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-rwxr-xr-xwqflask/base/data_set.py2
-rwxr-xr-xwqflask/wqflask/templates/collections/view.html12
-rw-r--r--wqflask/wqflask/templates/wgcna_results.html76
-rw-r--r--wqflask/wqflask/templates/wgcna_setup.html28
-rwxr-xr-xwqflask/wqflask/views.py21
-rwxr-xr-xwqflask/wqflask/wgcna/__init__.py0
-rw-r--r--wqflask/wqflask/wgcna/wgcna_analysis.py156
7 files changed, 292 insertions, 3 deletions
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index e2db9ad7..427fd991 100755
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -89,7 +89,7 @@ class Dataset_Types(object):
for group in data['datasets'][species]:
for dataset_type in data['datasets'][species][group]:
for dataset in data['datasets'][species][group][dataset_type]:
- print("dataset is:", dataset)
+ #print("dataset is:", dataset)
short_dataset_name = dataset[0]
if dataset_type == "Phenotypes":
diff --git a/wqflask/wqflask/templates/collections/view.html b/wqflask/wqflask/templates/collections/view.html
index 29c65058..5450f903 100755
--- a/wqflask/wqflask/templates/collections/view.html
+++ b/wqflask/wqflask/templates/collections/view.html
@@ -38,6 +38,18 @@
<input type="submit" class="btn btn-primary" value="Correlation Matrix" />
</div>
</form>
+ <form action="/wgcna_setup" method="post">
+ {% if uc %}
+ <input type="hidden" name="uc_id" id="uc_id" value="{{ uc.id }}" />
+ {% endif %}
+ <input type="hidden" name="trait_list" id="trait_list" value= "
+ {% for this_trait in trait_obs %}
+ {{ this_trait.name }}:{{ this_trait.dataset.name }},
+ {% endfor %}" >
+ <div class="col-xs-2 controls">
+ <input type="submit" class="btn btn-primary" value="WGCNA Analysis" />
+ </div>
+ </form>
<form action="/heatmap" method="post">
{% if uc %}
<input type="hidden" name="uc_id" id="uc_id" value="{{ uc.id }}" />
diff --git a/wqflask/wqflask/templates/wgcna_results.html b/wqflask/wqflask/templates/wgcna_results.html
new file mode 100644
index 00000000..0dc030b1
--- /dev/null
+++ b/wqflask/wqflask/templates/wgcna_results.html
@@ -0,0 +1,76 @@
+{% extends "base.html" %}
+{% block title %}WCGNA results{% endblock %}
+
+{% block content %} <!-- Start of body -->
+ <div class="container">
+ <h1>WGCNA Results</h1>
+ Analysis found {{results['nmod']}} modules when scanning {{results['nphe']}} phenotypes, measured on {{results['nstr']}} strains.<br>
+ Additional parameters settings:
+ <ul>
+ <li>Soft thresholds checked = {{results['requestform']['SoftThresholds']}}</li>
+ <li>Power used for this analysis = {{results['Power']}}</li>
+ <li>TomType = {{results['requestform']['TOMtype']}}</li>
+ <li>Minimum module size = {{results['requestform']['MinModuleSize'] }}</li>
+ <li>mergeCutHeight = {{results['requestform']['mergeCutHeight'] }}</li>
+ </ul>
+
+ <h3>Soft threshold table</h3>
+ <table width="80%">
+ <tr><th>Power</th><th>SFT.R.sq</th><th>slope</th><th>truncated.R.sq</th><th>mean.k</th><th>median.k</th><th>max.k</th><th>Analysis</th></tr>
+ {% for r in range(powers[0][0]|length) %}
+ {% if powers[0][1][r] > 0.85 %}
+ <tr style="color: #00ff00;">
+ {% elif powers[0][1][r] > 0.75 %}
+ <tr style="color: #aaaa00;">
+ {% else %}
+ <tr style="color: #ff0000;">
+ {% endif %}
+ {% for c in range(powers[0]|length) %}
+ <td>{{powers[0][c][r]|round(3)}}</td>
+ {% endfor %}
+ <td style="color: #000000;">
+ {% if powers[0][1][r] > 0.75 %}
+ <input type="submit" value="Redo use power = {{powers[0][0][r]}}" /></td>
+ {% endif %}
+ </tr>
+ {% endfor %}
+ </table>
+ <h3>WGCNA module plot</h3>
+ <a href="/tmp/{{ results['imgurl'] }}">
+ <img alt="Embedded Image" src="data:image/png;base64,
+ {% for elem in results['imgdata'] -%}
+ {% print("%c"|format(elem)) %}
+ {%- endfor %}
+ " /></a>
+
+
+ <h3>Phenotype / Module table</h3>
+ <table width="80%">
+ <tr><th>Phenotype</th><th>Module</th></tr>
+ {% for r in range(results['nphe']) %}
+ <tr>
+ <td>{{results['phenotypes'][r][0]}}</td>
+ <td>{{results['network'][0][r]}}</td>
+ </tr>
+ {% endfor %}
+ </table>
+
+ <h3>Module eigen genes</h3>
+ <table width="80%">
+ <tr><th>Phenotype</th>
+ {% for m in range(results['nmod']) %}
+ <th><input type="submit" value="Add module {{m}} to collection" /></th>
+ {% endfor %}
+ </tr>
+ {% for r in range(results['nstr']) %}
+ <tr>
+ <td>{{results['strains'][r][0]}}</td>
+ {% for m in range(results['nmod']) %}
+ <td>{{results['network'][2][m][r]}}</td>
+ {% endfor %}
+ </tr>
+ {% endfor %}
+ </table>
+ </div>
+{% endblock %}
+
diff --git a/wqflask/wqflask/templates/wgcna_setup.html b/wqflask/wqflask/templates/wgcna_setup.html
new file mode 100644
index 00000000..49181938
--- /dev/null
+++ b/wqflask/wqflask/templates/wgcna_setup.html
@@ -0,0 +1,28 @@
+{% extends "base.html" %}
+{% block title %}WCGNA analysis{% endblock %}
+
+{% block content %} <!-- Start of body -->
+<form action="/wgcna_results" method="post">
+ <div class="container">
+
+ <h1>WGCNA analysis parameters</h1>
+ {% if request.form['trait_list'].split(",")|length <= 4 %}
+
+ <h2 style="color: #ff0000;">ERROR: Too few phenotypes as input</h2>
+ Please make sure you select enough phenotypes / genes to perform WGCNA,
+ your collection needs to contain at least 4 different phenotypes. You provided {{request.form['trait_list'].split(',')|length}} phenotypes as input
+
+ {% else %}
+ <input type="hidden" name="trait_list" id="trait_list" value= "{{request.form['trait_list']}}">
+ <table>
+ <tr><td>Soft threshold:</td><td><input type="text" class="form-inline" name="SoftThresholds" id="SoftThresholds" value="1,2,3,4,5,6,7,8,9"></td></tr>
+ <tr><td>Minimum module size:</td><td><input type="text" class="form-inline" name="MinModuleSize" id="MinModuleSize" value="30"></td></tr>
+ <tr><td>TOMtype:</td><td><input type="text" class="form-inline" name="TOMtype" id="TOMtype" value="unsigned"></td></tr>
+ <tr><td>mergeCutHeight:</td><td><input type="text" class="form-inline" name="mergeCutHeight" id="mergeCutHeight" value="0.25"></td></tr>
+ </table>
+ <input type="submit" class="btn btn-primary" value="Run WGCNA using these settings" />
+ {% endif %}
+ </div>
+</form>
+{% endblock %}
+
diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py
index 7a8a0f03..dc199ab2 100755
--- a/wqflask/wqflask/views.py
+++ b/wqflask/wqflask/views.py
@@ -46,6 +46,9 @@ from wqflask.interval_mapping import interval_mapping
from wqflask.correlation import show_corr_results
from wqflask.correlation_matrix import show_corr_matrix
from wqflask.correlation import corr_scatter_plot
+
+from wqflask.wgcna import wgcna_analysis
+
from utility import temp_data
from base import webqtlFormData
@@ -93,7 +96,7 @@ def tmp_page(img_path):
print("img_path:", img_path)
initial_start_vars = request.form
print("initial_start_vars:", initial_start_vars)
- imgfile = open('/home/zas1024/tmp/' + img_path, 'rb')
+ imgfile = open(webqtlConfig.TMPDIR + img_path, 'rb')
imgdata = imgfile.read()
imgB64 = imgdata.encode("base64")
bytesarray = array.array('B', imgB64)
@@ -174,6 +177,20 @@ def help():
doc = docs.Docs("help")
return render_template("docs.html", **doc.__dict__)
+@app.route("/wgcna_setup", methods=('POST',))
+def wcgna_setup():
+ print("In wgcna, request.form is:", request.form) # We are going to get additional user input for the analysis
+ return render_template("wgcna_setup.html", **request.form) # Display them using the template
+
+@app.route("/wgcna_results", methods=('POST',))
+def wcgna_results():
+ print("In wgcna, request.form is:", request.form)
+ wgcna = wgcna_analysis.WGCNA() # Start R, load the package and pointers and create the analysis
+ wgcnaA = wgcna.run_analysis(request.form) # Start the analysis, a wgcnaA object should be a separate long running thread
+ result = wgcna.process_results(wgcnaA) # After the analysis is finished store the result
+ return render_template("wgcna_results.html", **result) # Display them using the template
+
+
@app.route("/news")
def news_route():
newsobject = news.News()
@@ -328,7 +345,7 @@ def marker_regression_page():
'mapmethod_rqtl_geno',
'mapmodel_rqtl_geno'
)
- print("Random Print too see if it is running:", initial_start_vars)
+ print("Marker regression called with initial_start_vars:", initial_start_vars)
start_vars = {}
for key, value in initial_start_vars.iteritems():
if key in wanted or key.startswith(('value:')):
diff --git a/wqflask/wqflask/wgcna/__init__.py b/wqflask/wqflask/wgcna/__init__.py
new file mode 100755
index 00000000..e69de29b
--- /dev/null
+++ b/wqflask/wqflask/wgcna/__init__.py
diff --git a/wqflask/wqflask/wgcna/wgcna_analysis.py b/wqflask/wqflask/wgcna/wgcna_analysis.py
new file mode 100644
index 00000000..a714319d
--- /dev/null
+++ b/wqflask/wqflask/wgcna/wgcna_analysis.py
@@ -0,0 +1,156 @@
+# WGCNA analysis for GN2
+# Author / Maintainer: Danny Arends <Danny.Arends@gmail.com>
+import sys
+from numpy import *
+import scipy as sp # SciPy
+import rpy2.robjects as ro # R Objects
+import rpy2.rinterface as ri
+
+from base import webqtlConfig # For paths and stuff
+from utility import webqtlUtil # Random number for the image
+
+import base64
+import array
+
+from utility import helper_functions
+
+from rpy2.robjects.packages import importr
+utils = importr("utils")
+
+## Get pointers to some common R functions
+r_library = ro.r["library"] # Map the library function
+r_options = ro.r["options"] # Map the options function
+r_read_csv = ro.r["read.csv"] # Map the read.csv function
+r_dim = ro.r["dim"] # Map the dim function
+r_c = ro.r["c"] # Map the c function
+r_cat = ro.r["cat"] # Map the cat function
+r_paste = ro.r["paste"] # Map the paste function
+r_unlist = ro.r["unlist"] # Map the unlist function
+r_unique = ro.r["unique"] # Map the unique function
+r_length = ro.r["length"] # Map the length function
+r_unlist = ro.r["unlist"] # Map the unlist function
+r_list = ro.r.list # Map the list function
+r_matrix = ro.r.matrix # Map the matrix function
+r_seq = ro.r["seq"] # Map the seq function
+r_table = ro.r["table"] # Map the table function
+r_names = ro.r["names"] # Map the names function
+r_sink = ro.r["sink"] # Map the sink function
+r_is_NA = ro.r["is.na"] # Map the is.na function
+r_file = ro.r["file"] # Map the file function
+r_png = ro.r["png"] # Map the png function for plotting
+r_dev_off = ro.r["dev.off"] # Map the dev.off function
+
+class WGCNA(object):
+ def __init__(self):
+ print("Initialization of WGCNA")
+ log = r_file("/tmp/genenetwork_wcgna.log", open = "wt")
+ #r_sink(log) # Uncomment the r_sink() commands to log output from stdout/stderr to a file
+ #r_sink(log, type = "message")
+ r_library("WGCNA") # Load WGCNA - Should only be done once, since it is quite expensive
+ r_options(stringsAsFactors = False)
+ print("Initialization of WGCNA done, package loaded in R session")
+ self.r_enableWGCNAThreads = ro.r["enableWGCNAThreads"] # Map the enableWGCNAThreads function
+ self.r_pickSoftThreshold = ro.r["pickSoftThreshold"] # Map the pickSoftThreshold function
+ self.r_blockwiseModules = ro.r["blockwiseModules"] # Map the blockwiseModules function
+ self.r_labels2colors = ro.r["labels2colors"] # Map the labels2colors function
+ self.r_plotDendroAndColors = ro.r["plotDendroAndColors"] # Map the plotDendroAndColors function
+ print("Obtained pointers to WGCNA functions")
+
+ def run_analysis(self, requestform):
+ print("Starting WGCNA analysis on dataset")
+ self.r_enableWGCNAThreads() # Enable multi threading
+ self.trait_db_list = [trait.strip() for trait in requestform['trait_list'].split(',')]
+ print("Retrieved phenotype data from database", requestform['trait_list'])
+ helper_functions.get_trait_db_obs(self, self.trait_db_list)
+
+ self.input = {} # self.input contains the phenotype values we need to send to R
+ strains = [] # All the strains we have data for (contains duplicates)
+ traits = [] # All the traits we have data for (should not contain duplicates)
+ for trait in self.trait_list:
+ traits.append(trait[0].name)
+ self.input[trait[0].name] = {}
+ for strain in trait[0].data:
+ strains.append(strain)
+ self.input[trait[0].name][strain] = trait[0].data[strain].value
+
+ # Transfer the load data from python to R
+ uStrainsR = r_unique(ro.Vector(strains)) # Unique strains in R vector
+ uTraitsR = r_unique(ro.Vector(traits)) # Unique traits in R vector
+
+ r_cat("The number of unique strains:", r_length(uStrainsR), "\n")
+ r_cat("The number of unique traits:", r_length(uTraitsR), "\n")
+
+ # rM is the datamatrix holding all the data in R /rows = strains columns = traits
+ rM = ro.r.matrix(ri.NA_Real, nrow=r_length(uStrainsR), ncol=r_length(uTraitsR), dimnames = r_list(uStrainsR, uTraitsR))
+ for t in uTraitsR:
+ trait = t[0] # R uses vectors every single element is a vector
+ for s in uStrainsR:
+ strain = s[0] # R uses vectors every single element is a vector
+ rM.rx[strain, trait] = self.input[trait].get(strain) # Update the matrix location
+ #DEBUG: print(trait, strain, " in python: ", self.input[trait].get(strain), "in R:", rM.rx(strain,trait)[0])
+ sys.stdout.flush()
+
+ self.results = {}
+ self.results['nphe'] = r_length(uTraitsR)[0] # Number of phenotypes/traits
+ self.results['nstr'] = r_length(uStrainsR)[0] # Number of strains
+ self.results['phenotypes'] = uTraitsR # Traits used
+ self.results['strains'] = uStrainsR # Strains used in the analysis
+ self.results['requestform'] = requestform # Store the user specified parameters for the output page
+
+ # Calculate soft threshold if the user specified the SoftThreshold variable
+ if requestform.get('SoftThresholds') is not None:
+ powers = [int(threshold.strip()) for threshold in requestform['SoftThresholds'].rstrip().split(",")]
+ rpow = r_unlist(r_c(powers))
+ print "SoftThresholds: {} == {}".format(powers, rpow)
+ self.sft = self.r_pickSoftThreshold(rM, powerVector = rpow, verbose = 5)
+
+ print "PowerEstimate: {}".format(self.sft[0])
+ self.results['PowerEstimate'] = self.sft[0]
+ if self.sft[0][0] is ri.NA_Integer:
+ print "No power is suitable for the analysis, just use 1"
+ self.results['Power'] = 1 # No power could be estimated
+ else:
+ self.results['Power'] = self.sft[0][0] # Use the estimated power
+ else:
+ # The user clicked a button, so no soft threshold selection
+ self.results['Power'] = requestform.get('Power') # Use the power value the user gives
+
+ # Create the block wise modules using WGCNA
+ network = self.r_blockwiseModules(rM, power = self.results['Power'], TOMType = requestform['TOMtype'], minModuleSize = requestform['MinModuleSize'], verbose = 3)
+
+ # Save the network for the GUI
+ self.results['network'] = network
+
+ # How many modules and how many gene per module ?
+ print "WGCNA found {} modules".format(r_table(network[1]))
+ self.results['nmod'] = r_length(r_table(network[1]))[0]
+
+ # The iconic WCGNA plot of the modules in the hanging tree
+ self.results['imgurl'] = webqtlUtil.genRandStr("WGCNAoutput_") + ".png"
+ self.results['imgloc'] = webqtlConfig.TMPDIR + self.results['imgurl']
+ r_png(self.results['imgloc'], width=1000, height=600)
+ mergedColors = self.r_labels2colors(network[1])
+ self.r_plotDendroAndColors(network[5][0], mergedColors, "Module colors", dendroLabels = False, hang = 0.03, addGuide = True, guideHang = 0.05)
+ r_dev_off()
+ sys.stdout.flush()
+
+ def render_image(self, results):
+ print("pre-loading imgage results:", self.results['imgloc'])
+ imgfile = open(self.results['imgloc'], 'rb')
+ imgdata = imgfile.read()
+ imgB64 = imgdata.encode("base64")
+ bytesarray = array.array('B', imgB64)
+ self.results['imgdata'] = bytesarray
+
+ def process_results(self, results):
+ print("Processing WGCNA output")
+ template_vars = {}
+ template_vars["input"] = self.input
+ template_vars["powers"] = self.sft[1:] # Results from the soft threshold analysis
+ template_vars["results"] = self.results
+ self.render_image(results)
+ sys.stdout.flush()
+ #r_sink(type = "message") # This restores R output to the stdout/stderr
+ #r_sink() # We should end the Rpy session more or less
+ return(dict(template_vars))
+