Changes to BCRPhylo to allow for mapping BCR sequence to affinity and expression

bin/GCutils.py

@@ -3,7 +3,6 @@
 from __future__ import absolute_import
 from Bio.Seq import Seq
 from Bio.Seq import translate as bio_translate
-from Bio.Alphabet import generic_dna
 from ete3 import TreeNode, NodeStyle, TreeStyle, TextFace, CircleFace, PieChartFace, faces, SVG_COLORS
 import scipy
 import numpy as np
@@ -15,18 +14,18 @@
 except:
     import pickle
 
-try:
-    import jellyfish
-
-    def hamming_distance(s1, s2):
-        if s1 == s2:
-            return 0
-        else:
-            return jellyfish.hamming_distance(unicode(s1), unicode(s2))
-except:
-    def hamming_distance(seq1, seq2):
-        '''Hamming distance between two sequences of equal length'''
-        return sum(x != y for x, y in zip(seq1, seq2))
+#try:
+    #import jellyfish
+
+    #def hamming_distance(s1, s2):
+        #if s1 == s2:
+            #return 0
+        #else:
+            #return jellyfish.hamming_distance(s1, s2)
+#except:
+def hamming_distance(seq1, seq2):
+    '''Hamming distance between two sequences of equal length'''
+    return sum(x != y for x, y in zip(seq1, seq2))
     print('Couldn\'t find the python module "jellyfish" which is used for fast string comparison. Falling back to pure python function.')
 
 global ISO_TYPE_ORDER
@@ -44,10 +43,11 @@ def local_translate(seq):
 
 # ----------------------------------------------------------------------------------------
 def replace_codon_in_aa_seq(new_nuc_seq, old_aa_seq, inuc):  # <inuc>: single nucleotide position that was mutated from old nuc seq (which corresponds to old_aa_seq) to new_nuc_seq
-    istart = 3 * int(math.floor(inuc / 3.))  # nucleotide position of start of mutated codon
+    istart = 3 * math.floor(inuc / 3)  # nucleotide position of start of mutated codon
+    aa_new = math.floor(inuc / 3)
     new_codon = local_translate(new_nuc_seq[istart : istart + 3])
-    return old_aa_seq[:inuc / 3] + new_codon + old_aa_seq[inuc / 3 + 1:]  # would be nice to check for synonymity and not do any translation unless we need to
-
+    new_seq = old_aa_seq[:aa_new] + new_codon + old_aa_seq[aa_new + 1:]  # would be nice to check for synonymity and not do any translation unless we need to
+    return new_seq 
 # ----------------------------------------------------------------------------------------
 class TranslatedSeq(object):
     # ----------------------------------------------------------------------------------------
@@ -270,7 +270,7 @@ def my_layout(node):
         if idlabel:
             aln = MultipleSeqAlignment([])
             for node in self.tree.traverse():
-                aln.append(SeqRecord(Seq(str(node.nuc_seq), generic_dna), id=node.name, description='abundance={}'.format(node.frequency)))
+                aln.append(SeqRecord(Seq(str(node.nuc_seq)), id=node.name, description='abundance={}'.format(node.frequency)))
             AlignIO.write(aln, open(os.path.splitext(outfile)[0] + '.fasta', 'w'), 'fasta')
 
     def write(self, file_name):

bin/selection_utils.py

@@ -92,16 +92,16 @@ def init_blosum():
 blinfo = {aa1 : {aa2 : None for aa2 in all_amino_acids} for aa1 in all_amino_acids}
 init_blosum()
 sdists = {  # config info necessary for rescaling the two versions of aa similarity distance
-    'ascii' : {
-        'scale_min' : 0.,  # you want the mean to be kinda sorta around 1, so the --target_dist ends up being comparable
-        'scale_max' : 4.7,
-    },
-    'blosum' : {
-        'scale_min' : 0.,
-        # 'scale_max' : 3.95,  # use this value if you go back to taking the exp() (note that now the exp() would need to be added in two places)
-        'scale_max' : 1.55,
-    },
-}
+        'ascii' : {
+            'scale_min' : 0.,  # you want the mean to be kinda sorta around 1, so the --target_dist ends up being comparable
+            'scale_max' : 4.7,
+            },
+        'blosum' : {
+            'scale_min' : 0.,
+            # 'scale_max' : 3.95,  # use this value if you go back to taking the exp() (note that now the exp() would need to be added in two places)
+            'scale_max' : 1.55,
+            },
+        }
 for sdtype, sdinfo in sdists.items():
     if sdtype == 'ascii':
         dfcn = aa_ascii_code_distance
@@ -134,9 +134,9 @@ def plot_sdists():
         print('       raw  rescaled   raw   rescaled')
         for aa2 in all_amino_acids:
             print('   %s  %5.2f  %5.2f    %5.1f  %5.2f' % (aa2,
-                                                           aa_inverse_similarity(aa1, aa2, 'blosum', dont_rescale=True), aa_inverse_similarity(aa1, aa2, 'blosum'),
-                                                           aa_inverse_similarity(aa1, aa2, 'ascii', dont_rescale=True), aa_inverse_similarity(aa1, aa2, 'ascii')))
-    import plotutils
+                aa_inverse_similarity(aa1, aa2, 'blosum', dont_rescale=True), aa_inverse_similarity(aa1, aa2, 'blosum'),
+                aa_inverse_similarity(aa1, aa2, 'ascii', dont_rescale=True), aa_inverse_similarity(aa1, aa2, 'ascii')))
+            import plotutils
     for sdtype in ['ascii', 'blosum']:
         print(sdtype)
         all_rescaled_vals = [aa_inverse_similarity(aa1, aa2, sdtype=sdtype) for aa1, aa2 in itertools.combinations_with_replacement(all_amino_acids, 2)]
@@ -161,58 +161,98 @@ def target_distance_fcn(args, this_seq, target_seqs):
     return itarget, tdist
 
 # ----------------------------------------------------------------------------------------
+def count_aa(aa_seq,which_aa):
+    list_of_aa = list(aa_seq)
+    res1 = []
+    for i in range(len(list_of_aa)):
+        if list_of_aa[i] == which_aa:
+            res1.append(i)
+    return len(res1)
+
+def count_not_that_aa(aa_seq,which_aa):
+    list_of_aa = list(aa_seq)
+    res1 = []
+    for i in range(len(list_of_aa)):
+        if list_of_aa[i] != which_aa:
+            res1.append(i)
+    return len(res1)
+
 def calc_kd(node, args):
     if has_stop_aa(node.aa_seq):  # nonsense sequences have zero affinity/infinite kd
         return float('inf')
     if not args.selection:
         return 1.
 
-    assert args.mature_kd < args.naive_kd
-    tdist = node.target_distance if args.min_target_distance is None else max(node.target_distance, args.min_target_distance)
-    kd = args.mature_kd + (args.naive_kd - args.mature_kd) * (tdist / float(args.target_distance))**args.k_exp  # transformation from distance to kd
-
+    #tdist = node.target_distance if args.min_target_distance is None else max(node.target_distance, args.min_target_distance)
+    #kd = args.mature_kd + (args.naive_kd - args.mature_kd) * (tdist / float(args.target_distance))**args.k_exp  # transformation from distance to kd
+    #kd = 100 - 3*min(count_not_that_aa(node.aa_seq,'A'),33)
+    kd = 100 - 12*min(count_aa(node.aa_seq,'L'),9)
     return kd
 
+def calc_B_exp(node, args):
+    if has_stop_aa(node.aa_seq):  # nonsense sequences have no production 
+        B_exp = 0
+    if not args.selection:
+        return 10000
+    #B_exp = 5000 + 250*min(count_aa(node.aa_seq,'T'),15)
+    #B_exp = 5000 + 250*min(count_aa(node.aa_seq,'L'),15)
+    B_exp = 5000
+    if B_exp == 0:
+        print(node.aa_seq)
+    return B_exp
+
 # ----------------------------------------------------------------------------------------
 def update_lambda_values(live_leaves, A_total, B_total, logi_params, selection_strength, lambda_min=10e-10):
     ''' update the lambda_ feature (parameter for the poisson progeny distribution) for each leaf in <live_leaves> '''
 
     # ----------------------------------------------------------------------------------------
-    def calc_BnA(Kd_n, A, B_total):
+    #Since each B cell now has its own BCR expression level, would need to calculate B_total
+    def sum_of_B(B_n):
+        B_total = numpy.sum(B_n)
+        return B_total
+
+    def calc_BnA(Kd_n, A, B_n):
         '''
         This calculates the fraction B:A (B bound to A), at equilibrium also referred to as "binding time",
         of all the different Bs in the population given the number of free As in solution.
         '''
-        BnA = B_total/(1+Kd_n/A)
+        BnA = B_n/(1+Kd_n/A)
         return(BnA)
 
     # ----------------------------------------------------------------------------------------
-    def return_objective_A(Kd_n, A_total, B_total):
+    def return_objective_A(Kd_n, A_total, B_n):
         '''
         The objective function that solves the set of differential equations setup to find the number of free As,
         at equilibrium, given a number of Bs with some affinity listed in Kd_n.
         '''
-        return lambda A: (A_total - (A + scipy.sum(B_total/(1+Kd_n/A))))**2
-
+        return lambda A: (A_total - (A + numpy.sum(B_n/(1+Kd_n/A))))**2
     # ----------------------------------------------------------------------------------------
-    def calc_binding_time(Kd_n, A_total, B_total):
+    def calc_binding_time(Kd_n, A_total, B_n):
         '''
         Solves the objective function to find the number of free As and then uses this,
         to calculate the fraction B:A (B bound to A) for all the different Bs.
         '''
-        obj = return_objective_A(Kd_n, A_total, B_total)
+        obj = return_objective_A(Kd_n, A_total, B_n)
         # Different minimizers have been tested and 'L-BFGS-B' was significant faster than anything else:
         obj_min = minimize(obj, A_total, bounds=[[1e-10, A_total]], method='L-BFGS-B', tol=1e-20)
-        BnA = calc_BnA(Kd_n, obj_min.x[0], B_total)
+        BnA = calc_BnA(Kd_n, obj_min.x[0], B_n)
+        #print(Kd_n)
+        #print(obj_min.x[0])
+        #print(B_n) 
         # Terminate if the precision is not good enough:
-        assert(BnA.sum()+obj_min.x[0]-A_total < A_total/100)
+        assert(BnA.sum()+obj_min.x[0]-A_total < A_total/100) 
         return BnA
 
     # ----------------------------------------------------------------------------------------
     def trans_BA(BnA):
         '''Transform the fraction B:A (B bound to A) to a poisson lambda between 0 and 2.'''
         # We keep alpha to enable the possibility that there is a minimum lambda_:
-        lambda_ = alpha + (2 - alpha) / (1 + Q*scipy.exp(-beta*BnA))
+        #print(beta)
+        #print(BnA)
+        power_val = -beta*BnA
+        power_val[power_val < -125] = -125
+        #lambda_ = 2*(alpha + (2 - alpha) / (1 + Q*numpy.exp(-beta*BnA))) 
+        lambda_ = 2*(alpha + (2 - alpha) / (1 + Q*numpy.exp(power_val))) ##multiplied by 2 here 
         return [max(lambda_min, l) for l in lambda_]
 
     # ----------------------------------------------------------------------------------------
@@ -240,8 +280,9 @@ def getvar(lvals):
 
     # ----------------------------------------------------------------------------------------
     alpha, beta, Q = logi_params
-    Kd_n = scipy.array([l.Kd for l in live_leaves])
-    BnA = calc_binding_time(Kd_n, A_total, B_total)  # get list of binding time values for each cell
+    Kd_n = numpy.array([l.Kd for l in live_leaves])
+    B_n = numpy.array([l.B_exp for l in live_leaves])
+    BnA = calc_binding_time(Kd_n, A_total, B_n)  # get list of binding time values for each cell
     new_lambdas = trans_BA(BnA)  # convert binding time list to list of poisson lambdas for each cell (which determine number of offspring)
     if selection_strength < 1:
         new_lambdas = apply_selection_strength_scaling(new_lambdas)
@@ -260,7 +301,7 @@ def A_obj(carry_cap, B_total, f_full, Kd_n, U):
         def obj(A): return((carry_cap - C_A(A, A_total_fun(A, B_total, Kd_n), f_full, U))**2)
         return obj
 
-    Kd_n = scipy.array([mature_kd] * carry_cap)
+    Kd_n = numpy.array([mature_kd] * carry_cap)
     obj = A_obj(carry_cap, B_total, f_full, Kd_n, U)
     # Some funny "zero encountered in true_divide" errors are not affecting results so ignore them:
     old_settings = scipy.seterr(all='ignore')  # Keep old settings
@@ -360,6 +401,80 @@ def make_bounds(tdist_hists):  # tdist_hists: list (over generations) of scipy.h
     plt.title('population over time of cells grouped by min. distance to target')
     fig.savefig(outbase + '.selection_sim.runstats.pdf')
 
+def make_list(array):
+    #print(type(array)) 
+    dummy_list = []
+    for row in array:
+        dummy_list.append(numpy.ndarray.tolist(row))
+        #dummy_list.append(row)
+        #print(row)
+        #print(type(row))
+    return dummy_list
+
+def make_equal_lengths(a):
+    row_lengths = []
+    for row in a:
+        row_lengths.append(len(row))
+        max_length = max(row_lengths)
+    #print(max_length)
+    b = make_list(a)
+    for row in b:
+        while len(row) < max_length:
+            row.append(None)
+#print(row)
+    return b
+
+def sort_it_out(bcell_index):
+    #inf = float("inf")
+    sorted_scatter = []
+    for element in bcell_index:
+        sort_row = sorted(element, key=lambda x: float('inf') if x is None else x)
+        sorted_scatter.append(sort_row)
+    return sorted_scatter
+
+def none_removed(index_sorted):
+    #inf = float("inf")
+    dummy = []
+    for element in index_sorted:
+        sort_row = []
+        for i in element:
+            if not math.isinf(i):
+                sort_row.append(i)
+        dummy.append(sort_row)
+    return dummy
+
+def plot_runstats2(scatter_value, scatter_index, desired_name):
+    fig = plt.figure()
+    ax = plt.subplot(111)
+    x_index = make_equal_lengths(scatter_index)
+    y_index = make_equal_lengths(scatter_value)
+    #print(x_index)
+    #print(y_index)
+    ax.scatter(x_index, y_index)
+    #plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)
+    # Shrink current axis by 20% to make the legend fit:
+    #box = ax.get_position()
+    #ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
+
+    plt.ylabel(desired_name)
+    plt.xlabel('GC generation')
+    plt.title('distribution of BCR value at each generation')
+    fig.savefig('GCsim' + desired_name + '.sim_scatter.pdf')
+
+def plot_runstats3(bcr_value, scatter_index, desired_name):
+    numpy.seterr(under='ignore')
+    fig = plt.figure()
+    ax = plt.subplot(111)
+    what_i_want_to_plot = none_removed(sort_it_out(bcr_value))
+    y_index = []
+    for i in range(len(scatter_index)//5):
+        y_index.append(what_i_want_to_plot[5*i+5])
+    ax.violinplot(y_index)
+    plt.ylabel(desired_name)
+    plt.xlabel('Every 5th GC generation')
+    plt.title('distribution of BCR value at each generation')
+    fig.savefig('GCsim' + desired_name + '.sim_violin.pdf')
+
 # ----------------------------------------------------------------------------------------
 # bash color codes
 Colors = {}