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-rwxr-xr-xwqflask/wqflask/marker_regression/marker_regression.py4
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/lmm.py223
-rw-r--r--wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js70
3 files changed, 145 insertions, 152 deletions
diff --git a/wqflask/wqflask/marker_regression/marker_regression.py b/wqflask/wqflask/marker_regression/marker_regression.py
index 4ddc89c6..4f6e8e1a 100755
--- a/wqflask/wqflask/marker_regression/marker_regression.py
+++ b/wqflask/wqflask/marker_regression/marker_regression.py
@@ -491,12 +491,12 @@ class MarkerRegression(object):
with Bench("LMM_ob fitting"):
lmm_ob.fit()
-
+
with Bench("Doing gwas"):
t_stats, p_values = lmm.GWAS(pheno_vector,
genotype_matrix,
kinship_matrix,
- REML=True,
+ restricted_max_likelihood=True,
refit=False,
temp_data=self.temp_data)
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
index 12f7c2ea..6c101ba1 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
@@ -95,179 +95,104 @@ 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, temp_data=None):
+def GWAS(pheno_vector,
+ genotype_matrix,
+ kinship_matrix,
+ kinship_eigen_vals=None,
+ kinship_eigen_vectors=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
+ 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
+
+ pheno_vector - n x 1 phenotype vector
+ genotype_matrix - n x m SNP matrix
+ kinship_matrix - n x n kinship matrix
+ kinship_eigen_vals, kinship_eigen_vectors = linalg.eigh(K) - or the eigen vectors and values for K
+ covariate_matrix - n x q covariate matrix
+ restricted_max_likelihood - use restricted maximum likelihood
+ refit - refit the variance component for each SNP
+
"""
- n = X.shape[0]
- m = X.shape[1]
+ if kinship_eigen_vals == None:
+ kinship_eigen_vals = []
+ if kinship_eigen_vectors= == None:
+ kinship_eigen_vectors = []
+
+ n = genotype_matrix.shape[0]
+ m = genotype_matrix.shape[1]
- if X0 == None: X0 = np.ones((n,1))
+ if covariate_matrix == None:
+ covariate_matrix = np.ones((n,1))
- # Remove missing values in Y and adjust associated parameters
- v = np.isnan(Y)
+ # Remove missing values in pheno_vector and adjust associated parameters
+ v = np.isnan(pheno_vector)
if v.sum():
keep = True - v
- Y = Y[keep]
- X = X[keep,:]
- X0 = X0[keep,:]
- K = K[keep,:][:,keep]
- Kva = []
- Kve = []
-
- L = LMM(Y,K,Kva,Kve,X0)
- if not refit: L.fit()
-
- PS = []
- TS = []
+ pheno_vector = pheno_vector[keep]
+ genotype_matrix = genotype_matrix[keep,:]
+ covariate_matrix = covariate_matrix[keep,:]
+ kinship_matrix = kinship_matrix[keep,:][:,keep]
+ kinship_eigen_vals = []
+ kinship_eigen_vectors = []
+
+ lmm_ob = LMM(pheno_vector,
+ kinship_matrix,
+ kinship_eigen_vals,
+ kinship_eigen_vectors,
+ covariate_matrix)
+ if not refit:
+ lmm_ob.fit()
+
+ p_values = []
+ t_statistics = []
for counter in range(m):
- x = X[:,counter].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:
- PS.append(np.nan)
- TS.append(np.nan)
+ p_values.append(np.nan)
+ t_statistics.append(np.nan)
continue
- Ys = Y[keep]
- X0s = X0[keep,:]
- Ks = K[keep,:][:,keep]
- Ls = LMM(Ys,Ks,X0=X0s)
- if refit: Ls.fit(X=xs)
- else: Ls.fit()
- ts,ps = Ls.association(xs,REML=REML)
+ pheno_vector = pheno_vector[keep]
+ covariate_matrix = covariate_matrix[keep,:]
+ kinship_matrix = kinship_matrix[keep,:][:,keep]
+ lmm_ob_2 = LMM(pheno_vector,
+ kinship_matrix,
+ X0=covariate_matrix)
+ if refit:
+ lmm_ob_2.fit(X=xs)
+ else:
+ lmm_ob_2.fit()
+ ts,ps = lmm_ob_2.association(xs, REML=restricted_max_likelihood)
else:
if x.var() == 0:
- PS.append(np.nan)
- TS.append(np.nan)
+ p_values.append(np.nan)
+ t_statistics.append(np.nan)
continue
- if refit: L.fit(X=x)
- ts,ps = L.association(x,REML=REML)
+ if refit:
+ lmm_ob.fit(X=x)
+ ts,ps = lmm_ob.association(x,REML=restricted_max_likelihood)
percent_complete = 45 + int(round((counter/m)*55))
print("Percent complete: ", percent_complete)
temp_data.store("percent_complete", percent_complete)
- 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
+ p_values.append(ps)
+ t_statistics.append(ts)
+
+ return t_statistics,p_values
+
class LMM:
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 c8b0aa7b..78459692 100644
--- a/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js
+++ b/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js
@@ -1,6 +1,74 @@
// 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);