diff --git a/CHANGELOG.md b/CHANGELOG.md index f2b39b5..d8833b1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,12 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [[3.2.1](https://github.com/CFIA-NCFAD/nf-flu/releases/tag/3.2.1)] - 2023-07-07 + +### Fixes + +* Empty BLAST results file parsing `NoDataError` (#27) (Thanks @MatFish for reporting this issue!) + ## [[3.2.0](https://github.com/CFIA-NCFAD/nf-flu/releases/tag/3.2.0)] - 2023-06-22 ### Added diff --git a/bin/parse_influenza_blast_results.py b/bin/parse_influenza_blast_results.py index 90881b7..5608325 100755 --- a/bin/parse_influenza_blast_results.py +++ b/bin/parse_influenza_blast_results.py @@ -189,19 +189,27 @@ def parse_blast_result( top: int = 3, pident_threshold: float = 0.85, min_aln_length: int = 50, -) -> Optional[ - Tuple[pl.DataFrame, Optional[pl.DataFrame], Optional[pl.DataFrame], Dict] -]: +) -> Optional[Tuple[pl.DataFrame, Dict]]: logging.info(f"Parsing BLAST results from {blast_result}") - df_filtered = pl.scan_csv( - blast_result, - has_header=False, - separator="\t", - new_columns=[name for name, coltype in blast_cols], - dtypes={name: coltype for name, coltype in blast_cols}, - ).filter((pl.col("pident") >= (pident_threshold * 100)) & - (pl.col("length") >= min_aln_length)).collect(streaming=True) + try: + df_filtered = ( + pl.scan_csv( + blast_result, + has_header=False, + separator="\t", + new_columns=[name for name, coltype in blast_cols], + dtypes=dict(blast_cols), + ) + .filter( + (pl.col("pident") >= (pident_threshold * 100)) + & (pl.col("length") >= min_aln_length) + ) + .collect(streaming=True) + ) + except pl.exceptions.NoDataError: + logging.warning(f"No BLAST results found in {blast_result}") + return None sample_name: str = re.sub(r"^(.+)_\d$", r"\1", df_filtered["qaccver"][0]) logging.info( @@ -231,22 +239,21 @@ def parse_blast_result( ) segments = df_merge["sample_segment"].unique().sort() - dfs = [] - for seg in segments: - dfs.append(df_merge.filter(pl.col("sample_segment") == seg).head(top)) + dfs = [ + df_merge.filter(pl.col("sample_segment") == seg).head(top) + for seg in segments + ] df_top_seg_matches = pl.concat(dfs, how="vertical") cols = pl.Series([x for x, _ in blast_results_report_columns]) df_top_seg_matches = df_top_seg_matches.select(pl.col(cols)) subtype_results_summary = {"sample": sample_name} if not get_top_ref: - is_iav = True - if df_top_seg_matches.select(pl.col("subtype").is_null().all())[0, 0]: - is_iav = False + is_iav = not df_top_seg_matches.select(pl.col("subtype").is_null().all())[0, 0] H_results = None N_results = None if "4" in segments: H_results = find_h_or_n_type(df_merge, "4", is_iav) - subtype_results_summary.update(H_results) + subtype_results_summary |= H_results if "6" in segments: N_results = find_h_or_n_type(df_merge, "6", is_iav) subtype_results_summary.update(N_results) @@ -258,27 +265,26 @@ def parse_blast_result( def get_subtype_value(H_results: Optional[Dict], N_results: Optional[Dict], is_iav: bool) -> str: subtype = "" if not is_iav: - subtype = "N/A" - return subtype + return "N/A" if H_results is None and N_results is None: subtype = "-" elif H_results is not None and N_results is None: H: str = H_results.get("H_type", "") subtype = f"H{H}" if H != "" else "-" - elif H_results is None and N_results is not None: + elif H_results is None: N: str = N_results.get("N_type", "") subtype = f"N{N}" if N != "" else "-" else: H: str = H_results.get("H_type", "") N: str = N_results.get("N_type", "") - if H == "" and N == "": - subtype = "-" - else: + if H or N: if H != "": H = f"H{H}" if N != "": N = f"N{N}" subtype = f"{H}{N}" + else: + subtype = "-" return subtype @@ -289,15 +295,11 @@ def find_h_or_n_type(df_merge, seg, is_iav): ], "Can only determine H or N type from segments 4 or 6, respectively!" type_name = "H_type" if seg == "4" else "N_type" h_or_n = type_name[0] - reg_h_or_n_type = "" - if h_or_n == "H": - reg_h_or_n_type = "[Hh]" - else: - reg_h_or_n_type = "[Nn]" df_segment = df_merge.filter(pl.col("sample_segment") == seg) if is_iav: type_counts = df_segment["subtype"].value_counts(sort=True) type_counts = type_counts.filter(~pl.col("subtype").is_null()) + reg_h_or_n_type = "[Hh]" if h_or_n == "H" else "[Nn]" df_type_counts = type_counts.with_columns(pl.lit(type_counts["subtype"].str.extract(reg_h_or_n_type + r"(\d+)"). alias(type_name))) df_type_counts = df_type_counts.filter(~pl.col(type_name).is_null()) @@ -305,7 +307,7 @@ def find_h_or_n_type(df_merge, seg, is_iav): type_to_count = defaultdict(int) for x in df_type_counts.iter_rows(named=True): type_to_count[x[type_name]] += x["counts"] - type_to_count = [(h, c) for h, c in type_to_count.items()] + type_to_count = list(type_to_count.items()) type_to_count.sort(key=lambda x: x[1], reverse=True) top_type, top_type_count = type_to_count[0] total_count = type_counts["counts"].sum() @@ -313,16 +315,20 @@ def find_h_or_n_type(df_merge, seg, is_iav): f"{h_or_n}{top_type} n={top_type_count}/{total_count} ({top_type_count / total_count:.1%})" ) df_segment = df_segment.with_columns( - pl.lit(df_segment["subtype"].str.contains(r".*" + reg_h_or_n_type + top_type + r".*") - .fill_null(False) - .alias("type_mask"))) + pl.lit( + df_segment["subtype"] + .str.contains(f".*{reg_h_or_n_type}" + top_type + r".*") + .fill_null(False) + .alias("type_mask") + ) + ) df_seg_top_type = df_segment.filter(pl.col("type_mask") == True).drop("type_mask") - top_result: pl.Series = [r for r in df_seg_top_type.head(1).iter_rows(named=True)][0] + top_result: pl.Series = list(df_seg_top_type.head(1).iter_rows(named=True))[0] else: top_type = "N/A" top_type_count = "N/A" total_count = "N/A" - top_result: pl.Series = [r for r in df_segment.head(1).iter_rows(named=True)][0] + top_result: pl.Series = list(df_segment.head(1).iter_rows(named=True))[0] results_summary = { f"{h_or_n}_type": top_type if is_iav else "N/A", @@ -388,7 +394,7 @@ def report(flu_metadata, blast_results, excel_report, top, pident_threshold, has_header=False, separator="\t", new_columns=[name for name, _ in md_cols], - dtypes={name: t for name, t in md_cols}, + dtypes=dict(md_cols), ) unique_subtypes = df_md.select("subtype").unique() @@ -414,7 +420,7 @@ def report(flu_metadata, blast_results, excel_report, top, pident_threshold, sample = subtype_results_summary["sample"] all_subtype_results[sample] = subtype_results_summary df_all_blast = pd.concat(dfs_blast).rename( - columns={k: v for k, v in blast_results_report_columns} + columns=dict(blast_results_report_columns) ) df_subtype_results = pd.DataFrame(all_subtype_results).transpose() cols = pd.Series(subtype_results_summary_columns) @@ -442,9 +448,7 @@ def report(flu_metadata, blast_results, excel_report, top, pident_threshold, ) else: df_blast, subtype_results_summary = results[0] - df_blast = df_blast.rename( - mapping={k: v for k, v in blast_results_report_columns} - ) + df_blast = df_blast.rename(mapping=dict(blast_results_report_columns)) df_ref_id = df_blast.select(pl.col(['Sample', 'Sample Genome Segment Number', 'Reference NCBI Accession', 'BLASTN Bitscore', 'Reference Sequence ID'])) df_ref_id = df_ref_id.with_columns( @@ -463,10 +467,9 @@ def report(flu_metadata, blast_results, excel_report, top, pident_threshold, def get_col_widths(df, index=False): """Calculate column widths based on column headers and contents""" if index: - idx_max = max( + yield max( [len(str(s)) for s in df.index.values] + [len(str(df.index.name))] ) - yield idx_max for c in df.columns: # get max length of column contents and length of column header yield np.max([df[c].astype(str).str.len().max() + 1, len(c) + 1]) @@ -493,7 +496,7 @@ def write_excel( # fixed max number of characters in sheet name due to compatibility if sheet_name_index: max_chars = 28 - fixed_sheetname = "{}_{}".format(idx, fixed_sheetname[:max_chars]) + fixed_sheetname = f"{idx}_{fixed_sheetname[:max_chars]}" else: max_chars = 31 fixed_sheetname = fixed_sheetname[:max_chars] @@ -506,7 +509,7 @@ def write_excel( len(fixed_sheetname), ) - logging.info('Writing table to Excel sheet "{}"'.format(fixed_sheetname)) + logging.info(f'Writing table to Excel sheet "{fixed_sheetname}"') df.to_excel( writer, sheet_name=fixed_sheetname, index=False, freeze_panes=(1, 1) ) diff --git a/nextflow.config b/nextflow.config index b3bd9bf..7ff7d09 100644 --- a/nextflow.config +++ b/nextflow.config @@ -153,7 +153,7 @@ manifest { description = 'Influenza A virus genome assembly pipeline' homePage = 'https://github.com/CFIA-NCFAD/nf-flu' author = 'Peter Kruczkiewicz, Hai Nguyen' - version = '3.2.0' + version = '3.2.1' nextflowVersion = '>=21.10' mainScript = 'main.nf' doi = '10.5281/zenodo.7011213'