aboutsummaryrefslogtreecommitdiff
diff options
context:
space:
mode:
authorzsloan2021-05-20 21:44:12 +0000
committerzsloan2021-05-20 21:44:12 +0000
commitc2fac869f5ac1a398677d5646d34a1a0704e29e4 (patch)
tree8f5debbfac3dfab6943ef5e8e547d73d8b74d42b
parent0149b5da461945350be2fe9b95d3c42cf4211f5b (diff)
downloadgenenetwork2-c2fac869f5ac1a398677d5646d34a1a0704e29e4.tar.gz
Re-implemented R/qtl to get the results from the GN3 API
-rw-r--r--wqflask/wqflask/marker_regression/rqtl_mapping.py569
-rw-r--r--wqflask/wqflask/marker_regression/run_mapping.py6
2 files changed, 135 insertions, 440 deletions
diff --git a/wqflask/wqflask/marker_regression/rqtl_mapping.py b/wqflask/wqflask/marker_regression/rqtl_mapping.py
index 1c8477bf..3f4899b0 100644
--- a/wqflask/wqflask/marker_regression/rqtl_mapping.py
+++ b/wqflask/wqflask/marker_regression/rqtl_mapping.py
@@ -1,460 +1,155 @@
-import rpy2.robjects as ro
-import rpy2.robjects.numpy2ri as np2r
-import numpy as np
-import json
-
-from flask import g
+import csv
+import hashlib
+import io
+import requests
+import shutil
+from typing import Dict
+from typing import List
+from typing import Optional
+from typing import TextIO
from base.webqtlConfig import TMPDIR
from base.trait import create_trait
-from base.data_set import create_dataset
-from utility import webqtlUtil
-from utility.tools import locate, TEMPDIR
-from wqflask.marker_regression.gemma_mapping import generate_random_n_string
-from flask import g
+from utility.tools import locate
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"
-
+GN3_RQTL_URL = "http://localhost:8086/api/rqtl/compute"
+GN3_TMP_PATH = "/export/local/home/zas1024/genenetwork3/tmp"
-# Run qtl mapping using R/qtl
-def run_rqtl_geno(vals, samples, dataset, mapping_scale, 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
- plot = ro.r["plot"] # Map the plot function
- png = ro.r["png"] # Map the png function
- dev_off = ro.r["dev.off"] # Map the device off function
+def run_rqtl(trait_name, vals, samples, dataset, mapping_scale, method, model, permCheck, num_perm, perm_strata_list, do_control, control_marker, manhattan_plot, pair_scan, cofactors):
+ """Run R/qtl by making a request to the GN3 endpoint and reading in the output file(s)"""
- 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
- # Map the calc.genoprob function
- calc_genoprob = ro.r["calc.genoprob"]
-
- crossname = dataset.group.name
- # try:
- # generate_cross_from_rdata(dataset)
- # read_cross_from_rdata = ro.r["generate_cross_from_rdata"] # Map the local read_cross_from_rdata function
- # genofilelocation = locate(crossname + ".RData", "genotype/rdata")
- # cross_object = read_cross_from_rdata(genofilelocation) # Map the local GENOtoCSVR function
- # except:
-
- if mapping_scale == "morgan":
- scale_units = "cM"
- else:
- scale_units = "Mb"
-
- generate_cross_from_geno(dataset, scale_units)
- # Map the local GENOtoCSVR function
- GENOtoCSVR = ro.r["GENOtoCSVR"]
- crossfilelocation = TMPDIR + crossname + ".cross"
+ pheno_file = write_phenotype_file(trait_name, samples, vals, cofactors)
if dataset.group.genofile:
- genofilelocation = locate(dataset.group.genofile, "genotype")
- else:
- genofilelocation = locate(dataset.group.name + ".geno", "genotype")
- logger.info("Going to create a cross from geno")
- # TODO: Add the SEX if that is available
- cross_object = GENOtoCSVR(genofilelocation, crossfilelocation)
- logger.info("before calc_genoprob")
- if manhattan_plot:
- cross_object = calc_genoprob(cross_object)
+ geno_file = locate(dataset.group.genofile, "genotype")
else:
- 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")
- # Add the phenotype
- cross_object = add_phenotype(cross_object, pheno_string, "the_pheno")
- # Add the phenotype
- cross_object = add_names(cross_object, names_string, "the_names")
- logger.info("Added pheno and names")
- # Create the additive covariate markers
- marker_covars = create_marker_covariates(control_marker, cross_object)
- logger.info("Marker covars done")
- if cofactors != "":
- logger.info("Cofactors: " + cofactors)
- # Create the covariates from selected traits
- cross_object, trait_covars = add_cofactors(
- cross_object, dataset, cofactors, samples)
- ro.r('all_covars <- cbind(marker_covars, trait_covars)')
- else:
- ro.r('all_covars <- marker_covars')
- covars = ro.r['all_covars']
- # DEBUG to save the session object to file
- 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)
- else:
- logger.info("No covariates")
- result_data_frame = scantwo(
- cross_object, pheno="the_pheno", model=model, method=method, n_cluster=16)
-
- pair_scan_filename = webqtlUtil.genRandStr("scantwo_") + ".png"
- png(file=TEMPDIR + pair_scan_filename)
- plot(result_data_frame)
- dev_off()
-
- return process_pair_scan_results(result_data_frame)
- else:
- if do_control == "true" or cofactors != "":
- logger.info("Using covariate")
- ro.r(f"qtl_results = scanone(the_cross, pheno='the_pheno', addcovar=all_covars, model='{model}', method='{method}')")
- result_data_frame = ro.r("qtl_results")
- else:
- ro.r(f"qtl_results = scanone(the_cross, pheno='the_pheno', model='{model}', method='{method}')")
- result_data_frame = np.asarray(ro.r("qtl_results")).T
+ geno_file = locate(dataset.group.name + ".geno", "genotype")
- marker_names = np.asarray(ro.r("row.names(qtl_results)"))
+ post_data = {
+ "pheno_file": pheno_file,
+ "geno_file": geno_file,
+ "model": model,
+ "method": method,
+ "nperm": num_perm,
+ "scale": mapping_scale
+ }
- # Do permutation (if requested by user)
- if num_perm > 0 and permCheck == "ON":
- # ZS: The strata list would only be populated if "Stratified" was checked on before mapping
- if len(perm_strata_list) > 0:
- cross_object, strata_ob = add_perm_strata(
- cross_object, perm_strata_list)
-
- if do_control == "true" or cofactors != "":
- perm_data_frame = scanone(cross_object, pheno_col="the_pheno", addcovar=covars, n_perm=int(
- num_perm), perm_strata=strata_ob, model=model, method=method)
- else:
- perm_data_frame = scanone(
- cross_object, pheno_col="the_pheno", n_perm=num_perm, perm_strata=strata_ob, model=model, method=method)
- else:
- if do_control == "true" or cofactors != "":
- perm_data_frame = scanone(cross_object, pheno_col="the_pheno", addcovar=covars, n_perm=int(
- num_perm), model=model, method=method)
- else:
- perm_data_frame = scanone(
- cross_object, pheno_col="the_pheno", n_perm=num_perm, model=model, method=method)
-
- # Functions that sets the thresholds for the webinterface
- perm_output, suggestive, significant = process_rqtl_perm_results(
- num_perm, perm_data_frame)
- return perm_output, suggestive, significant, process_rqtl_results(marker_names, result_data_frame, dataset.group.species)
- else:
- return process_rqtl_results(marker_names, result_data_frame, dataset.group.species)
-
-
-def generate_cross_from_rdata(dataset):
- rdata_location = locate(dataset.group.name + ".RData", "genotype/rdata")
- ro.r("""
- generate_cross_from_rdata <- function(filename = '%s') {
- load(file=filename)
- cross = cunique
- return(cross)
- }
- """ % (rdata_location))
-
-
-# TODO: Need to figure out why some genofiles have the wrong format and don't convert properly
-def generate_cross_from_geno(dataset, scale_units):
-
- cross_filename = (f"{str(dataset.group.name)}_"
- f"{generate_random_n_string(6)}")
-
- ro.r("""
- trim <- function( x ) { gsub("(^[[:space:]]+|[[:space:]]+$)", "", x) }
- getGenoCode <- function(header, name = 'unk'){
- mat = which(unlist(lapply(header,function(x){ length(grep(paste('@',name,sep=''), x)) })) == 1)
- return(trim(strsplit(header[mat],':')[[1]][2]))
- }
- GENOtoCSVR <- function(genotypes = '%s', out = '%s.csvr', phenotype = NULL, sex = NULL, verbose = FALSE){
- header = readLines(genotypes, 40) # Assume a geno header is not longer than 40 lines
- toskip = which(unlist(lapply(header, function(x){ length(grep("Chr\t", x)) })) == 1)-1 # Major hack to skip the geno headers
- type <- getGenoCode(header, 'type')
- if(type == '4-way'){
- genocodes <- NULL
- } else {
- 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')
- if(is.null(phenotype)) phenotype <- runif((ncol(genodata)-4)) # If there isn't a phenotype, generate a random one
- if(is.null(sex)) sex <- rep('m', (ncol(genodata)-4)) # If there isn't a sex phenotype, treat all as males
- outCSVR <- rbind(c('Pheno', '', '', phenotype), # Phenotype
- c('sex', '', '', sex), # Sex phenotype for the mice
- cbind(genodata[,c('Locus','Chr', '%s')], 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)
- if(type == '4-way'){
- cat('Loading in as 4-WAY\n')
- cross = read.cross(file=out, 'csvr', genotypes=NULL, crosstype="4way") # Load the created cross file using R/qtl read.cross
- }else if(type == 'f2'){
- cat('Loading in as F2\n')
- cross = read.cross(file=out, 'csvr', genotypes=genocodes, crosstype="f2") # 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, cross_filename, scale_units))
+ if do_control == "true" and control_marker:
+ post_data["control_marker"] = control_marker
+ if not manhattan_plot:
+ post_data["interval"] = True
+ if cofactors:
+ post_data["addcovar"] = True
-def add_perm_strata(cross, perm_strata):
- col_string = 'c("the_strata")'
- perm_strata_string = "c("
- for item in perm_strata:
- perm_strata_string += str(item) + ","
+ out_file = requests.post(GN3_RQTL_URL, data=post_data).json()['output_file']
- perm_strata_string = perm_strata_string[:-1] + ")"
+ return process_rqtl_results(out_file)
- cross = add_phenotype(cross, perm_strata_string, "the_strata")
- strata_ob = pull_var("perm_strata", cross, col_string)
+def process_rqtl_results(out_file: str) -> List:
+ """Given the output filename, read in results and
+ return as a list of dictionaries representing each
+ marker
- return cross, strata_ob
+ """
-
-def sanitize_rqtl_phenotype(vals):
- pheno_as_string = "c("
- for i, val in enumerate(vals):
- if val == "x":
- if i < (len(vals) - 1):
- pheno_as_string += "NA,"
+ marker_obs = []
+ # Later I should probably redo this using csv.read to avoid the
+ # awkwardness with removing quotes with [1:-1]
+ with open(GN3_TMP_PATH + "/output/" + out_file, "r") as the_file:
+ for line in the_file:
+ line_items = line.split(",")
+ if line_items[1][1:-1] == "chr" or not line_items:
+ continue
else:
- pheno_as_string += "NA"
+ # Convert chr to int if possible
+ try:
+ the_chr = int(line_items[1][1:-1])
+ except:
+ the_chr = line_items[1][1:-1]
+ this_marker = {
+ "name": line_items[0][1:-1],
+ "chr": the_chr,
+ "cM": float(line_items[2]),
+ "Mb": float(line_items[2]),
+ "lod_score": float(line_items[3])
+ }
+ marker_obs.append(this_marker)
+
+ return marker_obs
+
+def get_hash_of_textio(the_file: TextIO) -> str:
+ """Given a StringIO, return the hash of its contents"""
+
+ the_file.seek(0)
+ hash_of_file = hashlib.md5(the_file.read().encode()).hexdigest()
+
+ return hash_of_file
+
+
+def write_phenotype_file(trait_name: str,
+ samples: List[str],
+ vals: List,
+ dataset_ob,
+ cofactors: Optional[str] = None) -> TextIO:
+ """Given trait name, sample list, value list, dataset ob, and optional string
+ representing cofactors, return the file's full path/name
+
+ """
+
+ cofactor_data = cofactors_to_dict(cofactors, dataset_ob, samples)
+
+ pheno_file = io.StringIO()
+ writer = csv.writer(pheno_file, delimiter="\t", quoting=csv.QUOTE_NONE)
+
+ header_row = ["Samples", trait_name]
+ header_row += [cofactor for cofactor in cofactor_data]
+
+ writer.writerow(header_row)
+ for i, sample in enumerate(samples):
+ this_row = [sample]
+ if vals[i] != "x":
+ this_row.append(vals[i])
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 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('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_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$pheno <- cbind(pheno, ' + \
- col_name + ' = newcovar[,' + str(x) + '])')
- col_names.append(col_name)
- #logger.info("loop" + str(x) + "done");
-
- logger.info("returning from add_categorical_covar")
- return ro.r["the_cross"], col_names
-
-
-def add_names(cross, names_as_string, col_name):
- ro.globalenv["the_cross"] = cross
- ro.r('pheno <- data.frame(pull.pheno(the_cross))')
- ro.r('the_cross$pheno <- cbind(pheno, ' + \
- col_name + ' = ' + names_as_string + ')')
- return ro.r["the_cross"]
-
-
-def pull_var(var_name, cross, var_string):
- ro.globalenv["the_cross"] = cross
- ro.r(var_name + ' <- pull.pheno(the_cross, ' + var_string + ')')
-
- return ro.r[var_name]
-
-
-def add_cofactors(cross, this_dataset, covariates, samples):
- ro.numpy2ri.activate()
-
- 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]
- dataset_ob = create_dataset(covariate.split(":")[1])
- trait_ob = create_trait(dataset=dataset_ob,
- name=trait_name,
- cellid=None)
-
- this_dataset.group.get_samplelist()
- trait_samples = this_dataset.group.samplelist
- trait_sample_data = trait_ob.data
- 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)
- else:
- this_covar_data.append("NA")
-
- for j, item in enumerate(this_covar_data):
- if j < (len(this_covar_data) - 1):
- covar_as_string += str(item) + ","
- else:
- covar_as_string += str(item)
-
- covar_as_string += ")"
-
- 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")
- # Go through the additional covar names
- for z, col_name in enumerate(col_names):
- if i < (len(covariate_list) - 1):
- covar_name_string += '"' + col_name + '", '
- else:
- if(z < (len(col_names) - 1)):
- covar_name_string += '"' + col_name + '", '
+ this_row.append("NA")
+ for cofactor in cofactor_data:
+ this_row.append(cofactor_data[cofactor][i])
+ writer.writerow(this_row)
+
+ hash_of_file = get_hash_of_textio(pheno_file)
+ file_path = TMPDIR + hash_of_file + ".csv"
+
+ with open(file_path, "w") as fd:
+ pheno_file.seek(0)
+ shutil.copyfileobj(pheno_file, fd)
+
+ return file_path
+
+
+def cofactors_to_dict(cofactors: str, dataset_ob, samples) -> Dict:
+ """Given a string of cofactors, the trait being mapped's dataset ob,
+ and list of samples, return cofactor data as a Dict
+
+ """
+ cofactors = {}
+ if cofactors:
+ dataset_ob.group.get_samplelist()
+ sample_list = dataset_ob.group.samplelist
+ for cofactor in cofactors.split(","):
+ cofactor_name, cofactor_dataset = cofactor.split(":")
+ if cofactor_dataset == this_dataset.name:
+ cofactors[cofactor_name] = []
+ trait_ob = create_trait(dataset=dataset_ob,
+ name=cofactor_name)
+ sample_data = trait_ob.data
+ for index, sample in enumerate(samples):
+ #if (sample in sample_list) and (sample in sample_data):
+ if sample in sample_data:
+ sample_value = sample_data[sample].value
+ cofactors[cofactor_name].append(sample_value)
else:
- covar_name_string += '"' + col_name + '"'
- 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 += ")"
- covars_ob = pull_var("trait_covars", cross, covar_name_string)
- return cross, covars_ob
-
-
-def create_marker_covariates(control_marker, cross):
- ro.globalenv["the_cross"] = cross
- # Get the genotype matrix
- ro.r('genotypes <- pull.geno(the_cross)')
- # TODO: sanitize user input, Never Ever trust a user
- userinput_sanitized = control_marker.replace(" ", "").split(",")
- logger.debug(userinput_sanitized)
- if len(userinput_sanitized) > 0:
- covariate_names = ', '.join('"{0}"'.format(w)
- for w in userinput_sanitized)
- ro.r('covnames <- c(' + covariate_names + ')')
- else:
- ro.r('covnames <- c()')
- ro.r('covInGeno <- which(covnames %in% colnames(genotypes))')
- ro.r('covnames <- covnames[covInGeno]')
- ro.r("cat('covnames (purged): ', covnames,'\n')")
- # Get the covariate matrix by using the marker name as index to the genotype file
- ro.r('marker_covars <- genotypes[,covnames]')
- # TODO: Create a design matrix from the marker covars for the markers in case of an F2, 4way, etc
- return ro.r["marker_covars"]
-
-
-def process_pair_scan_results(result):
- pair_scan_results = []
-
- result = result[1]
- output = [tuple([result[j][i] for j in range(result.ncol)])
- for i in range(result.nrow)]
-
- for i, line in enumerate(result.iter_row()):
- marker = {}
- marker['name'] = result.rownames[i]
- marker['chr1'] = output[i][0]
- marker['Mb'] = output[i][1]
- marker['chr2'] = int(output[i][2])
- pair_scan_results.append(marker)
-
- return pair_scan_results
-
-
-def process_rqtl_perm_results(num_perm, results):
- perm_vals = [item[0] for item in results]
-
- perm_output = perm_vals
- suggestive = np.percentile(np.array(perm_vals), 67)
- significant = np.percentile(np.array(perm_vals), 95)
-
- return perm_output, suggestive, significant
-
-
-def process_rqtl_results(marker_names, results, species_name): # TODO: how to make this a one liner and not copy the stuff in a loop
- qtl_results = []
-
- for i, line in enumerate(results):
- marker = {}
- marker['name'] = marker_names[i]
- if species_name == "mouse" and line[0] == 20:
- marker['chr'] = "X"
- else:
- try:
- marker['chr'] = int(line[0])
- except:
- marker['chr'] = line[0]
- marker['cM'] = marker['Mb'] = line[1]
- marker['lod_score'] = line[2]
- qtl_results.append(marker)
-
- return qtl_results
+ cofactors[cofactor_name].append("NA")
+ return cofactors \ No newline at end of file
diff --git a/wqflask/wqflask/marker_regression/run_mapping.py b/wqflask/wqflask/marker_regression/run_mapping.py
index a3b579ec..be1186c0 100644
--- a/wqflask/wqflask/marker_regression/run_mapping.py
+++ b/wqflask/wqflask/marker_regression/run_mapping.py
@@ -242,10 +242,10 @@ class RunMapping:
# if start_vars['pair_scan'] == "true":
# self.pair_scan = True
if self.permCheck and self.num_perm > 0:
- self.perm_output, self.suggestive, self.significant, results = rqtl_mapping.run_rqtl_geno(
- self.vals, self.samples, self.dataset, self.mapping_scale, self.method, self.model, self.permCheck, self.num_perm, perm_strata, self.do_control, self.control_marker, self.manhattan_plot, self.pair_scan, self.covariates)
+ self.perm_output, self.suggestive, self.significant, results = rqtl_mapping.run_rqtl(
+ self.this_trait.name, self.vals, self.samples, self.dataset, self.mapping_scale, self.method, self.model, self.permCheck, self.num_perm, perm_strata, self.do_control, self.control_marker, self.manhattan_plot, self.pair_scan, self.covariates)
else:
- results = rqtl_mapping.run_rqtl_geno(self.vals, self.samples, self.dataset, self.mapping_scale, self.method, self.model, self.permCheck,
+ results = rqtl_mapping.run_rqtl(self.this_trait.name, self.vals, self.samples, self.dataset, self.mapping_scale, self.method, self.model, self.permCheck,
self.num_perm, perm_strata, self.do_control, self.control_marker, self.manhattan_plot, self.pair_scan, self.covariates)
elif self.mapping_method == "reaper":
if "startMb" in start_vars: # ZS: Check if first time page loaded, so it can default to ON