API Reference

Complete API documentation for SATAY Tools modules.

CLI Module

satay.cli.main()

Main entry point for the CLI.

Core Modules

Script to map FASTQ files to the yeast genome using STAR aligner. Takes a directory of FASTQ files and aligns them to a specified yeast genome reference.

satay.fastq_to_bam.check_star_installed()

Check if STAR is installed and available

satay.fastq_to_bam.create_genome_index(genome_fasta, genome_dir, threads, logger)

satay.fastq_to_bam.find_fastq_files(fastq_dir, suffix=None)

Find all FASTQ files in the given directory

satay.fastq_to_bam.find_paired_files(fastq_files)

Group FASTQ files into pairs if they appear to be paired-end reads

satay.fastq_to_bam.get_sample_name(fastq_file)

Extract sample name from FASTQ file path

satay.fastq_to_bam.map_fastq_to_bam(fastq_dir, output_dir, genome_fasta, threads, suffix=[], single_end=True, limit_bam_sort_ram=2000000000)

satay.fastq_to_bam.run_star_alignment(fastq_file1, fastq_file2, output_dir, genome_dir, sample_name, threads, logger, limit_bam_sort_ram=2000000000)

Run STAR alignment for single or paired-end reads

satay.fastq_to_bam.setup_logger(log_dir)

Set up the logger to write to both console and file

satay.fastq_to_bam.verify_star_index(genome_dir, logger)

Verify that STAR actually created a valid index.

Parameters:

• genome_dir (str) – Directory containing the genome index

• logger – Logger object for logging messages

Returns:

True if valid index exists, False otherwise

Return type:

bool

satay.process_satay_bams.count_insertions_over_intervals(interval_files: List[str | Path], filtered_insertions_file: str | Path) → None

Count insertions over specified genomic intervals using bedtools map.

Parameters:

• interval_files (List[Union[str, Path]]) – List of paths to interval files (BED/GFF format)

• filtered_insertions_file (Union[str, Path]) – Path to the filtered insertions file

Notes

For each interval file, creates an output file with the format: {filtered_insertions_stem}_{interval_file_stem}.cnts containing the count of unique insertions and sum of insertions per interval.

satay.process_satay_bams.filter_bam(output_bam)

Filter BAM files and convert to BED format. Filter out q < 10

satay.process_satay_bams.filter_insertions(merged_file)

Remove insertions supported by only 1 read.

satay.process_satay_bams.map_sample(sample, bam_dir, output_dir, interval_files)

Process a sample

satay.process_satay_bams.merge_bams(sample, output_dir, bam_dir, bam_suffix='Aligned.sortedByCoord.out.bam')

Merge BAM files for each sample.

satay.process_satay_bams.merge_cnts_files(interval_file: str | Path, counts_dir: str | Path, name: str = '', format: str = 'gff') → Tuple[pandas.DataFrame, pandas.DataFrame]

Merge count files from multiple samples into consolidated transposon and read count matrices.

Parameters:

• interval_file (str or Path) – Path to the interval file (GFF or BED format)

• counts_dir (str or Path) – Directory containing the count files to merge

• name (str, optional) – Prefix for output files, by default “”

• format (str, optional) – Format of the interval file (‘gff’ or ‘bed’), by default ‘gff’

Returns:

Two DataFrames containing merged transposon counts and read counts respectively

Return type:

Tuple[pd.DataFrame, pd.DataFrame]

satay.process_satay_bams.merge_files(sorted_insertions_file)

Merge intervals mapping to same location using sorted files.

satay.process_satay_bams.process_orientation(filtered_bam_bed)

Process BED files based on orientation.

satay.process_satay_bams.run_command(cmd)

Run a subprocess command with error handling.

satay.process_satay_bams.setup_logging(output_dir)

satay.process_satay_bams.sort_files(insertions_file)

Sort insertion files.

satay.process_satay_bams.validate_environment()

satay.da_analysis.gene_da(counts_file, sample_data_file, output_dir, filter, comp_col, baseline, a, gff_file='', ids_to_keep=['locus_tag', 'gene', 'product'], sample_id_col='sample_id', n_cpus=None)

Run complete differential analysis pipeline.

satay.da_analysis.load_filter_deseq(counts_file, sample_data_file, filter=100, comp_col='conc', baseline='0nMaF', a=0.01, sample_id_col='sample_id', n_cpus=None)

Load count data and sample metadata, filter low-count genes, run DESeq2.

satay.da_analysis.merge_annotations(res_df, gff_file, ids_to_keep=['locus_tag', 'gene', 'product'])

Merge DESeq2 results with gene annotations from GFF file.

satay.da_analysis.run_deseq(count_df, sample_data, condition_col, baseline, a=0.01, n_cpus=None)

Run DESeq2 analysis on count data.

Parameters:

• count_df – DataFrame with count data (samples as rows, genes as columns)

• sample_data – DataFrame with sample metadata

• condition_col – Column name for condition/treatment in sample_data

• baseline – Baseline condition for comparisons

• a – Alpha value for significance testing

Returns:

(vst_counts DataFrame, results DataFrame)

Return type:

tuple