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"""
This module will contain functions to be used in computation of the data used to
generate various kinds of heatmaps.
"""
from gn3.computations.slink import slink
from gn3.computations.correlations2 import compute_correlation
def export_trait_data(
trait_data: dict, strainlist: Sequence[str], dtype: str="val",
var_exists: bool=False, n_exists: bool=False):
"""
Export data according to `strainlist`. Mostly used in calculating
correlations.
DESCRIPTION:
Migrated from
https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/base/webqtlTrait.py#L166-L211
PARAMETERS
trait: (dict)
The dictionary of key-value pairs representing a trait
strainlist: (list)
A list of strain names
type: (str)
... verify what this is ...
var_exists: (bool)
A flag indicating existence of variance
n_exists: (bool)
A flag indicating existence of ndata
"""
def __export_all_types(tdata, strain):
sample_data = []
if tdata[strain]["val"]:
sample_data.append(tdata[strain]["val"])
if var_exists:
if tdata[strain].var:
sample_data.append(tdata[strain]["var"])
else:
sample_data.append(None)
if n_exists:
if tdata[strain]["ndata"]:
sample_data.append(tdata[strain]["ndata"])
else:
sample_data.append(None)
else:
if var_exists and n_exists:
sample_data += [None, None, None]
elif var_exists or n_exists:
sample_data += [None, None]
else:
sample_data.append(None)
return tuple(sample_data)
def __exporter(accumulator, strain):
if tdata.has_key(strain):
if dtype == "val":
return accumulator + (tdata[strain]["val"], )
if dtype == "var":
return accumulator + (tdata[strain]["var"], )
if dtype == "N":
return tdata[strain]["ndata"]
if dtype == "all":
return accumulator + __export_all_types(
accumulator, tdata, strain)
else:
raise KeyError("Type `%s` is incorrect" % dtype)
else:
if var_exists and n_exists:
return accumulator + (None, None, None)
if var_exists or n_exists:
return accumulator + (None, None)
return accumulator + (None,)
return reduce(__exporter(strain), strainlist, tuple())
def trait_display_name(trait: Dict):
"""
Given a trait, return a name to use to display the trait on a heatmap.
DESCRIPTION
Migrated from
https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/base/webqtlTrait.py#L141-L157
"""
if trait.get("db", None) and trait.get("trait_name", None):
if trait["db"]["dataset_type"] == "Temp":
desc = trait["description"]
if desc.find("PCA") >= 0:
return "%s::%s" % (
trait["db"]["displayname"],
desc[desc.rindex(':')+1:].strip())
return "%s::%s" % (
trait["db"]["displayname"],
desc[:desc.index('entered')].strip())
else:
prefix = "%s::%s" % (
trait["db"]["dataset_name"], trait["trait_name"])
if trait["cellid"]:
return "%s::%s" % (prefix, trait["cellid"])
return prefix
return trait["description"]
def cluster_traits(traits_data_list: Sequence[Dict]):
"""
Clusters the trait values.
DESCRIPTION
Attempts to replicate the clustering of the traits, as done at
https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L138-L162
"""
def __compute_corr(tdata_i, tdata_j):
if tdata_j[0] < tdata_i[0]:
corr, nOverlap = compute_correlation(tdata_i, tdata_j)
if (1 - corr) < 0:
return 0.0
return 1 - corr
return 0.0
def __cluster(tdata_i):
res2 = tuple(
__compute_corr(tdata_i, tdata_j) for tdata_j in enumerate(traits))
return tuple(__cluster(tdata_i) for tdata_i in enumerate(traits_data_list))
def heatmap_data(
fd, search_result, conn: Any, colorScheme=None, userPrivilege=None,
userName=None):
"""
heatmap function
DESCRIPTION
This function is an attempt to reproduce the initialisation at
https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L46-L64
and also the clustering and slink computations at
https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L138-L165
with the help of the `gn3.computations.heatmap.cluster_traits` function.
It does not try to actually draw the heatmap image.
PARAMETERS:
TODO: Elaborate on the parameters here...
"""
cluster_checked = fd.formdata.getvalue("clusterCheck", "")
strainlist = [strain for strain in fd.strainlist if strain not in fd.parlist]
genotype = fd.genotype
def __retrieve_traitlist_and_datalist(threshold, fullname):
trait = retrieve_trait_info(threshold, fullname, conn)
return (trait, export_trait_data(retrieve_trait_data(trait), strainlist))
traits_details = [
__retrieve_traitlist_and_datalist(threshold, fullname)
for fullname in search_result]
traits_list = map(lambda x: x[0], traits_details)
traits_data_list = map(lambda x: x[1], traits_details)
return {
"target_description_checked": fd.formdata.getvalue(
"targetDescriptionCheck", ""),
"cluster_checked": cluster_checked,
"slink_data": (
slink(cluster_traits(traits_list, strainlist))
if cluster_checked else False)
"sessionfile": fd.formdata.getvalue("session"),
"genotype": genotype,
"nLoci": sum(map(lambda x: len(x), genotype))
"strainlist": strainlist,
"ppolar": fd.ppolar,
"mpolar":fd.mpolar,
"traits_list": traits_list
"traits_data_list": traits_data_list
}
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