bugs

README.md

@@ -1,3 +1,5 @@
+[![PyPI version](https://badge.fury.io/py/spycone.svg)](https://badge.fury.io/py/spycone)
+
 # Spycone - SPlicing-aware time-COurse Network Enricher
 
 Spycone is a python package that provides systematic analysis of time course transcriptomics data. Spycone uses gene or isoform expression as an input. Spycone features a novel method for IS detection and employs the sum of changes of all isoforms relative abundances (total isoform usage) across time points. Spycone provides downstream analysis such as clustering by total isoform usage, i.e. grouping genes that are most likely to be coregulated, and network enrichment, i.e. extracting subnetworks or pathways that are over-represented by a list of genes. These analyses can be coupled with gene set enrichment analysis and visualization.

setup.py

@@ -11,7 +11,7 @@
 
 def setup_package():
     metadata = dict(name='spycone',
-    version='0.0.6',
+    version='0.0.7',
     description='A splicing-aware time course network enricher',
     url='https://github.com/yollct/spycone.git',
     author='Chit Tong Lio',

spycone/DataSet.py

@@ -7,7 +7,7 @@
 
 def _map_to_gene_name(keys, keytype, species, transid_type = None):
     keymap = {'entrezgeneid': 'NCBI gene (formerly Entrezgene) ID', 'ensemblgeneid': 'Gene stable ID', 'ensembltransid' : 'Transcript stable ID', 'ensembltransidver': "Transcript stable ID version"}
-    ann = pd.read_csv(os.path.join(dir_path, "data/{}_ann.csv".format(species)), index_col=keymap[keytype], dtype=str)
+    ann = pd.read_csv(os.path.join(dir_path, "data/annotation/{}_ann.csv".format(species)), index_col=keymap[keytype], dtype=str)
     ann.index = list(map(str, list(map(lambda x: int(x) if not np.isnan(x) else x, ann.index))))
     ann_dict = ann.to_dict()
 
@@ -22,7 +22,7 @@ def _map_to_gene_name(keys, keytype, species, transid_type = None):
 
 def _map_trans_to_gene(keys, keytype, species, transid_type = None):
     keymap = {'entrezgeneid': 'NCBI gene (formerly Entrezgene) ID', 'ensemblgeneid': 'Gene stable ID', 'ensembltransid' : 'Transcript stable ID', 'ensembltransidver': "Transcript stable ID version"}
-    ann = pd.read_csv(os.path.join(dir_path, "data/{}_ann.csv".format(species)), index_col=keymap[transid_type], dtype=str)
+    ann = pd.read_csv(os.path.join(dir_path, "data/annotation/{}_ann.csv".format(species)), index_col=keymap[transid_type], dtype=str)
     ann_dict = ann.to_dict()
 
     a = []