reformatted as pypi package

MANIFEST.in

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+include README.md
+include lisa/config.ini
+include docs/*

bin/lisa

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+#!/Users/alynch/projects/.venvs/lisa_test/bin/python3
+
+from lisa import LISA, Log, _config, __version__
+import configparser
+import argparse
+import os
+import sys
+
+#____COMMAND LINE INTERFACE________
+
+INSTANTIATION_KWARGS = ['cores','isd_method','verbose','oneshot']
+PREDICTION_KWARGS = ['background_list','num_background_genes','background_strategy', 'seed']
+
+def extract_kwargs(args, keywords):
+    return {key : vars(args)[key] for key in keywords}
+
+def is_valid_prefix(prefix):
+
+    if os.path.isdir(prefix) or os.path.isfile(prefix) or os.path.isdir(os.path.dirname(prefix)):
+        return prefix
+
+    raise argparse.ArgumentTypeError('{}: Invalid file prefix.'.format(prefix))
+
+def lisa_download(args):
+    lisa = LISA(args.species)
+    lisa._download_data()
+
+
+def lisa_oneshot(args):
+
+    results, metadata = LISA(args.species, **extract_kwargs(args, INSTANTIATION_KWARGS)).predict(args.query_list.readlines(), **extract_kwargs(args, PREDICTION_KWARGS))
+
+    if args.save_metadata:
+        if args.output_prefix:
+            metadata_filename = args.output_prefix + '.metadata.json' 
+        else:
+            metadata_filename = os.path.basename(args.query_list.name) + '.metadata.json'
+
+        with open(metadata_filename, 'w') as f:
+            f.write(json.dumps(metadata, indent=4))
+
+    if not args.output_prefix is None:
+        with open(args.output_prefix + '.lisa.tsv', 'w') as f:
+            f.write(results.to_tsv())
+    else:
+        print(results.to_tsv())
+
+
+def save_and_get_top_TFs(args, query_name, results, metadata):
+
+    with open(args.output_prefix + query_name + '.lisa.tsv', 'w') as f:
+        f.write(results.to_tsv())
+
+    if args.save_metadata:
+        with open(args.output_prefix + query_name + '.metadata.json', 'w') as f:
+            f.write(json.dumps(metadata, indent=4))
+
+    top_TFs = results.filter_rows(lambda x : x <= 0.05, 'combined_p_value_adjusted').todict()['factor']
+
+    top_TFs_unique, encountered = [], set()
+    for TF in top_TFs:
+        if not TF in encountered:
+            top_TFs_unique.append(TF)
+            encountered.add(TF)
+
+    return top_TFs_unique
+
+
+def print_results_multi(results_summary):
+    print('Sample\tTop Regulatory Factors (p < 0.05)')
+    for result_line in results_summary:
+        print(result_line[0], ', '.join(result_line[1]), sep = '\t')
+
+
+def lisa_multi(args):
+
+    log = Log(target = sys.stderr, verbose = args.verbose)
+    lisa = LISA(args.species, **extract_kwargs(args, INSTANTIATION_KWARGS), log = log)
+
+    query_dict = {os.path.basename(query.name) : query.readlines() for query in args.query_lists}
+
+    results_summary = []
+    for query_name, query_list in query_dict.items():
+
+        with log.section('Modeling {}:'.format(str(query_name))):
+            try: 
+                results, metadata = lisa.predict(query_list, **extract_kwargs(args, PREDICTION_KWARGS))
+
+                top_TFs_unique = save_and_get_top_TFs(args, query_name, results, metadata)
+
+                results_summary.append((query_name, top_TFs_unique))
+
+            except AssertionError as err:
+                log.append(str(err))
+
+    print_results_multi(results_summary)
+
+
+def lisa_one_v_rest(args):
+
+    log = Log(target = sys.stderr, verbose = args.verbose)
+    lisa = LISA(args.species, **extract_kwargs(args, INSTANTIATION_KWARGS), log = log)
+
+    queries = {query.name : query.readlines() for query in args.query_lists}
+
+    cluster_lists = {
+        query_name : (query_genes, [
+            background_gene
+            for j, background in enumerate(queries.values()) for background_gene in background if not j == i
+        ])
+        for i, (query_name, query_genes) in enumerate(queries.items())
+    }
+
+    results_summary = []
+    for query_name, genelists in cluster_lists.items():
+
+        with log.section('Modeling {}:'.format(str(query_name))):
+            try:
+                results, metadata = lisa.predict(genelists[0], background_list = genelists[1], **extract_kwargs(args, PREDICTION_KWARGS))
+
+                top_TFs_unique = save_and_get_top_TFs(args, query_name, results, metadata)
+
+                results_summary.append((query_name, top_TFs_unique))
+
+            except AssertionError as err:
+                log.append(str(err))
+
+    print_results_multi(results_summary)
+
+
+def build_common_args(parser):
+    parser.add_argument('species', choices = ['hg38','mm10'], help = 'Find TFs associated with human (hg38) or mouse (mm10) genes')
+    parser.add_argument('-c','--cores', required = True, type = int, default = 1)
+    parser.add_argument('--seed', type = int, default = None, help = 'Random seed for gene selection. Allows for reproducing exact results.')
+    parser.add_argument('--use_motifs', action = 'store_const', const = 'motifs', default='chipseq',
+        dest = 'isd_method', help = 'Use motif hits instead of ChIP-seq peaks to represent TF binding (only recommended if TF-of-interest is not represented in ChIP-seq database).')
+    parser.add_argument('--save_metadata', action = 'store_true', default = False, help = 'Save json-formatted metadata from processing each gene list.')
+
+
+def build_multiple_lists_args(parser):
+    parser.add_argument('query_lists', type = argparse.FileType('r', encoding = 'utf-8'), nargs = "+", help = 'user-supplied gene lists. One gene per line in either symbol or refseqID format')
+    parser.add_argument('-o','--output_prefix', required = True, type = is_valid_prefix, help = 'Output file prefix.')
+    parser.add_argument('-v','--verbose',type = int, default = 2)
+
+if __name__ == "__main__":
+
+    #define command-line arguments
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        description =
+"""
+___________________________________________________________________________________________________________________________
+
+Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data
+https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1934-6
+X. Shirley Liu Lab, 2020
+___________________________________________________________________________________________________________________________
+""")
+
+    parser.add_argument('--version', action = 'version', version = __version__)
+
+    subparsers = parser.add_subparsers(help = 'commands')
+
+    #__ LISA oneshot command __#################
+
+    oneshot_parser = subparsers.add_parser('oneshot', help = 'Use LISA to infer genes from one gene list. If you have multiple lists, this option will be slower than using "multi" due to data-loading time.\n')
+    build_common_args(oneshot_parser)
+
+    oneshot_parser.add_argument('query_list', type = argparse.FileType('r', encoding = 'utf-8'), help = 'user-supplied gene lists. One gene per line in either symbol or refseqID format')
+    oneshot_parser.add_argument('-o','--output_prefix', required = False, type = is_valid_prefix, help = 'Output file prefix. If left empty, will write results to stdout.')
+    oneshot_parser.add_argument('--background_strategy', choices = _config.get('lisa_params', 'background_strategies').split(','),
+        default = 'regulatory',
+        help = """Background genes selection strategy. LISA samples background genes to compare to user\'s genes-of-interest from a diverse
+        regulatory background (regulatory - recommended), randomly from all genes (random), or uses a user-provided list (provided).
+        """)
+    background_genes_group = oneshot_parser.add_mutually_exclusive_group()
+    background_genes_group.add_argument('--background_list', type = argparse.FileType('r', encoding = 'utf-8'), required = False,
+        help = 'user-supplied list of backgroung genes. Used when --background_strategy flag is set to "provided"')
+    background_genes_group.add_argument('-b','--num_background_genes', type = int, default = _config.get('lisa_params', 'background_genes'),
+        help = 'Number of sampled background genes to compare to user-supplied genes')
+    oneshot_parser.add_argument('-v','--verbose',type = int, default = 4)
+    oneshot_parser.set_defaults(func = lisa_oneshot, oneshot = True)
+
+    #__ LISA multi command __#################
+
+    multi_parser = subparsers.add_parser('multi', help = 'Process multiple genelists. This reduces data-loading time if using the same parameters for all lists.\n')
+    build_common_args(multi_parser)
+    build_multiple_lists_args(multi_parser)
+    multi_parser.add_argument('-b','--num_background_genes', type = int, default = _config.get('lisa_params', 'background_genes'),
+        help = 'Number of sampled background genes to compare to user-supplied genes. These genes are selection from other gene lists.')
+    multi_parser.add_argument('--random_background', action = 'store_const', const = 'random', default = 'regulatory', dest = 'background_strategy', help = 'Use random background selection rather than "regulatory" selection.')
+    multi_parser.set_defaults(func = lisa_multi, oneshot = False, background_list = None)
+
+    #__ LISA one-vs-rest command __#################
+
+    one_v_rest_parser = subparsers.add_parser('one-vs-rest', help = 'Compare gene lists in a one-vs-rest fashion. Useful downstream of cluster analysis.\n')
+    build_common_args(one_v_rest_parser)
+    build_multiple_lists_args(one_v_rest_parser)
+    one_v_rest_parser.set_defaults(func = lisa_one_v_rest, oneshot = False, background_strategy = 'provided')
+
+    download_data_parser = subparsers.add_parser('download', help = 'Download data from CistromeDB. Use if data recieved is incomplete or malformed.')
+    download_data_parser.add_argument('species', choices = ['hg38','mm10'], help = 'Download data associated with human (hg38) or mouse (mm10) genes')   
+    download_data_parser.set_defaults(func = lisa_download)
+
+    args = parser.parse_args()
+
+    try:
+        args.func #first try accessing the .func attribute, which is empty if user tries ">>>lisa". In this case, don't throw error, display help!
+    except AttributeError:
+        print(parser.print_help(), file = sys.stderr)
+    else:
+        args.func(args)
+

docs/README.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!source /Users/alynch/projects/.venvs/lisa_test/bin/activate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lisa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = pd.read_csv('/Users/alynch/projects/.venvs/lisa_test/lib/python3.7/site-packages/lisa/data/hg38/metadata/Motifs.tsv',\n",
+    "               sep = '\\t', encoding = 'latin')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       ATF2::JUN\n",
+       "1            IRF1\n",
+       "2           TFCP2\n",
+       "3           RUNX1\n",
+       "4           ZBTB6\n",
+       "          ...    \n",
+       "1056         ELF2\n",
+       "1057         Etv5\n",
+       "1058         Elf4\n",
+       "1059         Etv6\n",
+       "1060       Gm5454\n",
+       "Name: symbol, Length: 1061, dtype: object"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.symbol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'edition', 'source', 'sourcefile', 'status', 'numseqs', 'pmid',\n",
+       "       'dbd', 'family', 'description', 'species', 'cellline', 'entrez',\n",
+       "       'symbol', 'synonym', 'refseq', 'cluster', 'comment2', 'comment2.1',\n",
+       "       'comment3', 'comment4', 'comment5', 'datasetid', 'zscore', 'seqfactors',\n",
+       "       'seqdbds', 'seqdatasetid', 'nmotifs', 'pssm'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "bioenv",
+   "language": "python",
+   "name": "bioenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

lisa/__init__.py

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+from .lisa import LISA, _config
+from .utils import Log
+from ._version import __version__

lisa/_version.py

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+__version__ = '2.0.0'