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-rw-r--r--wqflask/wqflask/correlation/rust_correlation.py21
1 files changed, 11 insertions, 10 deletions
diff --git a/wqflask/wqflask/correlation/rust_correlation.py b/wqflask/wqflask/correlation/rust_correlation.py
index 88133b31..5c4d0b8a 100644
--- a/wqflask/wqflask/correlation/rust_correlation.py
+++ b/wqflask/wqflask/correlation/rust_correlation.py
@@ -99,7 +99,7 @@ def chunk_dataset(dataset, steps, name):
for i in range(0, len(dataset), steps):
matrix = list(dataset[i:i + steps])
results.append([traits_name_dict[matrix[0][0]]] + [str(value)
- for (trait_name, strain, value) in matrix])
+ for (trait_name, strain, value) in matrix])
return results
@@ -159,9 +159,9 @@ def compute_top_n_sample(start_vars, dataset, trait_list):
corr_data, list(sample_data.values()), "pearson", ",")
-def compute_top_n_lit(corr_results, this_dataset, this_trait) -> dict:
+def compute_top_n_lit(corr_results, target_dataset, this_trait) -> dict:
(this_trait_geneid, geneid_dict, species) = do_lit_correlation(
- this_trait, this_dataset)
+ this_trait, target_dataset)
geneid_dict = {trait_name: geneid for (trait_name, geneid)
in geneid_dict.items() if
@@ -177,14 +177,14 @@ def compute_top_n_lit(corr_results, this_dataset, this_trait) -> dict:
return {}
-def compute_top_n_tissue(this_dataset, this_trait, traits, method):
+def compute_top_n_tissue(target_dataset, this_trait, traits, method):
# refactor lots of rpt
trait_symbol_dict = dict({
trait_name: symbol
for (trait_name, symbol)
- in this_dataset.retrieve_genes("Symbol").items()
+ in target_dataset.retrieve_genes("Symbol").items()
if traits.get(trait_name)})
corr_result_tissue_vals_dict = get_trait_symbol_and_tissue_values(
@@ -248,7 +248,6 @@ def __compute_sample_corr__(
target_dataset.get_trait_data(list(sample_data.keys()))
-
def __merge_key_and_values__(rows, current):
wo_nones = [value for value in current[1] if value is not None]
if len(wo_nones) > 0:
@@ -265,12 +264,14 @@ def __compute_sample_corr__(
target_data, list(sample_data.values()), method, ",", corr_type,
n_top)
+
def __datasets_compatible_p__(trait_dataset, target_dataset, corr_method):
return not (
corr_method in ("tissue", "Tissue r", "Literature r", "lit")
and (trait_dataset.type == "ProbeSet" and
target_dataset.type in ("Publish", "Geno")))
+
def __compute_tissue_corr__(
start_vars: dict, corr_type: str, method: str, n_top: int,
target_trait_info: tuple):
@@ -344,9 +345,9 @@ def compute_correlation_rust(
if corr_type == "sample":
top_a = compute_top_n_tissue(
- this_dataset, this_trait, results, method)
+ target_dataset, this_trait, results, method)
- top_b = compute_top_n_lit(results, this_dataset, this_trait)
+ top_b = compute_top_n_lit(results, target_dataset, this_trait)
elif corr_type == "lit":
@@ -355,14 +356,14 @@ def compute_correlation_rust(
top_a = compute_top_n_sample(
start_vars, target_dataset, list(results.keys()))
top_b = compute_top_n_tissue(
- this_dataset, this_trait, results, method)
+ target_dataset, this_trait, results, method)
else:
top_a = compute_top_n_sample(
start_vars, target_dataset, list(results.keys()))
- top_b = compute_top_n_lit(results, this_dataset, this_trait)
+ top_b = compute_top_n_lit(results, target_dataset, this_trait)
return {
"correlation_results": merge_results(