mirror of
https://github.com/chanzuckerberg/cellxgene.git
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6c23a72e5f
* Minor naming and phrasing changes from UX review * category-selection-limit -> max-category-items * Indicate load may taking a long time * program -> command (for launch, prepare) * debug -> verbose * flask-debug -> debug * Developer mode for debug verbose on open browser off * move examples from epilogue to prefix
116 lines
4.1 KiB
Python
116 lines
4.1 KiB
Python
from abc import ABCMeta, abstractmethod
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"""
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Sort order for methods
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1. Initialize
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2. Helper
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3. Filter
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4. Data & Metadata
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5. Computation
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"""
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class CXGDriver(metaclass=ABCMeta):
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def __init__(self, data, layout_method=None, diffexp_method=None, max_category_items=100):
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self.data = self._load_data(data)
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self.layout_method = layout_method
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self.diffexp_method = diffexp_method
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self.max_category_items = max_category_items
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self.cluster = None
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@property
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def features(self):
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features = {
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"cluster": {"available": False},
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"layout": {
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"obs": {"available": False},
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"var": {"available": False},
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},
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"diffexp": {"available": False}
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}
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# TODO - Interactive limit should be generated from the actual available methods see GH issue #94
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if self.layout_method:
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# TODO handle "var" when gene layout becomes available
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features["layout"]["obs"] = {"available": True, "interactiveLimit": 50000}
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if self.diffexp_method:
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features["diffexp"] = {"available": True, "interactiveLimit": 50000}
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if self.cluster:
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features["cluster"] = {"available": True, "interactiveLimit": 50000}
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return features
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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 cells(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_dataframe(self, filter):
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"""
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Filter cells from data and return a subset of the data. They can operate on both obs and var dimension with
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indexing and filtering by annotation value. Filters are combined with the and operator.
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See REST specs for info on filter format:
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https://github.com/chanzuckerberg/cellxgene/blob/master/docs/REST_API.md
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:param filter: dictionary with filter params
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:return: View into scanpy object with cells/genes filtered
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"""
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pass
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@abstractmethod
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def annotation(self, filter, axis, fields=None):
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"""
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Gets annotation value for each observation
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:param filter: filter: dictionary with filter params
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:param axis: string obs or var
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:param fields: list of keys for annotation to return, returns all annotation values if not set.
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:return: dict: names - list of fields in order, data - list of lists or metadata
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[observation ids, val1, val2...]
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"""
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pass
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@abstractmethod
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def data_frame(self, filter, axis):
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"""
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Retrieves data for each variable for observations in data frame
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:param filter: filter: dictionary with filter params
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:param axis: string obs or var
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:return: {
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"var": list of variable ids,
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"obs": [cellid, var1 expression, var2 expression, ...],
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}
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"""
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pass
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@abstractmethod
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def diffexp(self, filter1, filter2, top_n=None, interactive_limit=None):
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"""
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Computes the top differentially expressed variables between two observation sets. If dataframes
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contain a subset of variables, then statistics for all variables will be returned, otherwise
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only the top N vars will be returned.
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:param filter1: filter: dictionary with filter params for first set of observations
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:param filter2: filter: dictionary with filter params for second set of observations
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:param top_n: Limit results to top N (Top var mode only)
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:param interactive_limit: -- don't compute if total # genes in dataframes are larger than this
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:return: top genes, stats and expression values for variables
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"""
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pass
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@abstractmethod
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def layout(self, filter, interactive_limit=None):
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"""
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Computes a n-d layout for cells through dimensionality reduction.
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:param filter: filter: dictionary with filter params
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:param interactive_limit: -- don't compute if total # genes in dataframes are larger than this
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:return: [cellid, x, y, ...]
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"""
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pass
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