[Feat/macs3/memory consumption] To address the memory usage issue of hmmratac

MACS3/Commands/hmmratac_cmd.py

@@ -1,4 +1,4 @@
-# Time-stamp: <2023-08-02 17:32:47 Tao Liu>
+# Time-stamp: <2024-04-26 11:47:02 Tao Liu>
 
 """Description: Main HMMR command
 
@@ -16,6 +16,8 @@
 import gc
 import numpy as np
 import json
+import csv
+import tempfile
 from hmmlearn import hmm
 #from typing import Sized
 
@@ -176,7 +178,7 @@ def run( args ):
         options.info( f"#  Pile up ONLY short fragments" )
         fc_bdg = digested_atac_signals[ 0 ]
         (sum_v, n_v, max_v, min_v, mean_v, std_v) = fc_bdg.summary()
-        print(sum_v, n_v, max_v, min_v, mean_v, std_v)
+        #print(sum_v, n_v, max_v, min_v, mean_v, std_v)
         options.info( f"#  Convert pileup to fold-change over average signal" )
         fc_bdg.apply_func(lambda x: x/mean_v)
     else:
@@ -195,7 +197,7 @@ def run( args ):
 
         # Let MACS3 do the cutoff analysis to help decide the lower and upper cutoffs
         with open(cutoffanalysis_file, "w") as ofhd_cutoff:
-            ofhd_cutoff.write( fc_bdg.cutoff_analysis( min_length=minlen, max_gap=options.hmm_training_flanking, max_score = 1000 ) )
+            ofhd_cutoff.write( fc_bdg.cutoff_analysis( min_length=minlen, max_gap=options.hmm_training_flanking, max_score = 100 ) )
         #raise Exception("Cutoff analysis only.")
         sys.exit(1)
 
@@ -242,7 +244,7 @@ def run( args ):
 
         # Let MACS3 do the cutoff analysis to help decide the lower and upper cutoffs
         with open(cutoffanalysis_file, "w") as ofhd_cutoff:
-            ofhd_cutoff.write( fc_bdg.cutoff_analysis( min_length=minlen, max_gap=options.hmm_training_flanking, max_score = 1000 ) )
+            ofhd_cutoff.write( fc_bdg.cutoff_analysis( min_length=minlen, max_gap=options.hmm_training_flanking, max_score = 100 ) )
 
         # we will check if anything left after filtering
         if peaks.total > options.hmm_maxTrain:
@@ -377,11 +379,12 @@ def run( args ):
     options.info( f"#  We expand the candidate regions with {options.hmm_training_flanking} and merge overlap" )
     candidate_regions.expand( options.hmm_training_flanking )
     candidate_regions.merge_overlap()
+    options.info( f"#   after expanding and merging, we have {candidate_regions.total} candidate regions" )
 
     # remove peaks overlapping with blacklisted regions
     if options.blacklist:
         candidate_regions.exclude( blacklist_regions )
-        options.info( f"#  after removing those overlapping with provided blacklisted regions, we have {candidate_regions.total} left" )
+        options.info( f"#   after removing those overlapping with provided blacklisted regions, we have {candidate_regions.total} left" )
 
     # extract signals
     options.info( f"#  Extract signals in candidate regions and decode with HMM")
@@ -392,21 +395,39 @@ def run( args ):
     # Note: we implement in a way that we will decode the candidate regions 10000 regions at a time so 1. we can make it running in parallel in the future; 2. we can reduce the memory usage.
     options.info( f"#  Use HMM to predict states")
     n = 0
-    predicted_proba = []
-    candidate_bins = []
+    #predicted_proba_file_name = "predicted_proba.csv"
+    #predicted_proba_file = open(predicted_proba_file_name, "wb")
+    predicted_proba_file = tempfile.TemporaryFile(mode="w+b")
+    # predicted_proba_file = open("predicted_proba.csv", "w+b")
+
     while candidate_regions.total != 0:
         n += 1
-        cr = candidate_regions.pop( options.decoding_steps )
-        options.info( "#    decoding %d..." % ( n*options.decoding_steps ) )        
-        [ cr_bins, cr_data, cr_data_lengths ] = extract_signals_from_regions( digested_atac_signals, cr, binsize = options.hmm_binsize, hmm_type = options.hmm_type )
-        #options.info( "#     extracted signals in the candidate regions")
-        candidate_bins.extend( cr_bins )
-        #options.info( "#     saving information regarding the candidate regions")        
-        predicted_proba.extend( hmm_predict( cr_data, cr_data_lengths, hmm_model ) )
-        #options.info( "#     prediction done")
+        # we get DECODING_STEPS number of candidate regions first
+        cr = candidate_regions.pop(options.decoding_steps)
+        options.info("#    decoding %d..." % (n * options.decoding_steps))
+
+        # then extrac data from digested signals, create cr_bins, cr_data, and cr_data_lengths
+        [cr_bins, cr_data, cr_data_lengths] = extract_signals_from_regions( digested_atac_signals, cr, binsize = options.hmm_binsize, hmm_type = options.hmm_type )
+        #options.debug( "#     extract_signals_from_regions complete")
+
+        prob_data = hmm_predict(cr_data, cr_data_lengths, hmm_model)
+        assert len(prob_data) == len(cr_bins)
+        for i in range(len(prob_data)):
+            predicted_proba_file.write(b"%s,%d" % cr_bins[i])
+            predicted_proba_file.write(b",%f,%f,%f\n" % tuple(prob_data[i]))
+
+        cr_data = []
+        cr_data_lengths = []
+        cr_bins = []
+        prob_data = []
+
+        #options.debug( "#     clean up complete")
         gc.collect()
 
-
+    #predicted_proba_file.close()
+    predicted_proba_file.seek(0) # reset
+    options.info( f"# predicted_proba files written...")
+
 #############################################
 # 6. Output - add to OutputWriter
 #############################################
@@ -424,62 +445,68 @@ def run( args ):
         open_state_bdg_fhd = open( open_state_bdgfile, "w" )
         nuc_state_bdg_fhd = open( nuc_state_bdgfile, "w" )
         bg_state_bdg_fhd = open( bg_state_bdgfile, "w" )
-        save_proba_to_bedGraph( candidate_bins, predicted_proba, options.hmm_binsize, open_state_bdg_fhd, nuc_state_bdg_fhd, bg_state_bdg_fhd, i_open_region, i_nucleosomal_region, i_background_region )
+        save_proba_to_bedGraph( predicted_proba_file, options.hmm_binsize, open_state_bdg_fhd, nuc_state_bdg_fhd, bg_state_bdg_fhd, i_open_region, i_nucleosomal_region, i_background_region )
+        predicted_proba_file.seek(0) # reset
         open_state_bdg_fhd.close()
         nuc_state_bdg_fhd.close()
         bg_state_bdg_fhd.close()
+        options.info( f"# finished writing proba_to_bedgraph")
 
-    # Generate states path:
-    states_path = generate_states_path( candidate_bins, predicted_proba, options.hmm_binsize, i_open_region, i_nucleosomal_region, i_background_region )
-
+    # # Generate states path:
+    states_path = generate_states_path( predicted_proba_file, options.hmm_binsize, i_open_region, i_nucleosomal_region, i_background_region )
+    options.info( f"# finished generating states path")
+    predicted_proba_file.close()          #kill the tempfile
     # Save states path if needed
     # PS: we need to implement extra feature to include those regions NOT in candidate_bins and assign them as 'background state'.
     if options.save_states:
         options.info( f"# Write states assignments in a BED file: {options.name}_states.bed" )
-        f = open( states_file, "w" )
-        save_states_bed( states_path, f )
-        f.close()
+        with open( states_file, "w" ) as f:
+            save_states_bed( states_path, f )
 
     options.info( f"# Write accessible regions in a gappedPeak file: {options.name}_accessible_regions.gappedPeak")
-    ofhd = open( accessible_file, "w" )
-    save_accessible_regions( states_path, ofhd, options.openregion_minlen )
-    ofhd.close()
+    with open( accessible_file, "w" ) as ofhd:
+        save_accessible_regions( states_path, ofhd, options.openregion_minlen )
+
+    options.info( f"# Finished")
 
-def save_proba_to_bedGraph( candidate_bins, predicted_proba, binsize, open_state_bdg_file, nuc_state_bdg_file, bg_state_bdg_file, i_open, i_nuc, i_bg ):
+def save_proba_to_bedGraph( predicted_proba_file, binsize, open_state_bdg_file, nuc_state_bdg_file, bg_state_bdg_file, i_open, i_nuc, i_bg ):
     open_state_bdg = bedGraphTrackI( baseline_value = 0 )
     nuc_state_bdg = bedGraphTrackI( baseline_value = 0 )
     bg_state_bdg = bedGraphTrackI( baseline_value = 0 )
 
     prev_chrom_name = None
     prev_bin_end = None
-    for l in range(len(predicted_proba)):
-        # note that any region not in the candidate bins will be
-        # treated as absolutely the background
-        chrname = candidate_bins[l][0]
-        end_pos = candidate_bins[l][1]
+
+    for pp_line in predicted_proba_file:
+        pp_data = pp_line.strip().split(b',')
+
+        chrname = pp_data[0]
+        end_pos = int(pp_data[1])
         start_pos = end_pos - binsize
+        pp_open = float(pp_data[i_open+2])
+        pp_nuc = float(pp_data[i_nuc+2])
+        pp_bg = float(pp_data[i_bg+2])
+
         if chrname != prev_chrom_name:
-            prev_chrom_name = chrname
-            # add the first region as background
+            # we start a new chromosome
             if start_pos > 0:
+                # add the first unannotated region as background
                 open_state_bdg.add_loc( chrname, 0, start_pos, 0.0 )
                 nuc_state_bdg.add_loc( chrname, 0, start_pos, 0.0 )
                 bg_state_bdg.add_loc( chrname, 0, start_pos, 1.0 )
-                prev_bin_end = start_pos
-            elif start_pos == 0:
-                # if start_pos == 0, then the first bin has to be assigned, we set prev_bin_end as 0 
-                prev_bin_end = 0
-        # now check if the prev_bin_end is start_pos, if not, add a gap of background
-        if prev_bin_end < start_pos:
-            open_state_bdg.add_loc( chrname, prev_bin_end, start_pos, 0.0 )
-            nuc_state_bdg.add_loc( chrname, prev_bin_end, start_pos, 0.0 )
-            bg_state_bdg.add_loc( chrname, prev_bin_end, start_pos, 1.0 )
-
-        open_state_bdg.add_loc( chrname, start_pos, end_pos, predicted_proba[l][ i_open ] )
-        nuc_state_bdg.add_loc( chrname, start_pos, end_pos, predicted_proba[l][ i_nuc ] )
-        bg_state_bdg.add_loc( chrname, start_pos, end_pos, predicted_proba[l][ i_bg ] )
-        prev_bin_end = start_pos
-
+            prev_chrom_name = chrname
+        else:
+            # now check if the prev_bin_end is start_pos, if not, add a gap of background
+            if prev_bin_end < start_pos:
+                open_state_bdg.add_loc( chrname, prev_bin_end, start_pos, 0.0 )
+                nuc_state_bdg.add_loc( chrname, prev_bin_end, start_pos, 0.0 )
+                bg_state_bdg.add_loc( chrname, prev_bin_end, start_pos, 1.0 )
+
+        open_state_bdg.add_loc( chrname, start_pos, end_pos, pp_open )
+        nuc_state_bdg.add_loc( chrname, start_pos, end_pos, pp_nuc )
+        bg_state_bdg.add_loc( chrname, start_pos, end_pos, pp_bg )
+        prev_bin_end = end_pos
+
     open_state_bdg.write_bedGraph( open_state_bdg_file, "Open States", "Likelihoods of being Open States" )
     nuc_state_bdg.write_bedGraph( nuc_state_bdg_file, "Nucleosomal States", "Likelihoods of being Nucleosomal States" )
     bg_state_bdg.write_bedGraph( bg_state_bdg_file, "Background States", "Likelihoods of being Background States" )
@@ -489,31 +516,52 @@ def save_states_bed( states_path, states_bedfile ):
     # we do not need to output background state. 
     for l in range( len( states_path ) ):
         if states_path[l][3] != "bg":
-            states_bedfile.write( "%s\t%s\t%s\t%s\n" % states_path[l] )
+            states_bedfile.write( "%s\t" % states_path[l][0].decode() )
+            states_bedfile.write( "%d\t%d\t%s\n" % states_path[l][1:] )
     return
 
-def generate_states_path(candidate_bins, predicted_proba, binsize, i_open_region, i_nucleosomal_region, i_background_region):
+def generate_states_path(predicted_proba_file, binsize, i_open, i_nuc, i_bg):
     ret_states_path = []
     labels_list = ["open", "nuc", "bg"]
-    start_pos = candidate_bins[0][1] - binsize
-    for l in range(1, len(predicted_proba)):
-        chromosome = candidate_bins[l][0].decode()
-        prev_open, prev_nuc, prev_bg = predicted_proba[l-1][i_open_region], predicted_proba[l-1][i_nucleosomal_region], predicted_proba[l-1][i_background_region]
-        curr_open, curr_nuc, curr_bg = predicted_proba[l][i_open_region], predicted_proba[l][i_nucleosomal_region], predicted_proba[l][i_background_region]
+
+    prev_chrom_name = None
+    prev_bin_end = None
+    prev_label = None
+
+    for pp_line in predicted_proba_file:
+        pp_data = pp_line.strip().split(b',')
+
+        chrname = pp_data[0]
+        end_pos = int(pp_data[1])
+        start_pos = end_pos - binsize
+        pp_open = float(pp_data[i_open+2])
+        pp_nuc = float(pp_data[i_nuc+2])
+        pp_bg = float(pp_data[i_bg+2])
+
+        # find the best state as label
+        label = labels_list[max((pp_open, 0), (pp_nuc, 1), (pp_bg, 2), key=lambda x: x[0])[1]]
 
-        label_prev = labels_list[max((prev_open, 0), (prev_nuc, 1), (prev_bg, 2), key=lambda x: x[0])[1]]
-        label_curr = labels_list[max((curr_open, 0), (curr_nuc, 1), (curr_bg, 2), key=lambda x: x[0])[1]]
-
-        if candidate_bins[l-1][0] == candidate_bins[l][0]:  # if we are looking at the same chromosome ...
-            if label_prev != label_curr:
-                end_pos = candidate_bins[l-1][1]
-                ret_states_path.append((chromosome, start_pos, end_pos, label_prev))
-                start_pos = candidate_bins[l][1] - binsize
-            elif l == len(predicted_proba) - 1:
-                end_pos = candidate_bins[l][1]
-                ret_states_path.append((chromosome, start_pos, end_pos, label_prev))
+        if chrname != prev_chrom_name:
+            # we start a new chromosome
+            if start_pos > 0:
+                # add the first unannotated region as background
+                ret_states_path.append((chrname, 0, start_pos, "bg"))
+            ret_states_path.append((chrname, start_pos, end_pos, label))
+            prev_label = label
+            prev_chrom_name = chrname
         else:
-            start_pos = candidate_bins[l][1] - binsize
+            # now check if the prev_bin_end is start_pos, if not, add a gap of background
+            if prev_bin_end < start_pos:
+                ret_states_path.append((chrname, prev_bin_end, start_pos, "bg"))
+                prev_label = "bg"
+            # same label, just extend
+            if label == prev_label:
+                ret_states_path[-1] = (ret_states_path[-1][0], ret_states_path[-1][1], end_pos, label)
+            else:
+                ret_states_path.append((chrname, start_pos, end_pos, label))
+                prev_label = label
+
+        prev_bin_end = end_pos
     return ret_states_path
 
 def save_accessible_regions(states_path, accessible_region_file, openregion_minlen):
@@ -532,7 +580,6 @@ def add_regions(i, regions):
             states_path[i][2] == states_path[i+1][1] and states_path[i+1][2] == states_path[i+2][1] and 
             states_path[i+1][2] - states_path[i+1][1] > openregion_minlen):
             accessible_regions = add_regions(i, accessible_regions)
-
     # Group states by region
     list_of_groups = []
     one_group = [accessible_regions[0]]
@@ -550,7 +597,8 @@ def add_regions(i, regions):
         block_num = sum('open' in tup for tup in region)
         block_sizes = ','.join(str(region[j][2] - region[j][1]) for j in range(1, len(region) - 1, 2))
         block_starts = ','.join(str(region[j][1] - region[0][1]) for j in range(1, len(region) - 1, 2))
-        broadpeak.add(bytes(region[1][0], encoding="raw_unicode_escape"), region[0][1], region[-1][2],
+        broadpeak.add(region[1][0], region[0][1], region[-1][2],
+            #bytes(region[1][0], encoding="raw_unicode_escape"), region[0][1], region[-1][2],
                       thickStart=bytes(str(region[1][1]), encoding="raw_unicode_escape"),
                       thickEnd=bytes(str(region[-2][2]), encoding="raw_unicode_escape"),
                       blockNum=block_num,

MACS3/Signal/HMMR_Signal_Processing.pyx

@@ -1,6 +1,6 @@
 # cython: language_level=3
 # cython: profile=True
-# Time-stamp: <2023-07-28 12:14:57 Tao Liu>
+# Time-stamp: <2024-04-26 10:40:41 Tao Liu>
 
 """Module description:
 
@@ -146,6 +146,7 @@ cpdef list extract_signals_from_regions( list signals, object regions, int binsi
         list ret_training_data, ret_training_lengths, ret_training_bins
 
     regionsbdg = _make_bdg_of_bins_from_regions( regions, binsize )
+    debug('#      extract_signals_from_regions: regionsbdg completed')
     # now, let's overlap
     extracted_positions = []
     extracted_data = []
@@ -156,10 +157,14 @@ cpdef list extract_signals_from_regions( list signals, object regions, int binsi
         extracted_positions.append( positions )
         extracted_data.append( values )
         extracted_len.append( lengths )
+    positions = []
+    values = []
+    lengths = []
+    debug('#      extract_signals_from_regions: extracted positions, data, len')
     ret_training_bins = []
     ret_training_data = []
     ret_training_lengths = []
-    c = 0
+
     nn = len( extracted_data[0] )
     assert nn > 0
     assert nn == len( extracted_data[1] )
@@ -182,6 +187,7 @@ cpdef list extract_signals_from_regions( list signals, object regions, int binsi
                 counter = 0
             prev_c = c
             counter +=  1
+        debug('#      extract_signals_from_regions: ret_training bins, data, lengths - gaussian')
     #poisson can only take int values as input
     if hmm_type == "poisson":
         for i in range( nn ):
@@ -197,6 +203,7 @@ cpdef list extract_signals_from_regions( list signals, object regions, int binsi
                 counter = 0
             prev_c = c
             counter +=  1
+        debug('#      extract_signals_from_regions: ret_training bins, data, lengths - poisson')
     # last region
     ret_training_lengths.append( counter )
     assert sum(ret_training_lengths) == len(ret_training_data)