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-rw-r--r--wqflask/wqflask/marker_regression/rqtl_mapping.py79
1 files changed, 71 insertions, 8 deletions
diff --git a/wqflask/wqflask/marker_regression/rqtl_mapping.py b/wqflask/wqflask/marker_regression/rqtl_mapping.py
index 4070e9f5..f3d9a70e 100644
--- a/wqflask/wqflask/marker_regression/rqtl_mapping.py
+++ b/wqflask/wqflask/marker_regression/rqtl_mapping.py
@@ -1,16 +1,36 @@
import rpy2.robjects as ro
import rpy2.robjects.numpy2ri as np2r
import numpy as np
+import json
from base.webqtlConfig import TMPDIR
from base.trait import GeneralTrait
from base.data_set import create_dataset
from utility import webqtlUtil
from utility.tools import locate, TEMPDIR
+from flask import g
import utility.logger
logger = utility.logger.getLogger(__name__ )
+# Get a trait's type (numeric, categorical, etc) from the DB
+def get_trait_data_type(trait_db_string):
+ logger.info("get_trait_data_type");
+ the_query = "SELECT value FROM TraitMetadata WHERE type='trait_data_type'"
+ logger.info("the_query done");
+ results_json = g.db.execute(the_query).fetchone()
+ logger.info("the_query executed");
+ results_ob = json.loads(results_json[0])
+ logger.info("json results loaded");
+ if trait_db_string in results_ob:
+ logger.info("found");
+ return results_ob[trait_db_string]
+ else:
+ logger.info("not found");
+ return "numeric"
+
+
+# Run qtl mapping using R/qtl
def run_rqtl_geno(vals, samples, dataset, method, model, permCheck, num_perm, perm_strata_list, do_control, control_marker, manhattan_plot, pair_scan, cofactors):
logger.info("Start run_rqtl_geno");
## Get pointers to some common R functions
@@ -63,7 +83,7 @@ def run_rqtl_geno(vals, samples, dataset, method, model, permCheck, num_perm, pe
logger.info("Marker covars done");
if cofactors != "":
logger.info("Cofactors: " + cofactors);
- cross_object, trait_covars = add_cofactors(cross_object, dataset, cofactors, samples) # Create the covariates from selected traits
+ cross_object, trait_covars = add_cofactors(cross_object, dataset, cofactors, samples) # Create the covariates from selected traits
ro.r('all_covars <- cbind(marker_covars, trait_covars)')
else:
ro.r('all_covars <- marker_covars')
@@ -218,6 +238,34 @@ def add_phenotype(cross, pheno_as_string, col_name):
ro.r('the_cross$pheno <- cbind(pheno, ' + col_name + ' = as.numeric('+ pheno_as_string +'))')
return ro.r["the_cross"]
+def add_categorical_covar(cross, covar_as_string, i):
+ ro.globalenv["the_cross"] = cross
+ logger.info("cross set");
+ ro.r('covar <- as.factor(' + covar_as_string + ')')
+ logger.info("covar set");
+ ro.r('newcovar <- model.matrix(~covar-1)')
+ logger.info("model.matrix finished");
+ ro.r('cat("new covar columns", ncol(newcovar), "\n")')
+ nCol = ro.r('ncol(newcovar)')
+ logger.info("ncol covar done: " + str(nCol[0]));
+ ro.r('pheno <- data.frame(pull.pheno(the_cross))')
+ logger.info("pheno pulled from cross");
+ nCol = int(nCol[0])
+ logger.info("nCol python int:" + str(nCol));
+ col_names = []
+ #logger.info("loop")
+ for x in range(1, (nCol+1)):
+ #logger.info("loop" + str(x));
+ col_name = "covar_" + str(i) + "_" + str(x)
+ #logger.info("col_name" + col_name);
+ ro.r('the_cross$pheno <- cbind(pheno, ' + col_name + ' = newcovar[,' + str(x) + '])')
+ col_names.append(col_name)
+ #logger.info("loop" + str(x) + "done");
+
+ logger.info("returning from add_categorical_covar");
+ return ro.r["the_cross"], col_names
+
+
def add_names(cross, names_as_string, col_name):
ro.globalenv["the_cross"] = cross
ro.r('pheno <- data.frame(pull.pheno(the_cross))')
@@ -236,6 +284,7 @@ def add_cofactors(cross, this_dataset, covariates, samples):
covariate_list = covariates.split(",")
covar_name_string = "c("
for i, covariate in enumerate(covariate_list):
+ logger.info("Covariate: " + covariate);
this_covar_data = []
covar_as_string = "c("
trait_name = covariate.split(":")[0]
@@ -263,19 +312,33 @@ def add_cofactors(cross, this_dataset, covariates, samples):
covar_as_string += ")"
- col_name = "covar_" + str(i)
- cross = add_phenotype(cross, covar_as_string, col_name)
+ datatype = get_trait_data_type(covariate)
+ logger.info("Covariate: " + covariate + " is of type: " + datatype);
+ if(datatype == "categorical"): # Cat variable
+ logger.info("call of add_categorical_covar");
+ cross, col_names = add_categorical_covar(cross, covar_as_string, i) # Expand and add it to the cross
+ logger.info("add_categorical_covar returned");
+ for z, col_name in enumerate(col_names): # Go through the additional covar names
+ if i < (len(covariate_list) - 1):
+ covar_name_string += '"' + col_name + '", '
+ else:
+ if(z < (len(col_names) -1)):
+ covar_name_string += '"' + col_name + '", '
+ else:
+ covar_name_string += '"' + col_name + '"'
- if i < (len(covariate_list) - 1):
- covar_name_string += '"' + col_name + '", '
+ logger.info("covar_name_string:" + covar_name_string);
else:
+ col_name = "covar_" + str(i)
+ cross = add_phenotype(cross, covar_as_string, col_name)
+ if i < (len(covariate_list) - 1):
+ covar_name_string += '"' + col_name + '", '
+ else:
covar_name_string += '"' + col_name + '"'
covar_name_string += ")"
-
+ logger.info("covar_name_string:" + covar_name_string);
covars_ob = pull_var("trait_covars", cross, covar_name_string)
- # TODO: Pull in the types of the covars from MariaDB
- # TODO: Iterate through the covar types and create a design matrix based on it
return cross, covars_ob
def create_marker_covariates(control_marker, cross):