Merge pull request #29 from zsloan/feature/add_rqtl_pairscan

Feature/add rqtl pairscan

3 files changed, 267 insertions, 69 deletions

diff --git a/gn3/api/rqtl.py b/gn3/api/rqtl.py
index b5627c5..70ebe12 100644
--- a/gn3/api/rqtl.py
+++ b/gn3/api/rqtl.py
@@ -6,7 +6,8 @@ from flask import current_app
 from flask import jsonify
 from flask import request
 
-from gn3.computations.rqtl import generate_rqtl_cmd, process_rqtl_output, process_perm_output
+from gn3.computations.rqtl import generate_rqtl_cmd, process_rqtl_mapping, \
+                                  process_rqtl_pairscan, process_perm_output
 from gn3.computations.gemma import do_paths_exist
 
 rqtl = Blueprint("rqtl", __name__)
@@ -25,7 +26,7 @@ run the rqtl_wrapper script and return the results as JSON
 
     # Split kwargs by those with values and boolean ones that just convert to True/False
     kwargs = ["covarstruct", "model", "method", "nperm", "scale", "control_marker"]
-    boolean_kwargs = ["addcovar", "interval", "pstrata"]
+    boolean_kwargs = ["addcovar", "interval", "pstrata", "pairscan"]
     all_kwargs = kwargs + boolean_kwargs
 
     rqtl_kwargs = {"geno": genofile, "pheno": phenofile}
@@ -48,9 +49,11 @@ run the rqtl_wrapper script and return the results as JSON
                                        "output", rqtl_cmd.get('output_file'))):
         os.system(rqtl_cmd.get('rqtl_cmd'))
 
-    rqtl_output['results'] = process_rqtl_output(rqtl_cmd.get('output_file'))
+    if "pairscan" in rqtl_bool_kwargs:
+        rqtl_output['results'] = process_rqtl_pairscan(rqtl_cmd.get('output_file'), genofile)
+    else:
+        rqtl_output['results'] = process_rqtl_mapping(rqtl_cmd.get('output_file'))
 
-    rqtl_output['results'] = process_rqtl_output(rqtl_cmd.get('output_file'))
     if int(rqtl_kwargs['nperm']) > 0:
         rqtl_output['perm_results'], rqtl_output['suggestive'], rqtl_output['significant'] = \
         process_perm_output(rqtl_cmd.get('output_file'))
diff --git a/gn3/computations/rqtl.py b/gn3/computations/rqtl.py
index b3539a9..65ee6de 100644
--- a/gn3/computations/rqtl.py
+++ b/gn3/computations/rqtl.py
@@ -1,6 +1,7 @@
 """Procedures related rqtl computations"""
 import os
-from typing import Dict, List, Union
+from bisect import bisect
+from typing import Dict, List, Tuple, Union
 
 import numpy as np
 
@@ -15,9 +16,7 @@ def generate_rqtl_cmd(rqtl_wrapper_cmd: str,
                       rqtl_wrapper_bool_kwargs: list) -> Dict:
     """Given the base rqtl_wrapper command and
 dict of keyword arguments, return the full rqtl_wrapper command and an
-output filename generated from a hash of the genotype and phenotype files
-
-    """
+output filename generated from a hash of the genotype and phenotype files"""
 
     # Generate a hash from contents of the genotype and phenotype files
     _hash = get_hash_of_files(
@@ -47,11 +46,9 @@ output filename generated from a hash of the genotype and phenotype files
     }
 
 
-def process_rqtl_output(file_name: str) -> List:
+def process_rqtl_mapping(file_name: str) -> List:
     """Given an output file name, read in R/qtl results and return
-    a List of marker objects
-
-    """
+    a List of marker objects"""
     marker_obs = []
     # Later I should probably redo this using csv.read to avoid the
     # awkwardness with removing quotes with [1:-1]
@@ -80,12 +77,181 @@ def process_rqtl_output(file_name: str) -> List:
 
     return marker_obs
 
+def process_rqtl_pairscan(file_name: str, geno_file: str) -> List:
+    """Given an output file name, read in R/qtl pair-scan results and return
+a list of both the JSON needed for the d3panels figure and a list of results
+to be used when generating the results table (which will include marker names)"""
+    figure_data = pairscan_for_figure(file_name)
+    table_data = pairscan_for_table(file_name, geno_file)
 
-def process_perm_output(file_name: str):
-    """Given base filename, read in R/qtl permutation output and calculate
-    suggestive and significant thresholds
+    return [figure_data, table_data]
+
+def pairscan_for_figure(file_name: str) -> Dict:
+    """Given an output file name, read in R/qtl pair-scan results and return
+    the JSON needed for the d3panels figure"""
+    figure_data = {}
+
+    # Open the file with the actual results, written as a list of lists
+    with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
+                           "output", file_name), "r") as the_file:
+        lod_results = []
+        for i, line in enumerate(the_file):
+            if i == 0: # Skip first line
+                continue
+            line_items = [item.rstrip('\n') for item in line.split(",")]
+            lod_results.append(line_items[1:]) # Append all but first item in line
+        figure_data['lod'] = lod_results
+
+    # Open the map file with the list of markers/pseudomarkers and their positions
+    with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
+                           "output", "MAP_" + file_name), "r") as the_file:
+        chr_list = [] # type: List
+        pos_list = [] # type: List
+        for i, line in enumerate(the_file):
+            if i == 0: # Skip first line
+                continue
+            line_items = [item.rstrip('\n') for item in line.split(",")]
+            chr_list.append(line_items[1][1:-1])
+            pos_list.append(line_items[2])
+        figure_data['chr'] = chr_list
+        figure_data['pos'] = pos_list
+
+    return figure_data
+
+def get_marker_list(map_file: str) -> List:
+    """
+    Open the map file with the list of markers/pseudomarkers and create list of marker obs
+
+    PARAMETERS:
+    map_file: The map file output by R/qtl containing marker/pseudomarker positions
+    """
+
+    marker_list = []
+    with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
+                           "output", map_file), "r") as map_fh:
+        for line in map_fh.readlines()[1:]:
+            line_items = [item.rstrip('\n') for item in line.split(",")]
+            this_marker = {
+                'name': line_items[0],
+                'chr': line_items[1][1:-1], # Strip quotes from beginning and end of chr string
+                'pos': line_items[2]
+            }
+
+            marker_list.append(this_marker)
+
+    return marker_list
+
+def generate_table_rows(results_file: str, marker_list: List, original_markers: Dict) -> List:
+    """
+    Open the file with the actual R/qtl pair-scan results and write them as
+    they will be displayed in the results table
 
+    PARAMETERS:
+    results_file: The filename containing R/qtl pair-scan results
+    marker_list: List of marker/pseudomarker names/positions from results
+    original_markers: Dict of markers from the .geno file, for finding proximal/distal
+                      markers to each pseudomarker
     """
+
+    table_data = []
+    with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
+                           "output", results_file), "r") as the_file:
+        for i, line in enumerate(the_file.readlines()[1:]):
+            marker_1 = marker_list[i]
+            marker_1['proximal'], marker_1['distal'] = find_nearest_marker(marker_1['chr'],
+                                                                           marker_1['pos'],
+                                                                           original_markers)
+            line_items = [item.rstrip('\n') for item in line.split(",")]
+            for j, item in enumerate(line_items[1:]):
+                marker_2 = marker_list[j]
+                marker_2['proximal'], marker_2['distal'] = find_nearest_marker(marker_2['chr'],
+                                                                               marker_2['pos'],
+                                                                               original_markers)
+                try:
+                    lod_score = f"{float(item):.3f}"
+                except ValueError:
+                    lod_score = f"{item}"
+
+                table_data.append({
+                    'proximal1': marker_1['proximal'],
+                    'distal1': marker_1['distal'],
+                    'pos1': f"Chr {marker_1['chr']} @ {float(marker_1['pos']):.1f} cM",
+                    'lod': lod_score,
+                    'proximal2': marker_2['proximal'],
+                    'distal2': marker_2['distal'],
+                    'pos2': f"Chr {marker_2['chr']} @ {float(marker_2['pos']):.1f} cM"
+                })
+
+    return table_data
+
+def pairscan_for_table(file_name: str, geno_file: str) -> List:
+    """
+    Read in R/qtl pair-scan results and return as List representing
+    table row contents
+
+    PARAMETERS:
+    file_name: The filename containing R/qtl pair-scan results
+    geno_file: Filename of the genotype file (used to get marker positions)
+    """
+
+    # Open the map file with the list of markers/pseudomarkers and create list of marker obs
+    marker_list = get_marker_list("MAP_" + file_name)
+
+    # Get the list of original markers from the .geno file
+    original_markers = build_marker_pos_dict(geno_file)
+
+    # Open the file with the actual results and write the results as
+    # they will be displayed in the results table
+    table_data = generate_table_rows(file_name, marker_list, original_markers)
+
+    return sorted(table_data, key=lambda i: float(i['lod']), reverse=True)[:500]
+
+def build_marker_pos_dict(genotype_file: str) -> Dict:
+    """Gets list of markers and their positions from .geno file
+
+    Basically a pared-down version of parse_genotype_file for R/qtl pair-scan"""
+
+    with open(genotype_file, "r") as infile:
+        contents = infile.readlines()
+
+    # Get all lines after the metadata
+    lines = tuple(line for line in contents if
+                  ((not line.strip().startswith("#")) and
+                   (not line.strip().startswith("@")) and
+                   (not line.strip() == "")))
+
+    header_items = lines[0].split("\t")
+    mb_exists = "Mb" in header_items
+    pos_column = header_items.index("Mb") if mb_exists else header_items.index("cM")
+
+    the_markers = {"1": {}} # type: Dict[str, Dict]
+    for line in lines[1:]: # The lines with markers
+        line_items = line.split("\t")
+        this_chr = line_items[0]
+        if this_chr not in the_markers:
+            the_markers[this_chr] = {}
+        the_markers[this_chr][str(float(line_items[pos_column]))] = line_items[1]
+
+    return the_markers
+
+def find_nearest_marker(the_chr: str, the_pos: str, marker_list: Dict) -> Tuple[str, str]:
+    """Given a chromosome and position of a pseudomarker (from R/qtl pair-scan results),
+    return the nearest real marker"""
+
+    pos_list = [float(pos) for pos in marker_list[the_chr]]
+
+    # Get the position of the pseudomarker in the list of markers for the chr
+    the_pos_index = bisect(pos_list, float(the_pos))
+
+    proximal_marker = marker_list[the_chr][str(pos_list[the_pos_index-1])]
+    distal_marker = marker_list[the_chr][str(pos_list[the_pos_index])]
+
+    return proximal_marker, distal_marker
+
+def process_perm_output(file_name: str) -> Tuple[List, float, float]:
+    """Given base filename, read in R/qtl permutation output and calculate
+    suggestive and significant thresholds"""
+
     perm_results = []
     with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
                            "output", "PERM_" + file_name), "r", encoding="utf-8") as the_file:
diff --git a/scripts/rqtl_wrapper.R b/scripts/rqtl_wrapper.R
index 13c2684..ea2c345 100644
--- a/scripts/rqtl_wrapper.R
+++ b/scripts/rqtl_wrapper.R
@@ -12,6 +12,7 @@ option_list = list(
   make_option(c("--covarstruct"), type="character", help="File detailing which covariates are categorical or numerical"),
   make_option(c("--model"), type="character", default="normal", help="Mapping Model - Normal or Non-Parametric"),
   make_option(c("--method"), type="character", default="hk", help="Mapping Method - hk (Haley Knott), ehk (Extended Haley Knott), mr (Marker Regression), em (Expectation-Maximization), imp (Imputation)"),
+  make_option(c("--pairscan"), action="store_true", default=NULL, help="Run Pair Scan - the R/qtl function scantwo"),
   make_option(c("-i", "--interval"), action="store_true", default=NULL, help="Use interval mapping"),
   make_option(c("--nperm"), type="integer", default=0, help="Number of permutations"),
   make_option(c("--pstrata"), action="store_true", default=NULL, help="Use permutation strata (stored as final column/vector in phenotype input file)"),
@@ -171,9 +172,15 @@ verbose_print('Generating cross object\n')
 cross_object = geno_to_csvr(geno_file, trait_names, trait_vals, cross_file, type)
 
 # Calculate genotype probabilities
-if (!is.null(opt$interval)) {
+if (!is.null(opt$pairscan)) {
+  verbose_print('Calculating genotype probabilities for pair-scan\n')
+  cross_object <- calc.genoprob(cross_object, step=10)
+} else if (!is.null(opt$interval)) {
   verbose_print('Calculating genotype probabilities with interval mapping\n')
   cross_object <- calc.genoprob(cross_object, step=5, stepwidth="max")
+} else if (!is.null(opt$pairscan)) {
+  verbose_print('Calculating genotype probabilities for pair-scan\n')
+  cross_object <- calc.genoprob(cross_object, step=10)
 } else {
   verbose_print('Calculating genotype probabilities\n')
   cross_object <- calc.genoprob(cross_object)
@@ -188,6 +195,7 @@ if (type == "4-way") {
 # Pull covariates out of cross object, if they exist
 covars <- c() # Holds the covariates which should be passed to R/qtl
 if (!is.null(opt$addcovar)) {
+  verbose_print('Pulling covariates out of cross object\n')
   # If perm strata are being used, it'll be included as the final column in the phenotype file
   if (!is.null(opt$pstrata)) {
     covar_names = trait_names[2:(length(trait_names)-1)]
@@ -223,16 +231,30 @@ if (!is.null(opt$addcovar)) {
 # Pull permutation strata out of cross object, if it is being used
 perm_strata = vector()
 if (!is.null(opt$pstrata)) {
+  verbose_print('Pulling permutation strata out of cross object\n')
   strata_col = trait_names[length(trait_names)]
   perm_strata <- pull.pheno(cross_object, strata_col)
 }
 
 # If a marker name is supplied as covariate, get its vector of values and add them as a covariate
 if (!is.null(opt$control)) {
+  verbose_print('Creating marker covariates and binding them to covariates vector\n')
   marker_covars = create_marker_covars(cross_object, opt$control)
   covars <- cbind(covars, marker_covars)
 }
 
+if (!is.null(opt$pairscan)) {
+  verbose_print("Running scantwo")
+  scan_func <- function(...){
+    scantwo(...)
+  }
+} else {
+  verbose_print("Running scanone")
+  scan_func <- function(...){
+    scanone(...)
+  }
+}
+
 # Calculate permutations
 if (opt$nperm > 0) {
   if (!is.null(opt$filename)){
@@ -243,19 +265,19 @@ if (opt$nperm > 0) {
 
   if (!is.null(opt$addcovar) || !is.null(opt$control)){
     if (!is.null(opt$pstrata)) {
-      verbose_print('Running ', opt$nperm, ' permutations with cofactors and strata\n')
-      perm_results = scanone(cross_object, pheno.col=1, addcovar=covars, n.perm=opt$nperm, perm.strata=perm_strata, model=opt$model, method=opt$method)
+      verbose_print('Running permutations with cofactors and strata\n')
+      perm_results = scan_func(cross_object, pheno.col=1, addcovar=covars, n.perm=opt$nperm, perm.strata=perm_strata, model=opt$model, method=opt$method)
     } else {
-      verbose_print('Running ', opt$nperm, ' permutations with cofactors\n')
-      perm_results = scanone(cross_object, pheno.col=1, addcovar=covars, n.perm=opt$nperm, model=opt$model, method=opt$method)
+      verbose_print('Running permutations with cofactors\n')
+      perm_results = scan_func(cross_object, pheno.col=1, addcovar=covars, n.perm=opt$nperm, model=opt$model, method=opt$method)
     }
   } else {
     if (!is.null(opt$pstrata)) {
-      verbose_print('Running ', opt$nperm, ' permutations with strata\n')
-      perm_results = scanone(cross_object, pheno.col=1, n.perm=opt$nperm, perm.strata=perm_strata, model=opt$model, method=opt$method)
+      verbose_print('Running permutations with strata\n')
+      perm_results = scan_func(cross_object, pheno.col=1, n.perm=opt$nperm, perm.strata=perm_strata, model=opt$model, method=opt$method)
     } else {
-      verbose_print('Running ', opt$nperm, ' permutations\n')
-      perm_results = scanone(cross_object, pheno.col=1, n.perm=opt$nperm, model=opt$model, method=opt$method)
+      verbose_print('Running permutations\n')
+      perm_results = scan_func(cross_object, pheno.col=1, n.perm=opt$nperm, model=opt$model, method=opt$method)
     }
   }
   write.csv(perm_results, perm_out_file)
@@ -268,57 +290,64 @@ if (!is.null(opt$filename)){
 }
 
 if (!is.null(opt$addcovar) || !is.null(opt$control)){
-  verbose_print('Running scanone with cofactors\n')
-  qtl_results = scanone(cross_object, pheno.col=1, addcovar=covars, model=opt$model, method=opt$method)
+  verbose_print('Running scan with cofactors\n')
+  qtl_results = scan_func(cross_object, pheno.col=1, addcovar=covars, model=opt$model, method=opt$method)
 } else {
-  verbose_print('Running scanone\n')
-  qtl_results = scanone(cross_object, pheno.col=1, model=opt$model, method=opt$method)
+  verbose_print('Running scan\n')
+  qtl_results = scan_func(cross_object, pheno.col=1, model=opt$model, method=opt$method)
 }
 
-#QTL main effects on adjusted longevity
-getEffects <- function(sdata, gtsprob, marker = "1_24042124", model = "longevity ~ sex + site + cohort + treatment", trait = "longevity"){
-  rownames(sdata) <- 1:nrow(sdata)
-  rownames(gtsprob) <- 1:nrow(gtsprob)
-  mp <- gtsprob[, grep(marker, colnames(gtsprob))]
-  gts <- unlist(lapply(lapply(lapply(apply(mp,1,function(x){which(x > 0.85)}),names), strsplit, ":"), function(x){
-    if(length(x) > 0){ return(x[[1]][2]); }else{ return(NA) }
-  }))
-
-  ismissing <- which(apply(sdata, 1, function(x){any(is.na(x))}))
-  if(length(ismissing) > 0){
-    sdata <- sdata[-ismissing, ]
-    gts <- gts[-ismissing]
+verbose_print('Writing results to CSV file\n')
+if (!is.null(opt$pairscan)) {
+  map_out_file = file.path(opt$outdir, paste("MAP_", opt$filename, sep = "" ))
+  write.csv(qtl_results[1], out_file)
+  write.csv(qtl_results[2], map_out_file)
+} else {
+  # QTL main effects on adjusted longevity
+  getEffects <- function(sdata, gtsprob, marker = "1_24042124", model = "longevity ~ sex + site + cohort + treatment", trait = "longevity"){
+    rownames(sdata) <- 1:nrow(sdata)
+    rownames(gtsprob) <- 1:nrow(gtsprob)
+    mp <- gtsprob[, grep(marker, colnames(gtsprob))]
+    gts <- unlist(lapply(lapply(lapply(apply(mp,1,function(x){which(x > 0.85)}),names), strsplit, ":"), function(x){
+      if(length(x) > 0){ return(x[[1]][2]); }else{ return(NA) }
+    }))
+
+    ismissing <- which(apply(sdata, 1, function(x){any(is.na(x))}))
+    if(length(ismissing) > 0){
+      sdata <- sdata[-ismissing, ]
+      gts <- gts[-ismissing]
+    }
+
+    mlm <- lm(as.formula(model), data = sdata)
+    pheAdj <- rep(NA, nrow(sdata))
+    adj <- residuals(mlm) + mean(sdata[, trait])
+    pheAdj[as.numeric(names(adj))] <- adj
+    means <- c(mean(pheAdj[which(gts == "AC")],na.rm=TRUE),mean(pheAdj[which(gts == "AD")],na.rm=TRUE),mean(pheAdj[which(gts == "BC")],na.rm=TRUE),mean(pheAdj[which(gts == "BD")],na.rm=TRUE))
+    std <- function(x) sd(x,na.rm=TRUE)/sqrt(length(x))
+    stderrs <- c(std(pheAdj[which(gts == "AC")]),std(pheAdj[which(gts == "AD")]),std(pheAdj[which(gts == "BC")]),std(pheAdj[which(gts == "BD")]))
+    paste0(round(means,0), " ± ", round(stderrs,2))
   }
 
-  mlm <- lm(as.formula(model), data = sdata)
-  pheAdj <- rep(NA, nrow(sdata))
-  adj <- residuals(mlm) + mean(sdata[, trait])
-  pheAdj[as.numeric(names(adj))] <- adj
-  means <- c(mean(pheAdj[which(gts == "AC")],na.rm=TRUE),mean(pheAdj[which(gts == "AD")],na.rm=TRUE),mean(pheAdj[which(gts == "BC")],na.rm=TRUE),mean(pheAdj[which(gts == "BD")],na.rm=TRUE))
-  std <- function(x) sd(x,na.rm=TRUE)/sqrt(length(x))
-  stderrs <- c(std(pheAdj[which(gts == "AC")]),std(pheAdj[which(gts == "AD")]),std(pheAdj[which(gts == "BC")]),std(pheAdj[which(gts == "BD")]))
-  paste0(round(means,0), " ± ", round(stderrs,2))
-}
+  if (type == "4-way") {
+    verbose_print("Get phenotype name + genoprob + all phenotypes + models for 4-way crosses")
+    traitname <- colnames(pull.pheno(cross_object))[1]
+    gtsp <- pull.genoprob(cross_object)
+    allpheno <- pull.pheno(cross_object)
+    if (!is.null(opt$addcovar)) {
+      model <- paste0(traitname, " ~ ", paste0(covar_names, sep="", collapse=" + "))
+    } else {
+      model <- paste0(traitname, " ~ 1 ")
+    }
 
-if (type == "4-way") {
-  verbose_print("Get phenotype name + genoprob + all phenotypes + models for 4-way crosses")
-  traitname <- colnames(pull.pheno(cross_object))[1]
-  gtsp <- pull.genoprob(cross_object)
-  allpheno <- pull.pheno(cross_object)
-  if (!is.null(opt$addcovar)) {
-    model <- paste0(traitname, " ~ ", paste0(covar_names, sep="", collapse=" + "))
-  } else {
-    model <- paste0(traitname, " ~ 1 ")
+    meffects <- c()
+    verbose_print("Getting QTL main effects for 4-way crosses")
+    for(marker in  rownames(qtl_results)){
+      meff <- getEffects(allpheno, gtsp, marker = marker, model, trait = traitname)
+      meffects <- rbind(meffects, meff)
+    }
+    qtl_results <- cbind(data.frame(qtl_results[,1:3]), meffects)
+    colnames(qtl_results)[4:7] <- c("AC", "AD", "BC", "BD")
   }
 
-  meffects <- c()
-  verbose_print("Getting QTL main effects for 4-way crosses")
-  for(marker in  rownames(qtl_results)){
-    meff <- getEffects(allpheno, gtsp, marker = marker, model, trait = traitname)
-    meffects <- rbind(meffects, meff)
-  }
-  qtl_results <- cbind(data.frame(qtl_results[,1:3]), meffects)
-  colnames(qtl_results)[4:7] <- c("AC", "AD", "BC", "BD")
+  write.csv(qtl_results, out_file)
 }
-
-write.csv(qtl_results, out_file)