mirror of
https://github.com/chanzuckerberg/cellxgene.git
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77 lines
1.9 KiB
Python
77 lines
1.9 KiB
Python
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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@abstractmethod
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@staticmethod
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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 _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 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 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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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, cells_iterator, 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 cells_iterator: from filter cells, iterator for cellids
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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, cells_iterator):
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"""
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Computes a n-d layout for cells through dimensionality reduction.
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:param cells_iterator: from filter cells, iterator for cellids
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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, cells_iterator_1, cells_iterator_2):
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"""
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Computes the top differentially expressed genes between two clusters
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:param cells_iterator_1: First set of cell ids
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:param cells_iterator_2: Second set of cell ids
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:return: Up in the air: I recommend [gene name, mean_expression_cells1, mean_expression_cells2, average_difference, statistic_value]
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"""
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pass
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