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Reorganize order of methods in engine and rest (#305)

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This commit is contained in:
Charlotte Weaver
2018-10-10 16:44:05 -07:00
committed by GitHub
parent dce778f658
commit eb3b3fc59a
3 changed files with 287 additions and 258 deletions
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server/app/scanpy_engine/scanpy_engine.py
+55 -46
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@@ -11,6 +11,15 @@ from scipy import stats
from server.app.driver.driver import CXGDriver
from server.app.util.constants import Axis, DEFAULT_TOP_N, DiffExpMode
"""
Sort order for methods
1. Initialize
2. Helper
3. Filter
4. Data & Metadata
5. Computation
"""
class ScanpyEngine(CXGDriver):
@@ -151,7 +160,7 @@ class ScanpyEngine(CXGDriver):
Filter data based on index. ex. [1, 3, [111:200]]
:param filter: subset of filter dict for obs/var:index
:param index: np logical vector containing true for passing false for failing filter
:param axis: Axis
:param axis: string obs or var
:return: np logical vector for whether the data passes the filter
"""
if axis == Axis.OBS:
@@ -194,9 +203,8 @@ class ScanpyEngine(CXGDriver):
def annotation(self, df, axis, fields=None):
"""
Gets annotation value for each observation
:param axis:
:param df: from filter_cells, dataframe
:param axis: string obs or var
:param fields: list of keys for annotation to return, returns all annotation values if not set.
:return: dict: names - list of fields in order, data - list of lists or metadata
[observation ids, val1, val2...]
@@ -211,24 +219,37 @@ class ScanpyEngine(CXGDriver):
}
# @cache.memoize()
def layout(self, df):
def data_frame(self, df, axis):
"""
Computes a n-d layout for cells through dimensionality reduction.
Retrieves data for each variable for observations in data frame
:param df: from filter_cells, dataframe
:return: [cellid, x, y, ...]
"""
# TODO Filtering cells is fine, but filtering genes does nothing because the neighbors are
# calculated using the original vars (geneset) and this doesn’t get updated when you use less.
# Need to recalculate neighbors (long) if user requests new layout filtered by var
getattr(sc.tl, self.layout_method)(df, random_state=123)
df_layout = df.obsm[f"X_{self.layout_method}"]
normalized_layout = DataFrame((df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()),
index=df.obs.index)
return {
"ndims": normalized_layout.shape[1],
# reset_index gets obs' id into output
"coordinates": normalized_layout.reset_index().values.tolist()
:param axis: string obs or var
:return: {
"var": list of variable ids,
"obs": [cellid, var1 expression, var2 expression, ...],
}
"""
var_idx = df.var.index.tolist()
obs_idx = df.obs.index.tolist()
values = df.X
df_shape = df.shape
if df_shape[0] == 1:
values = values[None, :]
elif df_shape[1] == 1:
values = values[:, None]
if axis == Axis.OBS:
expression = DataFrame(values, index=obs_idx)
result = {
"var": var_idx,
"obs": expression.reset_index().values.tolist()
}
else:
expression = DataFrame(values.T, index=var_idx)
result = {
"obs": obs_idx,
"var": expression.reset_index().values.tolist(),
}
return result
# @cache.memoize()
def diffexp(self, df1, df2, top_n=None):
@@ -238,7 +259,7 @@ class ScanpyEngine(CXGDriver):
only the top N vars will be returned.
:param df1: from filter_cells, dataframe containing first set of observations
:param df2: from filter_cells, dataframe containing second set of observations
:param topN: Limit results to top N (Top var mode only)
:param top_n: Limit results to top N (Top var mode only)
:return: top genes, stats and expression values for variables
"""
# If not the same genes, test is wrong!
@@ -280,33 +301,21 @@ class ScanpyEngine(CXGDriver):
return sorted(result, key=lambda gene: gene[0])
# @cache.memoize()
def data_frame(self, df, axis):
def layout(self, df):
"""
Retrieves data for each variable for observations in data frame
Computes a n-d layout for cells through dimensionality reduction.
:param df: from filter_cells, dataframe
:return: {
"var": list of variable ids,
"obs": [cellid, var1 expression, var2 expression, ...],
}
:return: [cellid, x, y, ...]
"""
var_idx = df.var.index.tolist()
obs_idx = df.obs.index.tolist()
values = df.X
df_shape = df.shape
if df_shape[0] == 1:
values = values[None, :]
elif df_shape[1] == 1:
values = values[:, None]
if axis == Axis.OBS:
expression = DataFrame(values, index=obs_idx)
result = {
"var": var_idx,
"obs": expression.reset_index().values.tolist()
}
else:
expression = DataFrame(values.T, index=var_idx)
result = {
"obs": obs_idx,
"var": expression.reset_index().values.tolist(),
}
return result
# TODO Filtering cells is fine, but filtering genes does nothing because the neighbors are
# calculated using the original vars (geneset) and this doesn’t get updated when you use less.
# Need to recalculate neighbors (long) if user requests new layout filtered by var
getattr(sc.tl, self.layout_method)(df, random_state=123)
df_layout = df.obsm[f"X_{self.layout_method}"]
normalized_layout = DataFrame((df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()),
index=df.obs.index)
return {
"ndims": normalized_layout.shape[1],
# reset_index gets obs' id into output
"coordinates": normalized_layout.reset_index().values.tolist()
}