From 51db16394ebe5936a2078293c676744b7ea74fc6 Mon Sep 17 00:00:00 2001
From: Zachary Sloan
Date: Thu, 7 Mar 2013 23:42:58 +0000
Subject: Progress bar is now completely working
Still need to figure out the problem that occurred
with negative p-values after I refactored the LMM
code
---
wqflask/base/data_set.py | 1 +
.../wqflask/marker_regression/marker_regression.py | 3 +-
wqflask/wqflask/my_pylmm/pyLMM/lmm.py | 203 ++++++++++++++++-----
.../new/javascript/show_trait_mapping_tools.coffee | 1 -
.../new/javascript/show_trait_mapping_tools.js | 70 +------
.../wqflask/templates/show_trait_progress_bar.html | 2 +-
6 files changed, 159 insertions(+), 121 deletions(-)
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index c6d67e68..d474302c 100755
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -75,6 +75,7 @@ class Markers(object):
def add_pvalues(self, p_values):
for marker, p_value in itertools.izip(self.markers, p_values):
marker['p_value'] = p_value
+ print("p_value is:", marker['p_value'])
marker['lod_score'] = -math.log10(marker['p_value'])
#Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values
marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61
diff --git a/wqflask/wqflask/marker_regression/marker_regression.py b/wqflask/wqflask/marker_regression/marker_regression.py
index 412d9e35..4ddc89c6 100755
--- a/wqflask/wqflask/marker_regression/marker_regression.py
+++ b/wqflask/wqflask/marker_regression/marker_regression.py
@@ -497,7 +497,8 @@ class MarkerRegression(object):
genotype_matrix,
kinship_matrix,
REML=True,
- refit=False)
+ refit=False,
+ temp_data=self.temp_data)
Bench().report()
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
index cc2e32a7..12f7c2ea 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
@@ -95,53 +95,53 @@ def calculate_kinship(genotype_matrix, temp_data):
kinship_matrix = np.dot(genotype_matrix,genotype_matrix.T) * 1.0/float(m)
return kinship_matrix
-def GWAS(Y, X, K, Kva=[], Kve=[], X0=None, REML=True, refit=False):
- """
- Performs a basic GWAS scan using the LMM. This function
- uses the LMM module to assess association at each SNP and
- does some simple cleanup, such as removing missing individuals
- per SNP and re-computing the eigen-decomp
-
- Y - n x 1 phenotype vector
- X - n x m SNP matrix
- K - n x n kinship matrix
- Kva,Kve = linalg.eigh(K) - or the eigen vectors and values for K
- X0 - n x q covariate matrix
- REML - use restricted maximum likelihood
- refit - refit the variance component for each SNP
- """
- n = X.shape[0]
- m = X.shape[1]
+def GWAS(Y, X, K, Kva=[], Kve=[], X0=None, REML=True, refit=False, temp_data=None):
+ """
+ Performs a basic GWAS scan using the LMM. This function
+ uses the LMM module to assess association at each SNP and
+ does some simple cleanup, such as removing missing individuals
+ per SNP and re-computing the eigen-decomp
+
+ Y - n x 1 phenotype vector
+ X - n x m SNP matrix
+ K - n x n kinship matrix
+ Kva,Kve = linalg.eigh(K) - or the eigen vectors and values for K
+ X0 - n x q covariate matrix
+ REML - use restricted maximum likelihood
+ refit - refit the variance component for each SNP
+ """
+ n = X.shape[0]
+ m = X.shape[1]
- if X0 == None: X0 = np.ones((n,1))
-
- # Remove missing values in Y and adjust associated parameters
- v = np.isnan(Y)
- if v.sum():
- keep = True - v
- Y = Y[keep]
- X = X[keep,:]
- X0 = X0[keep,:]
- K = K[keep,:][:,keep]
- Kva = []
- Kve = []
+ if X0 == None: X0 = np.ones((n,1))
- L = LMM(Y,K,Kva,Kve,X0)
- if not refit: L.fit()
+ # Remove missing values in Y and adjust associated parameters
+ v = np.isnan(Y)
+ if v.sum():
+ keep = True - v
+ Y = Y[keep]
+ X = X[keep,:]
+ X0 = X0[keep,:]
+ K = K[keep,:][:,keep]
+ Kva = []
+ Kve = []
- PS = []
- TS = []
+ L = LMM(Y,K,Kva,Kve,X0)
+ if not refit: L.fit()
- for i in range(m):
- x = X[:,i].reshape((n,1))
- v = np.isnan(x).reshape((-1,))
- if v.sum():
+ PS = []
+ TS = []
+
+ for counter in range(m):
+ x = X[:,counter].reshape((n,1))
+ v = np.isnan(x).reshape((-1,))
+ if v.sum():
keep = True - v
xs = x[keep,:]
- if xs.var() == 0:
- PS.append(np.nan)
- TS.append(np.nan)
- continue
+ if xs.var() == 0:
+ PS.append(np.nan)
+ TS.append(np.nan)
+ continue
Ys = Y[keep]
X0s = X0[keep,:]
@@ -150,19 +150,124 @@ def GWAS(Y, X, K, Kva=[], Kve=[], X0=None, REML=True, refit=False):
if refit: Ls.fit(X=xs)
else: Ls.fit()
ts,ps = Ls.association(xs,REML=REML)
- else:
- if x.var() == 0:
- PS.append(np.nan)
- TS.append(np.nan)
- continue
+ else:
+ if x.var() == 0:
+ PS.append(np.nan)
+ TS.append(np.nan)
+ continue
if refit: L.fit(X=x)
ts,ps = L.association(x,REML=REML)
- PS.append(ps)
- TS.append(ts)
+ percent_complete = 45 + int(round((counter/m)*55))
+ print("Percent complete: ", percent_complete)
+ temp_data.store("percent_complete", percent_complete)
- return TS,PS
+ PS.append(ps)
+ TS.append(ts)
+
+ return TS,PS
+
+#def GWAS(pheno_vector,
+# genotype_matrix,
+# kinship_matrix,
+# kinship_eigenvals=None,
+# kinship_eigenvectors=None,
+# covariate_matrix=None,
+# restricted_max_likelihood=True,
+# refit=False,
+# temp_data=None):
+# """
+# Performs a basic GWAS scan using the LMM. This function
+# uses the LMM module to assess association at each SNP and
+# does some simple cleanup, such as removing missing individuals
+# per SNP and re-computing the eigen-decomp
+#
+# Y - n x 1 phenotype vector
+# X - n x m SNP matrix
+# K - n x n kinship matrix
+# Kva,Kve = linalg.eigh(K) - or the eigen vectors and values for K
+# X0 - n x q covariate matrix
+# REML - use restricted maximum likelihood
+# refit - refit the variance component for each SNP
+#
+# """
+#
+# assert temp_data, "You forgot to pass in temp_data"
+#
+# if kinship_eigenvals == None:
+# kinship_eigenvals = []
+# if kinship_eigenvectors == None:
+# kinship_eigenvectors = []
+#
+# n = genotype_matrix.shape[0]
+# m = genotype_matrix.shape[1]
+#
+# if covariate_matrix == None:
+# covariate_matrix = np.ones((n,1))
+#
+# # Remove missing values in Y and adjust associated parameters
+# pheno_not_number = np.isnan(pheno_vector)
+# if pheno_not_number.sum():
+# keep = True - pheno_not_number
+# pheno_vector = pheno_vector[keep]
+# genotype_matrix = genotype_matrix[keep,:]
+# covariate_matrix = covariate_matrix[keep,:]
+# kinship_matrix = kinship_matrix[keep,:][:,keep]
+# kinship_eigenvals = []
+# kinship_eigenvectors = []
+#
+# lmm_ob = LMM(pheno_vector,
+# kinship_matrix,
+# kinship_eigenvals,
+# kinship_eigenvectors,
+# covariate_matrix)
+# if not refit:
+# lmm_ob.fit()
+#
+# p_value_matrix = []
+# t_stats_matrix = []
+#
+# for counter in range(m):
+# #pheno_vector_2 = geno_vector[:, counter]
+# #x = pheno_vector_2.reshape((n,1))
+# x = genotype_matrix[:,counter].reshape((n,1))
+# v = np.isnan(x).reshape((-1,))
+# if v.sum():
+# keep = True - v
+# xs = x[keep,:]
+# if xs.var() == 0:
+# p_value_matrix.append(np.nan)
+# t_stats_matrix.append(np.nan)
+# continue
+#
+# pheno_vector_2 = pheno_vector[keep]
+# covariate_matrix_2 = covariate_matrix[keep,:]
+# kinship_matrix_2 = kinship_matrix[keep,:][:,keep]
+# lmm_ob_2 = LMM(pheno_vector, kinship_matrix, covariate_matrix=covariate_matrix)
+# if refit:
+# lmm_ob_2.fit(X=xs)
+# else:
+# lmm_ob_2.fit()
+# t_stats, p_values = lmm_ob_2.association(xs, REML=restricted_max_likelihood)
+# else:
+# if x.var() == 0:
+# p_value_matrix.append(np.nan)
+# t_stats_matrix.append(np.nan)
+# continue
+#
+# if refit:
+# lmm_ob.fit(X=x)
+# t_stats,p_values = lmm_ob.association(x, REML=restricted_max_likelihood)
+#
+# p_value_matrix.append(p_values)
+# t_stats_matrix.append(t_stats)
+#
+# percent_complete = 45 + int(round((counter/m)*55))
+# print("Percent complete: ", percent_complete)
+# temp_data.store("percent_complete", percent_complete)
+#
+# return p_value_matrix, t_stats_matrix
class LMM:
diff --git a/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.coffee b/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.coffee
index 35572f67..157f56a9 100644
--- a/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.coffee
+++ b/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.coffee
@@ -30,7 +30,6 @@ $ ->
$("#marker_regression").click(() =>
$("#progress_bar_container").modal()
-
url = "/marker_regression"
form_data = $('#trait_data_form').serialize()
console.log("form_data is:", form_data)
diff --git a/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js b/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js
index 78459692..c8b0aa7b 100644
--- a/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js
+++ b/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js
@@ -1,74 +1,6 @@
// Generated by CoffeeScript 1.4.0
(function() {
- $(function() {
- var composite_mapping_fields, get_progress, submit_special, toggle_enable_disable,
- _this = this;
- submit_special = function() {
- var url;
- console.log("In submit_special");
- console.log("this is:", this);
- console.log("$(this) is:", $(this));
- url = $(this).data("url");
- console.log("url is:", url);
- $("#trait_data_form").attr("action", url);
- return $("#trait_data_form").submit();
- };
- get_progress = function() {
- var params, params_str, temp_uuid, url,
- _this = this;
- console.log("temp_uuid:", $("#temp_uuid").val());
- temp_uuid = $("#temp_uuid").val();
- params = {
- key: temp_uuid
- };
- params_str = $.param(params);
- url = "/get_temp_data?" + params_str;
- console.log("url:", url);
- $.ajax({
- type: "GET",
- url: url,
- success: function(progress_data) {
- console.log("in get_progress data:", progress_data);
- console.log(progress_data['percent_complete'] + "%");
- return $('#marker_regression_progress').css("width", progress_data['percent_complete'] + "%");
- }
- });
- return false;
- };
- $("#marker_regression").click(function() {
- var form_data, url;
- $("#progress_bar_container").modal();
- url = "/marker_regression";
- form_data = $('#trait_data_form').serialize();
- console.log("form_data is:", form_data);
- $.ajax({
- type: "POST",
- url: url,
- data: form_data,
- success: function(data) {
- clearInterval(_this.my_timer);
- $('#progress_bar_container').modal('hide');
- return $("body").html(data);
- }
- });
- console.log("settingInterval");
- _this.my_timer = setInterval(get_progress, 1000);
- return false;
- });
- composite_mapping_fields = function() {
- return $(".composite_fields").toggle();
- };
- $("#use_composite_choice").change(composite_mapping_fields);
- toggle_enable_disable = function(elem) {
- return $(elem).prop("disabled", !$(elem).prop("disabled"));
- };
- $("#choose_closet_control").change(function() {
- return toggle_enable_disable("#control_locus");
- });
- return $("#display_all_lrs").change(function() {
- return toggle_enable_disable("#suggestive_lrs");
- });
- });
+
}).call(this);
diff --git a/wqflask/wqflask/templates/show_trait_progress_bar.html b/wqflask/wqflask/templates/show_trait_progress_bar.html
index 2984cc02..eff5c391 100644
--- a/wqflask/wqflask/templates/show_trait_progress_bar.html
+++ b/wqflask/wqflask/templates/show_trait_progress_bar.html
@@ -3,7 +3,7 @@
Loading...