delete unwanted correlation stuff (#5)

* delete unwanted correlation stuff

* Refactor/clean up correlations (#4)

* initial commit for Refactor/clean-up-correlation

* add python scipy dependency

* initial commit for sample correlation

* initial commit for sample correlation endpoint

* initial commit for integration and unittest

* initial commit for registering  correlation blueprint

* add and modify unittest and integration tests for correlation

* Add compute compute_all_sample_corr   method for correlation

* add scipy to requirement txt file

* add tissue correlation for trait list

* add unittest for tissue correlation

* add lit correlation for trait list

* add unittests for lit correlation for trait list

* modify lit correlarion for trait list

* add unittests for lit correlation for trait list

* add correlation metho  in dynamic url

* add file format for expected structure input  while doing sample correlation

* modify input data structure -> add  trait id

* update tests for sample r correlation

* add compute all lit correlation method

* add endpoint for computing lit_corr

* add unit and integration tests for computing lit corr

* add /api/correlation/tissue_corr/{corr_method} endpoint for tissue correlation

* add unittest and integration tests for tissue correlation

Co-authored-by: BonfaceKilz <bonfacemunyoki@gmail.com>

* update guix scm file

* fix pylint error for correlations api

Co-authored-by: BonfaceKilz <bonfacemunyoki@gmail.com>

1 files changed, 0 insertions, 96 deletions

diff --git a/gn3/correlation/correlation_functions.py b/gn3/correlation/correlation_functions.py
deleted file mode 100644
index be08c96..0000000
--- a/gn3/correlation/correlation_functions.py
+++ /dev/null
@@ -1,96 +0,0 @@
-
-"""
-# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
-#
-# This program is free software: you can redistribute it and/or modify it
-# under the terms of the GNU Affero General Public License
-# as published by the Free Software Foundation, either version 3 of the
-# License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-# See the GNU Affero General Public License for more details.
-#
-# This program is available from Source Forge: at GeneNetwork Project
-# (sourceforge.net/projects/genenetwork/).
-#
-# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
-# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
-#
-#
-#
-# This module is used by GeneNetwork project (www.genenetwork.org)
-#
-# Created by GeneNetwork Core Team 2010/08/10
-#
-# Last updated by NL 2011/03/23
-
-
-"""
-
-import rpy2.robjects
-from gn3.base.mrna_assay_tissue_data import MrnaAssayTissueData
-
-
-#####################################################################################
-# Input: primaryValue(list): one list of expression values of one probeSet,
-#       targetValue(list): one list of expression values of one probeSet,
-#               method(string): indicate correlation method ('pearson' or 'spearman')
-# Output: corr_result(list): first item is Correlation Value, second item is tissue number,
-#                           third item is PValue
-# Function: get correlation value,Tissue quantity ,p value result by using R;
-# Note : This function is special case since both primaryValue and targetValue are from
-# the same dataset. So the length of these two parameters is the same. They are pairs.
-# Also, in the datatable TissueProbeSetData, all Tissue values are loaded based on
-# the same tissue order
-#####################################################################################
-
-def cal_zero_order_corr_for_tiss(primaryValue=[], targetValue=[], method='pearson'):
-    """refer above for info on the function"""
-    # pylint: disable = E, W, R, C
-
-    #nb disabled pylint until tests are written for this function
-
-    R_primary = rpy2.robjects.FloatVector(list(range(len(primaryValue))))
-    N = len(primaryValue)
-    for i in range(len(primaryValue)):
-        R_primary[i] = primaryValue[i]
-
-    R_target = rpy2.robjects.FloatVector(list(range(len(targetValue))))
-    for i in range(len(targetValue)):
-        R_target[i] = targetValue[i]
-
-    R_corr_test = rpy2.robjects.r['cor.test']
-    if method == 'spearman':
-        R_result = R_corr_test(R_primary, R_target, method='spearman')
-    else:
-        R_result = R_corr_test(R_primary, R_target)
-
-    corr_result = []
-    corr_result.append(R_result[3][0])
-    corr_result.append(N)
-    corr_result.append(R_result[2][0])
-
-    return corr_result
-
-
-####################################################
-####################################################
-# input: cursor, symbolList (list), dataIdDict(Dict): key is symbol
-# output: SymbolValuePairDict(dictionary):one dictionary of Symbol and Value Pair.
-#        key is symbol, value is one list of expression values of one probeSet.
-# function: wrapper function for getSymbolValuePairDict function
-#          build gene symbol list if necessary, cut it into small lists if necessary,
-#          then call getSymbolValuePairDict function and merge the results.
-###################################################
-#####################################################
-
-def get_trait_symbol_and_tissue_values(symbol_list=None):
-    """function to get trait symbol and tissues values refer above"""
-    tissue_data = MrnaAssayTissueData(gene_symbols=symbol_list)
-
-    if len(tissue_data.gene_symbols) >= 1:
-        return tissue_data.get_symbol_values_pairs()
-
-    return None