summarizedexperiment package

Submodules

summarizedexperiment.BaseSE module

class summarizedexperiment.BaseSE.BaseSE(assays=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]

Bases: object

Base class for SummarizedExperiment. This class provides common properties and methods that can be utilized across all derived classes.

This container represents genomic experiment data in the form of assays, features in row_data, sample data in column_data, and any other relevant metadata.

If row_names are not provided, the row_names from row_data are used as the experiment’s row names. Similarly if column_names are not provided the row_names of the column_data are used as the experiment’s column names.

__copy__()[source]
Returns:

A shallow copy of the current BaseSE.

__deepcopy__(memo=None, _nil=[])[source]
Returns:

A deep copy of the current BaseSE.

__getitem__(args)[source]

Subset a SummarizedExperiment.

Parameters:

args (Union[int, str, Sequence, tuple]) –

Integer indices, a boolean filter, or (if the current object is named) names specifying the ranges to be extracted, see normalize_subscript().

Alternatively a tuple of length 1. The first entry specifies the rows to retain based on their names or indices.

Alternatively a tuple of length 2. The first entry specifies the rows to retain, while the second entry specifies the columns to retain, based on their names or indices.

Raises:

ValueError – If too many or too few slices provided.

Return type:

BaseSE

Returns:

Same type as caller with the sliced rows and columns.

__init__(assays=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]

Initialize an instance of BaseSE.

Parameters:

  • assays (Dict[str, Any]) –

    A dictionary containing matrices, with assay names as keys and 2-dimensional matrices represented as either ndarray or spmatrix.

    Alternatively, you may use any 2-dimensional matrix that has the shape property and implements the slice operation using the __getitem__ dunder method.

    All matrices in assays must be 2-dimensional and have the same shape (number of rows, number of columns).

  • row_data (Optional[BiocFrame]) –

    Features, must be the same length as the number of rows of the matrices in assays.

    Feature information is coerced to a BiocFrame. Defaults to None.

  • column_data (Optional[BiocFrame]) –

    Sample data, must be the same length as the number of columns of the matrices in assays.

    Sample information is coerced to a BiocFrame. Defaults to None.

  • row_names (Optional[List[str]]) –

    A list of strings, same as the number of rows.

    If row_names are not provided, these are inferred from row_data.

    Defaults to None.

  • column_names (Optional[List[str]]) –

    A list of string, same as the number of columns.

    if column_names are not provided, these are inferred from column_data.

    Defaults to None.

  • metadata (Optional[dict]) – Additional experimental metadata describing the methods. Defaults to None.

  • validate (bool) – Internal use only.

__len__()[source]
Return type:

int

Returns:

Number of rows.

__repr__()[source]
Return type:

str

Returns:

A string representation.

assay(assay)[source]

Convenience method to access an assays by name or index.

Parameters:

assay (Union[int, str]) – Name or index position of the assay.

Raises:
Return type:

Any

Returns:

Experiment data.

property assay_names: List[str]

Alias for get_assay_names.

property assays: Dict[str, Any]

Alias for get_assays().

property col_data: Dict[str, Any]

Alias for get_coldata().

property col_names: Names | None

Alias for get_column_names, provided for back-compatibility.

property coldata: Dict[str, Any]

Alias for get_coldata().

property colnames: Names | None

Alias for get_column_names, provided for back-compatibility.

property column_data: Dict[str, Any]

Alias for get_coldata().

property column_names: Names | None

Alias for get_column_names, provided for back-compatibility.

property columndata: Dict[str, Any]

Alias for get_coldata().

property columnnames: Names | None

Alias for get_column_names, provided for back-compatibility.

copy()[source]

Alias for __copy__().

property dims: Tuple[int, int]

Alias to shape.

Returns:

A tuple (m,n), where m is the number of features/rows, and n is the number of samples/columns.

Return type:

Tuple[int, int]

get_assay_names()[source]

Get assay names.

Return type:

List[str]

Returns:

List of assay names.

get_assays()[source]

Access assays/experimental data.

Return type:

Dict[str, Any]

Returns:

A dictionary with keys as assay names and value the experimental data.

get_column_data(replace_row_names=True)[source]

Get sample data.

Parameters:

replace_row_names (bool) –

Whether to replace column_data’s row_names with the row_names from the experiment.

Defaults to True.

Return type:

BiocFrame

Returns:

Sample information.

get_column_names()[source]
Return type:

Optional[Names]

Returns:

List of column names, or None if no column names are available.

get_metadata()[source]
Return type:

dict

Returns:

Dictionary of metadata for this object.

get_row_data(replace_row_names=True)[source]

Get features, the row_names of row_data are replaced by the row_names from the experiment.

Parameters:

replace_row_names (bool) –

Whether to replace row_data’s row_names with the row_names from the experiment.

Defaults to True.

Return type:

BiocFrame

Returns:

Feature information.

get_row_names()[source]
Return type:

Optional[Names]

Returns:

List of row names, or None if no row names are available.

get_slice(rows, columns)[source]

Alias for __getitem__, for back-compatibility.

Return type:

BaseSE

property metadata: dict

Alias for get_metadata.

property row_data: Dict[str, Any]

Alias for get_rowdata().

property row_names: Names | None

Alias for get_row_names, provided for back-compatibility.

property rowdata: Dict[str, Any]

Alias for get_rowdata().

property rownames: Names | None

Alias for get_row_names, provided for back-compatibility.

set_assay_names(names, in_place=False)[source]

Replace assays’s names.

Parameters:

  • names (List[str]) – New names.

  • in_place (bool) – Whether to modify the BaseSE in place.

Return type:

BaseSE

Returns:

A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_assays(assays, in_place=False)[source]

Set new experiment data (assays).

Parameters:

  • assays (Dict[str, Any]) – New assays.

  • in_place (bool) – Whether to modify the BaseSE in place.

Return type:

BaseSE

Returns:

A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_column_data(cols, replace_column_names=False, in_place=False)[source]

Set sample data.

Parameters:

  • cols (Optional[BiocFrame]) –

    New sample data.

    If cols is None, an empty BiocFrame object is created.

  • replace_column_names (bool) – Whether to replace experiment’s column_names with the names from the new object. Defaults to False.

  • in_place (bool) – Whether to modify the BaseSE in place.

Return type:

BaseSE

Returns:

A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_column_names(names, in_place=False)[source]

Set new column names.

Parameters:

  • names (Optional[List[str]]) –

    New names, same as the number of columns.

    May be None to remove column names.

  • in_place (bool) – Whether to modify the BaseSE in place.

Return type:

BaseSE

Returns:

A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_metadata(metadata, in_place=False)[source]

Set additional metadata.

Parameters:

  • metadata (dict) – New metadata for this object.

  • in_place (bool) – Whether to modify the BaseSE in place.

Return type:

BaseSE

Returns:

A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_row_data(rows, replace_row_names=False, in_place=False)[source]

Set new feature information.

Parameters:

  • rows (Optional[BiocFrame]) –

    New feature information.

    If rows is None, an empty BiocFrame object is created.

  • replace_row_names (bool) – Whether to replace experiment’s row_names with the names from the new object. Defaults to False.

  • in_place (bool) – Whether to modify the BaseSE in place.

Return type:

BaseSE

Returns:

A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_row_names(names, in_place=False)[source]

Set new row names.

Parameters:

  • names (Optional[List[str]]) –

    New names, same as the number of rows.

    May be None to remove row names.

  • in_place (bool) – Whether to modify the BaseSE in place.

Return type:

BaseSE

Returns:

A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

property shape: Tuple[int, int]

Get shape of the experiment.

Returns:

A tuple (m,n), where m is the number of features/rows, and n is the number of samples/columns.

Return type:

Tuple[int, int]

subset_assays(rows, columns)[source]

Subset all assays by the slice defined by rows and columns.

If both row_indices and col_indices are None, a shallow copy of the current assays is returned.

Parameters:

  • rows (Union[str, int, bool, Sequence, None]) –

    Row indices to subset.

    Integer indices, a boolean filter, or (if the current object is named) names specifying the ranges to be extracted, see normalize_subscript().

  • columns (Union[str, int, bool, Sequence, None]) –

    Column indices to subset.

    Integer indices, a boolean filter, or (if the current object is named) names specifying the ranges to be extracted, see normalize_subscript().

Return type:

Dict[str, Any]

Returns:

Sliced experiment data.

to_anndata()[source]

Transform BaseSE-like into a AnnData representation.

Returns:

An AnnData representation of the experiment.

summarizedexperiment.BaseSE.SliceResult

alias of SlicerResult

summarizedexperiment.RangedSummarizedExperiment module

class summarizedexperiment.RangedSummarizedExperiment.RangedSummarizedExperiment(assays=None, row_ranges=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]

Bases: SummarizedExperiment

RangedSummarizedExperiment class to represent genomic experiment data, genomic features as GenomicRanges or GenomicRangesList sample data and any additional experimental metadata.

Note: If row_ranges is empty, None or not a genomicranges.GenomicRanges.GenomicRanges object, use a SummarizedExperiment instead.

__annotations__ = {}
__copy__()[source]
Returns:

A shallow copy of the current RangedSummarizedExperiment.

__deepcopy__(memo=None, _nil=[])[source]
Returns:

A deep copy of the current RangedSummarizedExperiment.

__init__(assays=None, row_ranges=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]

Initialize a RangedSummarizedExperiment (RSE) object.

Parameters:

  • assays (Dict[str, Any]) –

    A dictionary containing matrices, with assay names as keys and 2-dimensional matrices represented as either ndarray or spmatrix.

    Alternatively, you may use any 2-dimensional matrix that has the shape property and implements the slice operation using the __getitem__ dunder method.

    All matrices in assays must be 2-dimensional and have the same shape (number of rows, number of columns).

  • row_ranges (Union[GenomicRanges, GenomicRangesList, None]) – Genomic features, must be the same length as the number of rows of the matrices in assays.

  • row_data (Optional[BiocFrame]) –

    Features, must be the same length as the number of rows of the matrices in assays.

    Feature information is coerced to a BiocFrame. Defaults to None.

  • column_data (Optional[BiocFrame]) –

    Sample data, must be the same length as the number of columns of the matrices in assays.

    Sample information is coerced to a BiocFrame. Defaults to None.

  • row_names (Optional[List[str]]) –

    A list of strings, same as the number of rows.

    If row_names are not provided, these are inferred from row_data.

    Defaults to None.

  • column_names (Optional[List[str]]) –

    A list of string, same as the number of columns.

    if column_names are not provided, these are inferred from column_data.

    Defaults to None.

  • metadata (Optional[dict]) – Additional experimental metadata describing the methods. Defaults to None.

  • validate (bool) – Internal use only.

__repr__()[source]
Return type:

str

Returns:

A string representation.

copy()[source]

Alias for __copy__().

coverage(shift=0, width=None, weight=1)[source]

Calculate coverage for each chromosome.

Parameters:

  • shift (int) – Shift all genomic positions. Defaults to 0.

  • width (Optional[int]) – Restrict the width of all chromosomes. Defaults to None.

  • weight (int) – Weight to use. Defaults to 1.

Return type:

Dict[str, ndarray]

Returns:

A dictionary with chromosome names as keys and the coverage vector as value.

property end: ndarray

Get genomic end positions for each feature or row in experimental data.

Returns:

A numpy.ndarray of end positions.

find_overlaps(query, query_type='any', select='all', max_gap=-1, min_overlap=1, ignore_strand=False)[source]

Find overlaps between subject (self) and query ranges.

Parameters:

  • query (Union[GenomicRanges, GenomicRangesList, RangedSummarizedExperiment]) –

    Query intervals to find nearest positions.

    query may be a GenomicRanges or a RangedSummarizedExperiment object.

  • query_type (str) –

    Overlap query type, must be one of

    • ”any”: Any overlap is good

    • ”start”: Overlap at the beginning of the intervals

    • ”end”: Must overlap at the end of the intervals

    • ”within”: Fully contain the query interval

    Defaults to “any”.

  • select (Literal['all', 'first', 'last', 'arbitrary']) – Determine what hit to choose when there are multiple hits for an interval in subject.

  • max_gap (int) – Maximum gap allowed in the overlap. Defaults to -1 (no gap allowed).

  • min_overlap (int) – Minimum overlap with query. Defaults to 1.

  • ignore_strand (bool) – Whether to ignore strands. Defaults to False.

Raises:

TypeError – If query is not a RangedSummarizedExperiment or GenomicRanges.

Return type:

List[List[int]]

Returns:

A list with the same length as query, containing hits to overlapping indices.

flank(width, start=True, both=False, ignore_strand=False, in_place=False)[source]

Compute flanking ranges for each range.

Refer to either flank() or the Bioconductor documentation for more details.

Parameters:

  • width (int) – Width to flank by. May be negative.

  • start (bool) – Whether to only flank starts. Defaults to True.

  • both (bool) – Whether to flank both starts and ends. Defaults to False.

  • ignore_strand (bool) – Whether to ignore strands. Defaults to False.

  • in_place (bool) – Whether to modify the GenomicRanges object in place.

Return type:

RangedSummarizedExperiment

Returns:

A new RangedSummarizedExperiment object with the flanked ranges, either as a copy of the original or as a reference to the (in-place-modified) original.

follow(query, select='all', ignore_strand=False)[source]

Search nearest positions only upstream that overlap with each range in query.

Parameters:
Raises:

  • If query is not a RangedSummarizedExperiment or –

  • GenomicRanges` –

Return type:

Optional[List[Optional[int]]]

Returns:

A List with the same length as query, containing hits to nearest indices.

get_row_ranges()[source]

Get genomic feature information.

Return type:

Union[GenomicRanges, GenomicRangesList]

Returns:

Genomic feature information.

get_slice(rows, columns)[source]

Alias for __getitem__, for back-compatibility.

Return type:

RangedSummarizedExperiment

narrow(start=None, width=None, end=None, in_place=False)[source]

Narrow genomic positions by provided start, width and end parameters.

Important: these parameters are relative shift in positions for each range.

Parameters:
Return type:

RangedSummarizedExperiment

Returns:

A new RangedSummarizedExperiment object with narrow positions, either as a copy of the original or as a reference to the (in-place-modified) original.

nearest(query, select='all', ignore_strand=False)[source]

Search nearest positions both upstream and downstream that overlap with each range in query.

Parameters:
Raises:

TypeError – If query is not a RangedSummarizedExperiment or GenomicRanges.

Return type:

Optional[List[Optional[int]]]

Returns:

A list with the same length as query, containing hits to nearest indices.

order(decreasing=False)[source]

Get the order of indices to sort.

Parameters:

decreasing (bool) – Whether to sort in descending order. Defaults to False.

Return type:

ndarray

Returns:

NumPy vector containing index positions in the sorted order.

precede(query, select='all', ignore_strand=False)[source]

Search nearest positions only downstream that overlap with each range in query.

Parameters:
Raises:

TypeError – If query is not a RangedSummarizedExperiment or GenomicRanges.

Return type:

Optional[List[Optional[int]]]

Returns:

A List with the same length as query, containing hits to nearest indices.

promoters(upstream=2000, downstream=200, in_place=False)[source]

Extend intervals to promoter regions.

Parameters:

  • upstream (int) – Number of positions to extend in the 5’ direction. Defaults to 2000.

  • downstream (int) – Number of positions to extend in the 3’ direction. Defaults to 200.

  • in_place (bool) – Whether to modify the GenomicRanges object in place.

Return type:

RangedSummarizedExperiment

Returns:

A new RangedSummarizedExperiment object with the extended ranges for promoter regions, either as a copy of the original or as a reference to the (in-place-modified) original.

resize(width, fix='start', ignore_strand=False, in_place=False)[source]

Resize ranges to the specified width where either the start, end, or center is used as an anchor.

Parameters:

  • width (Union[int, List[int], ndarray]) – Width to resize, cannot be negative!

  • fix (Literal['start', 'end', 'center']) – Fix positions by “start”, “end”, or “center”. Defaults to “start”.

  • ignore_strand (bool) – Whether to ignore strands. Defaults to False.

  • in_place (bool) – Whether to modify the GenomicRanges object in place.

Raises:

ValueError – If fix is neither start, center, or end.

Return type:

RangedSummarizedExperiment

Returns:

A new RangedSummarizedExperiment object with the resized ranges, either as a copy of the original or as a reference to the (in-place-modified) original.

restrict(start=None, end=None, keep_all_ranges=False, in_place=False)[source]

Restrict ranges to a given start and end positions.

Parameters:

  • start (Union[int, List[int], ndarray, None]) – Start position. Defaults to None.

  • end (Union[int, List[int], ndarray, None]) – End position. Defaults to None.

  • keep_all_ranges (bool) – Whether to keep intervals that do not overlap with start and end. Defaults to False.

  • in_place (bool) – Whether to modify the GenomicRanges object in place.

Return type:

RangedSummarizedExperiment

Returns:

A new RangedSummarizedExperiment object with restricted intervals, either as a copy of the original or as a reference to the (in-place-modified) original.

property row_ranges: GenomicRanges | GenomicRangesList

Alias for get_rowranges().

property seq_info: SeqInfo

Get sequence information object (if available).

Returns:

Sequence information.

property seqnames: List[str]

Get sequence or chromosome names.

Returns:

List of all chromosome names.

set_row_ranges(row_ranges, in_place=False)[source]

Set new genomic features.

Parameters:

  • row_ranges (Union[GenomicRanges, GenomicRangesList, None]) – Genomic features, must be the same length as the number of rows of the matrices in assays.

  • in_place (bool) – Whether to modify the RangeSummarizedExperiment in place.

Return type:

RangedSummarizedExperiment

Returns:

A modified RangeSummarizedExperiment object, either as a copy of the original or as a reference to the (in-place-modified) original.

shift(shift=0, in_place=False)[source]

Shift all intervals.

shift may be be negative.

Parameters:

  • shift (Union[int, List[int], ndarray]) – Shift interval. If shift is 0, the current object is returned. Defaults to 0.

  • in_place (bool) – Whether to modify the GenomicRanges object in place.

Return type:

RangedSummarizedExperiment

Returns:

A new RangedSummarizedExperiment object with the shifted ranges, either as a copy of the original or as a reference to the (in-place-modified) original.

sort(decreasing=False, in_place=False)[source]

Sort by ranges.

Parameters:

  • decreasing (bool) – Whether to sort in descending order. Defaults to False.

  • in_place (bool) – Whether to modify the object in place. Defaults to False.

Return type:

RangedSummarizedExperiment

Returns:

A new sorted RangedSummarizedExperiment object.

property start: ndarray

Get genomic start positions for each feature or row in experimental data.

Returns:

A numpy.ndarray of start positions.

property strand: ndarray

Get strand information.

Returns:

A numpy.ndarray of strand information.

subset_by_overlaps(query, query_type='any', max_gap=-1, min_overlap=1, ignore_strand=False)[source]

Subset a RangedSummarizedExperiment by feature overlaps.

Parameters:

  • query (Union[GenomicRanges, GenomicRangesList, RangedSummarizedExperiment]) –

    Query GenomicRanges.

    query may be a GenomicRanges or a RangedSummarizedExperiment object.

  • query_type (str) –

    Overlap query type, must be one of

    • ”any”: Any overlap is good

    • ”start”: Overlap at the beginning of the intervals

    • ”end”: Must overlap at the end of the intervals

    • ”within”: Fully contain the query interval

    Defaults to “any”.

  • max_gap (int) – Maximum gap allowed in the overlap. Defaults to -1 (no gap allowed).

  • min_overlap (int) – Minimum overlap with query. Defaults to 1.

  • ignore_strand (bool) – Whether to ignore strands. Defaults to False.

Raises:

TypeError – If query is not a RangedSummarizedExperiment or GenomicRanges.

Return type:

RangedSummarizedExperiment

Returns:

A new RangedSummarizedExperiment object. None if there are no indices to slice.

property width: ndarray

Get widths of row_ranges.

Returns:

A numpy.ndarray of widths for each interval.

summarizedexperiment.RangedSummarizedExperiment.combine_columns(*x)[source]

Combine multiple RangedSummarizedExperiment objects by column.

All assays must contain the same assay names. If you need a flexible combine operation, checkout relaxed_combine_columns().

Return type:

RangedSummarizedExperiment

Returns:

A combined RangedSummarizedExperiment.

summarizedexperiment.RangedSummarizedExperiment.combine_rows(*x)[source]

Combine multiple RangedSummarizedExperiment objects by row.

All assays must contain the same assay names. If you need a flexible combine operation, checkout relaxed_combine_rows().

Return type:

RangedSummarizedExperiment

Returns:

A combined RangedSummarizedExperiment.

summarizedexperiment.RangedSummarizedExperiment.relaxed_combine_columns(*x)[source]

A relaxed version of the combine_columns() method for RangedSummarizedExperiment objects. Whereas combine_columns expects that all objects have the same rows, relaxed_combine_columns allows for different rows. Absent columns in any object are filled in with appropriate placeholder values before combining.

Parameters:

x (RangedSummarizedExperiment) – One or more RangedSummarizedExperiment objects, possibly with differences in the number and identity of their rows.

Return type:

RangedSummarizedExperiment

Returns:

A RangedSummarizedExperiment that combines all experiments along their columns and contains the union of all rows. Rows absent in any x are filled in with placeholders consisting of Nones or masked NumPy values.

summarizedexperiment.RangedSummarizedExperiment.relaxed_combine_rows(*x)[source]

A relaxed version of the combine_rows() method for RangedSummarizedExperiment objects. Whereas combine_rows expects that all objects have the same columns, relaxed_combine_rows allows for different columns. Absent columns in any object are filled in with appropriate placeholder values before combining.

Parameters:

x (RangedSummarizedExperiment) – One or more RangedSummarizedExperiment objects, possibly with differences in the number and identity of their columns.

Return type:

RangedSummarizedExperiment

Returns:

A RangedSummarizedExperiment that combines all experiments along their rows and contains the union of all columns. Columns absent in any x are filled in with placeholders consisting of Nones or masked NumPy values.

summarizedexperiment.SummarizedExperiment module

class summarizedexperiment.SummarizedExperiment.SummarizedExperiment(assays=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]

Bases: BaseSE

Container to represents genomic experiment data (assays), features (row_data), sample data (column_data) and any other metadata.

SummarizedExperiment follows the R/Bioconductor specification; rows are features, columns are samples.

__annotations__ = {}
__init__(assays=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]

Initialize a Summarized Experiment (SE).

Parameters:

  • assays (Dict[str, Any]) –

    A dictionary containing matrices, with assay names as keys and 2-dimensional matrices represented as either ndarray or spmatrix.

    Alternatively, you may use any 2-dimensional matrix that has the shape property and implements the slice operation using the __getitem__ dunder method.

    All matrices in assays must be 2-dimensional and have the same shape (number of rows, number of columns).

  • row_data (Optional[BiocFrame]) –

    Features, must be the same length as the number of rows of the matrices in assays.

    Feature information is coerced to a BiocFrame. Defaults to None.

  • column_data (Optional[BiocFrame]) –

    Sample data, must be the same length as the number of columns of the matrices in assays.

    Sample information is coerced to a BiocFrame. Defaults to None.

  • row_names (Optional[List[str]]) –

    A list of strings, same as the number of rows.

    If row_names are not provided, these are inferred from row_data.

    Defaults to None.

  • column_names (Optional[List[str]]) –

    A list of string, same as the number of columns.

    if column_names are not provided, these are inferred from column_data.

    Defaults to None.

  • metadata (Optional[dict]) – Additional experimental metadata describing the methods. Defaults to None.

  • validate (bool) – Internal use only.

summarizedexperiment.SummarizedExperiment.combine_columns(*x)[source]

Combine multiple SummarizedExperiment objects by column.

All assays must contain the same assay names. If you need a flexible combine operation, checkout relaxed_combine_columns().

Return type:

SummarizedExperiment

Returns:

A combined SummarizedExperiment.

summarizedexperiment.SummarizedExperiment.combine_rows(*x)[source]

Combine multiple SummarizedExperiment objects by row.

All assays must contain the same assay names. If you need a flexible combine operation, checkout relaxed_combine_rows().

Return type:

SummarizedExperiment

Returns:

A combined SummarizedExperiment.

summarizedexperiment.SummarizedExperiment.relaxed_combine_columns(*x)[source]

A relaxed version of the combine_columns() method for SummarizedExperiment objects. Whereas combine_columns expects that all objects have the same rows, relaxed_combine_columns allows for different rows. Absent columns in any object are filled in with appropriate placeholder values before combining.

Parameters:

x (SummarizedExperiment) – One or more SummarizedExperiment objects, possibly with differences in the number and identity of their rows.

Return type:

SummarizedExperiment

Returns:

A SummarizedExperiment that combines all experiments along their columns and contains the union of all rows. Rows absent in any x are filled in with placeholders consisting of Nones or masked NumPy values.

summarizedexperiment.SummarizedExperiment.relaxed_combine_rows(*x)[source]

A relaxed version of the combine_rows() method for SummarizedExperiment objects. Whereas combine_rows expects that all objects have the same columns, relaxed_combine_rows allows for different columns. Absent columns in any object are filled in with appropriate placeholder values before combining.

Parameters:

x (SummarizedExperiment) – One or more SummarizedExperiment objects, possibly with differences in the number and identity of their columns.

Return type:

SummarizedExperiment

Returns:

A SummarizedExperiment that combines all experiments along their rows and contains the union of all columns. Columns absent in any x are filled in with placeholders consisting of Nones or masked NumPy values.

summarizedexperiment.type_checks module

summarizedexperiment.type_checks.is_matrix_like(x)[source]

Check if x is a matrix-like object.

Matrix must support the matrix protocol, has the shape property and allows slicing by implementing the __getitem__ dunder method.

Parameters:

x (Any) – Any object.

Return type:

bool

Returns:

True if ``x``is matrix-like.

Module contents