common.py 文件源码

def calculate_plate_summaries(self):
        """Get mean reads, percent mapping, etc summaries for each plate"""
        well_map = self.cell_metadata.groupby(Plates.SAMPLE_MAPPING)

        # these stats are from STAR mapping
        star_cols = ['Number of input reads', 'Uniquely mapped reads number']
        star_stats = self.mapping_stats[star_cols].groupby(
                self.cell_metadata[Plates.SAMPLE_MAPPING]).sum()

        total_reads = star_stats['Number of input reads']
        unique_reads = star_stats['Uniquely mapped reads number']

        percent_ercc = well_map.sum()['ercc'].divide(total_reads, axis=0)
        percent_mapped_reads = unique_reads / total_reads - percent_ercc

        plate_summaries = pd.DataFrame(OrderedDict([
            (Plates.MEAN_READS_PER_CELL, total_reads / well_map.size()),
            (Plates.MEDIAN_GENES_PER_CELL, well_map.median()['n_genes']),
            ('Percent not uniquely aligned', 100 * well_map.sum()['alignment_not_unique'].divide(total_reads, axis=0)),
            (Plates.PERCENT_MAPPED_READS, 100 * percent_mapped_reads),
            ('Percent no feature', 100 * well_map.sum()['no_feature'].divide(total_reads, axis=0)),
            ('Percent Rn45s', 100 * self.genes['Rn45s'].groupby(
                    self.cell_metadata[Plates.SAMPLE_MAPPING]).sum() / total_reads),
            (Plates.PERCENT_ERCC, 100 * percent_ercc),
            ('n_wells', well_map.size())
        ]))

        return plate_summaries