Merge pull request #1 from wtsi-hgi/main

Modified yascp keras

CITATIONS.md

@@ -1,12 +1,5 @@
-# nf-core/yascp: Citations
-
-## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)
-
-> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.
-
-## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)
-
-> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.
+# yascp: Citations
+Mention the use of this repo in manuscript.
 
 ## Pipeline tools
 
@@ -34,15 +27,6 @@
 
 ## Software packaging/containerisation tools
 
-* [Anaconda](https://anaconda.com)
-    > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.
-
-* [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)
-    > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.
-
-* [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)
-    > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.
-
 * [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)
 
 * [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

bin/dynamic_donor_exclusive_snp_selection.py

@@ -13,7 +13,9 @@
 import multiprocessing as mp
 from multiprocessing import Lock
 import os
-
+add_noninformative=True
+
+
 use_only_informative_snps=True #When this is set to True we ignore any sites that have no difference between all the individuals.
 class VCF_Loader:
 
@@ -68,15 +70,20 @@ def load_sample_mp(self,line,obs_ids,count,format_list):
             random.shuffle(c)
             obs_with_gt, list_val_with_gt = zip(*c)
             test_if_there_is_difference = set(pd.DataFrame(list_val_with_gt)[0].str.split(":").str[0])
+            test_if_there_is_difference.discard('./.')
+            test_if_there_is_difference.discard('.|.')
+            test_if_there_is_difference.discard('.')
+
             nr_genotypes = len(test_if_there_is_difference)
-            if use_only_informative_snps==True:
+            if nr_genotypes==0:
+                _='here we only have missing genotypes' #here we only have missing genotypes
+            else:
                 if nr_genotypes>1:
-                    return [obs_with_gt,list_val_with_gt,idx,list_val,count,coment_fields]
+                    return [obs_with_gt,list_val_with_gt,idx,list_val,count,coment_fields,'informative']
                 else:
-                    _='Site is not informative'
-                    # print('Site is not informative')
-            else:
-                return [obs_with_gt,list_val_with_gt,idx,list_val,count,coment_fields]
+                    return [obs_with_gt,list_val_with_gt,idx,list_val,count,coment_fields,'constant']
+                        # print('Site is not informative')
+
 
 
     def set_results(self,to_set,id):
@@ -116,7 +123,7 @@ def append_results(self,result):
                 alleles = list_val_with_gt[donor_loc_in_list].split(':')[idx]
                 if alleles!='.':
                     ids = "_".join([list_val[x] for x in [0, 1, 3, 4]])
-                    donor_var = f"{ids}_{alleles}"
+                    donor_var = f"{result[6]}_{ids}_{alleles}"
                     while ob_id in self.curently_pushing:
                         time.sleep(r*0.01)
                     self.curently_pushing.append(ob_id)           
@@ -314,32 +321,50 @@ def donor_exclusive_sites(exclusive_don_variants2):
     for key1 in iteration_dataframe.keys():
         all_sites = pd.DataFrame(iteration_dataframe[key1],columns=['f1'])
         splits = all_sites['f1'].str.split('_')
-        all_sites['#CHROM']=splits.str[0]
-        all_sites['POS']=splits.str[1]
+        all_sites['informative']=splits.str[0]
+        all_sites['#CHROM']=splits.str[1]
+        all_sites['POS']=splits.str[2]
         all_sites['ID']=f'.'
-        all_sites['REF']=splits.str[2]
-        all_sites['ALT']=splits.str[3]
+        all_sites['REF']=splits.str[3]
+        all_sites['ALT']=splits.str[4]
         all_sites['QUAL']=f'.'
         all_sites['FILTER']=f'.'
         all_sites['INFO']=f'.'
         all_sites = all_sites.drop(columns=['f1'])
         exta_snps=pd.concat([exta_snps,all_sites])
     # All_Extra_informative_Sites = exta_snps.drop_duplicates()
+    informative_sites = exta_snps[exta_snps['informative']=='informative'].drop('informative',axis=1)
+    constant_sites = exta_snps[exta_snps['informative']=='constant'].drop('informative',axis=1)
+    constant_sites = constant_sites.drop_duplicates(subset=['#CHROM', 'POS'])
+    constant_sites.index=constant_sites['#CHROM']+'_'+constant_sites['POS']
+
+    exta_snps_back = exta_snps.copy()
+    exta_snps = informative_sites
     exta_snps.columns = cellsnp.columns   
     exta_snps=exta_snps.drop_duplicates(subset=[0, 1])
-    cellsnp_exta_snps=pd.concat([cellsnp,exta_snps])
+
     exta_snps.index=exta_snps[0]+'_'+exta_snps[1]
     cellsnp.index=cellsnp[0]+'_'+cellsnp[1]
-    set1_uninformative_sites=set(cellsnp.index)-set(exta_snps.index)
+
+
     informative_sites_covered_in_default_panel = set(exta_snps.index)-set(cellsnp.index)
+    constant_sites_covered_in_default_panel = set(constant_sites.index)-set(cellsnp.index)
 
+
+    if add_noninformative:
+        constant_sites.columns = cellsnp.columns
+        cellsnp_exta_snps=pd.concat([cellsnp,exta_snps,constant_sites])
+    else:
+        cellsnp_exta_snps=pd.concat([cellsnp,exta_snps])
+
     cellsnp_exta_snps = cellsnp_exta_snps.drop_duplicates(subset=[0, 1])
-    set1_uninformative_sites = cellsnp.loc[set1_uninformative_sites]
+
     set2_informative_sites =exta_snps
-    description = pd.DataFrame([{'total sites':len(cellsnp_exta_snps),'informative sites':len(set2_informative_sites),'uninformative sites':len(set1_uninformative_sites),'informative sites covered in initial panel':len(informative_sites_covered_in_default_panel)}])
+    set1_uninformative_sites=constant_sites
+    description = pd.DataFrame([{'total sites':len(cellsnp_exta_snps),'informative sites':len(set2_informative_sites),'uninformative sites':len(set1_uninformative_sites),'informative sites covered in initial panel':len(informative_sites_covered_in_default_panel), 'constant_sites_covered_in_default_panel':len(constant_sites_covered_in_default_panel)}])
     description.to_csv('variants_description.tsv',sep='\t',index=False)
     cellsnp_exta_snps.to_csv('cellsnp_variants.tsv',sep='\t',index=False,header=False)
-    set1_uninformative_sites.to_csv('set1_uninformative_sites.tsv',sep='\t',index=False,header=False)
+    constant_sites.to_csv('set1_uninformative_sites.tsv',sep='\t',index=False,header=False)
     set2_informative_sites.to_csv('set2_informative_sites.tsv',sep='\t',index=False,header=False)
     print('Done')