def write_multimodal_data(self, data: MultimodalData, overwrite: bool = True) -> None:
""" Write MultimodalData
"""
if overwrite:
for key in data.list_data():
self.write_unimodal_data(self.root, key, data.get_data(key), overwrite = overwrite)
else:
for key in data.data.deleted:
del self.root[key]
for key in data.data.accessed:
self.write_unimodal_data(self.root, key, data.get_data(key), overwrite = key in data.data.modified)
self.root.attrs['_selected'] = data._selected
def write_mtx_file(data: MultimodalData, output_directory: str, precision: int = 2):
""" Write output to mtx files in output_directory
"""
output_dir = os.path.abspath(output_directory)
os.makedirs(output_dir, exist_ok = True)
for key in data.list_data():
_write_mtx(data.get_data(key), os.path.join(output_dir, key), precision)
logger.info("Mtx files are written.")
def _run_filter_data(
data: MultimodalData,
focus_list: List[str] = None,
output_filt: str = None,
plot_filt: str = None,
plot_filt_figsize: Tuple[int, int] = None,
min_genes_before_filt: int = 100,
select_singlets: bool = False,
remap_string: str = None,
subset_string: str = None,
min_genes: int = 500,
max_genes: int = 6000,
min_umis: int = None,
max_umis: int = None,
mito_prefix: str = "MT-",
percent_mito: float = 20.0,
percent_cells: float = 0.05,
) -> None:
""" This function is for command line use.
"""
if focus_list is None:
focus_list = [data.current_key()]
mito_dict = DictWithDefault(mito_prefix)
for key in focus_list:
unidata = data.get_data(key)
qc_metrics(
unidata,
select_singlets,
remap_string,
subset_string,
min_genes,
max_genes,
min_umis,
max_umis,
mito_dict.get(unidata.get_genome()),
percent_mito,
)
if output_filt is not None:
group_key = unidata.get_uid()
writer = pd.ExcelWriter(f"{output_filt}.{group_key}.filt.xlsx",
engine="xlsxwriter")
df_cells = get_filter_stats(
unidata, min_genes_before_filt=min_genes_before_filt)
df_cells.to_excel(writer, sheet_name="Cell filtration stats")
writer.save()
logger.info(f"Filtration results for {group_key} are written.")
if plot_filt is not None:
_generate_filter_plots(unidata,
plot_filt,
plot_filt_figsize=plot_filt_figsize,
min_genes_before_filt=min_genes_before_filt)
filter_data(data, focus_list=focus_list)
for key in focus_list:
unidata = data.get_data(key)
identify_robust_genes(unidata, percent_cells=percent_cells)
def add_data(self, data: MultimodalData) -> None:
for key in data.list_data():
self.aggr[key].append(data.get_data(key))
def write_scp_file(data: MultimodalData, output_name: str, is_sparse: bool = True, precision: int = 2) -> None:
"""Generate outputs for single cell portal from a MultimodalData object
Parameters
----------
data: MultimodalData
A MultimodalData object.
output_name: ``str``
Name prefix for output files.
is_sparse: ``bool``, optional, default: ``True``
If ``True``, enforce the count matrix to be sparse after written into files.
precision: ``int``, optional, default: ``2``
Round numbers to ``precision`` decimal places.
Returns
-------
``None``
If data contains only one modality, files are generated as follows:
* ``output_name.scp.basis.coords.txt``, where ``basis`` is for each key in ``adata.obsm`` field.
* ``output_name.scp.metadata.txt``.
* Gene expression files:
* If in sparse format:
* ``output_name.scp.features.tsv``, information on genes;
* ``output_name.scp.barcodes.tsv``, information on cell barcodes;
* ``output_name.scp.matrix.mtx``, count matrix.
* If not in sparse:
* ``output_name.scp.expr.txt``.
Otherwise, under the directory os.path.dirname(output_name), we will create separate folders per key and each separate folder has its own files in the format above.
Examples
--------
>>> io.write_scp_file(data, output_name = "scp_result")
"""
output_name = os.path.abspath(output_name)
valid_keys = []
for key in data.list_data():
try:
mat = data.get_data(key).get_matrix("X")
if mat.data.size > 0:
valid_keys.append(key)
except ValueError:
pass
if len(valid_keys) == 1:
unidata = data.get_data(valid_keys[0])
_write_scp_metadata(unidata, output_name, precision = precision)
_write_scp_coords(unidata, output_name, precision = precision)
_write_scp_expression(unidata, output_name, is_sparse, precision = precision)
else:
path = os.path.dirname(output_name)
fname = os.path.basename(output_name)
for key in valid_keys:
subpath = os.path.join(path, key)
if not os.path.isdir(subpath):
os.path.mkdir(subpath)
out_new_name = os.path.join(subpath, fname)
unidata = data.get_data(key)
_write_scp_metadata(unidata, out_new_name, precision = precision)
_write_scp_coords(unidata, out_new_name, precision = precision)
_write_scp_expression(unidata, out_new_name, is_sparse, precision = precision)
logger.info("write_scp_file is done.")
