Index

A

add_cum_mid() (in module cns.process.aggregation) add_cytoband_legend() (in module cns.analyze.plot) add_gap_legend() (in module cns.analyze.plot) add_mid() (in module cns.process.aggregation) add_missing() (in module cns.process.imputation) add_tails() (in module cns.process.imputation)

add_total_cn() (in module cns.process.aggregation) aggregate_by_break_type() (in module cns.process.aggregation) aggregate_by_breaks() (in module cns.process.aggregation) aggregate_by_segments() (in module cns.process.aggregation) align_segs_to_assembly() (in module cns.process.segments) Assembly (class in cns.utils.assemblies)

B

bins_to_features() (in module cns.utils.conversions)

breaks_to_segments() (in module cns.utils.conversions)

C

calc_ane_bases() (in module cns.analyze.aneuploidy) calc_angles() (in module cns.utils.anomaly) calc_angles_cons() (in module cns.utils.anomaly) calc_breaks_per_chr() (in module cns.analyze.breakage) calc_breaks_per_sample() (in module cns.analyze.breakage) calc_chrom_mean() (in module cns.analyze.aneuploidy) calc_chrom_var() (in module cns.analyze.aneuploidy) calc_cum_mid() (in module cns.utils.conversions) calc_imb_bases() (in module cns.analyze.aneuploidy) calc_lengths() (in module cns.utils.conversions) calc_loh_bases() (in module cns.analyze.aneuploidy) calc_mid() (in module cns.utils.conversions) calc_nan_cols() (in module cns.utils.conversions) calc_ploidy_per_column() (in module cns.analyze.aneuploidy) calc_step_per_chr() (in module cns.analyze.breakage) calc_step_per_sample() (in module cns.analyze.breakage) calculate_signed_angle() (in module cns.utils.anomaly) canonize_chroms() (in module cns.utils.canonization) canonize_cns_df() (in module cns.utils.canonization) canonize_name() (in module cns.utils.canonization) canonize_positions() (in module cns.utils.canonization) canonize_sample_id() (in module cns.utils.canonization) chr_lens (cns.utils.assemblies.Assembly attribute) chr_x (cns.utils.assemblies.Assembly attribute) chr_y (cns.utils.assemblies.Assembly attribute) chrom_to_sortable() (in module cns.utils.conversions) cluster_segments() (in module cns.process.clustering) cn_not_nan() (in module cns.utils.selection) cns.analyze.aneuploidy module cns.analyze.breakage module cns.analyze.coverage module cns.analyze.plot module cns.data_utils module

cns.pipelines module cns.process.aggregation module cns.process.breakpoints module cns.process.clustering module cns.process.imputation module cns.process.normalize module cns.process.segments module cns.utils.anomaly module cns.utils.assemblies module cns.utils.canonization module cns.utils.conversions module cns.utils.files module cns.utils.logging module cns.utils.selection module cns_df_to_segments() (in module cns.process.segments) cns_head() (in module cns.utils.selection) cns_infer() (in module cns.process.imputation) cns_random() (in module cns.utils.selection) cns_tail() (in module cns.utils.selection) count_below_lim() (in module cns.utils.anomaly) count_cum_val() (in module cns.utils.anomaly) count_segments() (in module cns.process.segments) cytobands (cns.utils.assemblies.Assembly attribute) cytobands_to_df() (in module cns.utils.conversions)

D

dataframe_array_split() (in module cns.utils.selection)

do_segments_overlap() (in module cns.process.segments) drop_Y() (in module cns.utils.selection)

F

fig_bars() (in module cns.analyze.plot) fig_dots() (in module cns.analyze.plot) fig_heatmap() (in module cns.analyze.plot) fig_lines() (in module cns.analyze.plot) fig_steps() (in module cns.analyze.plot) fill_gaps() (in module cns.process.imputation)

fill_nans_with_zeros() (in module cns.process.imputation) fill_sex_if_missing() (in module cns.utils.files) filter_cons_size() (in module cns.process.segments) filter_min_size() (in module cns.process.segments) find_bends() (in module cns.utils.anomaly) find_knee() (in module cns.utils.anomaly) find_overlaps() (in module cns.process.segments)

G

gaps (cns.utils.assemblies.Assembly attribute) gaps_to_df() (in module cns.utils.conversions) genome_to_segments() (in module cns.utils.conversions) get_assembly() (in module cns.utils.assemblies) get_chr_sets() (in module cns.utils.selection) get_cn_cols() (in module cns.utils.canonization)

get_consecutive_segs() (in module cns.process.segments) get_covered_bases() (in module cns.analyze.coverage) get_missing_chroms() (in module cns.analyze.coverage) get_norm_sizes() (in module cns.process.normalize) get_root_path() (in module cns.data_utils) group_samples() (in module cns.process.aggregation)

H

hg19 (in module cns.utils.assemblies)

hg38 (in module cns.utils.assemblies)

L

load_cns() (in module cns.utils.files) load_cns_file() (in module cns.data_utils) load_COSMIC() (in module cns.data_utils) load_ENSEMBL() (in module cns.data_utils) load_samples() (in module cns.utils.files)

load_samples_file() (in module cns.data_utils) load_segments() (in module cns.utils.files) load_segs_out() (in module cns.data_utils) log_info() (in module cns.utils.logging) log_warn() (in module cns.utils.logging)

M

main_aggregate() (in module cns.pipelines) main_align() (in module cns.pipelines) main_breakage() (in module cns.pipelines) main_coverage() (in module cns.pipelines) main_impute() (in module cns.pipelines) main_infer() (in module cns.pipelines) main_load() (in module cns.data_utils) main_ploidy() (in module cns.pipelines) main_seg_agg() (in module cns.pipelines) main_segment() (in module cns.pipelines) make_breaks() (in module cns.process.breakpoints) make_segments() (in module cns.process.segments) mean_value_per_seg() (in module cns.process.aggregation) merge_cns_df() (in module cns.process.imputation) merge_segments() (in module cns.process.segments) module cns.analyze.aneuploidy cns.analyze.breakage cns.analyze.coverage cns.analyze.plot cns.data_utils cns.pipelines cns.process.aggregation cns.process.breakpoints cns.process.clustering cns.process.imputation cns.process.normalize cns.process.segments cns.utils.anomaly cns.utils.assemblies cns.utils.canonization cns.utils.conversions cns.utils.files cns.utils.logging cns.utils.selection

N

name (cns.utils.assemblies.Assembly attribute) no_x_ticks() (in module cns.analyze.plot)

no_y_ticks() (in module cns.analyze.plot) normalize_feature() (in module cns.process.normalize)

O

obtain_segments() (in module cns.utils.files)

only_aut() (in module cns.utils.selection) only_sex() (in module cns.utils.selection)

P

plot_bars() (in module cns.analyze.plot) plot_chr_bg() (in module cns.analyze.plot) plot_cytobands() (in module cns.analyze.plot) plot_dots() (in module cns.analyze.plot) plot_gaps() (in module cns.analyze.plot)

plot_heatmap() (in module cns.analyze.plot) plot_lines() (in module cns.analyze.plot) plot_steps() (in module cns.analyze.plot) plot_x_lines() (in module cns.analyze.plot) plot_x_ticks() (in module cns.analyze.plot)

R

remove_outliers() (in module cns.process.imputation)

rename_cn_cols() (in module cns.utils.canonization)

S

sample_head() (in module cns.utils.selection) sample_random() (in module cns.utils.selection) sample_tail() (in module cns.utils.selection) samples_above_threshold() (in module cns.data_utils) samples_df_from_cns_df() (in module cns.utils.files) save_cns() (in module cns.utils.files) save_samples() (in module cns.utils.files) save_segments() (in module cns.utils.files) segment_difference() (in module cns.process.segments)

segment_union() (in module cns.process.segments) segments_to_breaks() (in module cns.utils.conversions) segments_to_cns_df() (in module cns.utils.conversions) select_cns_by_type() (in module cns.utils.selection) select_CNS_samples() (in module cns.utils.selection) sortable_to_chrom() (in module cns.utils.conversions) split_into_bins() (in module cns.process.breakpoints) split_segment() (in module cns.process.segments) split_segments() (in module cns.process.segments) stack_groups() (in module cns.process.aggregation)

T

tuples_to_segments() (in module cns.utils.conversions)

V

values_count() (in module cns.utils.conversions)

X

x_limits() (in module cns.analyze.plot)

Y

y_limits() (in module cns.analyze.plot)

Z

z_score_filter() (in module cns.utils.anomaly)