diff options
| -rw-r--r-- | wqflask/wqflask/wgcna/wgcna_analysis.py | 37 |
1 files changed, 33 insertions, 4 deletions
diff --git a/wqflask/wqflask/wgcna/wgcna_analysis.py b/wqflask/wqflask/wgcna/wgcna_analysis.py index 5316fcd4..74bb6f8e 100644 --- a/wqflask/wqflask/wgcna/wgcna_analysis.py +++ b/wqflask/wqflask/wgcna/wgcna_analysis.py @@ -6,6 +6,11 @@ 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 + +import base64 +import array + from utility import helper_functions from rpy2.robjects.packages import importr @@ -22,8 +27,8 @@ 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_matrix = ro.r["matrix"] # Map the matrix function -r_list = ro.r["list"] # Map the list 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 @@ -82,19 +87,43 @@ class WGCNA(object): print(trait, strain, " in python: ", self.input[trait].get(strain), "in R:", rM.rx(strain,trait)[0]) sys.stdout.flush() - # TODO: + # TODO: Get the user specified parameters + self.results = {} # Calculate a good soft threshold + powers = r_c(r_c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), r_seq(12, 20, 2)) + sft = self.r_pickSoftThreshold(rM, powerVector = powers, verbose = 5) + # Create block wise modules using WGCNA + network = self.r_blockwiseModules(rM, power = 6, verbose = 3) + + self.results['network'] = network + # How many modules and how many gene per module ? - # Show the iconic WCGNA plot of the modules in the hanging tree + print(r_table(network[1])) + + # The iconic WCGNA plot of the modules in the hanging tree + self.results['imgurl'] = webqtlConfig.TMPDIR + "WGCNAoutput.png" + r_png(self.results['imgurl']) + 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['imgurl']) + imgfile = open(self.results['imgurl'], '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["results"] = self.results + self.render_image(results) #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)) |