AnnData stores a data matrix X together with annotations of observations obs (obsm, obsp), variables var (varm, varp), and unstructured annotations uns.

An AnnData object adata can be sliced like a data frame, for instance adata_subset <- adata[, list_of_variable_names]. AnnData’s basic structure is similar to R's ExpressionSet.

If setting an h5ad-formatted HDF5 backing file filename, data remains on the disk but is automatically loaded into memory if needed. See this blog post for more details.

AnnData(
  X = NULL,
  obs = NULL,
  var = NULL,
  uns = NULL,
  obsm = NULL,
  varm = NULL,
  layers = NULL,
  raw = NULL,
  dtype = "float32",
  shape = NULL,
  filename = NULL,
  filemode = NULL,
  obsp = NULL,
  varp = NULL
)

Raw(adata, X = NULL, var = NULL, varm = NULL)

Arguments

X

A #observations × #variables data matrix.

obs

Key-indexed one-dimensional observations annotation of length #observations.

var

Key-indexed one-dimensional variables annotation of length #variables.

uns

Key-indexed unstructured annotation.

obsm

Key-indexed multi-dimensional observations annotation of length #observations. If passing a ~numpy.ndarray, it needs to have a structured datatype.

varm

Key-indexed multi-dimensional variables annotation of length #variables.

layers

Key-indexed multi-dimensional arrays aligned to dimensions of X.

raw

Store raw version of X and var as $raw$X and $raw$var.

dtype

Data type used for storage.

shape

Shape list (#observations, #variables). Can only be provided if X is NULL.

filename

Name of backing file. See h5py.File.

filemode

Open mode of backing file. See h5py.File.

obsp

Pairwise annotation of observations, a mutable mapping with array-like values.

varp

Pairwise annotation of observations, a mutable mapping with array-like values.

adata

An AnnData object.

Details

AnnData stores observations (samples) of variables/features in the rows of a matrix. This is the convention of the modern classics of statistic and machine learning, the convention of dataframes both in R and Python and the established statistics and machine learning packages in Python (statsmodels, scikit-learn).

Single dimensional annotations of the observation and variables are stored in the obs and var attributes as data frames. This is intended for metrics calculated over their axes. Multi-dimensional annotations are stored in obsm and varm, which are aligned to the objects observation and variable dimensions respectively. Square matrices representing graphs are stored in obsp and varp, with both of their own dimensions aligned to their associated axis. Additional measurements across both observations and variables are stored in layers.

Indexing into an AnnData object can be performed by relative position with numeric indices, or by labels. To avoid ambiguity with numeric indexing into observations or variables, indexes of the AnnData object are converted to strings by the constructor.

Subsetting an AnnData object by indexing into it will also subset its elements according to the dimensions they were aligned to. This means an operation like adata[list_of_obs, ] will also subset obs, obsm, and layers.

Subsetting an AnnData object returns a view into the original object, meaning very little additional memory is used upon subsetting. This is achieved lazily, meaning that the constituent arrays are subset on access. Copying a view causes an equivalent “real” AnnData object to be generated. Attempting to modify a view (at any attribute except X) is handled in a copy-on-modify manner, meaning the object is initialized in place. Here’s an example

batch1 <- adata[adata$obs["batch"] == "batch1", ]
batch1$obs["value"] = 0 # This makes batch1 a “real” AnnData object

At the end of this snippet: adata was not modified, and batch1 is its own AnnData object with its own data.

Similar to Bioconductor’s ExpressionSet and scipy.sparse matrices, subsetting an AnnData object retains the dimensionality of its constituent arrays. Therefore, unlike with the classes exposed by pandas, numpy, and xarray, there is no concept of a one dimensional AnnData object. AnnDatas always have two inherent dimensions, obs and var. Additionally, maintaining the dimensionality of the AnnData object allows for consistent handling of scipy.sparse matrices and numpy arrays.

See also

Active bindings

X

Data matrix of shape n_obs × n_vars.

filename

Name of the backing file.Change to backing mode by setting the filename of a .h5ad file.

  • Setting the filename writes the stored data to disk.

  • Setting the filename when the filename was previously another name moves the backing file from the previous file to the new file. If you want to copy the previous file, use copy(filename='new_filename').

layers

A list-like object with values of the same dimensions as X. Layers in AnnData are inspired by loompy's layers.Overwrite the layers:

adata$layers <- list(spliced = spliced, unspliced = unspliced)
Return the layer named "unspliced":
adata$layers["unspliced"]
Create or replace the "spliced" layer:
adata$layers["spliced"] = example_matrix
Assign the 10th column of layer "spliced" to the variable a:
a <- adata$layers["spliced"][, 10]
Delete the "spliced":
adata$layers["spliced"] <- NULL
Return layers' names:
names(adata$layers)

T

Transpose whole object.Data matrix is transposed, observations and variables are interchanged.Ignores .raw.

is_view

TRUE if object is view of another AnnData object, FALSE otherwise.

isbacked

TRUE if object is backed on disk, FALSE otherwise.

n_obs

Number of observations.

obs

One-dimensional annotation of observations (data.frame).

obs_names

Names of observations.

obsm

Multi-dimensional annotation of observations (matrix).Stores for each key a two or higher-dimensional matrix with n_obs rows.

obsp

Pairwise annotation of observations, a mutable mapping with array-like values.Stores for each key a two or higher-dimensional matrix whose first two dimensions are of length n_obs.

n_vars

Number of variables.

var

One-dimensional annotation of variables (data.frame).

var_names

Names of variables.

varm

Multi-dimensional annotation of variables (matrix).Stores for each key a two or higher-dimensional matrix with n_vars rows.

varp

Pairwise annotation of variables, a mutable mapping with array-like values.Stores for each key a two or higher-dimensional matrix whose first two dimensions are of length n_vars.

shape

Shape of data matrix (n_obs, n_vars).

uns

Unstructured annotation (ordered dictionary).

raw

Store raw version of X and var as $raw$X and $raw$var.The raw attribute is initialized with the current content of an object by setting:

adata$raw = adata
Its content can be deleted:
adata$raw <- NULL
Upon slicing an AnnData object along the obs (row) axis, raw is also sliced. Slicing an AnnData object along the vars (columns) axis leaves raw unaffected. Note that you can call:
adata$raw[, 'orig_variable_name']$X
to retrieve the data associated with a variable that might have been filtered out or "compressed away" inX`.

Methods

Public methods


Method new()

Create a new AnnData object

Usage

AnnDataR6$new(obj)

Arguments

obj

A Python anndata object

Examples

\dontrun{
# use AnnData() instead of AnnDataR6$new()
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2"))
)
}


Method obs_keys()

List keys of observation annotation obs.

Usage

AnnDataR6$obs_keys()

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2"))
)
ad$obs_keys()
}


Method obs_names_make_unique()

Makes the index unique by appending a number string to each duplicate index element: 1, 2, etc.

If a tentative name created by the algorithm already exists in the index, it tries the next integer in the sequence.

The first occurrence of a non-unique value is ignored.

Usage

AnnDataR6$obs_names_make_unique(join = "-")

Arguments

join

The connecting string between name and integer (default: "-").

Examples

\dontrun{
ad <- AnnData(
  X = matrix(rep(1, 6), nrow = 3),
  obs = data.frame(field = c(1, 2, 3))
)
ad$obs_names <- c("a", "a", "b")
ad$obs_names_make_unique()
ad$obs_names
}


Method obsm_keys()

List keys of observation annotation obsm.

Usage

AnnDataR6$obsm_keys()

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  obsm = list(
    ones = matrix(rep(1L, 10), nrow = 2),
    rand = matrix(rnorm(6), nrow = 2),
    zeros = matrix(rep(0L, 10), nrow = 2)
  )
)
ad$obs_keys()
}


Method var_keys()

List keys of variable annotation var.

Usage

AnnDataR6$var_keys()

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2"))
)
ad$var_keys()
}


Method var_names_make_unique()

Makes the index unique by appending a number string to each duplicate index element: 1, 2, etc.

If a tentative name created by the algorithm already exists in the index, it tries the next integer in the sequence.

The first occurrence of a non-unique value is ignored.

Usage

AnnDataR6$var_names_make_unique(join = "-")

Arguments

join

The connecting string between name and integer (default: "-").

Examples

\dontrun{
ad <- AnnData(
  X = matrix(rep(1, 6), nrow = 2),
  var = data.frame(field = c(1, 2, 3))
)
ad$var_names <- c("a", "a", "b")
ad$var_names_make_unique()
ad$var_names
}


Method varm_keys()

List keys of variable annotation varm.

Usage

AnnDataR6$varm_keys()

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
  varm = list(
    ones = matrix(rep(1L, 10), nrow = 2),
    rand = matrix(rnorm(6), nrow = 2),
    zeros = matrix(rep(0L, 10), nrow = 2)
  )
)
ad$varm_keys()
}


Method uns_keys()

List keys of unstructured annotation uns.

Usage

AnnDataR6$uns_keys()

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
  uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4))
)
}


Method chunk_X()

Return a chunk of the data matrix X with random or specified indices.

Usage

AnnDataR6$chunk_X(select = 1000L, replace = TRUE)

Arguments

select

Depending on the values:

  • 1 integer: A random chunk with select rows will be returned.

  • multiple integers: A chunk with these indices will be returned.

replace

if select is an integer then TRUE means random sampling of indices with replacement, FALSE without replacement.

Examples

\dontrun{
ad <- AnnData(
  X = matrix(runif(10000), nrow = 50)
)

ad$chunk_X(select = 10L) # 10 random samples
ad$chunk_X(select = 1:3) # first 3 samples
}


Method chunked_X()

Return an iterator over the rows of the data matrix X.

Usage

AnnDataR6$chunked_X(chunk_size = NULL)

Arguments

chunk_size

Row size of a single chunk.

Examples

\dontrun{
ad <- AnnData(
  X = matrix(runif(10000), nrow = 50)
)
ad$chunked_X(10)
}


Method concatenate()

Concatenate along the observations axis.

Usage

AnnDataR6$concatenate(...)

Arguments

...

Deprecated


Method copy()

Full copy, optionally on disk.

Usage

AnnDataR6$copy(filename = NULL)

Arguments

filename

Path to filename (default: NULL).

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2)
)
ad$copy()
ad$copy("file.h5ad")
}


Method rename_categories()

Rename categories of annotation key in obs, var, and uns. Only supports passing a list/array-like categories argument. Besides calling self.obs[key].cat.categories = categories – similar for var - this also renames categories in unstructured annotation that uses the categorical annotation key.

Usage

AnnDataR6$rename_categories(key, categories)

Arguments

key

Key for observations or variables annotation.

categories

New categories, the same number as the old categories.

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2"))
)
ad$rename_categories("group", c(a = "A", b = "B")) # ??
}


Method strings_to_categoricals()

Transform string annotations to categoricals.

Only affects string annotations that lead to less categories than the total number of observations.

Usage

AnnDataR6$strings_to_categoricals(df = NULL)

Arguments

df

If df is NULL, modifies both obs and var, otherwise modifies df inplace.

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
)
ad$strings_to_categoricals() # ??
}


Method to_df()

Generate shallow data frame.

The data matrix X is returned as data frame, where obs_names are the rownames, and var_names the columns names.

No annotations are maintained in the returned object.

The data matrix is densified in case it is sparse.

Usage

AnnDataR6$to_df(layer = NULL)

Arguments

layer

Key for layers

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
  layers = list(
    spliced = matrix(c(4, 5, 6, 7), nrow = 2),
    unspliced = matrix(c(8, 9, 10, 11), nrow = 2)
  )
)

ad$to_df()
ad$to_df("unspliced")
}


Method transpose()

transpose Transpose whole object.

Data matrix is transposed, observations and variables are interchanged.

Ignores .raw.

Usage

AnnDataR6$transpose()

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2"))
)

ad$transpose()
}


Method write_csvs()

Write annotation to .csv files.

It is not possible to recover the full AnnData from these files. Use write_h5ad() for this.

Usage

AnnDataR6$write_csvs(dirname, skip_data = TRUE, sep = ",")

Arguments

dirname

Name of the directory to which to export.

skip_data

Skip the data matrix X.

sep

Separator for the data

anndata

An AnnData() object

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
  varm = list(
    ones = matrix(rep(1L, 10), nrow = 2),
    rand = matrix(rnorm(6), nrow = 2),
    zeros = matrix(rep(0L, 10), nrow = 2)
  ),
  uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4))
)

ad$to_write_csvs("output")

unlink("output", recursive = TRUE)
}


Method write_h5ad()

Write .h5ad-formatted hdf5 file.

Generally, if you have sparse data that are stored as a dense matrix, you can dramatically improve performance and reduce disk space by converting to a csr_matrix:

Usage

AnnDataR6$write_h5ad(
  filename,
  compression = NULL,
  compression_opts = NULL,
  as_dense = list()
)

Arguments

filename

Filename of data file. Defaults to backing file.

compression

See the h5py filter pipeline. Options are "gzip", "lzf" or NULL.

compression_opts

See the h5py filter pipeline.

as_dense

Sparse in AnnData object to write as dense. Currently only supports "X" and "raw/X".

anndata

An AnnData() object

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
  varm = list(
    ones = matrix(rep(1L, 10), nrow = 2),
    rand = matrix(rnorm(6), nrow = 2),
    zeros = matrix(rep(0L, 10), nrow = 2)
  ),
  uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4))
)

ad$write_h5ad("output.h5ad")

file.remove("output.h5ad")
}


Method write_loom()

Write .loom-formatted hdf5 file.

Usage

AnnDataR6$write_loom(filename, write_obsm_varm = FALSE)

Arguments

filename

The filename.

write_obsm_varm

Whether or not to also write the varm and obsm.

anndata

An AnnData() object

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
  varm = list(
    ones = matrix(rep(1L, 10), nrow = 2),
    rand = matrix(rnorm(6), nrow = 2),
    zeros = matrix(rep(0L, 10), nrow = 2)
  ),
  uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4))
)

ad$write_loom("output.loom")

file.remove("output.loom")
}


Method print()

Print AnnData object

Usage

AnnDataR6$print(...)

Arguments

...

optional arguments to print method.

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
  layers = list(
    spliced = matrix(c(4, 5, 6, 7), nrow = 2),
    unspliced = matrix(c(8, 9, 10, 11), nrow = 2)
  ),
  obsm = list(
    ones = matrix(rep(1L, 10), nrow = 2),
    rand = matrix(rnorm(6), nrow = 2),
    zeros = matrix(rep(0L, 10), nrow = 2)
  ),
  varm = list(
    ones = matrix(rep(1L, 10), nrow = 2),
    rand = matrix(rnorm(6), nrow = 2),
    zeros = matrix(rep(0L, 10), nrow = 2)
  ),
  uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4))
)

ad$print()
print(ad)
}


Method .set_py_object()

Set internal Python object

Usage

AnnDataR6$.set_py_object(obj)

Arguments

obj

A python anndata object


Method .get_py_object()

Get internal Python object

Usage

AnnDataR6$.get_py_object()

Active bindings

X

Data matrix of shape n_obs × n_vars.

n_obs

Number of observations.

obs_names

Names of observations.

n_vars

Number of variables.

var

One-dimensional annotation of variables (data.frame).

var_names

Names of variables.

varm

Multi-dimensional annotation of variables (matrix).Stores for each key a two or higher-dimensional matrix with n_var rows.

shape

Shape of data matrix (n_obs, n_vars).

Methods

Public methods


Method new()

Create a new Raw object

Usage

RawR6$new(obj)

Arguments

obj

A Python Raw object


Method copy()

Full copy, optionally on disk.

Usage

RawR6$copy()

Arguments

filename

Path to filename (default: NULL).

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2)
)
ad$copy()
ad$copy("file.h5ad")
}


Method to_adata()

Create a full AnnData object

Usage

RawR6$to_adata()

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
  layers = list(
    spliced = matrix(c(4, 5, 6, 7), nrow = 2),
    unspliced = matrix(c(8, 9, 10, 11), nrow = 2)
  )
)
ad$raw <- ad

ad$raw$to_adata()
}


Method print()

Print Raw object

Usage

RawR6$print(...)

Arguments

...

optional arguments to print method.

Examples

\dontrun{
ad <- AnnData(
  X = matrix(c(0, 1, 2, 3), nrow = 2),
  obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
  var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
  layers = list(
    spliced = matrix(c(4, 5, 6, 7), nrow = 2),
    unspliced = matrix(c(8, 9, 10, 11), nrow = 2)
  ),
  obsm = list(
    ones = matrix(rep(1L, 10), nrow = 2),
    rand = matrix(rnorm(6), nrow = 2),
    zeros = matrix(rep(0L, 10), nrow = 2)
  ),
  varm = list(
    ones = matrix(rep(1L, 10), nrow = 2),
    rand = matrix(rnorm(6), nrow = 2),
    zeros = matrix(rep(0L, 10), nrow = 2)
  ),
  uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4))
)
ad$raw <- ad

library(reticulate)
sc <- import("scanpy")
sc$pp$normalize_per_cell(ad)

ad[]
ad$raw[]

ad$print()
print(ad)
}


Method .set_py_object()

Set internal Python object

Usage

RawR6$.set_py_object(obj)

Arguments

obj

A Python Raw object


Method .get_py_object()

Get internal Python object

Usage

RawR6$.get_py_object()

Examples

if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), layers = list( spliced = matrix(c(4, 5, 6, 7), nrow = 2), unspliced = matrix(c(8, 9, 10, 11), nrow = 2) ), obsm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), varm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4)) ) value <- matrix(c(1,2,3,4), nrow = 2) ad$X <- value ad$X ad$layers ad$layers["spliced"] ad$layers["test"] <- value ad$layers ad$to_df() ad$uns as.matrix(ad) as.matrix(ad, layer = "unspliced") dim(ad) rownames(ad) colnames(ad) } ## ------------------------------------------------ ## Method `AnnDataR6$new` ## ------------------------------------------------ if (FALSE) { # use AnnData() instead of AnnDataR6$new() ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")) ) } ## ------------------------------------------------ ## Method `AnnDataR6$obs_keys` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")) ) ad$obs_keys() } ## ------------------------------------------------ ## Method `AnnDataR6$obs_names_make_unique` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(rep(1, 6), nrow = 3), obs = data.frame(field = c(1, 2, 3)) ) ad$obs_names <- c("a", "a", "b") ad$obs_names_make_unique() ad$obs_names } ## ------------------------------------------------ ## Method `AnnDataR6$obsm_keys` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), obsm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ) ) ad$obs_keys() } ## ------------------------------------------------ ## Method `AnnDataR6$var_keys` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")) ) ad$var_keys() } ## ------------------------------------------------ ## Method `AnnDataR6$var_names_make_unique` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(rep(1, 6), nrow = 2), var = data.frame(field = c(1, 2, 3)) ) ad$var_names <- c("a", "a", "b") ad$var_names_make_unique() ad$var_names } ## ------------------------------------------------ ## Method `AnnDataR6$varm_keys` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), varm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ) ) ad$varm_keys() } ## ------------------------------------------------ ## Method `AnnDataR6$uns_keys` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4)) ) } ## ------------------------------------------------ ## Method `AnnDataR6$chunk_X` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(runif(10000), nrow = 50) ) ad$chunk_X(select = 10L) # 10 random samples ad$chunk_X(select = 1:3) # first 3 samples } ## ------------------------------------------------ ## Method `AnnDataR6$chunked_X` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(runif(10000), nrow = 50) ) ad$chunked_X(10) } ## ------------------------------------------------ ## Method `AnnDataR6$copy` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2) ) ad$copy() ad$copy("file.h5ad") } ## ------------------------------------------------ ## Method `AnnDataR6$rename_categories` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")) ) ad$rename_categories("group", c(a = "A", b = "B")) # ?? } ## ------------------------------------------------ ## Method `AnnDataR6$strings_to_categoricals` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), ) ad$strings_to_categoricals() # ?? } ## ------------------------------------------------ ## Method `AnnDataR6$to_df` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), layers = list( spliced = matrix(c(4, 5, 6, 7), nrow = 2), unspliced = matrix(c(8, 9, 10, 11), nrow = 2) ) ) ad$to_df() ad$to_df("unspliced") } ## ------------------------------------------------ ## Method `AnnDataR6$transpose` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")) ) ad$transpose() } ## ------------------------------------------------ ## Method `AnnDataR6$write_csvs` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), varm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4)) ) ad$to_write_csvs("output") unlink("output", recursive = TRUE) } ## ------------------------------------------------ ## Method `AnnDataR6$write_h5ad` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), varm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4)) ) ad$write_h5ad("output.h5ad") file.remove("output.h5ad") } ## ------------------------------------------------ ## Method `AnnDataR6$write_loom` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), varm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4)) ) ad$write_loom("output.loom") file.remove("output.loom") } ## ------------------------------------------------ ## Method `AnnDataR6$print` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), layers = list( spliced = matrix(c(4, 5, 6, 7), nrow = 2), unspliced = matrix(c(8, 9, 10, 11), nrow = 2) ), obsm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), varm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4)) ) ad$print() print(ad) } if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), layers = list( spliced = matrix(c(4, 5, 6, 7), nrow = 2), unspliced = matrix(c(8, 9, 10, 11), nrow = 2) ), obsm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), varm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4)) ) ad$raw <- ad library(reticulate) sc <- import("scanpy") sc$pp$normalize_per_cell(ad) ad[] ad$raw[] } ## ------------------------------------------------ ## Method `RawR6$copy` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2) ) ad$copy() ad$copy("file.h5ad") } ## ------------------------------------------------ ## Method `RawR6$to_adata` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), layers = list( spliced = matrix(c(4, 5, 6, 7), nrow = 2), unspliced = matrix(c(8, 9, 10, 11), nrow = 2) ) ) ad$raw <- ad ad$raw$to_adata() } ## ------------------------------------------------ ## Method `RawR6$print` ## ------------------------------------------------ if (FALSE) { ad <- AnnData( X = matrix(c(0, 1, 2, 3), nrow = 2), obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")), var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")), layers = list( spliced = matrix(c(4, 5, 6, 7), nrow = 2), unspliced = matrix(c(8, 9, 10, 11), nrow = 2) ), obsm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), varm = list( ones = matrix(rep(1L, 10), nrow = 2), rand = matrix(rnorm(6), nrow = 2), zeros = matrix(rep(0L, 10), nrow = 2) ), uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4)) ) ad$raw <- ad library(reticulate) sc <- import("scanpy") sc$pp$normalize_per_cell(ad) ad[] ad$raw[] ad$print() print(ad) }