fix merge conflicts

1 files changed, 395 insertions, 0 deletions

diff --git a/gn3/heatmaps.py b/gn3/heatmaps.py
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+++ b/gn3/heatmaps.py
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+"""
+This module will contain functions to be used in computation of the data used to
+generate various kinds of heatmaps.
+"""
+
+from functools import reduce
+from typing import Any, Dict, Sequence
+
+import numpy as np
+import plotly.graph_objects as go # type: ignore
+import plotly.figure_factory as ff # type: ignore
+from plotly.subplots import make_subplots # type: ignore
+
+from gn3.settings import TMPDIR
+from gn3.random import random_string
+from gn3.computations.slink import slink
+from gn3.computations.correlations2 import compute_correlation
+from gn3.db.genotypes import (
+    build_genotype_file, load_genotype_samples)
+from gn3.db.traits import (
+    retrieve_trait_data, retrieve_trait_info)
+from gn3.computations.qtlreaper import (
+    run_reaper,
+    generate_traits_file,
+    chromosome_sorter_key_fn,
+    parse_reaper_main_results,
+    organise_reaper_main_results)
+
+def export_trait_data(
+        trait_data: dict, samplelist: Sequence[str], dtype: str = "val",
+        var_exists: bool = False, n_exists: bool = False):
+    """
+    Export data according to `samplelist`. Mostly used in calculating
+    correlations.
+
+    DESCRIPTION:
+    Migrated from
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/base/webqtlTrait.py#L166-L211
+
+    PARAMETERS
+    trait: (dict)
+      The dictionary of key-value pairs representing a trait
+    samplelist: (list)
+      A list of sample names
+    dtype: (str)
+      ... verify what this is ...
+    var_exists: (bool)
+      A flag indicating existence of variance
+    n_exists: (bool)
+      A flag indicating existence of ndata
+    """
+    def __export_all_types(tdata, sample):
+        sample_data = []
+        if tdata[sample]["value"]:
+            sample_data.append(tdata[sample]["value"])
+            if var_exists:
+                if tdata[sample]["variance"]:
+                    sample_data.append(tdata[sample]["variance"])
+                else:
+                    sample_data.append(None)
+            if n_exists:
+                if tdata[sample]["ndata"]:
+                    sample_data.append(tdata[sample]["ndata"])
+                else:
+                    sample_data.append(None)
+        else:
+            if var_exists and n_exists:
+                sample_data += [None, None, None]
+            elif var_exists or n_exists:
+                sample_data += [None, None]
+            else:
+                sample_data.append(None)
+
+        return tuple(sample_data)
+
+    def __exporter(accumulator, sample):
+        # pylint: disable=[R0911]
+        if sample in trait_data["data"]:
+            if dtype == "val":
+                return accumulator + (trait_data["data"][sample]["value"], )
+            if dtype == "var":
+                return accumulator + (trait_data["data"][sample]["variance"], )
+            if dtype == "N":
+                return accumulator + (trait_data["data"][sample]["ndata"], )
+            if dtype == "all":
+                return accumulator + __export_all_types(trait_data["data"], sample)
+            raise KeyError("Type `%s` is incorrect" % dtype)
+        if var_exists and n_exists:
+            return accumulator + (None, None, None)
+        if var_exists or n_exists:
+            return accumulator + (None, None)
+        return accumulator + (None,)
+
+    return reduce(__exporter, samplelist, tuple())
+
+def trait_display_name(trait: Dict):
+    """
+    Given a trait, return a name to use to display the trait on a heatmap.
+
+    DESCRIPTION
+    Migrated from
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/base/webqtlTrait.py#L141-L157
+    """
+    if trait.get("db", None) and trait.get("trait_name", None):
+        if trait["db"]["dataset_type"] == "Temp":
+            desc = trait["description"]
+            if desc.find("PCA") >= 0:
+                return "%s::%s" % (
+                    trait["db"]["displayname"],
+                    desc[desc.rindex(':')+1:].strip())
+            return "%s::%s" % (
+                trait["db"]["displayname"],
+                desc[:desc.index('entered')].strip())
+        prefix = "%s::%s" % (
+            trait["db"]["dataset_name"], trait["trait_name"])
+        if trait["cellid"]:
+            return "%s::%s" % (prefix, trait["cellid"])
+        return prefix
+    return trait["description"]
+
+def cluster_traits(traits_data_list: Sequence[Dict]):
+    """
+    Clusters the trait values.
+
+    DESCRIPTION
+    Attempts to replicate the clustering of the traits, as done at
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L138-L162
+    """
+    def __compute_corr(tdata_i, tdata_j):
+        if tdata_i[0] == tdata_j[0]:
+            return 0.0
+        corr_vals = compute_correlation(tdata_i[1], tdata_j[1])
+        corr = corr_vals[0]
+        if (1 - corr) < 0:
+            return 0.0
+        return 1 - corr
+
+    def __cluster(tdata_i):
+        return tuple(
+            __compute_corr(tdata_i, tdata_j)
+            for tdata_j in enumerate(traits_data_list))
+
+    return tuple(__cluster(tdata_i) for tdata_i in enumerate(traits_data_list))
+
+def build_heatmap(traits_names, conn: Any):
+    """
+    heatmap function
+
+    DESCRIPTION
+    This function is an attempt to reproduce the initialisation at
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L46-L64
+    and also the clustering and slink computations at
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L138-L165
+    with the help of the `gn3.computations.heatmap.cluster_traits` function.
+
+    It does not try to actually draw the heatmap image.
+
+    PARAMETERS:
+    TODO: Elaborate on the parameters here...
+    """
+    # pylint: disable=[R0914]
+    threshold = 0 # webqtlConfig.PUBLICTHRESH
+    traits = [
+        retrieve_trait_info(threshold, fullname, conn)
+        for fullname in traits_names]
+    traits_data_list = [retrieve_trait_data(t, conn) for t in traits]
+    genotype_filename = build_genotype_file(traits[0]["group"])
+    samples = load_genotype_samples(genotype_filename)
+    exported_traits_data_list = [
+        export_trait_data(td, samples) for td in traits_data_list]
+    clustered = cluster_traits(exported_traits_data_list)
+    slinked = slink(clustered)
+    traits_order = compute_traits_order(slinked)
+    samples_and_values = retrieve_samples_and_values(
+        traits_order, samples, exported_traits_data_list)
+    traits_filename = "{}/traits_test_file_{}.txt".format(
+        TMPDIR, random_string(10))
+    generate_traits_file(
+        samples_and_values[0][1],
+        [t[2] for t in samples_and_values],
+        traits_filename)
+
+    main_output, _permutations_output = run_reaper(
+        genotype_filename, traits_filename, separate_nperm_output=True)
+
+    qtlresults = parse_reaper_main_results(main_output)
+    organised = organise_reaper_main_results(qtlresults)
+
+    traits_ids = [# sort numerically, but retain the ids as strings
+        str(i) for i in sorted({int(row["ID"]) for row in qtlresults})]
+    chromosome_names = sorted(
+        {row["Chr"] for row in qtlresults}, key=chromosome_sorter_key_fn)
+    ordered_traits_names = dict(
+        zip(traits_ids,
+            [traits[idx]["trait_fullname"] for idx in traits_order]))
+
+    return generate_clustered_heatmap(
+        process_traits_data_for_heatmap(
+            organised, traits_ids, chromosome_names),
+        clustered,
+        "single_heatmap_{}".format(random_string(10)),
+        y_axis=tuple(
+            ordered_traits_names[traits_ids[order]]
+            for order in traits_order),
+        y_label="Traits",
+        x_axis=chromosome_names,
+        x_label="Chromosomes")
+
+def compute_traits_order(slink_data, neworder: tuple = tuple()):
+    """
+    Compute the order of the traits for clustering from `slink_data`.
+
+    This function tries to reproduce the creation and update of the `neworder`
+    variable in
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L120
+    and in the `web.webqtl.heatmap.Heatmap.draw` function in GN1
+    """
+    def __order_maker(norder, slnk_dt):
+        if isinstance(slnk_dt[0], int) and isinstance(slnk_dt[1], int):
+            return norder + (slnk_dt[0], slnk_dt[1])
+
+        if isinstance(slnk_dt[0], int):
+            return __order_maker((norder + (slnk_dt[0], )), slnk_dt[1])
+
+        if isinstance(slnk_dt[1], int):
+            return __order_maker(norder, slnk_dt[0]) + (slnk_dt[1], )
+
+        return __order_maker(__order_maker(norder, slnk_dt[0]), slnk_dt[1])
+
+    return __order_maker(neworder, slink_data)
+
+def retrieve_samples_and_values(orders, samplelist, traits_data_list):
+    """
+    Get the samples and their corresponding values from `samplelist` and
+    `traits_data_list`.
+
+    This migrates the code in
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L215-221
+    """
+    # This feels nasty! There's a lot of mutation of values here, that might
+    # indicate something untoward in the design of this function and its
+    # dependents  ==>  Review
+    samples = []
+    values = []
+    rets = []
+    for order in orders:
+        temp_val = traits_data_list[order]
+        for i, sample in enumerate(samplelist):
+            if temp_val[i] is not None:
+                samples.append(sample)
+                values.append(temp_val[i])
+        rets.append([order, samples[:], values[:]])
+        samples = []
+        values = []
+
+    return rets
+
+def nearest_marker_finder(genotype):
+    """
+    Returns a function to be used with `genotype` to compute the nearest marker
+    to the trait passed to the returned function.
+
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L425-434
+    """
+    def __compute_distances(chromo, trait):
+        loci = chromo.get("loci", None)
+        if not loci:
+            return None
+        return tuple(
+            {
+                "name": locus["name"],
+                "distance": abs(locus["Mb"] - trait["mb"])
+            } for locus in loci)
+
+    def __finder(trait):
+        _chrs = tuple(
+            _chr for _chr in genotype["chromosomes"]
+            if str(_chr["name"]) == str(trait["chr"]))
+        if len(_chrs) == 0:
+            return None
+        distances = tuple(
+            distance for dists in
+            filter(
+                lambda x: x is not None,
+                (__compute_distances(_chr, trait) for _chr in _chrs))
+            for distance in dists)
+        nearest = min(distances, key=lambda d: d["distance"])
+        return nearest["name"]
+    return __finder
+
+def get_nearest_marker(traits_list, genotype):
+    """
+    Retrieves the nearest marker for each of the traits in the list.
+
+    DESCRIPTION:
+    This migrates the code in
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L419-L438
+    """
+    if not genotype["Mbmap"]:
+        return [None] * len(traits_list)
+
+    marker_finder = nearest_marker_finder(genotype)
+    return [marker_finder(trait) for trait in traits_list]
+
+def get_lrs_from_chr(trait, chr_name):
+    """
+    Retrieve the LRS values for a specific chromosome in the given trait.
+    """
+    chromosome = trait["chromosomes"].get(chr_name)
+    if chromosome:
+        return [
+            locus["LRS"] for locus in
+            sorted(chromosome["loci"], key=lambda loc: loc["Locus"])]
+    return [None]
+
+def process_traits_data_for_heatmap(data, trait_names, chromosome_names):
+    """
+    Process the traits data in a format useful for generating heatmap diagrams.
+    """
+    hdata = [
+        [get_lrs_from_chr(data[trait], chr_name) for trait in trait_names]
+        for chr_name in chromosome_names]
+    return hdata
+
+def generate_clustered_heatmap(
+        data, clustering_data, image_filename_prefix, x_axis=None,
+        x_label: str = "", y_axis=None, y_label: str = "",
+        output_dir: str = TMPDIR,
+        colorscale=(
+            (0.0, '#5D5D5D'), (0.4999999999999999, '#ABABAB'),
+            (0.5, '#F5DE11'), (1.0, '#FF0D00'))):
+    """
+    Generate a dendrogram, and heatmaps for each chromosome, and put them all
+    into one plot.
+    """
+    # pylint: disable=[R0913, R0914]
+    num_cols = 1 + len(x_axis)
+    fig = make_subplots(
+        rows=1,
+        cols=num_cols,
+        shared_yaxes="rows",
+        horizontal_spacing=0.001,
+        subplot_titles=["distance"] + x_axis,
+        figure=ff.create_dendrogram(
+            np.array(clustering_data), orientation="right", labels=y_axis))
+    hms = [go.Heatmap(
+        name=chromo,
+        y=y_axis,
+        z=data_array,
+        showscale=False) for chromo, data_array in zip(x_axis, data)]
+    for i, heatmap in enumerate(hms):
+        fig.add_trace(heatmap, row=1, col=(i + 2))
+
+    fig.update_layout(
+        {
+            "width": 1500,
+            "height": 800,
+            "xaxis": {
+                "mirror": False,
+                "showgrid": True,
+                "title": x_label
+            },
+            "yaxis": {
+                "title": y_label
+            }
+        })
+
+    x_axes_layouts = {
+        "xaxis{}".format(i+1 if i > 0 else ""): {
+            "mirror": False,
+            "showticklabels": i == 0,
+            "ticks": "outside" if i == 0 else ""
+        }
+        for i in range(num_cols)}
+
+    fig.update_layout(
+        {
+            "width": 4000,
+            "height": 800,
+            "yaxis": {
+                "mirror": False,
+                "ticks": ""
+            },
+            **x_axes_layouts})
+    fig.update_traces(
+        showlegend=False,
+        colorscale=colorscale,
+        selector={"type": "heatmap"})
+    fig.update_traces(
+        showlegend=True,
+        showscale=True,
+        selector={"name": x_axis[-1]})
+    image_filename = "{}/{}.html".format(output_dir, image_filename_prefix)
+    fig.write_html(image_filename)
+    return image_filename, fig