diff options
| -rw-r--r-- | wqflask/wqflask/wgcna/wgcna_analysis.py | 5 |
1 files changed, 4 insertions, 1 deletions
diff --git a/wqflask/wqflask/wgcna/wgcna_analysis.py b/wqflask/wqflask/wgcna/wgcna_analysis.py index 9ab7950b..0cf4eeaf 100644 --- a/wqflask/wqflask/wgcna/wgcna_analysis.py +++ b/wqflask/wqflask/wgcna/wgcna_analysis.py @@ -74,6 +74,7 @@ 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") @@ -93,7 +94,7 @@ class WGCNA(object): 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) + self.sft = self.r_pickSoftThreshold(rM, powerVector = powers, verbose = 5) # Create block wise modules using WGCNA network = self.r_blockwiseModules(rM, power = 6, verbose = 3) @@ -125,6 +126,8 @@ 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) sys.stdout.flush() |