Fix bug in SIF generation for replicated blanks

src/qp_klp/Assays.py

@@ -395,6 +395,7 @@ def generate_prep_file(self):
         if 'GenPrepFileJob' not in self.skip_steps:
             job.run(callback=self.job_callback)
 
+        self.dereplicated_input_file_paths = job.dereplicated_input_file_paths
         self.prep_file_paths = job.prep_file_paths
         self.has_replicates = job.has_replicates
 
@@ -592,6 +593,7 @@ def generate_prep_file(self):
         if 'GenPrepFileJob' not in self.skip_steps:
             job.run(callback=self.job_callback)
 
+        self.dereplicated_input_file_paths = job.dereplicated_input_file_paths
         self.prep_file_paths = job.prep_file_paths
         self.has_replicates = job.has_replicates
 

src/qp_klp/Workflows.py

@@ -37,6 +37,7 @@ def __init__(self, **kwargs):
         self.output_path = None
         self.pipeline = None
         self.prep_copy_index = 0
+        self.dereplicated_input_file_paths = None
         self.prep_file_paths = None
         self.qclient = None
         self.run_prefixes = {}
@@ -158,39 +159,13 @@ def generate_special_map(self):
     def generate_sifs(self):
         """
         Generates sample-info files for each project, containing
-        metadata on BLANKS.
+        metadata on blanks.
         """
-        from_qiita = {}
-
-        for study_id in self.prep_file_paths:
-            url = f'/api/v1/study/{study_id}/samples'
-            logging.debug(url)
-            samples = list(self.qclient.get(url))
-            from_qiita[study_id] = samples
-
-        add_sif_info = []
-
-        qid_pn_map = {proj['qiita_id']: proj['project_name'] for
-                      proj in self.pipeline.get_project_info()}
-
-        # in case we really do need to query for samples again:
-        # assume set of valid study_ids can be determined from prep_file_paths.
-        for study_id in from_qiita:
-            samples = from_qiita[study_id]
-            # generate a list of (sample-name, project-name) pairs.
-            project_name = qid_pn_map[study_id]
-            samples = [(x, project_name) for x in samples]
-            add_sif_info.append(pd.DataFrame(data=samples,
-                                             columns=['sample_name',
-                                                      'project_name']))
-
-        # convert the list of dataframes into a single dataframe.
-        add_sif_info = pd.concat(add_sif_info,
-                                 ignore_index=True).drop_duplicates()
-
-        # generate SIF files with add_sif_info as additional metadata input.
-        # duplicate sample-names and non-blanks will be handled properly.
-        self.sifs = self.pipeline.generate_sample_info_files(add_sif_info)
+
+        # generate SIF files with paths to the input file(s) (multiples when
+        # there are replicates)
+        self.sifs = self.pipeline.generate_sample_info_files(
+            self.dereplicated_input_file_paths)
 
         return self.sifs
 
@@ -202,30 +177,28 @@ def update_blanks_in_qiita(self):
 
         for sif_path in self.sifs:
             # get study_id from sif_file_name ...something_14385_blanks.tsv
-            study_id = sif_path.split('_')[-2]
+            study_id = self.pipeline.get_qiita_id_from_sif_fp(sif_path)
 
             df = pd.read_csv(sif_path, delimiter='\t', dtype=str)
 
             # Prepend study_id to make them compatible w/list from Qiita.
             df['sample_name'] = f'{study_id}.' + df['sample_name'].astype(str)
 
-            # SIFs only contain BLANKs. Get the list of potentially new BLANKs.
-            blank_ids = [i for i in df['sample_name'] if 'blank' in i.lower()]
-            blanks = df[df['sample_name'].isin(blank_ids)]['sample_name']
+            # SIFs only contain blanks. Get the list of potentially new blanks.
+            blanks = df['sample_name'].tolist()
             if len(blanks) == 0:
                 # we have nothing to do so let's return early
                 return
 
-            # Get list of BLANKs already registered in Qiita.
+            # Get list of samples already registered in Qiita
+            # (will include any already-registered blanks)
             from_qiita = self.qclient.get(f'/api/v1/study/{study_id}/samples')
-            from_qiita = [x for x in from_qiita if
-                          x.startswith(f'{study_id}.BLANK')]
 
-            # Generate list of BLANKs that need to be ADDED to Qiita.
+            # Generate list of blanks that need to be ADDED to Qiita.
             new_blanks = (set(blanks) | set(from_qiita)) - set(from_qiita)
 
             if len(new_blanks):
-                # Generate dummy entries for each new BLANK, if any.
+                # Generate dummy entries for each new blank, if any.
                 url = f'/api/v1/study/{study_id}/samples/info'
                 logging.debug(url)
                 categories = self.qclient.get(url)['categories']
@@ -324,7 +297,7 @@ def _process_blanks(self):
         :return:
         """
         results = [x for x in listdir(self.pipeline.output_path) if
-                   x.endswith('_blanks.tsv')]
+                   self.pipeline.is_sif_fp(x)]
 
         results.sort()
 
@@ -495,7 +468,7 @@ def _compare_samples_against_qiita(self):
             else:
                 samples = set(self.pipeline.get_sample_names(p_name))
 
-            # do not include BLANKs. If they are unregistered, we will add
+            # do not include blanks. If they are unregistered, we will add
             # them downstream.
             samples = {smpl for smpl in samples
                        if not smpl.startswith('BLANK')}

src/sequence_processing_pipeline/GenPrepFileJob.py

@@ -27,6 +27,7 @@ def __init__(self, run_dir, convert_job_path, qc_job_path, output_path,
         self.seqpro_path = seqpro_path
         self.qiita_job_id = qiita_job_id
         self.is_amplicon = is_amplicon
+        self.dereplicated_input_file_paths = None
         self.prep_file_paths = None
         self.commands = []
         self.has_replicates = False
@@ -120,6 +121,10 @@ def __init__(self, run_dir, convert_job_path, qc_job_path, output_path,
                 # overwrite default setting
                 file_paths = self._write_to_file(demux_sample_sheet(fp))
 
+        # if no replicate demuxing was needed, then this will be a list
+        # containing only the original input file path.
+        self.dereplicated_input_file_paths = file_paths
+
         for fp in file_paths:
             # generate a seqpro command-line using the new sample-sheet.
             if self.has_replicates:

src/sequence_processing_pipeline/Pipeline.py

@@ -609,10 +609,12 @@ def _validate_mapping_file(self, mapping_file_path):
 
         return df
 
-    def generate_sample_info_files(self, addl_info=None):
+    def generate_sample_info_files(self, dereplicated_input_file_paths):
         """
         Generate sample-information files in self.output_path.
-        :param addl_info: A df of (sample-name, project-name) pairs.
+        :param dereplicated_input_file_paths: a list of input files (either
+        sample-sheets or pre-prep files) that have been dereplicated (split
+        into separate files for each replicate).
         :return: A list of paths to sample-information-files.
         """
         if self.pipeline_type == Pipeline.AMPLICON_PTYPE:
@@ -631,8 +633,15 @@ def generate_sample_info_files(self, addl_info=None):
             # next record from mapping file df
             df = pd.DataFrame(blanks_dicts_list)
         else:
-            controls = self.sample_sheet.get_denormalized_controls_list()
-            df = pd.DataFrame(controls)
+            all_controls = []
+            for curr_input_path in dereplicated_input_file_paths:
+                curr_sample_sheet = load_sample_sheet(curr_input_path)
+                curr_controls = \
+                    curr_sample_sheet.get_denormalized_controls_list()
+                all_controls.extend(curr_controls)
+            # next path
+            df = pd.DataFrame(all_controls)
+            df.drop_duplicates(inplace=True)
 
         paths = []
 

tests/data/good_sheet_w_replicates_and_context.csv

@@ -0,0 +1,103 @@
+[Header],,,,,,,,,,,,
+IEMFileVersion,4,,,,,,,,,,,
+SheetType,standard_metag,,,,,,,,,,,
+SheetVersion,101,,,,,,,,,,,
+Investigator Name,Knight,,,,,,,,,,,
+Experiment Name,RKL0042,,,,,,,,,,,
+Date,2/26/20,,,,,,,,,,,
+Workflow,GenerateFASTQ,,,,,,,,,,,
+Application,FASTQ Only,,,,,,,,,,,
+Assay,Metagenomic,,,,,,,,,,,
+Description,,,,,,,,,,,,
+Chemistry,Default,,,,,,,,,,,
+,,,,,,,,,,,,
+[Reads],,,,,,,,,,,,
+150,,,,,,,,,,,,
+150,,,,,,,,,,,,
+,,,,,,,,,,,,
+[Settings],,,,,,,,,,,,
+ReverseComplement,0,,,,,,,,,,,
+,,,,,,,,,,,,
+[Data],,,,,,,,,,,,
+orig_name,Sample_ID,Sample_Name,source_well_384,destination_well_384,Sample_Plate,well_id_384,I7_Index_ID,index,I5_Index_ID,index2,Sample_Project,Well_description
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+SP464350A04,SP464350A04_D14,SP464350A04.D14,C13,D14,ProjectN_13059_P1,D14,iTru7_109_01,GTCGTCTT,iTru5_05_B,AAGCATCG,ProjectN_13059,yellow_quadrant
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+,,,,,,,,,,,,
+[Bioinformatics],,,,,,,,,,,,
+Sample_Project,QiitaID,BarcodesAreRC,ForwardAdapter,ReverseAdapter,HumanFiltering,library_construction_protocol,experiment_design_description,contains_replicates,,,,
+ProjectF_11661,11661,FALSE,AACC,GGTT,FALSE,Nextera,Equipment,TRUE,,,,
+ProjectN_13059,13059,FALSE,AACC,GGTT,FALSE,Knight Lab Kapa HP,Equipment,TRUE,,,,
+TMI_10317,10317,FALSE,AACC,GGTT,FALSE,Knight Lab Kapa HP,Eqiiperiment,TRUE,,,,
+Other_11223,11223,FALSE,AACC,GGTT,FALSE,Knight Lab Kapa HP,Eqiiperiment,TRUE,,,,
+,,,,,,,,,,,,
+[Contact],,,,,,,,,,,,
+Email,Sample_Project,,,,,,,,,,,
+[email protected],ProjectF_11661,,,,,,,,,,,
+[email protected],ProjectN_13059,,,,,,,,,,,
+,,,,,,,,,,,,
+[SampleContext],,,,,,,,,,,,
+sample_name,sample_type,primary_qiita_study,secondary_qiita_studies,,,,,,,,,
+BLANK.43.12G.A1,control blank,11661,10317;11223,,,,,,,,,
+BLANK.43.12H.A3,control blank,11661,10317,,,,,,,,,
+BLANK.43.12G.A2,control blank,11661,10317;11223,,,,,,,,,
+BLANK.43.12H.A4,control blank,11661,10317,,,,,,,,,
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+BLANK.43.12H.B4,control blank,11661,10317,,,,,,,,,