diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/wqflask/wgcna/wgcna_analysis.py | 38 |
1 files changed, 29 insertions, 9 deletions
diff --git a/wqflask/wqflask/wgcna/wgcna_analysis.py b/wqflask/wqflask/wgcna/wgcna_analysis.py index 0cf4eeaf..b5e01ece 100644 --- a/wqflask/wqflask/wgcna/wgcna_analysis.py +++ b/wqflask/wqflask/wgcna/wgcna_analysis.py @@ -28,12 +28,14 @@ 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 @@ -74,7 +76,6 @@ class WGCNA(object): # 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 - self.phenotypes = uTraitsR r_cat("The number of unique strains:", r_length(uStrainsR), "\n") r_cat("The number of unique traits:", r_length(uTraitsR), "\n") @@ -86,29 +87,49 @@ class WGCNA(object): 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 - print(trait, strain, " in python: ", self.input[trait].get(strain), "in R:", rM.rx(strain,trait)[0]) + #print(trait, strain, " in python: ", self.input[trait].get(strain), "in R:", rM.rx(strain,trait)[0]) sys.stdout.flush() # 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)) - self.sft = self.r_pickSoftThreshold(rM, powerVector = powers, verbose = 5) + self.results['nphe'] = r_length(uTraitsR)[0] + self.results['nstr'] = r_length(uStrainsR)[0] + self.results['phenotypes'] = uTraitsR + self.results['strains'] = uStrainsR + self.results['requestform'] = requestform + + # 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 r_is_NA(self.sft[0]): + self.results['Power'] = 1 + else: + self.results['Power'] = self.sft[0][0] + else: + # The user clicked a button, so no soft threshold selection, just use the value the user gives + self.results['Power'] = requestform.get('Power') # Create block wise modules using WGCNA - network = self.r_blockwiseModules(rM, power = 6, verbose = 3) + 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(r_table(network[1])) + 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']) + 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() @@ -126,7 +147,6 @@ class WGCNA(object): print("Processing WGCNA output") template_vars = {} template_vars["input"] = self.input - template_vars["phenotypes"] = self.phenotypes template_vars["powers"] = self.sft[1:] # Results from the soft threshold analysis template_vars["results"] = self.results self.render_image(results) |