prophet.py

"""Function predictor for lncRNA that uses gene coexpression.

This script is used to apply statistics in order to figure out
possible Gene Ontology terms for lncRNAs, based on gene 
expression counts and an annotation for coding genes. The gene
expression is read from a counts table (see test_data/counts)
and the expression of lncRNA and coding genes is compared
by calculating correlation coefficients through several
differente metrics. The pairs of (lncRNA,gene) with 
coefficients passing the required minimum value to be 
considered inside the confidence level are taken as
coexpressed.
The associations between coding genes and GO terms in the 
functional annotation (see test_data/annotation) of the
coding genes are passed as possible associations for their
lncRNA coexpressed pairs. Statistical tests are used to filter
which of these possible associations are statistically
relevant, calculating P-Values and FDRs.
The associations passing the filtering are grouped together in
a output file, the functional prediction.

Author: Pitágoras Alves (github.com/pentalpha)
"""

import sys
from gatherer.functional_prediction import *
from gatherer.util import *
from gatherer.confidence_levels import *
from gatherer.bioinfo import short_ontology_name, long_ontology_name
import obonet
import networkx
import numpy as np
import argparse
import multiprocessing
import json
'''
Loading configurations and command line arguments
'''
from config import configs, require_files
mandatory_files = ["go_obo"]
require_files(mandatory_files)

#set configuration values
confs = {}
for conf in configs:
    confs[conf] = configs[conf]

available_species = get_available_species()

display_cache = get_cache()

all_methods = ["MIC","DC","PRS","SPR","SOB","FSH"]
all_ontologies = ["molecular_function","cellular_component","biological_process"]
#default_methods = load_metrics(confs["metrics_table"])
#highest_confidence = str(max([int(conf) for conf in default_methods.keys()]))

def getArgs():
    ap = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("-cr", "--count-reads", required=True,
        help=("Count reads table path. TSV file with where the first column must be the gene names and the " +
            " following columns are the FPKM normalized counts of each sample."))
    ap.add_argument("-reg", "--regulators", required=True,
        help=("A list of regulators, where each line contains the name of one gene."))
    ap.add_argument("-ann", "--annotation", required=True,
        help=("Functional annotation file of the genes."))
    ap.add_argument("-o", "--output-dir", required=True, help=("Output directory."))
    ap.add_argument("-conf", "--confidence-level", required=False, 
    help=("Overwrites 'quality'. Level of confidence in the correlation metrics. Values: 0 to 24."))
    ap.add_argument("-ont", "--ontology-type", required=False,
        default="molecular_function", help=("One of the following: molecular_function (default),"
        +" cellular_component or biological_process."))
    ap.add_argument("-q", "--quality", help=("'normal': normal precision, more predictions."
        + " 'high': high precision, very few predictions. Default=high."))
    '''ap.add_argument("-met", "--method", required=False,
        default=None, help=("Correlation coefficient calculation method:"
        +" MIC (Maximal Information Coefficient), "
        +"DC (Distance Correlation), SPR (Spearman Coefficient), PRS (Pearson Coefficient), "
        +"FSH (Fisher Information Metric) or SOB (Sobolev Metric)."
        +"\nRNA Gatherer is configured to use the best method combination for each ontology and "
        +"confidence level, if you set this option the defaults will be overwriten."))'''
    ap.add_argument("-bh", "--benchmarking", required=False,
        default=False, help=("Enables the (much slower) leave one out strategy: regulators can be regulated too."
        +"Default: False."))
    default_threads = max(2, multiprocessing.cpu_count()-1)
    ap.add_argument("-p", "--processes", required=False,
        default=default_threads, help=("CPUs to use. Default: " + str(default_threads)+"."))
    ap.add_argument("-md", "--model-species", required=False,
        default="mus_musculus", help=("Model organism used to calculate confidence levels: "
        + ",".join(available_species)) + ". Default: mus_musculus.")
    ap.add_argument("-th", "--threshold", required=False,
        default=None, help=("Sets an unique correlation coefficient threshold for all methods: 0.5 <= x <= 0.99."))
    ap.add_argument("-K", "--k-min-coexpressions", required=False,
        default=1, help=("The minimum number of ncRNAs a Coding Gene must be coexpressed with."
                        +" Increasing the value improves accuracy of functional assignments, but"
                        +" may restrict the results. Default: 1."))
    ap.add_argument("-pv", "--pvalue", required=False,
        default=0.05, help=("Maximum pvalue. Default: 0.05"))
    ap.add_argument("-fdr", "--fdr", required=False,
        default=0.05, help=("Maximum FDR. Default: 0.05"))
    ap.add_argument("-m", "--min-m", required=False,
        default=1, help=("Minimum m value. Default: 1"))
    ap.add_argument("-M", "--min-M", required=False,
        default=1, help=("Minimum M value. Default: 1"))
    ap.add_argument("-n", "--min-n", required=False,
        default=1, help=("Minimum n value. Default: 1"))

    ap.add_argument("-chu", "--cache-usage", required=False,
        default=0.6, help=("Portion of the cache memory to use for storing the counts table."))
    ap.add_argument("-ch", "--cache-size", required=False,
        default=display_cache, help=("Sets the size of the cache memory. Default: auto-detection of CPU cache size."))
    return vars(ap.parse_args())

cmdArgs = getArgs()
count_reads_path = cmdArgs["count_reads"]
regulators_path = cmdArgs["regulators"]
coding_gene_ontology_path = cmdArgs["annotation"]
tempDir = cmdArgs["output_dir"]
go_path = confs["go_obo"]

ontology_types_arg = cmdArgs["ontology_type"].split(",")
if ontology_types_arg[0] == "ALL":
    ontology_types_arg = all_ontologies

benchmarking = cmdArgs["benchmarking"]
threads = int(cmdArgs["processes"])

cache_usage = float(cmdArgs["cache_usage"])
available_cache = get_cache(usage=cache_usage)
if "cache_size" in cmdArgs:
    available_cache = int(int(cmdArgs["cache_size"]) * cache_usage)
print("Available cache memory: " + str(int(available_cache/1024)) + "KB")

model_name = cmdArgs["model_species"]
if not model_name in available_species:
    print(model_name + " is not a valid model species.")
    print("Available species are: " + str(available_species))
    quit()

print("Loading confidence levels for " + model_name)
confidence_thresholds = load_confidence_levels(model_name)
default_methods = get_best_metrics(model_name)
conf_presets = get_preset_confs(model_name)
confidence_levels = conf_presets['high']
if "quality" in cmdArgs:
    if cmdArgs["quality"] == "normal":
        confidence_levels = conf_presets['normal']
    elif cmdArgs["quality"] != "high":
        print("Invalid quality preset. Valid values: " + str(list(conf_presets.keys())))
elif "confidence_level" in cmdArgs:
    print("Loading custom confidence levels.")
    for onto in all_ontologies:
        confidence_levels[onto] = [int(x) for x in cmdArgs["confidence_level"].split(",")]
print("Confidence levels used:", confidence_levels)

for onto in all_ontologies:
    vals = confidence_levels[onto]
    if not isinstance(vals, list):
        confidence_levels[onto] = [vals]

if cmdArgs["threshold"] != None:
    universal_th = float(cmdArgs["threshold"])
    #print(str(len(confidence_thresholds)))
    #print(str(confidence_levels))
    for onto, confs in confidence_levels:
        for i in confs:
            for metric in confidence_thresholds[onto][i].keys():
                confidence_thresholds[onto][i][metric] = universal_th

min_confidence = min([min(confs) for onto, confs in confidence_levels.items()])
print("Min confidence:", min_confidence)
#min_confidence = confidence_levels[0]
'''min_thresholds_by_onto = {onto: confs[min_confidence] 
                        for onto, confs in confidence_thresholds.items()}'''
min_thresholds_by_onto = {onto: confs[confidence_levels[long_ontology_name(onto)][0]] 
                        for onto, confs in confidence_thresholds.items()}
print(str(min_thresholds_by_onto))

min_thresholds = {}
for metric in min_thresholds_by_onto["MF"].keys():
    values = [mins[metric] 
                for onto, mins in min_thresholds_by_onto.items() 
                if mins[metric] is not None]
    if len(values) > 0:
        if metric == "SOB" or metric == "FSH":
            min_thresholds[metric] = max(values)
        else:
            min_thresholds[metric] = min(values)
    else:
        min_thresholds[metric] = None

print("Minimum thresholds to load correlations:")
print(str(min_thresholds))

'''if cmdArgs["method"] != None:
    new_metrics = {}
    method = cmdArgs["method"].split(",")
    if method[0] == "ALL":
        method = all_methods

    metrics_with_minimum_conf = set(method)
    for metric_name in method:
        if min_thresholds[metric_name] == None:
            metrics_with_minimum_conf.remove(metric_name)
            print(metric_name + " does not have the minimum confidence level for " 
                + onto + ", not using it.")
    method = list(metrics_with_minimum_conf)

    for onto, conf in confidence_levels:
        new_metrics[str(conf)] = {o: method for o in all_ontologies}    
    default_methods = new_metrics'''

metrics_used = set()
for onto, confs in confidence_levels.items():
    print(onto, confs)
    if onto in ontology_types_arg:
        print("using this ontology")
        for conf, metrics_by_onto in default_methods.items():
            print("\t",conf,metrics_by_onto)
            if int(conf) in confs:
                print("\t\tusing")
                metrics_used.update(metrics_by_onto[onto])
            else:
                print("\t\tnot using")
    else:
        print("not using this ontology")
'''if int(conf) >= min_confidence:
        for onto, metrics in metrics_by_onto.items():
            if onto in ontology_types_arg:
                metrics_used.update(metrics)'''
print("Metrics used:", metrics_used)
pval = float(cmdArgs["pvalue"])
fdr = float(cmdArgs["fdr"])
min_n = int(cmdArgs["min_n"])
min_M = int(cmdArgs["min_M"])
min_m = int(cmdArgs["min_m"])
K = int(cmdArgs["k_min_coexpressions"])

regulators_max_portion = 0.4

'''
Creating output directory
'''

if not os.path.exists(tempDir):
    os.mkdir(tempDir)

correlations_dir = tempDir + "/correlations"
if not os.path.exists(correlations_dir):
    os.mkdir(correlations_dir)

def get_metric_file(metric_name):
    return open(correlations_dir + "/" + metric_name + ".tsv", 'a+')

def find_correlated(reads, regulators, tempDir, methods, method_streams, threads, separate_regulators = False):
    """Find coexpressed pairs using a set of metrics."""
    if len(reads) < threads*2:
        threads = len(reads)/2
    coding_noncoding_pairs = []
    func = try_find_coexpression_process
    if separate_regulators:
        print("Running 'leave one out' (benchmarking) mode.")
        func = leave_one_out
    genes_per_process = int(len(reads) / threads)
    
    limit = len(reads)-1
    end = 0
    last = -1
    manager = multiprocessing.Manager()
    return_dict = manager.dict()
    processes = []
    last_pid = 0
    print('Spawning', threads, 'threads')
    for i in range(threads-1):
        start = last+1
        end = start + genes_per_process
        if end >= limit:
            end = limit
        parcial_df = reads.iloc[start:end]
        p = multiprocessing.Process(target=func, 
            args=(i, parcial_df, regulators, methods, min_thresholds, return_dict, ))
        processes.append(p)
        print("Spawned process from gene " + str(start) + " to " + str(end))
        p.start()
        last = end

        last_pid = i
        if end == limit:
            break
    if end < limit:
        parcial_df = reads.iloc[end:limit]
        p = multiprocessing.Process(target=func, 
            args=(last_pid+1, parcial_df, regulators, methods, min_thresholds, return_dict, ))
        processes.append(p)
        print("Spawned process from gene " + str(end) + " to " + str(limit))
        p.start()
    
    for p in processes:
        p.join()

    #print("Merging results")
    for value in return_dict.values():
        coding_noncoding_pairs += value

    #print(str(len(coding_noncoding_pairs)) + " correlation pairs found.")
    output = tempDir+"/correlated.tsv"
    for coding_name, noncoding_name, corr, method_name in coding_noncoding_pairs:
        method_streams[method_name].write("\t".join([coding_name,noncoding_name,str(corr)]) + "\n")
    manager._process.terminate()
    manager.shutdown()
    del manager

'''
Pre-processing of the count-reads table
'''

print("Ontology type is " + str(ontology_types_arg))
print("Used metrics are: " + str(metrics_used))
reads = pd.read_csv(count_reads_path, sep='\t')
print(str(len(reads)) + " raw rows.")
reads["constant"] = reads.drop([reads.columns[0]], axis=1).apply(
        lambda row: is_constant(np.array(row.values,dtype=np.float32)),axis=1
    )
mask = reads["constant"] == False
reads = reads[mask]
del reads["constant"]
print(str(len(reads)) + " rows after removing constant rows.")
print(reads.head())

print("Reading regulators")
regulators = []
with open(regulators_path,'r') as stream:
    for line in stream.readlines():
        regulators.append(line.rstrip("\n").lstrip(">"))

'''
Looking for metrics not calculated yet.
'''

correlation_files = {method_name:correlations_dir+"/"+method_name+".tsv" for method_name in metrics_used}

for m,f in correlation_files.items():
    delete_if_empty(f,min_cells=3,sep="\t")

missing_metrics = []
for key,val in correlation_files.items():
    if not os.path.exists(val):
        missing_metrics.append(key)

if len(missing_metrics) > 0:
    '''
    Calculate any missing metrics
    '''
    print("Calculating correlation coefficients for the following metrics: "
        + str(missing_metrics))
    print("Separating regulators from regulated.")
    print("\tRegulator IDs: " + str(len(regulators)))
    mask = reads[reads.columns[0]].isin(regulators)
    regulators_reads = reads.loc[mask]
    print("\tRegulator IDs in dataframe: " 
        + str(len(regulators_reads[regulators_reads.columns[0]].tolist())))
    non_regulators_reads = reads
    if not benchmarking:
        non_regulators_reads = reads.loc[~mask]
    print(str(len(non_regulators_reads)) + " regulated.")
    print(str(len(regulators_reads)) + " regulators.")
    
    available_size = available_cache

    '''
    Split the table into cache-sized smaller parts.
    '''
    max_for_regulators = available_size*regulators_max_portion
    #print("Available for regulators: " + str(int(max_for_regulators/1024)) + "KB")
    regs_size = getsizeof(regulators_reads)
    #print("Regulators size: " + str(int(regs_size/1024)) + "KB")
    regulator_dfs = [regulators_reads]
    print("Dividing regulator rows:")
    regulator_dfs = split_df_to_max_mem(regulators_reads, max_for_regulators)
    available_size -= getsizeof(regulator_dfs[0])
    print("Dividing non-regulator rows:")
    dfs = split_df_to_max_mem(non_regulators_reads, available_size)
    
    '''print("Chunks for regulated: " + str(len(dfs)) 
    + "\nChunks for regulators: " + str(len(regulator_dfs)))'''

    df_pairs = []
    for df in dfs:
        for regulator_df in regulator_dfs:
            df_pairs.append((df,regulator_df))

    
    method_streams = {metric_name:get_metric_file(metric_name) for metric_name in missing_metrics}

    '''
    Calculate the correlations for each part of the table
    '''
    i = 1
    for df,regulator_df in tqdm(df_pairs):
        find_correlated(df, regulators_reads, tempDir, missing_metrics, 
                    method_streams, int(threads), separate_regulators = benchmarking)
        i += 1
    
    for method_name, stream in method_streams.items():
        stream.close()

coding_genes = {}
genes_coexpressed_with_ncRNA = {}
correlation_values = {}

'''
Load correlation coefficients from the files where they are stored.
'''
for m, correlation_file_path in correlation_files.items():
    print("Loading correlations from " + correlation_file_path + ".")
    min_value = min_thresholds[m]
    load_condition = lambda x: x >= min_value
    if m == "SOB" or m == "FSH":
        load_condition = lambda x: x <= min_value
    with open(correlation_file_path,'r') as stream:
        lines = 0
        invalid_lines = 0
        loaded = 0
        for raw_line in stream.readlines():
            cells = raw_line.rstrip("\n").split("\t")
            if len(cells) == 3:
                gene = cells[0]
                rna = cells[1]
                corr = cells[2]
                corr_val = float(corr)
                
                #if corr_val >= min_value:
                if load_condition(corr_val):
                    if not gene in coding_genes:
                        coding_genes[gene] = 0
                    coding_genes[gene] += 1
                    
                    if not rna in genes_coexpressed_with_ncRNA:
                        genes_coexpressed_with_ncRNA[rna] = set()
                    genes_coexpressed_with_ncRNA[rna].add(gene)
                    correlation_values[(rna,gene,m)] = corr
                    loaded += 1
            else:
                invalid_lines += 1
            lines += 1
        if lines == 0:
            print("Fatal error, no correlations could be loaded from "
                    + correlation_file_path + "\n(The file may be "
                    + "corrupted or just empty)")
            quit()
        else: 
            print(str(float(invalid_lines)/lines)
                + " lines without proper number of columns (4 columns)")
            print(str(loaded), "total correlations loaded from file")
print("correlation_values = "+str(len(correlation_values.keys())))
print("genes_coexpressed_with_ncRNA = "+str(len(genes_coexpressed_with_ncRNA.keys())))
print("coding_genes = "+str(len(coding_genes.keys())))
N = len(reads)

print("Loading GO network.")
graph = obonet.read_obo(go_path)

onto_id2gos = {"biological_process":{},"molecular_function":{},"cellular_component":{}}
onto_genes_annotated_with_term = {"biological_process":{},"molecular_function":{},"cellular_component":{}}

print("Reading annotation of coding genes from " + coding_gene_ontology_path)
with open(coding_gene_ontology_path,'r') as stream:
    lines = 0
    invalid_lines = 0
    associations = 0
    for raw_line in stream.readlines():
        cells = raw_line.rstrip("\n").split("\t")
        if len(cells) == 3 or len(cells) == 4:
            gene = cells[0]
            go = cells[1]
            onto = cells[2]
            if onto in onto_id2gos.keys():
                id2gos = onto_id2gos[onto]
                genes_annotated_with_term = onto_genes_annotated_with_term[onto]
                #coding_genes.add(gene)
                if not (gene in coding_genes):
                    gene = gene.upper()
                if gene in coding_genes:
                    if not gene in id2gos:
                        id2gos[gene] = set()
                    id2gos[gene].add(go)
                    if not go in genes_annotated_with_term:
                        genes_annotated_with_term[go] = set()
                    genes_annotated_with_term[go].add(gene)
                    associations += 1
                else:
                    invalid_lines += 1
                    print("Invalid coding gene" + gene)
            else:
                invalid_lines += 1
        else:
            invalid_lines += 1
        lines += 1
    print(str(float(invalid_lines)/lines)
            + " lines without proper number of columns (3 or 4 columns)")
    print(str(associations) + " valid associations loaded.")

def predict(tempDir,ontology_type="molecular_function",current_method=["MIC","SPR","FSH"],
            conf_arg=None,benchmarking=False,k_min_coexpressions=1,
            pval_threshold=0.05,fdr_threshold=0.05,
            min_n=5, min_M=5, min_m=1):
    """Predict functions based on the loaded correlation coefficients."""
    
    K = k_min_coexpressions
    ontology_type_mini = short_ontology_name(ontology_type)
    confidence = conf_arg
    thresholds = confidence_thresholds[ontology_type_mini][confidence]
    
    print("Current method = " + str(current_method) + ", ontology type = " + ontology_type
            + ", pvalue = " + str(pval_threshold) + ", fdr = " + str(fdr_threshold))
    print("Current thresholds = " + str(thresholds))
    
    out_dir = tempDir+"/"+ontology_type_mini
    if not os.path.exists(out_dir):
        os.mkdir(out_dir)
    longer = []
    if K != 1 or min_n != 1 or min_M != 1 or min_m != 1:
        longer = ["K"+str(K),"n"+str(min_n),"M"+str(min_M),"m"+str(min_m)]
    run_mode = "LNC"
    if benchmarking:
        run_mode = "BENCH"
    output_file = (out_dir + "/" +".".join(["-".join(current_method),"c"+str(confidence),
            run_mode,"pval"+str(pval_threshold),"fdr"+str(fdr_threshold)]
            + longer + ["tsv"])).replace(".LNC.",".")
    output_file = output_file.replace(".thNone.",".")

    if os.path.exists(output_file):
        print(output_file + " already created, skiping.")
        return output_file, False

    print("Selecting significant genes for processing")
    valid_coding_genes = {}
    valid_genes_coexpressed_with_ncRNA = {}
    total = 0
    correct_method = 0
    valid_corr = 0
    for rna in genes_coexpressed_with_ncRNA.keys():
        for gene in genes_coexpressed_with_ncRNA[rna]:
            for metric in current_method:
                key = (rna,gene,metric)
                if key in correlation_values.keys():
                    corr = float(correlation_values[key])
                    correct_method += 1
                    if compare_to_th(corr, thresholds[metric], metric):
                    #if corr >= thresholds[metric] or corr <= -thresholds[metric]:
                        valid_corr += 1
                        if not gene in valid_coding_genes:
                            valid_coding_genes[gene] = 0
                        valid_coding_genes[gene] += 1
                        if not rna in valid_genes_coexpressed_with_ncRNA:
                            valid_genes_coexpressed_with_ncRNA[rna] = set()
                        valid_genes_coexpressed_with_ncRNA[rna].add(gene)
            total += 1
    
    print("len(valid_genes_coexpressed_with_ncRNA)=" + str(len(valid_genes_coexpressed_with_ncRNA)))
    print("valid correlations loaded:", str(valid_corr))
    #print("Discarding coding genes with too little correlations with regulators.")
    genes_to_discard = set()
    for coding_gene in valid_coding_genes.keys():
        if valid_coding_genes[coding_gene] < K:
            genes_to_discard.add(coding_gene)

    for gene in genes_to_discard:
        del valid_coding_genes[gene]
        for rna in valid_genes_coexpressed_with_ncRNA.keys():
            if gene in valid_genes_coexpressed_with_ncRNA[rna]:
                valid_genes_coexpressed_with_ncRNA[rna].remove(gene)
    #print("len(valid_genes_coexpressed_with_ncRNA)=" + str(len(valid_genes_coexpressed_with_ncRNA)))

    valid_id2gos = {}
    valid_genes_annotated_with_term = {}

    print("Reading annotation of coding genes from " + coding_gene_ontology_path)
    id2gos = onto_id2gos[ontology_type]
    for _id in id2gos.keys():
        if _id in valid_coding_genes.keys():
            valid_id2gos[_id] = id2gos[_id]
    
    genes_annotated_with_term = onto_genes_annotated_with_term[ontology_type]
    for term in genes_annotated_with_term.keys():
        for _id in genes_annotated_with_term[term]:
            if _id in valid_coding_genes.keys():
                if not term in valid_genes_annotated_with_term.keys():
                    valid_genes_annotated_with_term[term] = set()
                valid_genes_annotated_with_term[term].add(_id)

    genes_annotated_with_term2 = {}

    #print("Extending associations of terms to genes by including children")
    found = 0
    for go in valid_genes_annotated_with_term.keys():
        genes = set()
        genes.update(valid_genes_annotated_with_term[go])
        before = len(genes)
        if go in graph:
            found += 1
            childrens = get_ancestors(graph, go)
            for children_go in childrens:
                if children_go in valid_genes_annotated_with_term:
                    genes.update(valid_genes_annotated_with_term[children_go])
        genes_annotated_with_term2[go] = genes
    #print(str((float(found)/len(valid_genes_annotated_with_term.keys()))*100) + "% of the GO terms found in network.")
    valid_genes_annotated_with_term = genes_annotated_with_term2

    print("Listing possible associations between rnas and GOs")
    possible_gene_term = []
    for rna in valid_genes_coexpressed_with_ncRNA.keys():
        for go in valid_genes_annotated_with_term.keys():
            possible_gene_term.append((rna, go))
    if len(possible_gene_term) == 0:
        print("No possible association to make, under current parameters and data."
            + " Suggestions: Try a different correlation threshold or a different method.")
        return "", False
    #print("len(valid_genes_coexpressed_with_ncRNA)=" + str(len(valid_genes_coexpressed_with_ncRNA)))
    #print("len(valid_genes_annotated_with_term)= " + str(len(valid_genes_annotated_with_term)))
    #print("Possible gene,term = " + str(len(possible_gene_term)))
    valid_gene_term, n_lens, M_lens, m_lens = get_valid_associations(valid_genes_coexpressed_with_ncRNA,
                                            valid_genes_annotated_with_term,
                                            possible_gene_term,
                                            min_n=min_n, min_M=min_M, min_m=min_m)

    print("Calculating p-values")
    gene_term_pvalue = parallel_pvalues(N, possible_gene_term, 
                                        valid_gene_term, n_lens, M_lens, m_lens, 
                                        threads, available_cache)
    print("Calculating corrected p-value (FDR)")
    pvalues = [pval for gene, term, pval in gene_term_pvalue]
    reject, fdrs, alphacSidak, alphacBonf = multitest.multipletests(pvalues, alpha=0.05, method='fdr_by')
    #print("Finished calculating pvalues, saving now")
    with open(tempDir + "/association_pvalue.tsv", 'w') as stream:
        for i in range(len(gene_term_pvalue)):
            if valid_gene_term[i]:
                rna, term, pvalue = gene_term_pvalue[i]
                stream.write(rna+"\t"+term+"\t"+str(pvalue)+"\t" + str(fdrs[i]) + "\n")

    print("Selecting relevant pvalues and fdr")
    relevant_pvals = []
    rna_id2gos = {}
    pval_passed = 0
    fdr_passed = 0
    for i in tqdm(range(len(gene_term_pvalue))):
        rna, term, pvalue = gene_term_pvalue[i]
        fdr = fdrs[i]
        if pvalue <= pval_threshold:
            pval_passed += 1
            if fdr <= fdr_threshold:
                fdr_passed += 1
                relevant_pvals.append((rna, term, pvalue, fdr))
                if not rna in rna_id2gos:
                    rna_id2gos[rna] = set()
                rna_id2gos[rna].add(term)
    print(str(pval_passed) + " rna->go associations passed p-value threshold ("
            + str((pval_passed/len(gene_term_pvalue))*100) + "%)")
    print(str(fdr_passed) + " rna->go associations passed fdr threshold ("
            + str((fdr_passed/len(gene_term_pvalue))*100) + "%)")
    
    print("Writing results")
    print("Output annotation is " + output_file)
    with open(output_file, 'w') as stream:
        for rna, term, pvalue, fdr in relevant_pvals:
            stream.write("\t".join([rna,term,ontology_type,str(pvalue),str(fdr)])+"\n")
    return output_file, True

all_confs = set()
for onto, confs in confidence_levels.items():
    all_confs.update(confs)
all_confs = list(all_confs)
all_confs.sort(key=lambda x: int(x))

for conf in all_confs:
    output_files = []
    created = 0
    for onto in ontology_types_arg:
        confs_to_use = confidence_levels[onto]
        if conf in confs_to_use:
            metrics_for_params = default_methods[str(conf)][onto]

            valid_metrics = []
            current_ths = confidence_thresholds[short_ontology_name(onto)][conf]
            for metric_name in metrics_for_params:
                if current_ths[metric_name] != None:
                    valid_metrics.append(metric_name)
            if len(valid_metrics) > 0:
                metrics_for_params = valid_metrics
                out_file, made = predict(tempDir,ontology_type=onto,
                        current_method=metrics_for_params,
                        conf_arg=conf,
                        benchmarking=benchmarking,k_min_coexpressions=K,
                        pval_threshold=pval,fdr_threshold=fdr,
                        min_n=min_n, min_M=min_M, min_m=min_m)
                if out_file != "":
                    output_files.append((out_file,onto))
                if made:
                    created += 1
            else:
                print("No valid metrics for current confidence level")
        print("Writing annotation file with all ontologies")
    if len(output_files) > 1 and created > 1:
        lines = []
        ontos = set()
        for output_file,onto_value in output_files:
            with open(output_file,'r') as stream:
                new_lines = [line for line in stream.readlines()]
                lines += new_lines
            ontos.add(onto_value)
        ontos = list(ontos)
        ontos.sort()
        ontos_str = "_".join([short_ontology_name(str(onto)) 
                            for onto in ontos])
        if len(ontos) == 3:
            ontos_str = "ALL"
        onto_dir = tempDir + "/" + ontos_str
        if not os.path.exists(onto_dir):
            os.mkdir(onto_dir)
        output_file = (onto_dir + "/" + ".".join(["-".join(valid_metrics),
                            "c"+str(conf),"bh="+str(benchmarking),
                            "pval"+str(pval),"fdr"+str(fdr),"tsv"]
                        ))
        with open(output_file,'w') as stream:
            for line in lines:
                stream.write(line)