diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/wqflask/marker_regression/rqtl_mapping.py | 79 |
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): |