diff PDAUG_Fishers_Plot/PDAUG_Fishers_Plot.py @ 0:9e347250e3a1 draft

"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit a9bd83f6a1afa6338cb6e4358b63ebff5bed155e"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/PDAUG_Fishers_Plot/PDAUG_Fishers_Plot.py	Wed Oct 28 02:38:39 2020 +0000
@@ -0,0 +1,420 @@
+import matplotlib
+matplotlib.use('Agg')
+import os
+import sys
+sys.path.insert(0, os.path.abspath('..'))
+
+import quantiprot
+from quantiprot.utils.io import load_fasta_file
+from quantiprot.utils.feature import Feature, FeatureSet
+from quantiprot.metrics.aaindex import get_aa2volume, get_aa2hydropathy
+from quantiprot.metrics.basic import average
+
+from matplotlib import pyplot as plt
+
+
+from math import log10, floor
+import numpy as np
+from matplotlib import pyplot as plt
+from scipy.stats import fisher_exact
+from quantiprot.utils.sequence import SequenceSet, compact
+
+
+def _count_frame(data, frame_range, num_bins):
+    """
+    Count instances in a 2D frame
+
+    The function discretizes the feature space into a grid of cells.
+    Then it counts the number of instances that fall into each cell.
+    An efficient method for counting instances is used. It performs parallel
+    logical comparisons of data instances to vectors that hold information on
+    grid lines.
+
+    Args:
+        data (numpy.matrix): a Nx2 data matrix
+        frame_range (numpy.matrix): a 2x2 matrix which defines feature ranges
+        num_bins (list): a pair defining the resolution of the 2D grid
+    Returns:
+        cell_counts (numpy.matrix): a matrix holding counts of instances in
+            each grid cell
+        bin_ranges (tuple): a pair of numpy matrices holding information on
+            bin(grid_cell) ranges
+    """
+    grid_x = np.linspace(start=frame_range[0, 0], stop=frame_range[1, 0],\
+                          num=num_bins[0]+1, endpoint=True)
+    grid_y = np.linspace(start=frame_range[0, 1], stop=frame_range[1, 1],\
+                          num=num_bins[1]+1, endpoint=True)
+    # copy because we add ones in the next lines
+    bin_ranges = (np.copy(grid_x), np.copy(grid_y))
+
+
+    #Count points in each grid cell
+    grid_x[-1] += 1 # the last cell has to contain data at the border
+    grid_y[-1] += 1 # the last cell has to contain data at the border
+
+    gte_x = np.matrix(data[:, 0] >= grid_x, dtype='float64')
+    lt_x = np.matrix(data[:, 0] < grid_x, dtype='float64')
+    gte_y = np.matrix(data[:, 1] >= grid_y, dtype='float64')
+    lt_y = np.matrix(data[:, 1] < grid_y, dtype='float64')
+
+    dif_x = gte_x - lt_x
+    dif_y = gte_y - lt_y
+
+    bins_x = dif_x.argmin(axis=1) - 1
+    bins_y = dif_y.argmin(axis=1) - 1
+
+    coords = np.concatenate((bins_x, bins_y), axis=1)
+
+    cell_counts = np.zeros(shape=(len(grid_x)-1, len(grid_y)-1))
+
+    for i in range(coords.shape[0]):
+        cell_counts[coords[i, 0], coords[i, 1]] += 1
+
+    return cell_counts, bin_ranges
+
+
+def local_fisher_2d(set1, set2, features=None, \
+                    windows_per_frame=10, overlap_factor=1, frame_range=None):
+    """
+    Compare local and global distribution of samples from two populations
+    in the 2d feature space using the Fisher's exact test.
+
+    The function performs the Fisher Exact Test for comparing local and global
+    ratia of instance counts from two different populations. It uses the
+    '_count_frame' function to discretize the feature space and get instance
+    counts. Then it scans the 2d feature space with a sliding window and
+    performs the Fisher Exact test.
+
+        Args:
+            set1 (SequenceSet or numpy.matrix): the first set with at least
+                2 sequence features.
+            set2 (SequenceSet or numpy.matrix): the second set with at least
+                2 sequence features.
+            features (tuple or list): strings with feature names for running
+                the 2d Fisher test. If None then the first two features are
+                used. Relevant only if 'set1' or 'set2' are SequenceSets.
+            windows_per_frame (int): ratio between the whole feature space and
+                the sliding window (default 10).
+            overlap_factor (int):ratio between the size of a sliding window
+                and a discretization grid cell (default 1).
+            frame_range(numpy.matrix): 2x2 matrix with range of features
+                in both dimensions.
+
+        Returns final_res (dict): a dictionary including:
+            'odds_ratio' (numpy.matrix): a matrix of odds_ratios obtained
+                in each sliding window position.
+            'p_value' (numpy.matrix): a matrix containing Fisher test outcome
+                pvalues in each sliding window position.
+            'w_counts1' (numpy.matrix): a matrix with first population instance
+                counts in each sliding window position.
+            'w_counts2' (numpy.matrix): a matrix with second population instance
+                counts in each sliding window position.
+            'w_center_x' (numpy.matrix): matrix containing coordinates of window
+                centers in the X dimension.
+            'w_center_y' (numpy.matrix): matrix containing coordinates of window
+                centers in the Y dimension.
+            '_bin_ranges_x' (numpy.matrix): matrix containing bin(grid_cell)
+                ranges in the X dimension.
+            '_bin_ranges_y' (numpy.matrix): matrix containing bin(grid_cell)
+                ranges in the Y dimension.
+    """
+
+    if isinstance(set1, SequenceSet):
+        mat1 = np.transpose(np.matrix(compact(set1,
+                                              features=features).columns()))
+    if isinstance(set2, SequenceSet):
+        mat2 = np.transpose(np.matrix(compact(set2,
+                                              features=features).columns()))
+
+    #Deal with window_per_frame and overlap_factor
+    #given either as a scalar or as a list-like
+    if not hasattr(windows_per_frame, "__len__"):
+        w_per_frame = (windows_per_frame, windows_per_frame)
+    else:
+        w_per_frame = (windows_per_frame[0], windows_per_frame[1])
+
+    if not hasattr(overlap_factor, "__len__"):
+        w_size = (overlap_factor, overlap_factor)
+    else:
+        w_size = (overlap_factor[0], overlap_factor[1])
+
+    num_bins = (w_per_frame[0]*w_size[0], w_per_frame[1]*w_size[1])
+
+    if frame_range is None:
+        #Evaluate the range of features in both populations.
+
+        frame_range = np.concatenate((np.minimum(mat1.min(0), mat2.min(0)),\
+                                      np.maximum(mat1.max(0), mat2.max(0))))
+
+        margin_x = (frame_range[1, 0] - frame_range[0, 0])/w_per_frame[0]
+        margin_y = (frame_range[1, 1] - frame_range[0, 1])/w_per_frame[1]
+
+        frame_range[0, 0] -= margin_x
+        frame_range[1, 0] += margin_x
+
+        frame_range[0, 1] -= margin_y
+        frame_range[1, 1] += margin_y
+
+    #Discretize feature space into NxM grid,
+    #where N = w_per_frame[0]*w_size[0].
+    #      M = w_per_frame[1]*w_size[1].
+    #count instances of population1 and population2 in each grid cell.
+    #both bin ranges are always the same because the frame range is common.
+    cell_counts1, bin_ranges = _count_frame(mat1, frame_range=frame_range,\
+                                          num_bins=num_bins)
+    cell_counts2, _ = _count_frame(mat2, frame_range=frame_range,\
+                                          num_bins=num_bins)
+
+    #Number of windows that fit in a single row/column of a frame
+    w_number = (cell_counts1.shape[0]-w_size[0]+1,
+                cell_counts1.shape[1]-w_size[1]+1)
+
+    #Initialize matrices holding counts at scanning window positions.
+    window_counts1 = np.zeros(shape=w_number)
+    window_counts2 = np.zeros(shape=w_number)
+
+    #Initialize matrices holding window coordinates
+    window_center_x = np.zeros(shape=w_number[0])
+    window_center_y = np.zeros(shape=w_number[1])
+
+    #Initialize matrices holding Fisher Exact test results
+    fisher_pv = np.ones(shape=w_number)
+    odds_ratio = np.ones(shape=w_number)
+
+    #Calculate population totals in the whole feature space
+    all1 = cell_counts1.sum()
+    all2 = cell_counts2.sum()
+
+    #Calculate window centers
+    for start_x in range(0, w_number[0]):
+        window_center_x[start_x] = (bin_ranges[0][start_x]+ \
+                                    bin_ranges[0][start_x+w_size[0]])/2
+    for start_y in range(0, w_number[1]):
+        window_center_y[start_y] = (bin_ranges[1][start_y]+ \
+                                    bin_ranges[1][start_y+w_size[1]])/2
+
+    #Scan the feature space with a step of 1 cell.
+    for start_x in range(0, w_number[0]):
+
+        for start_y in range(0, w_number[1]):
+            #Count instances of each population in the window
+            window_counts1[start_x, start_y] = \
+                cell_counts1[start_x:(start_x+w_size[0]), \
+                             start_y:(start_y+w_size[1])].sum()
+            window_counts2[start_x, start_y] = \
+                cell_counts2[start_x:(start_x+w_size[0]), \
+                             start_y:(start_y+w_size[1])].sum()
+            #Perform the Fisher Exact Test against
+            #h0: population ratio in the window the same as in the whole space.
+            odds_ratio[start_x, start_y], fisher_pv[start_x, start_y] =\
+                fisher_exact([[all1, window_counts1[start_x, start_y]],\
+                              [all2, window_counts2[start_x, start_y]]])
+
+    fisher_res = {'p_value':fisher_pv, 'odds_ratio':odds_ratio,\
+                'w_counts1':window_counts1, 'w_counts2':window_counts2,\
+                'w_center_x':window_center_x, 'w_center_y':window_center_y,\
+                '_bin_ranges_x':bin_ranges[0], '_bin_ranges_y':bin_ranges[1]}
+
+    return fisher_res
+
+
+def _plot_local_fisher_2d(fisher_res, xlabel="feat_1", ylabel="feat_2",
+                          pop1_label="pop_1", pop2_label="pop_2", out_file_path=None, fig_width=8, fig_hight=8, fig_hspace=0.35, fig_wspace=0.25):
+    """
+    Plot results of the local Fisher's extact test in the 2d space.
+
+    Args:
+        fisher_res (dict): output from 'fisher_local_2d'.
+        xlabel (str): name of the 1st feature to appear in the plots
+            (default: "feat_1")
+        ylabel (str): name of the 2nd feature to appear in the plots
+            (default: "feat_2")
+        pop1_label (str): name of the 1st population to appear in the plots
+            (default: "pop_1")
+        pop2_label (str): name of the 2nd population to appear in the plots
+            (default: "pop_2")
+    """
+    fisher_or = fisher_res["odds_ratio"]
+    fisher_c1 = fisher_res["w_counts1"]
+    fisher_c2 = fisher_res["w_counts2"]
+    fisher_pv = fisher_res["p_value"]
+
+    for pos_x in range(len(fisher_or)):
+        for pos_y in range(len(fisher_or[0])):
+            if fisher_c1[pos_x][pos_y] == 0 and fisher_c2[pos_x][pos_y] == 0:
+                fisher_or[pos_x][pos_y] = np.nan
+            elif fisher_c1[pos_x][pos_y] == 0:
+                fisher_or[pos_x][pos_y] = np.inf
+            elif fisher_c2[pos_x][pos_y] == 0:
+                fisher_or[pos_x][pos_y] = -np.inf
+            elif fisher_or[pos_x][pos_y] < 1:
+                fisher_or[pos_x][pos_y] = -1.0/fisher_or[pos_x][pos_y]
+
+    vmax_abs = np.nanmax(np.abs([x for x in np.array(fisher_or).flatten()
+                                 if x > -np.inf and x < np.inf]))
+
+    for pos_x in range(len(fisher_or)):
+        for pos_y in range(len(fisher_or[0])):
+            if abs(fisher_or[pos_x][pos_y]) == np.inf:
+                fisher_or[pos_x][pos_y] = np.sign(fisher_or[pos_x][pos_y])*vmax_abs
+
+    ##### Extra Fig perimeters added ################################
+    plt.figure(figsize=(fig_width, fig_hight)) # Figure size 
+    plt.subplots_adjust(hspace = fig_hspace, wspace = fig_wspace) # space between the subplots. 
+    ##################################################################
+
+    plt.subplot(221)
+    plt.pcolormesh(fisher_res["w_center_x"], fisher_res["w_center_y"],
+                   np.ma.masked_invalid(fisher_c1).T, cmap="Reds")
+    plt.colorbar()
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.title("Counts "+pop1_label)
+
+    plt.subplot(222)
+    plt.pcolormesh(fisher_res["w_center_x"], fisher_res["w_center_y"],
+                   np.ma.masked_invalid(fisher_c2).T, cmap="Reds")
+    plt.colorbar()
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.title("Counts "+pop2_label)
+
+    cmap = plt.get_cmap('RdBu')
+    cmap.set_bad(color='k', alpha=1.)
+
+    cbar_lo = 1.0/vmax_abs
+    cbar_lo_places = max(0, -floor(log10(cbar_lo))+1)
+    cbar_hi = vmax_abs
+    cbar_hi_places = max(0, -floor(log10(cbar_hi))+1)
+
+    plt.subplot(223)
+    plt.pcolormesh(fisher_res["w_center_x"], fisher_res["w_center_y"],
+                   np.ma.masked_invalid(fisher_or).T, cmap=cmap,
+                   vmin=-vmax_abs, vmax=vmax_abs)
+    cbar = plt.colorbar(ticks=([-vmax_abs, 0, vmax_abs]))
+    cbar.ax.set_yticklabels(['< '+str(round(cbar_lo, int(cbar_lo_places))), '1',
+                             '> '+str(round(cbar_hi, int(cbar_hi_places)))])
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.title("Odds ratio")
+
+    plt.subplot(224)
+    plt.pcolormesh(fisher_res["w_center_x"], fisher_res["w_center_y"],
+                   np.log10(np.ma.masked_invalid(fisher_pv)).T, cmap="RdGy")
+    plt.colorbar()
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.title("Fisher test\np-value (logarithm of 10)")
+
+    #Savefig function added with preserving default behavior
+
+    if out_file_path==None:
+        plt.show()
+    else:
+        plt.savefig(out_file_path,dpi=300)
+
+
+def HTML_Gen(html):
+
+    out_html = open(html,'w')             
+    part_1 =  """
+
+    <!DOCTYPE html>
+    <html lang="en">
+    <head>
+      <title>Bootstrap Example</title>
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width, initial-scale=1">
+      <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.0/css/bootstrap.min.css">
+      <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.4.0/jquery.min.js"></script>
+      <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.0/js/bootstrap.min.js"></script>
+    <body>
+    <style>
+    div.container_1 {
+      width:600px;
+      margin: auto;
+     padding-right: 10; 
+    }
+    div.table {
+      width:600px;
+      margin: auto;
+     padding-right: 10; 
+    }
+    </style>
+    </head>
+    <div class="jumbotron text-center">
+      <h1> Fisher's Plot </h1>
+    </div>
+    <div class="container">
+      <div class="row">
+        <div class="col-sm-4">
+          <img src="1.png" alt="Smiley face" height="800" width="800">
+        </div>
+
+      </div>
+    </div>
+    </body>
+    </html>
+    """ 
+    out_html.write(part_1)
+    out_html.close()
+# Load sets of amyloidogenic and non-amyloidogenic peptides:
+
+def run(Fasta1, Fasta2, windows_per_frame, overlap_factor, xlabel, ylabel, pop1_label, pop2_label, htmlOutDir, htmlFname, Workdirpath):
+
+    if not os.path.exists(htmlOutDir):
+        os.makedirs(htmlOutDir)
+
+    amyload_pos_seq = load_fasta_file(Fasta1)
+    amyload_neg_seq = load_fasta_file(Fasta2)
+
+    # Calculate quantitive features: volume and hydropathy
+    mean_volume = Feature(get_aa2volume()).then(average)
+    mean_hydropathy = Feature(get_aa2hydropathy()).then(average)
+
+    fs = FeatureSet("volume'n'hydropathy")
+    fs.add(mean_volume)
+    fs.add(mean_hydropathy)
+
+    amyload_pos_conv_seq = fs(amyload_pos_seq)
+    amyload_neg_conv_seq = fs(amyload_neg_seq)
+
+    # Do local Fisher:
+    result = local_fisher_2d(amyload_pos_conv_seq, amyload_neg_conv_seq,
+                             windows_per_frame=int(windows_per_frame), overlap_factor=int(overlap_factor))
+
+    # Plot local Fisher:
+    _plot_local_fisher_2d(result, xlabel=xlabel,
+                                  ylabel=ylabel,
+                                  pop1_label=pop1_label,
+                                  pop2_label=pop2_label,
+                                  out_file_path =os.path.join(os.getcwd(), "out.png") 
+                                  )
+
+    
+    #   plt.savefig(os.path.join(Workdirpath, htmlOutDir, "1.png"))
+
+    HTML_Gen(os.path.join(Workdirpath, htmlOutDir, htmlFname))
+
+if __name__=="__main__":
+    
+    
+    import argparse
+    
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument("-f1", "--Fasta1", required=True, default=None, help="First fasta file ")                
+    parser.add_argument("-f2", "--Fasta2", required=True, default=None, help="Second fasta file")   
+    parser.add_argument("-o", "--overlap_factor", required=False, default=5, help="Overlap factor")  
+    parser.add_argument("-w", "--windows_per_frame", required=False, default=5, help="Windows per frame")  
+    parser.add_argument("-x", "--xlabel", required=True, default=None, help="X label")  
+    parser.add_argument("-y", "--ylabel", required=True, default=None, help="Y label")  
+    parser.add_argument("-p1", "--pop1_label", required=True, default=None, help="First population label")  
+    parser.add_argument("-p2", "--pop2_label", required=True, default=None, help="Second population label") 
+    parser.add_argument("--htmlOutDir", required=False, default=os.path.join(os.getcwd(),'report_dir'),  help="Path to html dir")
+    parser.add_argument("--htmlFname",  required=False, help="html output file", default="report.html")
+    parser.add_argument("--Workdirpath", required=False, default=os.getcwd(), help="Path to output Working Directory")                          
+    args = parser.parse_args()
+
+    run(args.Fasta1, args.Fasta2, args.windows_per_frame, args.overlap_factor, args.xlabel, args.ylabel, args.pop1_label, args.pop2_label, args.htmlOutDir, args.htmlFname, args.Workdirpath)
+