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malariagen_data.af1.Af1.gene_cnv#

-Af1.gene_cnv(region: Annotated[str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], '\n    Region of the reference genome. Can be a contig name, region string\n    (formatted like "{contig}:{start}-{end}"), or identifier of a genome\n    feature such as a gene or transcript. Can also be a sequence (e.g., list)\n    of regions.\n    '], sample_sets=None, sample_query=None, sample_query_options=None, max_coverage_variance=0.2, chunks: Annotated[int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]], "\n    Define how input data being read from zarr should be divided into chunks\n    for a dask computation. If 'native', use underlying zarr chunks. If a string\n    specifying a target memory size, e.g., '300 MiB', resize chunks in arrays\n    with more than one dimension to match this size. If 'auto', let dask decide\n    chunk size.  If 'ndauto', let dask decide chunk size but only for arrays with\n    more than one dimension. If 'ndauto0', as 'ndauto' but only vary the first\n    chunk dimension. If 'ndauto1', as 'ndauto' but only vary the second chunk\n    dimension. If 'ndauto01', as 'ndauto' but only vary the first and second\n    chunk dimensions. Also, can be a tuple of integers, or a callable which\n    accepts the native chunks as a single argument and returns a valid dask\n    chunks value.\n    "] = 'native', inline_array: Annotated[bool, 'Passed through to dask `from_array()`.'] = True)#
+Af1.gene_cnv(region: str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], sample_sets: Sequence[str] | str | None = None, sample_query: str | None = None, sample_query_options: dict | None = None, max_coverage_variance: float | None = 0.2, chunks: int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]] = 'native', inline_array: bool = True) → Dataset#

Compute modal copy number by gene, from HMM data.

Parameters#

-
region: str or list of str or Region or list of Region

Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic -region defined with coordinates (e.g., “2L:44989425-44998059”) or a -named tuple with genomic location Region(contig, start, end). -Multiple values can be provided as a list, in which case data will -be concatenated, e.g., [“3R”, “3L”].

+
regionstr or Region or Mapping or list of str or Region or Mapping or tuple of str or Region or Mapping

Region of the reference genome. Can be a contig name, region string +(formatted like “{contig}:{start}-{end}”), or identifier of a genome +feature such as a gene or transcript. Can also be a sequence (e.g., +list) of regions.

-
sample_setsstr or list of str

Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of -sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or -a release identifier (e.g., “3.0”) or a list of release identifiers.

+
sample_setssequence of str or str or None, optional

List of sample sets and/or releases. Can also be a single sample set +or release.

-
sample_querystr, optional

A pandas query string which will be evaluated against the sample -metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.

+
sample_querystr or None, optional

A pandas query string to be evaluated against the sample metadata, to +select samples to be included in the returned data.

-
sample_query_optionsdict, optional

A dictionary of arguments that will be passed through to pandas query() or -eval(), e.g. parser, engine, local_dict, global_dict, resolvers.

+
sample_query_optionsdict or None, optional

A dictionary of arguments that will be passed through to pandas +query() or eval(), e.g. parser, engine, local_dict, global_dict, +resolvers.

-
max_coverage_variancefloat, optional

Remove samples if coverage variance exceeds this value.

+
max_coverage_variancefloat or None, optional, default: 0.2

Remove samples if coverage variance exceeds this value.

+
+
chunksint or str or tuple of int or str or Callable[[typing.Tuple[int, …]], int or str or tuple of int or str], optional, default: ‘native’

Define how input data being read from zarr should be divided into +chunks for a dask computation. If ‘native’, use underlying zarr +chunks. If a string specifying a target memory size, e.g., ‘300 MiB’, +resize chunks in arrays with more than one dimension to match this +size. If ‘auto’, let dask decide chunk size. If ‘ndauto’, let dask +decide chunk size but only for arrays with more than one dimension. If +‘ndauto0’, as ‘ndauto’ but only vary the first chunk dimension. If +‘ndauto1’, as ‘ndauto’ but only vary the second chunk dimension. If +‘ndauto01’, as ‘ndauto’ but only vary the first and second chunk +dimensions. Also, can be a tuple of integers, or a callable which +accepts the native chunks as a single argument and returns a valid +dask chunks value.

+
+
inline_arraybool, optional, default: True

Passed through to dask from_array().

Returns#

-
dsxarray.Dataset

A dataset of modal copy number per gene and associated data.

+
Dataset

A dataset of modal copy number per gene and associated data.

diff --git a/latest/generated/malariagen_data.af1.Af1.gene_cnv_frequencies.html b/latest/generated/malariagen_data.af1.Af1.gene_cnv_frequencies.html index a50e5067d..ae0e09e56 100644 --- a/latest/generated/malariagen_data.af1.Af1.gene_cnv_frequencies.html +++ b/latest/generated/malariagen_data.af1.Af1.gene_cnv_frequencies.html @@ -367,46 +367,62 @@

malariagen_data.af1.Af1.gene_cnv_frequencies#

-Af1.gene_cnv_frequencies(region: Annotated[str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], '\n    Region of the reference genome. Can be a contig name, region string\n    (formatted like "{contig}:{start}-{end}"), or identifier of a genome\n    feature such as a gene or transcript. Can also be a sequence (e.g., list)\n    of regions.\n    '], cohorts, sample_query=None, sample_query_options=None, min_cohort_size=10, sample_sets=None, drop_invariant=True, max_coverage_variance=0.2, chunks: Annotated[int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]], "\n    Define how input data being read from zarr should be divided into chunks\n    for a dask computation. If 'native', use underlying zarr chunks. If a string\n    specifying a target memory size, e.g., '300 MiB', resize chunks in arrays\n    with more than one dimension to match this size. If 'auto', let dask decide\n    chunk size.  If 'ndauto', let dask decide chunk size but only for arrays with\n    more than one dimension. If 'ndauto0', as 'ndauto' but only vary the first\n    chunk dimension. If 'ndauto1', as 'ndauto' but only vary the second chunk\n    dimension. If 'ndauto01', as 'ndauto' but only vary the first and second\n    chunk dimensions. Also, can be a tuple of integers, or a callable which\n    accepts the native chunks as a single argument and returns a valid dask\n    chunks value.\n    "] = 'native', inline_array: Annotated[bool, 'Passed through to dask `from_array()`.'] = True)#
+Af1.gene_cnv_frequencies(region: str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], cohorts: str | Mapping[str, str], sample_query: str | None = None, sample_query_options: dict | None = None, min_cohort_size: int = 10, max_coverage_variance: float | None = 0.2, sample_sets: Sequence[str] | str | None = None, drop_invariant: bool = True, include_counts: bool = False, chunks: int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]] = 'native', inline_array: bool = True) → DataFrame#

Compute modal copy number by gene, then compute the frequency of amplifications and deletions in one or more cohorts, from HMM data.

Parameters#

-
region: str or list of str or Region or list of Region

Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic -region defined with coordinates (e.g., “2L:44989425-44998059”) or a -named tuple with genomic location Region(contig, start, end). -Multiple values can be provided as a list, in which case data will -be concatenated, e.g., [“3R”, “3L”].

+
regionstr or Region or Mapping or list of str or Region or Mapping or tuple of str or Region or Mapping

Region of the reference genome. Can be a contig name, region string +(formatted like “{contig}:{start}-{end}”), or identifier of a genome +feature such as a gene or transcript. Can also be a sequence (e.g., +list) of regions.

-
cohortsstr or dict

If a string, gives the name of a predefined cohort set, e.g., one of -{“admin1_month”, “admin1_year”, “admin2_month”, “admin2_year”}. -If a dict, should map cohort labels to sample queries, e.g., -{"bf_2012_col": "country == 'Burkina Faso' and year == 2012 and -taxon == 'coluzzii'"}.

+
cohortsstr or Mapping[str, str]

Either a string giving the name of a predefined cohort set (e.g., +“admin1_month”) or a dict mapping custom cohort labels to sample +queries.

-
sample_querystr, optional

A pandas query string which will be evaluated against the sample -metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.

+
sample_querystr or None, optional

A pandas query string to be evaluated against the sample metadata, to +select samples to be included in the returned data.

-
sample_query_optionsdict, optional

A dictionary of arguments that will be passed through to pandas query() or -eval(), e.g. parser, engine, local_dict, global_dict, resolvers.

+
sample_query_optionsdict or None, optional

A dictionary of arguments that will be passed through to pandas +query() or eval(), e.g. parser, engine, local_dict, global_dict, +resolvers.

-
min_cohort_sizeint

Minimum cohort size, below which cohorts are dropped.

+
min_cohort_sizeint, optional, default: 10

Minimum cohort size. Raise an error if the number of samples is less +than this value.

-
sample_setsstr or list of str, optional

Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of -sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a -release identifier (e.g., “3.0”) or a list of release identifiers.

+
max_coverage_variancefloat or None, optional, default: 0.2

Remove samples if coverage variance exceeds this value.

-
drop_invariantbool, optional

If True, drop any rows where there is no evidence of variation.

+
sample_setssequence of str or str or None, optional

List of sample sets and/or releases. Can also be a single sample set +or release.

-
max_coverage_variancefloat, optional

Remove samples if coverage variance exceeds this value.

+
drop_invariantbool, optional, default: True

If True, drop variants not observed in the selected samples.

+
+
include_countsbool, optional, default: False

Include columns with allele counts and number of non-missing allele +calls (nobs).

+
+
chunksint or str or tuple of int or str or Callable[[typing.Tuple[int, …]], int or str or tuple of int or str], optional, default: ‘native’

Define how input data being read from zarr should be divided into +chunks for a dask computation. If ‘native’, use underlying zarr +chunks. If a string specifying a target memory size, e.g., ‘300 MiB’, +resize chunks in arrays with more than one dimension to match this +size. If ‘auto’, let dask decide chunk size. If ‘ndauto’, let dask +decide chunk size but only for arrays with more than one dimension. If +‘ndauto0’, as ‘ndauto’ but only vary the first chunk dimension. If +‘ndauto1’, as ‘ndauto’ but only vary the second chunk dimension. If +‘ndauto01’, as ‘ndauto’ but only vary the first and second chunk +dimensions. Also, can be a tuple of integers, or a callable which +accepts the native chunks as a single argument and returns a valid +dask chunks value.

+
+
inline_arraybool, optional, default: True

Passed through to dask from_array().

Returns#

-
dfpandas.DataFrame

A dataframe of CNV amplification (amp) and deletion (del) +

DataFrame

A dataframe of CNV amplification (amp) and deletion (del) frequencies in the specified cohorts, one row per gene and CNV type (amp/del).

diff --git a/latest/generated/malariagen_data.af1.Af1.gene_cnv_frequencies_advanced.html b/latest/generated/malariagen_data.af1.Af1.gene_cnv_frequencies_advanced.html index 2217a6bcf..c7fcd61e8 100644 --- a/latest/generated/malariagen_data.af1.Af1.gene_cnv_frequencies_advanced.html +++ b/latest/generated/malariagen_data.af1.Af1.gene_cnv_frequencies_advanced.html @@ -367,52 +367,72 @@

malariagen_data.af1.Af1.gene_cnv_frequencies_advanced#

-Af1.gene_cnv_frequencies_advanced(region: Annotated[str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], '\n    Region of the reference genome. Can be a contig name, region string\n    (formatted like "{contig}:{start}-{end}"), or identifier of a genome\n    feature such as a gene or transcript. Can also be a sequence (e.g., list)\n    of regions.\n    '], area_by, period_by, sample_sets=None, sample_query=None, sample_query_options=None, min_cohort_size=10, variant_query=None, drop_invariant=True, max_coverage_variance=0.2, ci_method='wilson', chunks: Annotated[int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]], "\n    Define how input data being read from zarr should be divided into chunks\n    for a dask computation. If 'native', use underlying zarr chunks. If a string\n    specifying a target memory size, e.g., '300 MiB', resize chunks in arrays\n    with more than one dimension to match this size. If 'auto', let dask decide\n    chunk size.  If 'ndauto', let dask decide chunk size but only for arrays with\n    more than one dimension. If 'ndauto0', as 'ndauto' but only vary the first\n    chunk dimension. If 'ndauto1', as 'ndauto' but only vary the second chunk\n    dimension. If 'ndauto01', as 'ndauto' but only vary the first and second\n    chunk dimensions. Also, can be a tuple of integers, or a callable which\n    accepts the native chunks as a single argument and returns a valid dask\n    chunks value.\n    "] = 'native', inline_array: Annotated[bool, 'Passed through to dask `from_array()`.'] = True)#
+Af1.gene_cnv_frequencies_advanced(region: str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], area_by: str, period_by: Literal['year', 'quarter', 'month'], sample_sets: Sequence[str] | str | None = None, sample_query: str | None = None, sample_query_options: dict | None = None, min_cohort_size: int = 10, drop_invariant: bool = True, variant_query: str | None = None, max_coverage_variance: float | None = 0.2, nobs_mode: Literal['called', 'fixed'] = 'called', ci_method: Literal['normal', 'agresti_coull', 'beta', 'wilson', 'binom_test'] | None = 'wilson', chunks: int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]] = 'native', inline_array: bool = True) → Dataset#

Group samples by taxon, area (space) and period (time), then compute gene CNV counts and frequencies.

Parameters#

-
region: str or list of str or Region or list of Region

Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic -region defined with coordinates (e.g., “2L:44989425-44998059”) or a -named tuple with genomic location Region(contig, start, end). -Multiple values can be provided as a list, in which case data will -be concatenated, e.g., [“3R”, “3L”].

+
regionstr or Region or Mapping or list of str or Region or Mapping or tuple of str or Region or Mapping

Region of the reference genome. Can be a contig name, region string +(formatted like “{contig}:{start}-{end}”), or identifier of a genome +feature such as a gene or transcript. Can also be a sequence (e.g., +list) of regions.

area_bystr

Column name in the sample metadata to use to group samples spatially. E.g., use “admin1_iso” or “admin1_name” to group by level 1 administrative divisions, or use “admin2_name” to group by level 2 administrative divisions.

-
period_by{“year”, “quarter”, “month”}

Length of time to group samples temporally.

+
period_by{‘year’, ‘quarter’, ‘month’}

Length of time to group samples temporally.

-
sample_setsstr or list of str, optional

Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of -sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a -release identifier (e.g., “3.0”) or a list of release identifiers.

+
sample_setssequence of str or str or None, optional

List of sample sets and/or releases. Can also be a single sample set +or release.

-
sample_querystr, optional

A pandas query string which will be evaluated against the sample -metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.

+
sample_querystr or None, optional

A pandas query string to be evaluated against the sample metadata, to +select samples to be included in the returned data.

-
sample_query_optionsdict, optional

A dictionary of arguments that will be passed through to pandas query() or -eval(), e.g. parser, engine, local_dict, global_dict, resolvers.

+
sample_query_optionsdict or None, optional

A dictionary of arguments that will be passed through to pandas +query() or eval(), e.g. parser, engine, local_dict, global_dict, +resolvers.

-
min_cohort_sizeint, optional

Minimum cohort size. Any cohorts below this size are omitted.

+
min_cohort_sizeint, optional, default: 10

Minimum cohort size. Raise an error if the number of samples is less +than this value.

-
variant_querystr, optional

A pandas query string which will be evaluated against variants.

+
drop_invariantbool, optional, default: True

If True, drop variants not observed in the selected samples.

-
drop_invariantbool, optional

If True, drop any rows where there is no evidence of variation.

+
variant_querystr or None, optional

A pandas query to be evaluated against variants.

-
max_coverage_variancefloat, optional

Remove samples if coverage variance exceeds this value.

+
max_coverage_variancefloat or None, optional, default: 0.2

Remove samples if coverage variance exceeds this value.

-
ci_method{“normal”, “agresti_coull”, “beta”, “wilson”, “binom_test”}, optional

Method to use for computing confidence intervals, passed through to +

nobs_mode{‘called’, ‘fixed’}, optional, default: ‘called’

Method for calculating the denominator when computing frequencies. If +“called” then use the number of called alleles, i.e., number of +samples with non-missing genotype calls multiplied by 2. If “fixed” +then use the number of samples multiplied by 2.

+
+
ci_method{‘normal’, ‘agresti_coull’, ‘beta’, ‘wilson’, ‘binom_test’} or None, optional, default: ‘wilson’

Method to use for computing confidence intervals, passed through to statsmodels.stats.proportion.proportion_confint.

+
chunksint or str or tuple of int or str or Callable[[typing.Tuple[int, …]], int or str or tuple of int or str], optional, default: ‘native’

Define how input data being read from zarr should be divided into +chunks for a dask computation. If ‘native’, use underlying zarr +chunks. If a string specifying a target memory size, e.g., ‘300 MiB’, +resize chunks in arrays with more than one dimension to match this +size. If ‘auto’, let dask decide chunk size. If ‘ndauto’, let dask +decide chunk size but only for arrays with more than one dimension. If +‘ndauto0’, as ‘ndauto’ but only vary the first chunk dimension. If +‘ndauto1’, as ‘ndauto’ but only vary the second chunk dimension. If +‘ndauto01’, as ‘ndauto’ but only vary the first and second chunk +dimensions. Also, can be a tuple of integers, or a callable which +accepts the native chunks as a single argument and returns a valid +dask chunks value.

+
+
inline_arraybool, optional, default: True

Passed through to dask from_array().

+

Returns#

-
dsxarray.Dataset

The resulting dataset contains data has dimensions “cohorts” and +

Dataset

The resulting dataset contains data has dimensions “cohorts” and “variants”. Variables prefixed with “cohort” are 1-dimensional arrays with data about the cohorts, such as the area, period, taxon and cohort size. Variables prefixed with “variant” are 1-dimensional diff --git a/latest/generated/malariagen_data.ag3.Ag3.gene_cnv.html b/latest/generated/malariagen_data.ag3.Ag3.gene_cnv.html index ce489335f..eb5fa3d72 100644 --- a/latest/generated/malariagen_data.ag3.Ag3.gene_cnv.html +++ b/latest/generated/malariagen_data.ag3.Ag3.gene_cnv.html @@ -367,35 +367,49 @@

malariagen_data.ag3.Ag3.gene_cnv#

-Ag3.gene_cnv(region: Annotated[str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], '\n    Region of the reference genome. Can be a contig name, region string\n    (formatted like "{contig}:{start}-{end}"), or identifier of a genome\n    feature such as a gene or transcript. Can also be a sequence (e.g., list)\n    of regions.\n    '], sample_sets=None, sample_query=None, sample_query_options=None, max_coverage_variance=0.2, chunks: Annotated[int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]], "\n    Define how input data being read from zarr should be divided into chunks\n    for a dask computation. If 'native', use underlying zarr chunks. If a string\n    specifying a target memory size, e.g., '300 MiB', resize chunks in arrays\n    with more than one dimension to match this size. If 'auto', let dask decide\n    chunk size.  If 'ndauto', let dask decide chunk size but only for arrays with\n    more than one dimension. If 'ndauto0', as 'ndauto' but only vary the first\n    chunk dimension. If 'ndauto1', as 'ndauto' but only vary the second chunk\n    dimension. If 'ndauto01', as 'ndauto' but only vary the first and second\n    chunk dimensions. Also, can be a tuple of integers, or a callable which\n    accepts the native chunks as a single argument and returns a valid dask\n    chunks value.\n    "] = 'native', inline_array: Annotated[bool, 'Passed through to dask `from_array()`.'] = True)#
+Ag3.gene_cnv(region: str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], sample_sets: Sequence[str] | str | None = None, sample_query: str | None = None, sample_query_options: dict | None = None, max_coverage_variance: float | None = 0.2, chunks: int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]] = 'native', inline_array: bool = True) → Dataset#

Compute modal copy number by gene, from HMM data.

Parameters#

-
region: str or list of str or Region or list of Region

Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic -region defined with coordinates (e.g., “2L:44989425-44998059”) or a -named tuple with genomic location Region(contig, start, end). -Multiple values can be provided as a list, in which case data will -be concatenated, e.g., [“3R”, “3L”].

+
regionstr or Region or Mapping or list of str or Region or Mapping or tuple of str or Region or Mapping

Region of the reference genome. Can be a contig name, region string +(formatted like “{contig}:{start}-{end}”), or identifier of a genome +feature such as a gene or transcript. Can also be a sequence (e.g., +list) of regions.

-
sample_setsstr or list of str

Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of -sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or -a release identifier (e.g., “3.0”) or a list of release identifiers.

+
sample_setssequence of str or str or None, optional

List of sample sets and/or releases. Can also be a single sample set +or release.

-
sample_querystr, optional

A pandas query string which will be evaluated against the sample -metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.

+
sample_querystr or None, optional

A pandas query string to be evaluated against the sample metadata, to +select samples to be included in the returned data.

-
sample_query_optionsdict, optional

A dictionary of arguments that will be passed through to pandas query() or -eval(), e.g. parser, engine, local_dict, global_dict, resolvers.

+
sample_query_optionsdict or None, optional

A dictionary of arguments that will be passed through to pandas +query() or eval(), e.g. parser, engine, local_dict, global_dict, +resolvers.

-
max_coverage_variancefloat, optional

Remove samples if coverage variance exceeds this value.

+
max_coverage_variancefloat or None, optional, default: 0.2

Remove samples if coverage variance exceeds this value.

+
+
chunksint or str or tuple of int or str or Callable[[typing.Tuple[int, …]], int or str or tuple of int or str], optional, default: ‘native’

Define how input data being read from zarr should be divided into +chunks for a dask computation. If ‘native’, use underlying zarr +chunks. If a string specifying a target memory size, e.g., ‘300 MiB’, +resize chunks in arrays with more than one dimension to match this +size. If ‘auto’, let dask decide chunk size. If ‘ndauto’, let dask +decide chunk size but only for arrays with more than one dimension. If +‘ndauto0’, as ‘ndauto’ but only vary the first chunk dimension. If +‘ndauto1’, as ‘ndauto’ but only vary the second chunk dimension. If +‘ndauto01’, as ‘ndauto’ but only vary the first and second chunk +dimensions. Also, can be a tuple of integers, or a callable which +accepts the native chunks as a single argument and returns a valid +dask chunks value.

+
+
inline_arraybool, optional, default: True

Passed through to dask from_array().

Returns#

-
dsxarray.Dataset

A dataset of modal copy number per gene and associated data.

+
Dataset

A dataset of modal copy number per gene and associated data.

diff --git a/latest/generated/malariagen_data.ag3.Ag3.gene_cnv_frequencies.html b/latest/generated/malariagen_data.ag3.Ag3.gene_cnv_frequencies.html index ad61a6e6d..410d11750 100644 --- a/latest/generated/malariagen_data.ag3.Ag3.gene_cnv_frequencies.html +++ b/latest/generated/malariagen_data.ag3.Ag3.gene_cnv_frequencies.html @@ -367,46 +367,62 @@

malariagen_data.ag3.Ag3.gene_cnv_frequencies#

-Ag3.gene_cnv_frequencies(region: Annotated[str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], '\n    Region of the reference genome. Can be a contig name, region string\n    (formatted like "{contig}:{start}-{end}"), or identifier of a genome\n    feature such as a gene or transcript. Can also be a sequence (e.g., list)\n    of regions.\n    '], cohorts, sample_query=None, sample_query_options=None, min_cohort_size=10, sample_sets=None, drop_invariant=True, max_coverage_variance=0.2, chunks: Annotated[int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]], "\n    Define how input data being read from zarr should be divided into chunks\n    for a dask computation. If 'native', use underlying zarr chunks. If a string\n    specifying a target memory size, e.g., '300 MiB', resize chunks in arrays\n    with more than one dimension to match this size. If 'auto', let dask decide\n    chunk size.  If 'ndauto', let dask decide chunk size but only for arrays with\n    more than one dimension. If 'ndauto0', as 'ndauto' but only vary the first\n    chunk dimension. If 'ndauto1', as 'ndauto' but only vary the second chunk\n    dimension. If 'ndauto01', as 'ndauto' but only vary the first and second\n    chunk dimensions. Also, can be a tuple of integers, or a callable which\n    accepts the native chunks as a single argument and returns a valid dask\n    chunks value.\n    "] = 'native', inline_array: Annotated[bool, 'Passed through to dask `from_array()`.'] = True)#
+Ag3.gene_cnv_frequencies(region: str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], cohorts: str | Mapping[str, str], sample_query: str | None = None, sample_query_options: dict | None = None, min_cohort_size: int = 10, max_coverage_variance: float | None = 0.2, sample_sets: Sequence[str] | str | None = None, drop_invariant: bool = True, include_counts: bool = False, chunks: int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]] = 'native', inline_array: bool = True) → DataFrame#

Compute modal copy number by gene, then compute the frequency of amplifications and deletions in one or more cohorts, from HMM data.

Parameters#

-
region: str or list of str or Region or list of Region

Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic -region defined with coordinates (e.g., “2L:44989425-44998059”) or a -named tuple with genomic location Region(contig, start, end). -Multiple values can be provided as a list, in which case data will -be concatenated, e.g., [“3R”, “3L”].

+
regionstr or Region or Mapping or list of str or Region or Mapping or tuple of str or Region or Mapping

Region of the reference genome. Can be a contig name, region string +(formatted like “{contig}:{start}-{end}”), or identifier of a genome +feature such as a gene or transcript. Can also be a sequence (e.g., +list) of regions.

-
cohortsstr or dict

If a string, gives the name of a predefined cohort set, e.g., one of -{“admin1_month”, “admin1_year”, “admin2_month”, “admin2_year”}. -If a dict, should map cohort labels to sample queries, e.g., -{"bf_2012_col": "country == 'Burkina Faso' and year == 2012 and -taxon == 'coluzzii'"}.

+
cohortsstr or Mapping[str, str]

Either a string giving the name of a predefined cohort set (e.g., +“admin1_month”) or a dict mapping custom cohort labels to sample +queries.

-
sample_querystr, optional

A pandas query string which will be evaluated against the sample -metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.

+
sample_querystr or None, optional

A pandas query string to be evaluated against the sample metadata, to +select samples to be included in the returned data.

-
sample_query_optionsdict, optional

A dictionary of arguments that will be passed through to pandas query() or -eval(), e.g. parser, engine, local_dict, global_dict, resolvers.

+
sample_query_optionsdict or None, optional

A dictionary of arguments that will be passed through to pandas +query() or eval(), e.g. parser, engine, local_dict, global_dict, +resolvers.

-
min_cohort_sizeint

Minimum cohort size, below which cohorts are dropped.

+
min_cohort_sizeint, optional, default: 10

Minimum cohort size. Raise an error if the number of samples is less +than this value.

-
sample_setsstr or list of str, optional

Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of -sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a -release identifier (e.g., “3.0”) or a list of release identifiers.

+
max_coverage_variancefloat or None, optional, default: 0.2

Remove samples if coverage variance exceeds this value.

-
drop_invariantbool, optional

If True, drop any rows where there is no evidence of variation.

+
sample_setssequence of str or str or None, optional

List of sample sets and/or releases. Can also be a single sample set +or release.

-
max_coverage_variancefloat, optional

Remove samples if coverage variance exceeds this value.

+
drop_invariantbool, optional, default: True

If True, drop variants not observed in the selected samples.

+
+
include_countsbool, optional, default: False

Include columns with allele counts and number of non-missing allele +calls (nobs).

+
+
chunksint or str or tuple of int or str or Callable[[typing.Tuple[int, …]], int or str or tuple of int or str], optional, default: ‘native’

Define how input data being read from zarr should be divided into +chunks for a dask computation. If ‘native’, use underlying zarr +chunks. If a string specifying a target memory size, e.g., ‘300 MiB’, +resize chunks in arrays with more than one dimension to match this +size. If ‘auto’, let dask decide chunk size. If ‘ndauto’, let dask +decide chunk size but only for arrays with more than one dimension. If +‘ndauto0’, as ‘ndauto’ but only vary the first chunk dimension. If +‘ndauto1’, as ‘ndauto’ but only vary the second chunk dimension. If +‘ndauto01’, as ‘ndauto’ but only vary the first and second chunk +dimensions. Also, can be a tuple of integers, or a callable which +accepts the native chunks as a single argument and returns a valid +dask chunks value.

+
+
inline_arraybool, optional, default: True

Passed through to dask from_array().

Returns#

-
dfpandas.DataFrame

A dataframe of CNV amplification (amp) and deletion (del) +

DataFrame

A dataframe of CNV amplification (amp) and deletion (del) frequencies in the specified cohorts, one row per gene and CNV type (amp/del).

diff --git a/latest/generated/malariagen_data.ag3.Ag3.gene_cnv_frequencies_advanced.html b/latest/generated/malariagen_data.ag3.Ag3.gene_cnv_frequencies_advanced.html index 68ad03e08..c53bbf456 100644 --- a/latest/generated/malariagen_data.ag3.Ag3.gene_cnv_frequencies_advanced.html +++ b/latest/generated/malariagen_data.ag3.Ag3.gene_cnv_frequencies_advanced.html @@ -367,52 +367,72 @@

malariagen_data.ag3.Ag3.gene_cnv_frequencies_advanced#

-Ag3.gene_cnv_frequencies_advanced(region: Annotated[str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], '\n    Region of the reference genome. Can be a contig name, region string\n    (formatted like "{contig}:{start}-{end}"), or identifier of a genome\n    feature such as a gene or transcript. Can also be a sequence (e.g., list)\n    of regions.\n    '], area_by, period_by, sample_sets=None, sample_query=None, sample_query_options=None, min_cohort_size=10, variant_query=None, drop_invariant=True, max_coverage_variance=0.2, ci_method='wilson', chunks: Annotated[int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]], "\n    Define how input data being read from zarr should be divided into chunks\n    for a dask computation. If 'native', use underlying zarr chunks. If a string\n    specifying a target memory size, e.g., '300 MiB', resize chunks in arrays\n    with more than one dimension to match this size. If 'auto', let dask decide\n    chunk size.  If 'ndauto', let dask decide chunk size but only for arrays with\n    more than one dimension. If 'ndauto0', as 'ndauto' but only vary the first\n    chunk dimension. If 'ndauto1', as 'ndauto' but only vary the second chunk\n    dimension. If 'ndauto01', as 'ndauto' but only vary the first and second\n    chunk dimensions. Also, can be a tuple of integers, or a callable which\n    accepts the native chunks as a single argument and returns a valid dask\n    chunks value.\n    "] = 'native', inline_array: Annotated[bool, 'Passed through to dask `from_array()`.'] = True)#
+Ag3.gene_cnv_frequencies_advanced(region: str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], area_by: str, period_by: Literal['year', 'quarter', 'month'], sample_sets: Sequence[str] | str | None = None, sample_query: str | None = None, sample_query_options: dict | None = None, min_cohort_size: int = 10, drop_invariant: bool = True, variant_query: str | None = None, max_coverage_variance: float | None = 0.2, nobs_mode: Literal['called', 'fixed'] = 'called', ci_method: Literal['normal', 'agresti_coull', 'beta', 'wilson', 'binom_test'] | None = 'wilson', chunks: int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]] = 'native', inline_array: bool = True) → Dataset#

Group samples by taxon, area (space) and period (time), then compute gene CNV counts and frequencies.

Parameters#

-
region: str or list of str or Region or list of Region

Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic -region defined with coordinates (e.g., “2L:44989425-44998059”) or a -named tuple with genomic location Region(contig, start, end). -Multiple values can be provided as a list, in which case data will -be concatenated, e.g., [“3R”, “3L”].

+
regionstr or Region or Mapping or list of str or Region or Mapping or tuple of str or Region or Mapping

Region of the reference genome. Can be a contig name, region string +(formatted like “{contig}:{start}-{end}”), or identifier of a genome +feature such as a gene or transcript. Can also be a sequence (e.g., +list) of regions.

area_bystr

Column name in the sample metadata to use to group samples spatially. E.g., use “admin1_iso” or “admin1_name” to group by level 1 administrative divisions, or use “admin2_name” to group by level 2 administrative divisions.

-
period_by{“year”, “quarter”, “month”}

Length of time to group samples temporally.

+
period_by{‘year’, ‘quarter’, ‘month’}

Length of time to group samples temporally.

-
sample_setsstr or list of str, optional

Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of -sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a -release identifier (e.g., “3.0”) or a list of release identifiers.

+
sample_setssequence of str or str or None, optional

List of sample sets and/or releases. Can also be a single sample set +or release.

-
sample_querystr, optional

A pandas query string which will be evaluated against the sample -metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.

+
sample_querystr or None, optional

A pandas query string to be evaluated against the sample metadata, to +select samples to be included in the returned data.

-
sample_query_optionsdict, optional

A dictionary of arguments that will be passed through to pandas query() or -eval(), e.g. parser, engine, local_dict, global_dict, resolvers.

+
sample_query_optionsdict or None, optional

A dictionary of arguments that will be passed through to pandas +query() or eval(), e.g. parser, engine, local_dict, global_dict, +resolvers.

-
min_cohort_sizeint, optional

Minimum cohort size. Any cohorts below this size are omitted.

+
min_cohort_sizeint, optional, default: 10

Minimum cohort size. Raise an error if the number of samples is less +than this value.

-
variant_querystr, optional

A pandas query string which will be evaluated against variants.

+
drop_invariantbool, optional, default: True

If True, drop variants not observed in the selected samples.

-
drop_invariantbool, optional

If True, drop any rows where there is no evidence of variation.

+
variant_querystr or None, optional

A pandas query to be evaluated against variants.

-
max_coverage_variancefloat, optional

Remove samples if coverage variance exceeds this value.

+
max_coverage_variancefloat or None, optional, default: 0.2

Remove samples if coverage variance exceeds this value.

-
ci_method{“normal”, “agresti_coull”, “beta”, “wilson”, “binom_test”}, optional

Method to use for computing confidence intervals, passed through to +

nobs_mode{‘called’, ‘fixed’}, optional, default: ‘called’

Method for calculating the denominator when computing frequencies. If +“called” then use the number of called alleles, i.e., number of +samples with non-missing genotype calls multiplied by 2. If “fixed” +then use the number of samples multiplied by 2.

+
+
ci_method{‘normal’, ‘agresti_coull’, ‘beta’, ‘wilson’, ‘binom_test’} or None, optional, default: ‘wilson’

Method to use for computing confidence intervals, passed through to statsmodels.stats.proportion.proportion_confint.

+
chunksint or str or tuple of int or str or Callable[[typing.Tuple[int, …]], int or str or tuple of int or str], optional, default: ‘native’

Define how input data being read from zarr should be divided into +chunks for a dask computation. If ‘native’, use underlying zarr +chunks. If a string specifying a target memory size, e.g., ‘300 MiB’, +resize chunks in arrays with more than one dimension to match this +size. If ‘auto’, let dask decide chunk size. If ‘ndauto’, let dask +decide chunk size but only for arrays with more than one dimension. If +‘ndauto0’, as ‘ndauto’ but only vary the first chunk dimension. If +‘ndauto1’, as ‘ndauto’ but only vary the second chunk dimension. If +‘ndauto01’, as ‘ndauto’ but only vary the first and second chunk +dimensions. Also, can be a tuple of integers, or a callable which +accepts the native chunks as a single argument and returns a valid +dask chunks value.

+
+
inline_arraybool, optional, default: True

Passed through to dask from_array().

+

Returns#

-
dsxarray.Dataset

The resulting dataset contains data has dimensions “cohorts” and +

Dataset

The resulting dataset contains data has dimensions “cohorts” and “variants”. Variables prefixed with “cohort” are 1-dimensional arrays with data about the cohorts, such as the area, period, taxon and cohort size. Variables prefixed with “variant” are 1-dimensional diff --git a/latest/searchindex.js b/latest/searchindex.js index 002e8e370..31b97a689 100644 --- a/latest/searchindex.js +++ b/latest/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"AIM data access": [[1, "aim-data-access"]], "API documentation": [[194, "api-documentation"]], "About the data": [[194, "about-the-data"]], "Af1": [[0, null]], "Ag3": [[1, null]], "Amin1": [[2, null]], "Basic data access": [[0, "basic-data-access"], [1, "basic-data-access"]], "CNV data access": [[0, "cnv-data-access"], [1, "cnv-data-access"]], "Diplotype clustering": [[0, "diplotype-clustering"], [1, "diplotype-clustering"]], "Diversity analysis": [[0, "diversity-analysis"], [1, "diversity-analysis"]], "Fst analysis": [[0, "fst-analysis"], [1, "fst-analysis"]], "Genetic distance and neighbour-joining trees (NJT)": [[0, "genetic-distance-and-neighbour-joining-trees-njt"], [1, "genetic-distance-and-neighbour-joining-trees-njt"]], "Genome-wide 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101, 102, 103, 137, 139, 144, 145, 159, 163, 178, 182, 185], "bp": [6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "browser": [36, 131], "burkina": [23, 24, 25, 118, 119, 120], "cach": [44, 86, 139, 182], "calcul": [4, 14, 19, 20, 21, 22, 25, 28, 29, 30, 35, 37, 54, 55, 56, 58, 59, 60, 61, 62, 66, 79, 88, 92, 94, 108, 114, 115, 116, 117, 120, 123, 124, 125, 130, 132, 150, 151, 152, 154, 155, 156, 157, 158, 162, 175, 184, 188], "calibr": [21, 28, 55, 58, 116, 123, 151, 154], "call": [0, 1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 18, 36, 42, 44, 49, 67, 72, 81, 87, 88, 89, 90, 93, 94, 96, 100, 101, 102, 103, 105, 106, 107, 112, 131, 137, 139, 145, 163, 168, 177, 183, 184, 185, 186, 193], "callabl": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 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62, 65, 66, 72, 79, 142, 150, 152, 153, 155, 156, 157, 158, 161, 162, 168, 175], "case": [23, 24, 25, 60, 61, 118, 119, 120, 156, 157, 191, 193], "catalog": [91, 187], "category_ord": [48, 49, 50, 63, 64, 67, 69, 70, 73, 74, 144, 145, 146, 159, 160, 163, 165, 166, 169, 170], "caus": [51, 147], "cccccc": [49, 145], "cdn": [64, 160], "cds_deg_0": [6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "cds_deg_2_simpl": [6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "cds_deg_4": [6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "center": [52, 67, 76, 148, 163, 172], "center_i": [67, 163], "center_x": [67, 163], "centr": [20, 22, 29, 30, 37, 92, 115, 117, 124, 125, 132, 188], "centroid": [48, 49, 63, 144, 145, 159], "chang": [4, 7, 8, 9, 10, 29, 30, 42, 44, 67, 94, 100, 101, 102, 103, 124, 125, 137, 139, 163], "channel": [51, 68, 147, 164], "chart": [75, 171], "child": [48, 49, 63, 67, 144, 145, 159, 163], "choos": [48, 49, 63, 67, 69, 70, 144, 145, 159, 163, 165, 166], "chosen": [29, 30, 124, 125], "chromosom": [23, 24, 25, 37, 66, 79, 92, 118, 119, 120, 132, 162, 175, 188], "chunk": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 139, 144, 145, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 168, 175, 177, 180, 182, 183, 184, 185, 188, 191, 193], "ci_method": [4, 25, 35, 88, 94, 120, 130, 184], "circl": [46, 65, 72, 142, 161, 168], "circle_kwarg": [46, 65, 72, 142, 161, 168], "cityblock": [7, 42, 48, 49, 67, 100, 137, 144, 145, 163], "class": [6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "clear": [11, 104], "cli": 194, "client": [44, 139], "clip": [20, 54, 115, 150], "clip_min": [20, 54, 115, 150], "cloud": 194, "cluster": [48, 49, 63, 144, 145, 159], "cnv": [12, 13, 18, 24, 25, 46, 47, 49, 105, 106, 107, 112, 119, 120, 142, 143, 145], "cnv_colorscal": [49, 145], "cnv_max_coverage_vari": [49, 145], "cnv_region": [49, 145], "cnv_row_height": [49, 145], "code": [0, 1, 2, 6, 7, 8, 9, 14, 15, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 109, 114, 137, 138, 139, 163, 182, 185], "cohort": [3, 4, 6, 7, 8, 9, 14, 19, 20, 21, 22, 24, 25, 28, 29, 30, 31, 33, 34, 35, 37, 42, 43, 44, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 66, 67, 68, 77, 79, 86, 87, 88, 89, 92, 93, 94, 99, 100, 101, 102, 108, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 128, 129, 130, 132, 137, 138, 139, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 162, 163, 164, 173, 175, 182, 183, 184, 185, 188], "cohort1_queri": [6, 20, 30, 54, 62, 79, 92, 99, 115, 125, 150, 158, 175, 188], "cohort2_queri": [6, 20, 30, 54, 62, 79, 92, 99, 115, 125, 150, 158, 175, 188], "cohort_s": [6, 7, 8, 9, 14, 19, 20, 28, 29, 30, 31, 33, 42, 43, 44, 48, 49, 54, 58, 59, 60, 61, 62, 63, 67, 77, 86, 89, 99, 100, 101, 102, 108, 114, 115, 123, 124, 125, 126, 128, 137, 138, 139, 144, 145, 150, 154, 155, 156, 157, 158, 159, 163, 173, 182, 185], "cohort_set": [15, 109], "col_width": [51, 68, 147, 164], "colab": 194, "coloni": 113, "color": [48, 49, 50, 51, 59, 60, 62, 63, 64, 67, 68, 69, 70, 73, 74, 75, 141, 144, 145, 146, 147, 155, 156, 158, 159, 160, 163, 164, 165, 166, 169, 170, 171], "color_continuous_scal": [51, 68, 147, 164], "color_discrete_map": [48, 49, 50, 63, 64, 67, 69, 70, 144, 145, 146, 159, 160, 163, 165, 166], "color_discrete_sequ": [48, 49, 50, 63, 64, 67, 69, 70, 73, 74, 144, 145, 146, 159, 160, 163, 165, 166, 169, 170], "colorbar": [51, 147], "colormap": [49, 51, 68, 145, 147, 164], "colour": [48, 49, 50, 63, 64, 67, 69, 70, 73, 74, 77, 144, 145, 146, 159, 160, 163, 165, 166, 169, 170, 173], "column": [3, 4, 5, 17, 25, 26, 32, 35, 44, 51, 53, 69, 70, 75, 76, 86, 87, 88, 91, 93, 94, 95, 111, 120, 121, 127, 130, 139, 141, 147, 149, 165, 166, 171, 172, 182, 183, 184, 187, 190], "coluzzii": [23, 24, 25, 118, 119, 120], "com": [48, 49, 51, 63, 64, 67, 69, 144, 145, 147, 159, 160, 163, 165], "command": 194, "commun": 194, "comparison": 141, "compat": [49, 51, 68, 145, 147, 164], "complet": [48, 49, 63, 144, 145, 159], "complex": [1, 21, 22, 55, 56, 116, 117, 151, 152, 194], "compon": [44, 64, 69, 70, 71, 139, 160, 165, 166, 167], "comput": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 43, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 72, 73, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 99, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 138, 139, 144, 145, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 168, 169, 175, 177, 180, 182, 183, 184, 185, 188, 194], "compute_min_maf": [37, 66, 132, 162], "concaten": [23, 24, 25, 118, 119, 120, 191, 193], "confid": [4, 6, 14, 19, 25, 35, 43, 88, 94, 99, 108, 114, 120, 130, 138, 184], "confidence_level": [14, 19, 108, 114], "configur": [36, 131], "connect": [64, 160], "consid": [73, 169], "constant": [48, 49, 53, 63, 67, 69, 70, 144, 145, 149, 159, 163, 165, 166], "construct": [42, 64, 137, 160], "contain": [4, 5, 9, 10, 20, 21, 22, 25, 28, 29, 30, 35, 37, 53, 88, 89, 92, 94, 95, 96, 98, 102, 103, 113, 115, 116, 117, 120, 123, 124, 125, 130, 132, 149, 184, 185, 188], "contig": [4, 6, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 42, 43, 44, 46, 47, 48, 49, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 72, 77, 79, 81, 84, 86, 88, 89, 90, 92, 94, 99, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 121, 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185, 188], "necessarili": [6, 14, 19, 43, 99, 108, 114, 138], "need": [60, 61, 156, 157, 194], "neighbour": [42, 67, 137, 163], "net": 194, "network": [64, 160, 194], "nob": [3, 87, 93, 183], "nobs_mod": [4, 88, 94, 184], "node": [48, 49, 63, 64, 67, 144, 145, 159, 160, 163], "node_size_factor": [64, 160], "non": [3, 4, 6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 77, 86, 87, 88, 89, 93, 94, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 173, 182, 183, 184, 185], "non_synonymous_cod": [49, 145], "none": [3, 4, 6, 7, 8, 9, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 42, 43, 44, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 82, 83, 84, 86, 87, 88, 89, 92, 93, 94, 96, 99, 100, 101, 102, 103, 106, 107, 108, 111, 114, 115, 116, 117, 118, 119, 120, 121, 123, 124, 125, 126, 128, 129, 130, 131, 132, 135, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 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125, 132, 138, 150, 151, 152, 154, 155, 156, 157, 158, 160, 162, 164, 175, 188], "500": [21, 28, 48, 50, 55, 58, 63, 76, 116, 123, 144, 146, 151, 154, 159, 172], "5000": [21, 28, 55, 58, 116, 123, 151, 154], "52": [73, 74, 169, 170], "5254a3": [59, 62, 155, 158], "56": [73, 74, 169, 170], "5e3c99": [49, 145], "6": [67, 163], "600": [64, 67, 69, 70, 73, 74, 75, 160, 163, 165, 166, 169, 170, 171], "62": [73, 74, 169, 170], "637939": [59, 62, 155, 158], "64": [73, 74, 169, 170], "6b6ecf": [59, 62, 155, 158], "7": [47, 143], "70": [73, 74, 169, 170], "72": [73, 74, 169, 170], "75": [37, 66, 79, 92, 132, 162, 175, 188], "7f7f7f": [60, 156], "8": [44, 73, 74, 139, 169, 170], "80": [73, 74, 77, 169, 170, 173], "800": [67, 73, 74, 75, 163, 169, 170, 171], "84": [73, 74, 169, 170], "8c564b": [60, 156], "9": [69, 165], "90": [46, 47, 54, 56, 59, 60, 61, 62, 65, 66, 72, 77, 79, 142, 143, 150, 152, 155, 156, 157, 158, 161, 162, 168, 173, 175], "900": [69, 70, 71, 165, 166, 167], "9467bd": [60, 156], "95": [14, 19, 73, 74, 108, 114, 169, 170], "9c9ede": [59, 62, 155, 158], "A": [3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 37, 42, 43, 44, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 82, 83, 84, 86, 87, 88, 89, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 106, 107, 108, 109, 111, 113, 114, 115, 116, 117, 118, 119, 120, 121, 123, 124, 125, 126, 128, 129, 130, 132, 137, 138, 139, 141, 142, 143, 144, 145, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 177, 178, 179, 180, 182, 183, 184, 185, 188, 194], "By": 194, "For": [0, 1, 2, 96, 97, 98, 141, 194], "If": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 42, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 81, 84, 86, 87, 88, 89, 90, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 137, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 173, 174, 175, 177, 180, 182, 183, 184, 185, 186, 188, 191, 193, 194], "It": [10, 51, 103, 147], "One": [51, 72, 81, 91, 147, 168, 177, 187], "The": [4, 5, 7, 8, 9, 10, 20, 21, 22, 25, 28, 29, 30, 35, 37, 42, 44, 48, 49, 50, 51, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 71, 73, 74, 75, 79, 88, 92, 94, 95, 100, 101, 102, 103, 115, 116, 117, 120, 123, 124, 125, 130, 132, 137, 139, 141, 144, 145, 146, 147, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 162, 163, 164, 165, 166, 167, 169, 170, 171, 175, 184, 188, 194], "These": [18, 45, 85, 112, 140, 181], "To": [0, 1, 2], "aa_allele_frequencies_advanc": [52, 53, 148, 149], "abbrevi": [76, 172], "about": [0, 1, 2, 4, 25, 35, 37, 66, 81, 88, 94, 113, 120, 130, 132, 162, 177, 184], "abov": [57, 78, 153, 174], "accept": [3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 109, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 139, 144, 145, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 168, 175, 177, 180, 182, 183, 184, 185, 188], "access": [9, 12, 13, 26, 27, 32, 33, 38, 82, 83, 89, 96, 98, 102, 105, 106, 107, 121, 122, 127, 128, 133, 178, 179, 185, 190, 191, 192, 193, 194], "acid": [3, 4, 49, 93, 94, 145], "ad": [11, 44, 69, 70, 104, 139, 165, 166], "add": [5, 87, 95, 183], "addit": [5, 36, 44, 52, 64, 95, 131, 139, 148, 160], "admin1_iso": [4, 17, 25, 35, 88, 94, 111, 120, 130, 184], "admin1_month": [3, 15, 19, 24, 34, 43, 60, 61, 87, 93, 109, 114, 119, 129, 138, 156, 157, 183], "admin1_nam": [4, 17, 25, 35, 73, 74, 88, 94, 111, 120, 130, 169, 170, 184], "admin1_quart": [15, 109], "admin1_year": [15, 109], "admin2_month": [15, 109], "admin2_nam": [4, 17, 25, 35, 88, 94, 111, 120, 130, 184], "admin2_quart": [15, 109], "admin2_year": [15, 109], "administr": [4, 15, 25, 35, 88, 94, 109, 120, 130, 184], "advanc": [7, 8, 9, 10, 42, 44, 67, 82, 86, 89, 100, 101, 102, 103, 137, 139, 163, 178, 182, 185], "af1": [116, 117, 151, 152, 194], "africa": [73, 169, 194], "ag3": [21, 22, 55, 56, 194], "against": [3, 4, 6, 7, 8, 9, 10, 13, 17, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 33, 34, 35, 37, 42, 43, 44, 47, 48, 49, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 66, 67, 73, 74, 75, 76, 77, 79, 82, 86, 87, 88, 89, 92, 93, 94, 96, 99, 100, 101, 102, 103, 106, 107, 111, 114, 115, 116, 117, 118, 119, 120, 123, 124, 125, 126, 128, 129, 130, 132, 137, 138, 139, 141, 143, 144, 145, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 162, 163, 169, 170, 171, 172, 173, 175, 178, 182, 183, 184, 185, 188], "agresti_coul": [4, 25, 35, 88, 94, 120, 130, 184], "aim": [96, 97, 98, 141], "aim_id": [96, 98, 141], "algorithm": [42, 48, 49, 63, 64, 67, 137, 144, 145, 159, 160, 163], "align": [90, 186], "all": [0, 1, 2, 21, 22, 26, 55, 56, 60, 61, 116, 117, 121, 151, 152, 156, 157, 190], "allel": [3, 4, 7, 8, 9, 10, 12, 24, 25, 32, 35, 37, 42, 44, 49, 66, 67, 79, 86, 87, 88, 92, 93, 94, 96, 98, 100, 101, 102, 103, 105, 106, 119, 120, 127, 130, 132, 137, 139, 141, 145, 162, 163, 175, 182, 183, 184, 188], "allow": 194, "also": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 43, 44, 46, 47, 48, 49, 51, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 72, 73, 74, 75, 76, 77, 78, 79, 81, 82, 84, 86, 87, 88, 89, 92, 93, 94, 96, 99, 100, 101, 102, 103, 105, 106, 107, 108, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 138, 139, 141, 142, 143, 144, 145, 147, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 165, 168, 169, 170, 171, 172, 173, 174, 175, 177, 178, 180, 182, 183, 184, 185, 188, 191, 193, 194], "alt": [32, 127], "altern": [4, 8, 25, 32, 35, 37, 49, 66, 86, 88, 94, 101, 120, 127, 130, 132, 145, 162, 182, 184], "america": [73, 169], "amin002150": [191, 193], "amin1": 194, "amino": [3, 4, 49, 93, 94, 145], "amp": [24, 119], "amplif": [24, 119], "an": [3, 4, 6, 7, 8, 9, 12, 13, 14, 19, 20, 21, 22, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 42, 43, 44, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 76, 77, 79, 86, 87, 88, 89, 92, 93, 94, 99, 100, 101, 102, 105, 106, 107, 108, 114, 115, 116, 117, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 137, 138, 139, 142, 144, 145, 146, 147, 148, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 165, 166, 167, 168, 172, 173, 175, 182, 183, 184, 185, 188, 194], "analys": [18, 45, 112, 140, 194], "analysi": [7, 8, 9, 10, 12, 21, 22, 28, 29, 30, 31, 32, 33, 37, 42, 44, 45, 55, 56, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 79, 92, 100, 101, 102, 103, 105, 116, 117, 123, 124, 125, 126, 127, 128, 132, 137, 139, 140, 151, 152, 154, 155, 156, 157, 158, 159, 160, 162, 163, 165, 166, 167, 175, 188, 194], "ancestri": [96, 97, 98, 141], "angl": [67, 163], "ani": [11, 18, 36, 45, 85, 104, 112, 131, 140, 181, 194], "annot": [26, 49, 68, 84, 87, 97, 121, 145, 164, 180, 183, 190], "anophel": [0, 1, 2, 10, 103, 194], "api": [0, 1, 2, 44, 51, 139, 147], "app": [64, 160], "appear": [46, 47, 48, 49, 50, 54, 56, 57, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 72, 73, 74, 77, 79, 142, 143, 144, 145, 146, 150, 152, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 165, 166, 168, 169, 170, 173, 175], "append": [10, 103], "appli": [3, 4, 6, 7, 8, 9, 10, 14, 19, 20, 21, 22, 38, 42, 43, 44, 48, 49, 54, 55, 56, 65, 67, 72, 77, 81, 84, 86, 87, 88, 89, 93, 94, 99, 100, 101, 102, 103, 108, 114, 115, 116, 117, 133, 137, 138, 139, 144, 145, 150, 151, 152, 161, 163, 168, 173, 177, 180, 182, 183, 184, 185, 193], "applic": 194, "approxim": [7, 8, 9, 10, 21, 22, 42, 44, 55, 56, 67, 100, 101, 102, 103, 116, 117, 137, 139, 151, 152, 163], "ar": [0, 4, 7, 8, 9, 10, 15, 18, 20, 25, 35, 37, 42, 44, 45, 51, 54, 66, 67, 68, 73, 76, 77, 79, 82, 85, 86, 88, 89, 92, 94, 100, 101, 102, 103, 109, 112, 113, 115, 120, 130, 132, 137, 139, 140, 147, 150, 162, 163, 164, 169, 172, 173, 175, 178, 181, 182, 184, 185, 188, 194], "arc_start": [67, 163], "arc_stop": [67, 163], "area": [4, 25, 35, 46, 52, 53, 54, 56, 57, 59, 60, 61, 62, 64, 65, 66, 72, 79, 88, 94, 120, 130, 142, 148, 149, 150, 152, 153, 155, 156, 157, 158, 160, 161, 162, 168, 175, 184], "area_bi": [4, 25, 35, 88, 94, 120, 130, 184], "argument": [3, 4, 7, 8, 9, 10, 12, 13, 14, 17, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 43, 44, 47, 48, 49, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 72, 73, 74, 75, 76, 77, 79, 81, 82, 84, 86, 87, 88, 89, 92, 93, 94, 96, 100, 101, 102, 103, 105, 106, 107, 108, 111, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 138, 139, 141, 143, 144, 145, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 168, 169, 170, 171, 172, 173, 175, 177, 178, 180, 182, 183, 184, 185, 188], "arrai": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 71, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 139, 144, 145, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 167, 168, 175, 177, 180, 182, 183, 184, 185, 188, 191, 193], "ascertain": [44, 139], "asia": [2, 73, 169, 194], "aspect": [51, 147], "associ": [13, 23, 33, 107, 118, 128, 194], "attempt": [48, 49, 51, 52, 53, 63, 67, 144, 145, 147, 148, 149, 159, 163], "attribut": [26, 48, 49, 53, 63, 67, 69, 70, 121, 144, 145, 149, 159, 163, 165, 166, 190], "auth": 194, "authent": 194, "auto": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 46, 48, 49, 51, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 69, 72, 73, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 139, 142, 144, 145, 147, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 169, 175, 177, 180, 182, 183, 184, 185, 188, 191, 193], "automat": [54, 55, 56, 57, 59, 60, 62, 66, 78, 79, 150, 151, 152, 153, 155, 156, 158, 162, 174, 175, 194], "avail": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 14, 17, 18, 19, 20, 21, 22, 28, 29, 30, 31, 32, 33, 37, 38, 42, 43, 44, 45, 48, 49, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 72, 75, 76, 77, 79, 80, 81, 84, 85, 86, 87, 88, 89, 92, 93, 94, 97, 99, 100, 101, 102, 103, 105, 108, 111, 112, 114, 115, 116, 117, 123, 124, 125, 126, 127, 128, 132, 133, 137, 138, 139, 140, 144, 145, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 168, 171, 172, 173, 175, 176, 177, 180, 181, 182, 183, 184, 185, 188, 194], "averag": [6, 43, 48, 49, 63, 68, 99, 138, 144, 145, 159, 164], "axi": [46, 51, 57, 65, 69, 70, 72, 75, 78, 142, 147, 153, 161, 165, 166, 168, 171, 174], "b": [6, 14, 19, 43, 99, 108, 114, 138], "b2abd2": [49, 145], "backend": [46, 48, 49, 54, 56, 57, 59, 60, 61, 62, 63, 65, 66, 67, 69, 72, 79, 142, 144, 145, 150, 152, 153, 155, 156, 157, 158, 159, 161, 162, 163, 165, 168, 175], "bam": [91, 187], "bar": [50, 51, 71, 75, 146, 147, 167, 171], "bar_plot_height": [50, 146], "bar_width": [50, 146], "base": [10, 37, 66, 79, 90, 92, 103, 132, 162, 175, 186, 188], "basemap": [76, 172], "basic": [46, 47, 54, 56, 57, 59, 60, 61, 62, 65, 66, 72, 77, 78, 79, 142, 143, 150, 152, 153, 155, 156, 157, 158, 161, 162, 168, 173, 174, 175], "bcbd22": [60, 156], "becom": [51, 90, 147, 186], "bed": [10, 103], "begin": [67, 163], "behaviour": [68, 164], "being": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 68, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 139, 144, 145, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 164, 168, 175, 177, 180, 182, 183, 184, 185, 188], "belong": [6, 7, 8, 9, 14, 19, 41, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 136, 137, 138, 139, 163, 182, 185], "below": [0, 1, 2, 7, 8, 9, 10, 20, 37, 42, 44, 49, 52, 54, 57, 66, 67, 78, 79, 92, 100, 101, 102, 103, 115, 132, 137, 139, 145, 148, 150, 153, 162, 163, 174, 175, 188], "best": 194, "beta": [4, 25, 35, 88, 94, 120, 130, 184], "better": [6, 14, 19, 43, 99, 108, 114, 138], "between": [6, 7, 20, 30, 31, 37, 42, 43, 48, 49, 54, 62, 63, 64, 66, 67, 72, 79, 81, 92, 99, 100, 115, 125, 126, 132, 137, 138, 141, 144, 145, 150, 158, 159, 160, 162, 163, 168, 175, 177, 188], "beyond": [37, 66, 79, 92, 132, 162, 175, 188], "biallel": [7, 8, 9, 10, 42, 44, 67, 100, 101, 102, 103, 137, 139, 163], "biallelic_snp_cal": [10, 103], "bim": [10, 103], "bin": [37, 66, 132, 162], "binari": [10, 103], "binom_test": [4, 25, 35, 88, 94, 120, 130, 184], "black": [48, 49, 63, 144, 145, 159], "block": [6, 14, 19, 43, 99, 108, 114, 138], "blue": [66, 79, 162, 175], "bokeh": [46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 65, 66, 72, 77, 78, 79, 142, 143, 150, 151, 152, 153, 154, 155, 156, 157, 158, 161, 162, 168, 173, 174, 175], "bold": [73, 74, 169, 170], "bool": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 42, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 81, 84, 86, 87, 88, 89, 90, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 137, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 173, 174, 175, 177, 180, 182, 183, 184, 185, 186, 188, 191, 193], "boolean": [10, 38, 103, 133], "both": [7, 8, 9, 10, 42, 44, 48, 49, 63, 67, 82, 86, 89, 100, 101, 102, 103, 137, 139, 144, 145, 159, 163, 178, 182, 185], "bp": [6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "browser": [36, 131], "cach": [44, 86, 139, 182], "calcul": [4, 14, 19, 20, 21, 22, 25, 28, 29, 30, 35, 37, 54, 55, 56, 58, 59, 60, 61, 62, 66, 79, 88, 92, 94, 108, 114, 115, 116, 117, 120, 123, 124, 125, 130, 132, 150, 151, 152, 154, 155, 156, 157, 158, 162, 175, 184, 188], "calibr": [21, 28, 55, 58, 116, 123, 151, 154], "call": [0, 1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 18, 24, 25, 36, 42, 44, 49, 67, 72, 81, 87, 88, 89, 90, 93, 94, 96, 100, 101, 102, 103, 105, 106, 107, 112, 119, 120, 131, 137, 139, 145, 163, 168, 177, 183, 184, 185, 186, 193], "callabl": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 139, 144, 145, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 168, 175, 177, 180, 182, 183, 184, 185, 188], "can": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 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9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "cds_deg_2_simpl": [6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "cds_deg_4": [6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "center": [52, 67, 76, 148, 163, 172], "center_i": [67, 163], "center_x": [67, 163], "centr": [20, 22, 29, 30, 37, 92, 115, 117, 124, 125, 132, 188], "centroid": [48, 49, 63, 144, 145, 159], "chang": [4, 7, 8, 9, 10, 29, 30, 42, 44, 67, 94, 100, 101, 102, 103, 124, 125, 137, 139, 163], "channel": [51, 68, 147, 164], "chart": [75, 171], "child": [48, 49, 63, 67, 144, 145, 159, 163], "choos": [48, 49, 63, 67, 69, 70, 144, 145, 159, 163, 165, 166], "chosen": [29, 30, 124, 125], "chromosom": [37, 66, 79, 92, 132, 162, 175, 188], "chunk": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 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185], "cohort_set": [15, 109], "col_width": [51, 68, 147, 164], "colab": 194, "coloni": 113, "color": [48, 49, 50, 51, 59, 60, 62, 63, 64, 67, 68, 69, 70, 73, 74, 75, 141, 144, 145, 146, 147, 155, 156, 158, 159, 160, 163, 164, 165, 166, 169, 170, 171], "color_continuous_scal": [51, 68, 147, 164], "color_discrete_map": [48, 49, 50, 63, 64, 67, 69, 70, 144, 145, 146, 159, 160, 163, 165, 166], "color_discrete_sequ": [48, 49, 50, 63, 64, 67, 69, 70, 73, 74, 144, 145, 146, 159, 160, 163, 165, 166, 169, 170], "colorbar": [51, 147], "colormap": [49, 51, 68, 145, 147, 164], "colour": [48, 49, 50, 63, 64, 67, 69, 70, 73, 74, 77, 144, 145, 146, 159, 160, 163, 165, 166, 169, 170, 173], "column": [3, 4, 5, 17, 24, 25, 26, 32, 35, 44, 51, 53, 69, 70, 75, 76, 86, 87, 88, 91, 93, 94, 95, 111, 119, 120, 121, 127, 130, 139, 141, 147, 149, 165, 166, 171, 172, 182, 183, 184, 187, 190], "com": [48, 49, 51, 63, 64, 67, 69, 144, 145, 147, 159, 160, 163, 165], "command": 194, "commun": 194, "comparison": 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163, 165, 166], "construct": [42, 64, 137, 160], "contain": [4, 5, 9, 10, 20, 21, 22, 25, 28, 29, 30, 35, 37, 53, 88, 89, 92, 94, 95, 96, 98, 102, 103, 113, 115, 116, 117, 120, 123, 124, 125, 130, 132, 149, 184, 185, 188], "contig": [4, 6, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 42, 43, 44, 46, 47, 48, 49, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 72, 77, 79, 81, 84, 86, 88, 89, 90, 92, 94, 99, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 137, 138, 139, 142, 143, 144, 145, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 168, 173, 175, 177, 180, 182, 184, 185, 186, 188, 191, 193], "contig_color": [59, 62, 155, 158], "control": [48, 49, 50, 63, 64, 67, 69, 70, 73, 74, 144, 145, 146, 159, 160, 163, 165, 166, 169, 170, 194], "conveni": 194, "coordin": [48, 49, 63, 67, 69, 70, 144, 145, 159, 163, 165, 166, 191, 193], "copi": [23, 24, 49, 118, 119, 145], "corner": [68, 164], "correspond": [7, 8, 9, 10, 42, 44, 48, 49, 53, 63, 64, 67, 69, 70, 76, 82, 86, 89, 100, 101, 102, 103, 137, 139, 144, 145, 149, 159, 160, 163, 165, 166, 172, 178, 182, 185], "cose": [64, 160], "count": [3, 4, 7, 8, 9, 10, 17, 24, 25, 35, 37, 42, 44, 66, 67, 76, 86, 87, 88, 93, 94, 100, 101, 102, 103, 111, 119, 120, 130, 132, 137, 139, 162, 163, 172, 182, 183, 184], "count_bi": [76, 172], "count_sort": [48, 49, 63, 67, 144, 145, 159, 163], "countri": [15, 17, 75, 109, 111, 171], "cours": 194, "cover": [68, 164], "coverag": [12, 13, 18, 23, 24, 25, 47, 49, 105, 107, 112, 118, 119, 120, 143, 145], "coverage_calls_analysi": [18, 112], "coverage_calls_analysis_id": [12, 105], "crash": [51, 147], "creat": [17, 36, 52, 53, 111, 131, 141, 148, 149], "credenti": 194, "cross": 113, "css": [49, 51, 68, 145, 147, 164], "curat": [0, 1, 2], "current": [0, 36, 80, 131, 176, 194], "custom": [3, 14, 19, 24, 34, 43, 46, 47, 54, 56, 57, 59, 60, 61, 62, 65, 66, 72, 77, 79, 87, 93, 108, 114, 119, 129, 138, 142, 143, 150, 152, 153, 155, 156, 157, 158, 161, 162, 168, 173, 175, 183], "cytoscap": [64, 160], "d": [52, 53, 148, 149, 191, 193], "d62728": [60, 156], "dash": [64, 160], "dask": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 139, 144, 145, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 168, 175, 177, 180, 182, 183, 184, 185, 188, 191, 193], "data": [3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 43, 44, 45, 46, 47, 48, 49, 51, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 111, 112, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 138, 139, 140, 141, 142, 143, 144, 145, 147, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 170, 171, 172, 173, 175, 176, 177, 178, 180, 181, 182, 183, 184, 185, 186, 187, 188, 191, 193], "datafram": [3, 5, 15, 17, 19, 24, 26, 34, 43, 44, 50, 51, 68, 69, 70, 81, 82, 83, 87, 93, 95, 109, 111, 113, 114, 119, 121, 129, 138, 139, 146, 147, 164, 165, 166, 177, 178, 179, 183, 190, 192], "dataset": [4, 9, 12, 13, 23, 25, 33, 35, 48, 49, 51, 52, 53, 63, 67, 84, 88, 89, 94, 96, 98, 102, 105, 106, 107, 118, 120, 128, 130, 144, 145, 147, 148, 149, 159, 163, 180, 184, 185, 193], "decai": [37, 66, 79, 92, 132, 162, 175, 188], "decid": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 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180, 182, 183, 184, 185, 186, 188, 194], "defin": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 139, 144, 145, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 168, 175, 177, 180, 182, 183, 184, 185, 188, 191, 193], "degener": [6, 7, 8, 9, 14, 19, 42, 43, 44, 67, 86, 89, 99, 100, 101, 102, 108, 114, 137, 138, 139, 163, 182, 185], "del": [24, 119], "delet": [24, 119], "dendrogram_height": [49, 145], "denomin": [4, 25, 88, 94, 120, 184], "depend": [10, 44, 86, 103, 139, 182], "descend": [48, 49, 63, 67, 144, 145, 159, 163], "descript": [48, 49, 63, 144, 145, 159, 190], "desir": [7, 8, 9, 10, 42, 44, 67, 100, 101, 102, 103, 137, 139, 163], "detect": [30, 62, 125, 158], "determin": [48, 49, 53, 63, 67, 69, 70, 144, 145, 149, 159, 163, 165, 166], "df": [51, 113, 147, 190, 192], "df_pca": [44, 139], "df_sampl": [0, 1, 2], "df_stat": [50, 146], "diagnost": [37, 66, 132, 162], "dict": [3, 4, 7, 8, 9, 13, 17, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 33, 34, 35, 37, 42, 43, 44, 47, 48, 49, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 66, 67, 73, 74, 75, 76, 77, 79, 82, 86, 87, 88, 89, 92, 93, 94, 96, 100, 101, 102, 106, 107, 111, 114, 115, 116, 117, 118, 119, 120, 123, 124, 125, 126, 128, 129, 130, 132, 137, 138, 139, 141, 143, 144, 145, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 162, 163, 169, 170, 171, 172, 173, 175, 178, 182, 183, 184, 185, 188], "dictionari": [3, 4, 7, 8, 9, 13, 17, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 33, 34, 35, 37, 42, 43, 44, 47, 48, 49, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 66, 67, 73, 74, 75, 76, 77, 79, 82, 86, 87, 88, 89, 92, 93, 94, 96, 100, 101, 102, 106, 107, 111, 114, 115, 116, 117, 118, 119, 120, 123, 124, 125, 126, 128, 129, 130, 132, 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186, 188, 190, 191, 193], "natgeoworldmap": [76, 172], "nativ": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 139, 144, 145, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 168, 175, 177, 180, 182, 183, 184, 185, 188, 191, 193], "natur": 194, "ndarrai": [7, 8, 20, 21, 22, 28, 29, 30, 31, 37, 38, 42, 44, 71, 79, 86, 92, 100, 101, 115, 116, 117, 123, 124, 125, 126, 132, 133, 137, 139, 167, 175, 182, 188], "ndauto": [3, 4, 7, 8, 9, 10, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 44, 48, 49, 55, 56, 58, 59, 62, 63, 64, 65, 66, 67, 72, 79, 81, 84, 86, 87, 88, 89, 92, 93, 94, 100, 101, 102, 103, 105, 106, 107, 108, 114, 115, 116, 117, 118, 119, 120, 122, 123, 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139, 163, 173, 182, 183, 184, 185], "non_synonymous_cod": [49, 145], "none": [3, 4, 6, 7, 8, 9, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 42, 43, 44, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 82, 83, 84, 86, 87, 88, 89, 92, 93, 94, 96, 99, 100, 101, 102, 103, 106, 107, 108, 111, 114, 115, 116, 117, 118, 119, 120, 121, 123, 124, 125, 126, 128, 129, 130, 131, 132, 135, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 177, 178, 179, 180, 182, 183, 184, 185, 188], "normal": [4, 25, 35, 88, 94, 120, 130, 184], "north": [73, 169], "note": [0, 48, 49, 63, 67, 144, 145, 159, 163, 194], "notebook": [36, 51, 131, 147], "now": [44, 139], "nucleotid": [27, 122], "number": [3, 4, 6, 7, 8, 9, 10, 14, 17, 19, 20, 21, 22, 23, 24, 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161, 162, 168, 173, 174, 175], "other": [57, 68, 76, 78, 153, 164, 172, 174, 194], "otherwis": [48, 49, 51, 52, 53, 63, 64, 67, 144, 145, 147, 148, 149, 159, 160, 163], "ought": [10, 103], "output": [3, 10, 34, 46, 50, 51, 54, 56, 57, 59, 60, 61, 62, 65, 66, 72, 79, 87, 93, 103, 129, 142, 146, 147, 150, 152, 153, 155, 156, 157, 158, 161, 162, 168, 175, 183], "output_backend": [46, 54, 56, 57, 59, 60, 61, 62, 65, 66, 72, 79, 142, 150, 152, 153, 155, 156, 157, 158, 161, 162, 168, 175], "output_dir": [10, 103], "outsid": [72, 81, 168, 177], "over": [37, 65, 66, 72, 79, 81, 92, 132, 161, 162, 168, 175, 177, 188], "overlaid": [60, 156], "overlap": [29, 30, 124, 125], "overrid": [37, 64, 66, 132, 160, 162], "overwrit": [10, 103], "overwritten": [10, 103], "owner": 194, "packag": 194, "page": [0, 1, 2], "pair": [14, 37, 66, 79, 90, 92, 108, 132, 162, 175, 186, 188], "pairwis": [7, 31, 43, 67, 68, 100, 126, 138, 163, 164], "palett": [66, 79, 141, 162, 175], "panda": [3, 4, 6, 7, 8, 9, 10, 13, 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158, 159, 160, 162, 163, 169, 170, 171, 172, 173, 175, 178, 182, 183, 184, 185, 188], "partner_sample_id": [5, 95], "pass": [3, 4, 7, 8, 9, 10, 12, 13, 14, 17, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 42, 43, 44, 46, 47, 48, 49, 51, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 84, 86, 87, 88, 89, 92, 93, 94, 96, 100, 101, 102, 103, 105, 106, 107, 108, 111, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 137, 138, 139, 141, 142, 143, 144, 145, 147, 149, 150, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 175, 177, 178, 180, 182, 183, 184, 185, 188, 191, 193], "path": [10, 103], "pc1": [44, 69, 70, 139, 165, 166], "pc2": [44, 69, 70, 139, 165, 166], "pc3": [44, 69, 70, 139, 165, 166], "pca": [69, 70, 71, 165, 166, 167], "per": [3, 5, 8, 15, 23, 24, 26, 34, 37, 44, 47, 50, 51, 53, 65, 66, 71, 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