Files
cellxgene/server/app/driver/driver.py
2018-06-26 11:41:32 -07:00

79 lines
2.1 KiB
Python

from abc import ABCMeta, abstractmethod
class CXGDriver(metaclass=ABCMeta):
def __init__(self, data, schema=None, graph_method=None, diffexp_method=None):
self.data = self._load_data(data)
@staticmethod
@abstractmethod
def _load_data(data):
pass
@abstractmethod
def _load_or_infer_schema(data):
pass
@abstractmethod
def _set_cell_ids(self):
pass
@abstractmethod
def cells(self):
pass
@abstractmethod
def cellids(self):
pass
@abstractmethod
def genes(self):
pass
@abstractmethod
def filter_cells(self, filter):
"""
Filter cells from data and return a subset of the data
:param filter:
:return: filtered dataframe
"""
pass
# Should this return the order of metadata fields as the first value?
@abstractmethod
def metadata(self, df, fields=None):
"""
Generator for metadata. Gets the metadata values cell by cell and returns all value
or only certain values if names is not None
:param df: from filter_cells, dataframe
:param fields: list of keys for metadata to return, returns all metadata values if not set.
:return: Iterator for cellid + list of cells metadata values ex. [cell-id, val1, val2, val3]
"""
pass
@abstractmethod
def create_graph(self, df):
"""
Computes a n-d layout for cells through dimensionality reduction.
:param df: from filter_cells, dataframe
:return: Iterator for [cellid-1, pos1, pos2], [cellid-2, pos1, pos2]
"""
pass
@abstractmethod
def diffexp(self, df1, df2):
"""
Computes the top differentially expressed genes between two clusters
:param df1: First set of cells
:param df2: Second set of cells
:return: Up in the air: I recommend [gene name, mean_expression_cells1,
mean_expression_cells2, average_difference, statistic_value]
"""
pass
@abstractmethod
def expression(self, df):
pass