diff options
| -rw-r--r-- | wqflask/wqflask/ctl/ctl_analysis.py | 51 |
1 files changed, 48 insertions, 3 deletions
diff --git a/wqflask/wqflask/ctl/ctl_analysis.py b/wqflask/wqflask/ctl/ctl_analysis.py index 7a42b2f8..e21b8500 100644 --- a/wqflask/wqflask/ctl/ctl_analysis.py +++ b/wqflask/wqflask/ctl/ctl_analysis.py @@ -6,6 +6,8 @@ import scipy as sp # SciPy import rpy2.robjects as ro # R Objects import rpy2.rinterface as ri +import simplejson as json + from base.webqtlConfig import GENERATED_IMAGE_DIR from utility import webqtlUtil # Random number for the image from utility import genofile_parser # genofile_parser @@ -73,6 +75,8 @@ class CTL(object): self.r_CTLnetwork = ro.r["CTLnetwork"] # Map the CTLnetwork function self.r_CTLprofiles = ro.r["CTLprofiles"] # Map the CTLprofiles function self.r_plotCTLobject = ro.r["plot.CTLobject"] # Map the CTLsignificant function + self.nodes_list = [] + self.edges_list = [] print("Obtained pointers to CTL functions") def run_analysis(self, requestform): @@ -99,7 +103,7 @@ class CTL(object): genofilelocation = locate(dataset.group.name + ".geno", "genotype") parser = genofile_parser.ConvertGenoFile(genofilelocation) parser.process_csv() - + print(dataset.group) # Create a genotype matrix individuals = parser.individuals markers = [] @@ -129,9 +133,11 @@ class CTL(object): rPheno = r_t(ro.r.matrix(r_as_numeric(r_unlist(traits)), nrow=len(self.trait_db_list), ncol=len(individuals), dimnames = r_list(self.trait_db_list, individuals), byrow=True)) + print(rPheno) + # Use a data frame to store the objects - rPheno = r_data_frame(rPheno) - rGeno = r_data_frame(rGeno) + rPheno = r_data_frame(rPheno, check_names = False) + rGeno = r_data_frame(rGeno, check_names = False) # Debug: Print the genotype and phenotype files to disk #r_write_table(rGeno, "~/outputGN/geno.csv") @@ -169,6 +175,44 @@ class CTL(object): # Flush any output from R sys.stdout.flush() + # Create the interactive graph for cytoscape visualization (Nodes) + # TODO DA : make this a function + for trait in self.trait_db_list: + if trait != "": + ts = trait.split(':') + gt = TRAIT.GeneralTrait(name = ts[0], dataset_name = ts[1]) + node_dict = { 'data' : {'id' : str(gt.name) + ":" + str(gt.dataset.name), + 'sid' : str(gt.name), + 'dataset' : str(gt.dataset.name), + 'label' : gt.name, + 'symbol' : gt.symbol, + 'geneid' : gt.geneid, + 'omim' : gt.omim } } + self.nodes_list.append(node_dict) + + # Create the interactive graph for cytoscape visualization (Edges) + # TODO DA : make this a function + print(type(significant)) + if not type(significant) == ri.RNULLType: + for x in range(len(significant[0])): + print(significant[0][x], significant[1][x], significant[2][x]) # Debug to console + tsS = significant[0][x].split(':') # Source + tsT = significant[2][x].split(':') # Target + gtS = TRAIT.GeneralTrait(name = tsS[0], dataset_name = tsS[1]) # Retrieve Source info from the DB + gtT = TRAIT.GeneralTrait(name = tsT[0], dataset_name = tsT[1]) # Retrieve Target info from the DB + edge_data = {'id' : str(gtS.symbol) + '_' + significant[1][x] + '_' + str(gtT.symbol), + 'source' : str(gtS.name) + ":" + str(gtS.dataset.name), + 'target' : str(gtT.name) + ":" + str(gtT.dataset.name), + 'lod' : significant[3][x], + 'color' : "#ff0000", + 'width' : significant[3][x] } + edge_dict = { 'data' : edge_data } + self.edges_list.append(edge_dict) + significant[0][x] = gtS.symbol + " (" + gtS.name + ")" + significant[2][x] = gtT.symbol + " (" + gtT.name + ")" + + self.elements = json.dumps(self.nodes_list + self.edges_list) + def loadImage(self, path, name): print("pre-loading imgage results:", self.results[path]) imgfile = open(self.results[path], 'rb') @@ -188,6 +232,7 @@ class CTL(object): print("Processing CTL output") template_vars = {} template_vars["results"] = self.results + template_vars["elements"] = self.elements self.render_image(self.results) sys.stdout.flush() return(dict(template_vars)) |