CXGDriver
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class ScanpyEngine(CXGDriver):
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def __init__(self, data, schema=None, graph_method="umap", diffexp_method="ttest"):
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self.data = self._load_data(data)
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self.schema = self._load_or_infer_schema(data, schema)
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self._set_cell_ids()
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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 __init__(self, data, schema=None, graph_method="umap", diffexp_method="ttest"):
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self.data = self._load_data(data)
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self.schema = self._load_or_infer_schema(data, schema)
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self._set_cell_ids()
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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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@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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@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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@staticmethod
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def _load_or_infer_schema(data, schema):
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data_schema = None
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if not schema:
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pass
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else:
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data_schema = parse_schema(os.path.join(data, schema))
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return data_schema
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@staticmethod
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def _load_or_infer_schema(data, schema):
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data_schema = None
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if not schema:
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pass
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else:
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data_schema = parse_schema(os.path.join(data, schema))
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return data_schema
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def _set_cell_ids(self):
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self.data.obs['cxg_cell_id'] = list(range(self.data.obs.shape[0]))
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self.data.obs["cell_name"] = list(self.data.obs.index)
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self.data.obs.set_index('cxg_cell_id', inplace=True)
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def _set_cell_ids(self):
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self.data.obs['cxg_cell_id'] = list(range(self.data.obs.shape[0]))
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self.data.obs["cell_name"] = list(self.data.obs.index)
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self.data.obs.set_index('cxg_cell_id', inplace=True)
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def cells(self):
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return list(self.data.obs.index)
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def cells(self):
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return list(self.data.obs.index)
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def cellids(self, df=None):
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if df:
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return list(df.obs.index)
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else:
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return list(self.data.obs.index)
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def cellids(self, df=None):
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if df:
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return list(df.obs.index)
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else:
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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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def genes(self):
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return self.data.var.index.tolist()
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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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:param filter:
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:return: iterator through cell ids
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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_:
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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_:
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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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return self.data[cell_idx, :]
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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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:param filter:
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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_:
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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_:
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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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return self.data[cell_idx, :]
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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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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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def metadata(self, df, fields=None):
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"""
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Generator for metadata. Gets the metadata values cell by cell and returns all value
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or only certain values if names is not None
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def metadata(self, df, fields=None):
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"""
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Generator for metadata. Gets the metadata values cell by cell and returns all value
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or only certain values if names is not None
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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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"""
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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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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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"""
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getattr(sc.tl, self.graph_method)(df)
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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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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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"""
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getattr(sc.tl, self.graph_method)(df)
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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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def diffexp(self, cell_list_1, cell_list_2, pval, num_genes):
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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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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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def diffexp(self, cell_list_1, cell_list_2, pval, num_genes):
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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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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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def expression(self, cells=None, genes=None):
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"""
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:param df:
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:return:
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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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def expression(self, cells=None, genes=None):
|
||||
"""
|
||||
:param df:
|
||||
:return:
|
||||
"""
|
||||
if cells:
|
||||
cells_idx = np.in1d(self.data.obs["cell_name"], cells)
|
||||
else:
|
||||
cells_idx = np.ones((self.cell_count,), dtype=bool)
|
||||
if genes:
|
||||
genes_idx = np.in1d(self.data.var_names, genes)
|
||||
else:
|
||||
genes_idx = np.ones((self.gene_count,), dtype=bool)
|
||||
index = np.ix_(cells_idx, genes_idx)
|
||||
expression = self.data.X[index]
|
||||
|
||||
if not genes:
|
||||
genes = self.data.var.index.tolist()
|
||||
if not cells:
|
||||
cells = self.data.obs["cell_name"].tolist()
|
||||
if not genes:
|
||||
genes = self.data.var.index.tolist()
|
||||
if not cells:
|
||||
cells = self.data.obs["cell_name"].tolist()
|
||||
|
||||
cell_data = []
|
||||
for idx, cell in enumerate(cells):
|
||||
cell_data.append({
|
||||
"cellname": cell,
|
||||
"e": list(expression[idx]),
|
||||
})
|
||||
cell_data = []
|
||||
for idx, cell in enumerate(cells):
|
||||
cell_data.append({
|
||||
"cellname": cell,
|
||||
"e": list(expression[idx]),
|
||||
})
|
||||
|
||||
return {
|
||||
"genes": genes,
|
||||
"cells": cell_data,
|
||||
"nonzero_gene_count": int(np.sum(expression.any(axis=0)))
|
||||
}
|
||||
return {
|
||||
"genes": genes,
|
||||
"cells": cell_data,
|
||||
"nonzero_gene_count": int(np.sum(expression.any(axis=0)))
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user