expect_flat_log2 ( is_male_reference=None ) ¶ Substituted to avoid domain or divide-by-zero errors. A very small log2 ratio or coverage value may have been These are generally bins that had no reads mapped due to sample-specific femaleĭrop_low_coverage ( verbose=False ) ¶ĭrop bins with extremely low log2 coverage or copy ratio values. dict – Calculated values used for the inference: relative log2 ratios ofĬhromosomes X and Y versus the autosomes the Mann-Whitney U valuesįrom each test and ratios of U values for male vs.bool – True if the sample appears male.Nonparametric and p-values are not used here. Included for completeness,īut shouldn’t affect the result much since the M-W test is skip_low ( bool) – If True, drop very-low-coverage bins (via drop_low_coverage)īefore comparing log2 coverage ratios.Y, and female 0 on X, deep negative (below -3) on Y. Male sample should have log2 values of -1 on X and 0 on Y, and female +1 on X, deep negative (below -3) on Y. If so, a male sample should have log2 values of 0 on X and male_reference ( bool) – Whether a male reference copy number profile was used to normalize.Y, separately shifting for assumed male and female chromosomal sex.Ĭompare the chi-squared values obtained to infer whether the male orįemale assumption fits the data better. Perform 4 Mood’s median tests of the log2 coverages on chromosomes X and Otherwise, applyĬompare_sex_chromosomes ( male_reference=False, skip_low=False ) ¶Ĭompare coverage ratios of sex chromosomes versus autosomes. ![]() Of uneven targeting or extreme aneuploidy. by_chrom ( bool) – If True, first apply estimator to each chromosome separately, thenĪpply estimator to the per-chromosome values, to reduce the impact.skip_low ( bool) – Whether to drop very-low-coverage bins (via drop_low_coverage).‘mean’, ‘median’, ‘mode’, ‘biweight’ (for biweight location). ![]() estimator ( str or callable) – Function to estimate central tendency.Re-center log2 values to the autosomes’ average (in-place). Tuple – Pairs of: (gene name, CNA of rows with same name)Ĭenter_all ( estimator=.median>, by_chrom=True, skip_low=False, verbose=False ) ¶ These bins will still retain their name in the output. ![]() Grouping these bins with the surrounding gene or intergenic region. Ignore ( list or tuple of str) – Gene names to treat as “Antitarget” bins instead of real genes, “Antitarget” bins within a gene are grouped with that gene.īins’ gene names are split on commas to accommodate overlapping genesĪnd bins that cover multiple genes. Group each series of intergenic bins as an “Antitarget” gene any Iterate over probes grouped by gene name. Optional columns: gc, rmask, spread, weight, probes by_gene ( ignore=('-', '.', 'CGH') ) ¶ Required columns: chromosome, start, end, gene, log2 CopyNumArray ( data_table, meta_dict=None ) ¶Īn array of genomic intervals, treated like aCGH probes. CNVkit’s core data structure, a copy number array.
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