Commiting a few minor changes plus returning rqtl_mapping.py to the same as on testing branch before merging Danny's changes and testing them

4 files changed, 327 insertions, 319 deletions

diff --git a/wqflask/utility/redis_tools.py b/wqflask/utility/redis_tools.py
index 0754e16f..573f9945 100644
--- a/wqflask/utility/redis_tools.py
+++ b/wqflask/utility/redis_tools.py
@@ -78,3 +78,21 @@ def check_verification_code(code):
     else:
         return None
         flash("Invalid code: Password reset code does not exist or might have expired!", "error")
+
+def get_user_groups(user_id):
+    #ZS: Get the groups where a user is an admin or a member and return lists corresponding to those two sets of groups
+    admin_group_ids = []  #ZS: Group IDs where user is an admin
+    user_group_ids = []   #ZS: Group IDs where user is a regular user
+    groups_list = Redis.hgetall("groups")
+    for key in groups_list:
+        group_ob = json.loads(groups_list[key])
+        group_admins = set(group_ob['admins'])
+        group_users = set(group_ob['users'])
+        if user_id in group_admins:
+            admin_group_ids.append(group_ob['id'])
+        elif user_id in group_users:
+            user_group_ids.append(group_ob['id'])
+        else:
+            continue
+
+    return admin_group_ids, user_group_ids
diff --git a/wqflask/wqflask/marker_regression/rqtl_mapping.py b/wqflask/wqflask/marker_regression/rqtl_mapping.py
index ed63ad92..f111b6d3 100644
--- a/wqflask/wqflask/marker_regression/rqtl_mapping.py
+++ b/wqflask/wqflask/marker_regression/rqtl_mapping.py
@@ -1,313 +1,307 @@
-import rpy2.robjects as ro

-import rpy2.robjects.numpy2ri as np2r

-import numpy as np

-

-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

-

-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):

-    ## 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

-

-    print(r_library("qtl"))                         # Load R/qtl

-

-    ## Get pointers to some R/qtl functions

-    scanone                    = ro.r["scanone"]               # Map the scanone function

-    scantwo                    = ro.r["scantwo"]               # Map the scantwo function

-    calc_genoprob              = ro.r["calc.genoprob"]         # Map the calc.genoprob function

-

-    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:

-    generate_cross_from_geno(dataset)

-    GENOtoCSVR                 = ro.r["GENOtoCSVR"]            # Map the local GENOtoCSVR function

-    crossfilelocation = TMPDIR + crossname + ".cross"

-    if dataset.group.genofile:

-        genofilelocation  = locate(dataset.group.genofile, "genotype")

-    else:

-        genofilelocation = locate(dataset.group.name + ".geno", "genotype")

-    cross_object = GENOtoCSVR(genofilelocation, crossfilelocation)      # TODO: Add the SEX if that is available

-

-    the_version = ro.r["packageVersion('qtl')"]

-    logger.debug("THE R VERSION:", the_version)

-

-    ro.r('save.image(file = "/home/zas1024/gn2-zach/tmp/HET3_cofactor_test2.RData")')

-

-    if manhattan_plot:

-        cross_object = calc_genoprob(cross_object)

-    else:

-        cross_object = calc_genoprob(cross_object, step=1, stepwidth="max")

-

-    pheno_string = sanitize_rqtl_phenotype(vals)

-

-    cross_object = add_phenotype(cross_object, pheno_string, "the_pheno")                 # Add the phenotype

-

-    # Scan for QTLs

-    marker_covars = create_marker_covariates(control_marker, cross_object)  # Create the additive covariate markers

-

-    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')

-

-    covars = ro.r['all_covars']

-

-    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"); result_data_frame = scanone(cross_object, pheno = "the_pheno", addcovar = covars, model=model, method=method)

-        else:

-            logger.info("No covariates"); result_data_frame = scanone(cross_object, pheno = "the_pheno", model=model, method=method)

-

-        if num_perm > 0 and permCheck == "ON":                                                                   # Do permutation (if requested by user)

-            if len(perm_strata_list) > 0: #ZS: The strata list would only be populated if "Stratified" was checked on before mapping

-                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)

-

-            perm_output, suggestive, significant = process_rqtl_perm_results(num_perm, perm_data_frame)          # Functions that sets the thresholds for the webinterface

-            the_scale = check_mapping_scale(genofilelocation)

-            return perm_output, suggestive, significant, process_rqtl_results(result_data_frame, dataset.group.species), the_scale

-        else:

-            the_scale = check_mapping_scale(genofilelocation)

-            return process_rqtl_results(result_data_frame, dataset.group.species), the_scale

-

-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))

-

-def generate_cross_from_geno(dataset):        # TODO: Need to figure out why some genofiles have the wrong format and don't convert properly

-

-    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 = 'cross.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 <- c('1','2','3','4')

-         } 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', '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, crosstype="4way", convertXdata=FALSE)                 # Load the created cross file using R/qtl read.cross

-         #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

-         return(cross)

-      }

-    """ % (dataset.group.genofile))

-

-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) + ","

-

-    perm_strata_string = perm_strata_string[:-1] + ")"

-

-    cross = add_phenotype(cross, perm_strata_string, "the_strata")

-

-    strata_ob = pull_var("perm_strata", cross, col_string)

-

-    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,"

-            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 +')')

-    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):

-        this_covar_data = []

-        covar_as_string = "c("

-        trait_name = covariate.split(":")[0]

-        dataset_ob = create_dataset(covariate.split(":")[1])

-        trait_ob = GeneralTrait(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(trait_samples):

-            if sample in 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 += ")"

-

-        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

-    ro.r('genotypes <- pull.geno(the_cross)')                             # Get the genotype matrix

-    userinputS = control_marker.replace(" ", "").split(",")               # TODO: sanitize user input, Never Ever trust a user

-    covariate_names = ', '.join('"{0}"'.format(w) for w in userinputS)

-    ro.r('covnames <- c(' + covariate_names + ')')

-    ro.r('covInGeno <- which(covnames %in% colnames(genotypes))')

-    ro.r('covnames <- covnames[covInGeno]')

-    ro.r("cat('covnames (purged): ', covnames,'\n')")

-    ro.r('marker_covars <- genotypes[,covnames]')                            # Get the covariate matrix by using the marker name as index to the genotype file

-

-    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 = []

-    for line in str(results).split("\n")[1:(num_perm+1)]:

-        #print("R/qtl permutation line:", line.split())

-        perm_vals.append(float(line.split()[1]))

-

-    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(result, species_name):        # TODO: how to make this a one liner and not copy the stuff in a loop

-    qtl_results = []

-    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]

-        if species_name == "mouse" and output[i][0] == 20: #ZS: This is awkward, but I'm not sure how to change the 20s to Xs in the RData file

-            marker['chr'] = "X"

-        else:

-            marker['chr'] = output[i][0]

-        marker['cM'] = output[i][1]

-        marker['Mb'] = output[i][1]

-        marker['lod_score'] = output[i][2]

-        qtl_results.append(marker)

-

-    return qtl_results

-

-def check_mapping_scale(genofile_location):

-    scale = "physic"

-    with open(genofile_location, "r") as geno_fh:

-        for line in geno_fh:

-            if line[0] == "@" or line[0] == "#":

-

-                if "@scale" in line:

-                    scale = line.split(":")[1].strip()

-                    break

-                else:

-                    continue

-            else:

-                break

-

+import rpy2.robjects as ro
+import rpy2.robjects.numpy2ri as np2r
+import numpy as np
+
+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
+
+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):
+    ## 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
+
+    print(r_library("qtl"))                         # Load R/qtl
+
+    ## Get pointers to some R/qtl functions
+    scanone                    = ro.r["scanone"]               # Map the scanone function
+    scantwo                    = ro.r["scantwo"]               # Map the scantwo function
+    calc_genoprob              = ro.r["calc.genoprob"]         # Map the calc.genoprob function
+
+    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:
+    generate_cross_from_geno(dataset)
+    GENOtoCSVR                 = ro.r["GENOtoCSVR"]            # Map the local GENOtoCSVR function
+    crossfilelocation = TMPDIR + crossname + ".cross"
+    if dataset.group.genofile:
+        genofilelocation  = locate(dataset.group.genofile, "genotype")
+    else:
+        genofilelocation = locate(dataset.group.name + ".geno", "genotype")
+    cross_object = GENOtoCSVR(genofilelocation, crossfilelocation)      # TODO: Add the SEX if that is available
+
+    if manhattan_plot:
+        cross_object = calc_genoprob(cross_object)
+    else:
+        cross_object = calc_genoprob(cross_object, step=1, stepwidth="max")
+
+    pheno_string = sanitize_rqtl_phenotype(vals)
+
+    cross_object = add_phenotype(cross_object, pheno_string, "the_pheno")                 # Add the phenotype
+
+    # Scan for QTLs
+    marker_covars = create_marker_covariates(control_marker, cross_object)  # Create the additive covariate markers
+
+    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')
+
+    covars = ro.r['all_covars']
+
+    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"); result_data_frame = scanone(cross_object, pheno = "the_pheno", addcovar = covars, model=model, method=method)
+        else:
+            logger.info("No covariates"); result_data_frame = scanone(cross_object, pheno = "the_pheno", model=model, method=method)
+
+        if num_perm > 0 and permCheck == "ON":                                                                   # Do permutation (if requested by user)
+            if len(perm_strata_list) > 0: #ZS: The strata list would only be populated if "Stratified" was checked on before mapping
+                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)
+
+            perm_output, suggestive, significant = process_rqtl_perm_results(num_perm, perm_data_frame)          # Functions that sets the thresholds for the webinterface
+            the_scale = check_mapping_scale(genofilelocation)
+            return perm_output, suggestive, significant, process_rqtl_results(result_data_frame, dataset.group.species), the_scale
+        else:
+            the_scale = check_mapping_scale(genofilelocation)
+            return process_rqtl_results(result_data_frame, dataset.group.species), the_scale
+
+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))
+
+def generate_cross_from_geno(dataset):        # TODO: Need to figure out why some genofiles have the wrong format and don't convert properly
+
+    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 = 'cross.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 <- c('1','2','3','4')
+         } 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', '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
+         return(cross)
+      }
+    """ % (dataset.group.genofile))
+
+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) + ","
+
+    perm_strata_string = perm_strata_string[:-1] + ")"
+
+    cross = add_phenotype(cross, perm_strata_string, "the_strata")
+
+    strata_ob = pull_var("perm_strata", cross, col_string)
+
+    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,"
+            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 +')')
+    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):
+        this_covar_data = []
+        covar_as_string = "c("
+        trait_name = covariate.split(":")[0]
+        dataset_ob = create_dataset(covariate.split(":")[1])
+        trait_ob = GeneralTrait(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(trait_samples):
+            if sample in 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 += ")"
+
+        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
+    ro.r('genotypes <- pull.geno(the_cross)')                             # Get the genotype matrix
+    userinputS = control_marker.replace(" ", "").split(",")               # TODO: sanitize user input, Never Ever trust a user
+    covariate_names = ', '.join('"{0}"'.format(w) for w in userinputS)
+    ro.r('covnames <- c(' + covariate_names + ')')
+    ro.r('covInGeno <- which(covnames %in% colnames(genotypes))')
+    ro.r('covnames <- covnames[covInGeno]')
+    ro.r("cat('covnames (purged): ', covnames,'\n')")
+    ro.r('marker_covars <- genotypes[,covnames]')                            # Get the covariate matrix by using the marker name as index to the genotype file
+
+    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 = []
+    for line in str(results).split("\n")[1:(num_perm+1)]:
+        #print("R/qtl permutation line:", line.split())
+        perm_vals.append(float(line.split()[1]))
+
+    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(result, species_name):        # TODO: how to make this a one liner and not copy the stuff in a loop
+    qtl_results = []
+    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]
+        if species_name == "mouse" and output[i][0] == 20: #ZS: This is awkward, but I'm not sure how to change the 20s to Xs in the RData file
+            marker['chr'] = "X"
+        else:
+            marker['chr'] = output[i][0]
+        marker['cM'] = output[i][1]
+        marker['Mb'] = output[i][1]
+        marker['lod_score'] = output[i][2]
+        qtl_results.append(marker)
+
+    return qtl_results
+
+def check_mapping_scale(genofile_location):
+    scale = "physic"
+    with open(genofile_location, "r") as geno_fh:
+        for line in geno_fh:
+            if line[0] == "@" or line[0] == "#":
+
+                if "@scale" in line:
+                    scale = line.split(":")[1].strip()
+                    break
+                else:
+                    continue
+            else:
+                break
+
     return scale
\ 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 8fea295f..5f7710ab 100644
--- a/wqflask/wqflask/marker_regression/run_mapping.py
+++ b/wqflask/wqflask/marker_regression/run_mapping.py
@@ -257,13 +257,9 @@ class RunMapping(object):
             #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.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_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)
             else:
-                results = rqtl_mapping.run_rqtl_geno(self.vals, self.samples, self.dataset, 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)
-            # 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)
-            # else:
-            #     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)
+                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)
         elif self.mapping_method == "reaper":
             if "startMb" in start_vars: #ZS: Check if first time page loaded, so it can default to ON
                 if "additiveCheck" in start_vars:
diff --git a/wqflask/wqflask/templates/show_trait_mapping_tools.html b/wqflask/wqflask/templates/show_trait_mapping_tools.html
index 7c897409..a2416ced 100755
--- a/wqflask/wqflask/templates/show_trait_mapping_tools.html
+++ b/wqflask/wqflask/templates/show_trait_mapping_tools.html
@@ -399,7 +399,7 @@
              <dt style="padding-top: 20px;">GEMMA</dt>
              <dd>Maps traits with correction for kinship among samples using a linear mixed model method, and also allows users to fit multiple covariates such as sex, age, treatment, and genetic markers (<a href="https://www.ncbi.nlm.nih.gov/pubmed/24531419">PMID: 2453419</a>, and <a href="https://github.com/genetics-statistics/GEMMA"> GitHub code</a>). GEMMA incorporates the Leave One Chromosome Out (LOCO) method to ensure that the correction for kinship does not remove useful genetic variance near each marker. Markers can be filtered to include only those with minor allele frequencies (MAF) above a threshold. The default MAF is 0.05.</dd>
              {% elif mapping_method == "R/qtl" %}
-             <dt style="margin-top: 20px;">R/qtl</dt>
+             <dt style="margin-top: 20px;">R/qtl (version 1.44.9</dt>
              <dd>The original R/qtl mapping package that supports classic experimental crosses including 4-parent F2 intercrosses (e.g., NIA ITP UM-HET3). R/qtl is ideal for populations that do not have complex kinship or admixture (<a href="https://www.ncbi.nlm.nih.gov/pubmed/12724300">PMID: 12724300</a>). Both R/qtl as implemented here, and R/qtl2 (<a href="https://www.ncbi.nlm.nih.gov/pubmed/30591514">PMID: 30591514</a>) are available as <a href="https://kbroman.org/pages/software.html">R suites</a>.</dd>
              {% elif mapping_method == "QTLReaper" %}
              <dt style="margin-top: 20px;">Haley-Knott Regression</dt>