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-rw-r--r--wqflask/wqflask/marker_regression/rqtl_mapping.py71
1 files changed, 57 insertions, 14 deletions
diff --git a/wqflask/wqflask/marker_regression/rqtl_mapping.py b/wqflask/wqflask/marker_regression/rqtl_mapping.py
index e1aa290b..152bb5be 100644
--- a/wqflask/wqflask/marker_regression/rqtl_mapping.py
+++ b/wqflask/wqflask/marker_regression/rqtl_mapping.py
@@ -12,6 +12,7 @@ import utility.logger
logger = utility.logger.getLogger(__name__ )
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
r_library = ro.r["library"] # Map the library function
r_c = ro.r["c"] # Map the c function
@@ -21,6 +22,8 @@ def run_rqtl_geno(vals, samples, dataset, method, model, permCheck, num_perm, pe
print(r_library("qtl")) # Load R/qtl
+ logger.info("QTL library loaded");
+
## Get pointers to some R/qtl functions
scanone = ro.r["scanone"] # Map the scanone function
scantwo = ro.r["scantwo"] # Map the scantwo function
@@ -40,28 +43,35 @@ def run_rqtl_geno(vals, samples, dataset, method, model, permCheck, num_perm, pe
genofilelocation = locate(dataset.group.genofile, "genotype")
else:
genofilelocation = locate(dataset.group.name + ".geno", "genotype")
+ logger.info("Going to create a cross from geno");
cross_object = GENOtoCSVR(genofilelocation, crossfilelocation) # TODO: Add the SEX if that is available
-
+ logger.info("before calc_genoprob");
if manhattan_plot:
cross_object = calc_genoprob(cross_object)
else:
- cross_object = calc_genoprob(cross_object, step=1, stepwidth="max")
-
+ cross_object = calc_genoprob(cross_object, step=5, stepwidth="max")
+ logger.info("after calc_genoprob");
pheno_string = sanitize_rqtl_phenotype(vals)
-
+ logger.info("phenostring done");
+ names_string = sanitize_rqtl_names(samples)
+ logger.info("sanitized pheno and names");
cross_object = add_phenotype(cross_object, pheno_string, "the_pheno") # Add the phenotype
-
+ cross_object = add_names(cross_object, names_string, "the_names") # Add the phenotype
+ logger.info("Added pheno and names");
# Scan for QTLs
marker_covars = create_marker_covariates(control_marker, cross_object) # Create the additive covariate markers
-
+ logger.info("Marker covars done");
if cofactors != "":
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')
-
+ #logger.info("Saving");
+ #ro.r('save.image(file = "/home/dannya/gn2-danny/cross.RData")')
+ #logger.info("Saving Done");
covars = ro.r['all_covars']
-
+ #DEBUG to save the session object to file
+ #ro.r('save.image(file = "/home/dannya/gn2-danny/all.RData")')
if pair_scan:
if do_control == "true":
logger.info("Using covariate"); result_data_frame = scantwo(cross_object, pheno = "the_pheno", addcovar = covars, model=model, method=method, n_cluster = 16)
@@ -128,7 +138,7 @@ def generate_cross_from_geno(dataset): # TODO: Need to figure out why som
if(type == '4-way'){
genocodes <- c('1','2','3','4')
} else {
- genocodes <- c(getGenoCode(header, 'mat'), getGenoCode(header, 'het'), getGenoCode(header, 'pat')) # Get the genotype codes
+ genocodes <- c(getGenoCode(header, 'mat'), getGenoCode(header, 'het'), getGenoCode(header, 'pat')) # Get the genotype codes
}
genodata <- read.csv(genotypes, sep='\t', skip=toskip, header=TRUE, na.strings=getGenoCode(header,'unk'), colClasses='character', comment.char = '#')
cat('Genodata:', toskip, " ", dim(genodata), genocodes, '\n')
@@ -139,8 +149,17 @@ def generate_cross_from_geno(dataset): # TODO: Need to figure out why som
cbind(genodata[,c('Locus','Chr', 'cM')], genodata[, 5:ncol(genodata)])) # Genotypes
write.table(outCSVR, file = out, row.names=FALSE, col.names=FALSE,quote=FALSE, sep=',') # Save it to a file
require(qtl)
- cross = read.cross(file=out, 'csvr', genotypes=genocodes) # Load the created cross file using R/qtl read.cross
- if(type == 'riset') cross <- convert2riself(cross) # If its a RIL, convert to a RIL in R/qtl
+ if(type == '4-way'){
+ cat('Loading in as 4-WAY\n')
+ cross = read.cross(file=out, 'csvr', genotypes=genocodes, crosstype="4way", convertXdata=FALSE) # Load the created cross file using R/qtl read.cross
+ }else{
+ cat('Loading in as normal\n')
+ cross = read.cross(file=out, 'csvr', genotypes=genocodes) # Load the created cross file using R/qtl read.cross
+ }
+ if(type == 'riset'){
+ cat('Converting to RISELF\n')
+ cross <- convert2riself(cross) # If its a RIL, convert to a RIL in R/qtl
+ }
return(cross)
}
""" % (dataset.group.genofile))
@@ -176,9 +195,33 @@ def sanitize_rqtl_phenotype(vals):
return pheno_as_string
+def sanitize_rqtl_names(vals):
+ pheno_as_string = "c("
+ for i, val in enumerate(vals):
+ if val == "x":
+ if i < (len(vals) - 1):
+ pheno_as_string += "NA,"
+ else:
+ pheno_as_string += "NA"
+ else:
+ if i < (len(vals) - 1):
+ pheno_as_string += "'" + str(val) + "',"
+ else:
+ pheno_as_string += "'" + str(val) + "'"
+ pheno_as_string += ")"
+
+ return pheno_as_string
+
def add_phenotype(cross, pheno_as_string, col_name):
ro.globalenv["the_cross"] = cross
- ro.r('the_cross$pheno <- cbind(pull.pheno(the_cross), ' + col_name + ' = '+ pheno_as_string +')')
+ ro.r('pheno <- data.frame(pull.pheno(the_cross))')
+ ro.r('the_cross$pheno <- cbind(pheno, ' + col_name + ' = as.numeric('+ pheno_as_string +'))')
+ return ro.r["the_cross"]
+
+def add_names(cross, names_as_string, col_name):
+ ro.globalenv["the_cross"] = cross
+ ro.r('pheno <- data.frame(pull.pheno(the_cross))')
+ ro.r('the_cross$pheno <- cbind(pheno, ' + col_name + ' = '+ names_as_string +')')
return ro.r["the_cross"]
def pull_var(var_name, cross, var_string):
@@ -204,8 +247,8 @@ def add_cofactors(cross, this_dataset, covariates, samples):
this_dataset.group.get_samplelist()
trait_samples = this_dataset.group.samplelist
trait_sample_data = trait_ob.data
- for index, sample in enumerate(trait_samples):
- if sample in samples:
+ for index, sample in enumerate(samples):
+ if sample in trait_samples:
if sample in trait_sample_data:
sample_value = trait_sample_data[sample].value
this_covar_data.append(sample_value)