Python loompy.create方法代碼示例

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import create [as 別名]
def export_regulons(regulons: Sequence[Regulon], fname: str) -> None:
    """
    Export regulons as GraphML.
    :param regulons: The sequence of regulons to export.
    :param fname: The name of the file to create.
    """
    graph = nx.DiGraph()
    for regulon in regulons:
        src_name = regulon.transcription_factor
        graph.add_node(src_name, group='transcription_factor')
        edge_type = 'activating' if 'activating' in regulon.context else 'inhibiting'
        node_type = 'activated_target' if 'activating' in regulon.context else 'inhibited_target'
        for dst_name, edge_strength in regulon.gene2weight.items():
            graph.add_node(dst_name, group=node_type, **regulon.context)
            graph.add_edge(src_name, dst_name, weight=edge_strength, interaction=edge_type, **regulon.context)
    nx.readwrite.write_graphml(graph, fname) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import create [as 別名]
def save_df_as_loom(df: pd.DataFrame, fname: str) -> None:
    """
    Save pandas dataframe as single layer loom file. Can be used to save expression matrix or AUC value matrix
    as binary loom file.

    :param df: The 2-dimensional dataframe (rows = cells x columns = genes).
    :param fname: The name of the loom file to create.
    """
    assert df.ndim == 2
    # The orientation of the loom file is always:
    #   - Columns represent cells or aggregates of cells
    # 	- Rows represent genes
    column_attrs = { ATTRIBUTE_NAME_CELL_IDENTIFIER: df.index.values.astype('str'), }
    row_attrs = { ATTRIBUTE_NAME_GENE: df.columns.values.astype('str'), }
    lp.create(filename=fname,
              layers=df.T.values,
              row_attrs=row_attrs,
              col_attrs=column_attrs) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import create [as 別名]
def save_loom(self, filename: str) -> None:
        """Save an ExpressionMatrix as a loom file

        Parameters
        ----------
        filename : str
            Name of loom file
        """
        import loompy

        row_attrs = {k: self[Features.CELLS][k].values for k in self[Features.CELLS].coords}
        col_attrs = {k: self[Features.GENES][k].values for k in self[Features.GENES].coords}

        loompy.create(filename, self.data, row_attrs, col_attrs) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import create [as 別名]
def create_append(filename: str, layers: Union[np.ndarray, Dict[str, np.ndarray], loompy.LayerManager], row_attrs: Dict[str, np.ndarray], col_attrs: Dict[str, np.ndarray], *, file_attrs: Dict[str, str] = None, fill_values: Dict[str, np.ndarray] = None) -> None:
	"""
	**DEPRECATED** - Use `new` instead; see https://github.com/linnarsson-lab/loompy/issues/42
	"""
	deprecated("'create_append' is deprecated. See https://github.com/linnarsson-lab/loompy/issues/42")
	if os.path.exists(filename):
		with connect(filename) as ds:
			ds.add_columns(layers, col_attrs, fill_values=fill_values)
	else:
		create(filename, layers, row_attrs, col_attrs, file_attrs=file_attrs) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import create [as 別名]
def test_file_with_empty_col_attrs_is_valid(self) -> None:
        f = NamedTemporaryFile(suffix=".loom")
        f.close()
        loompy.create(f.name, np.zeros((5, 5)), {}, {})
        try:
            self.assertTrue(
                LoomValidator().validate(f.name),
                "File with empty col_attrs or row_attrs should be valid"
            )
        finally:
            os.remove(f.name) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import create [as 別名]
def setUp(self) -> None:
        self.file = NamedTemporaryFile(suffix=".loom")
        self.file.close()
        loompy.create(
            self.file.name,
            np.random.random((5, 5)),
            row_attrs={
                "key": np.fromiter(range(5), dtype=np.int)
            },
            col_attrs={
                "key": np.fromiter(range(5), dtype=np.int)
            }) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import create [as 別名]
def doGeneSetEnrichment(self, request, context):
        gene_set_file_path = os.path.join(self.dfh.get_gene_sets_dir(), request.geneSetFilePath)
        loom = self.lfh.get_loom(loom_file_path=request.loomFilePath)
        gse = _gse.GeneSetEnrichment(
            scope=self, method="AUCell", loom=loom, gene_set_file_path=gene_set_file_path, annotation=""
        )

        # Running AUCell...
        yield gse.update_state(step=-1, status_code=200, status_message="Running AUCell...", values=None)
        time.sleep(1)

        # Reading gene set...
        yield gse.update_state(step=0, status_code=200, status_message="Reading the gene set...", values=None)
        with open(gse.gene_set_file_path, "r") as f:
            # Skip first line because it contains the name of the signature
            gs = GeneSignature(
                name="Gene Signature #1", gene2weight=[line.strip() for idx, line in enumerate(f) if idx > 0]
            )
        time.sleep(1)

        if not gse.has_AUCell_rankings():
            # Creating the matrix as DataFrame...
            yield gse.update_state(step=1, status_code=200, status_message="Creating the matrix...", values=None)
            loom = self.lfh.get_loom(loom_file_path=request.loomFilePath)
            dgem = np.transpose(loom.get_connection()[:, :])
            ex_mtx = pd.DataFrame(data=dgem, index=loom.get_ca_attr_by_name("CellID"), columns=loom.get_genes())
            # Creating the rankings...
            start_time = time.time()
            yield gse.update_state(step=2.1, status_code=200, status_message="Creating the rankings...", values=None)
            rnk_mtx = create_rankings(ex_mtx=ex_mtx)
            # Saving the rankings...
            yield gse.update_state(step=2.2, status_code=200, status_message="Saving the rankings...", values=None)
            lp.create(
                gse.get_AUCell_ranking_filepath(),
                rnk_mtx.as_matrix(),
                {"CellID": loom.get_cell_ids()},
                {"Gene": loom.get_genes()},
            )
            logger.debug("{0:.5f} seconds elapsed generating rankings ---".format(time.time() - start_time))
        else:
            # Load the rankings...
            yield gse.update_state(step=2, status_code=200, status_message="Rankings exists: loading...", values=None)
            rnk_loom = self.lfh.get_loom_connection(gse.get_AUCell_ranking_filepath())
            rnk_mtx = pd.DataFrame(data=rnk_loom[:, :], index=rnk_loom.ra.CellID, columns=rnk_loom.ca.Gene)

        # Calculating AUCell enrichment...
        start_time = time.time()
        yield gse.update_state(step=3, status_code=200, status_message="Calculating AUCell enrichment...", values=None)
        aucs = enrichment(rnk_mtx, gs).loc[:, "AUC"].values

        logger.debug("{0:.5f} seconds elapsed calculating AUC ---".format(time.time() - start_time))
        yield gse.update_state(
            step=4, status_code=200, status_message=gse.get_method() + " enrichment done!", values=aucs
        ) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import create [as 別名]
def create_from_cellranger(indir: str, outdir: str = None, genome: str = None) -> str:
	"""
	Create a .loom file from 10X Genomics cellranger output

	Args:
		indir (str):	path to the cellranger output folder (the one that contains 'outs')
		outdir (str):	output folder wher the new loom file should be saved (default to indir)
		genome (str):	genome build to load (e.g. 'mm10'; if None, determine species from outs folder)

	Returns:
		path (str):		Full path to the created loom file.

	Remarks:
		The resulting file will be named ``{sampleID}.loom``, where the sampleID is the one given by cellranger.
	"""
	if outdir is None:
		outdir = indir
	sampleid = os.path.split(os.path.abspath(indir))[-1]
	matrix_folder = os.path.join(indir, 'outs', 'filtered_gene_bc_matrices')
	if os.path.exists(matrix_folder):
		if genome is None:
			genome = [f for f in os.listdir(matrix_folder) if not f.startswith(".")][0]
		matrix_folder = os.path.join(matrix_folder, genome)
		matrix = mmread(os.path.join(matrix_folder, "matrix.mtx")).todense()
		genelines = open(os.path.join(matrix_folder, "genes.tsv"), "r").readlines()
		bclines = open(os.path.join(matrix_folder, "barcodes.tsv"), "r").readlines()
	else:  # cellranger V3 file locations
		if genome is None:
			genome = ""  # Genome is not visible from V3 folder
		matrix_folder = os.path.join(indir, 'outs', 'filtered_feature_bc_matrix')
		matrix = mmread(os.path.join(matrix_folder, "matrix.mtx.gz")).todense()
		genelines = [l.decode() for l in gzip.open(os.path.join(matrix_folder, "features.tsv.gz"), "r").readlines()]
		bclines = [l.decode() for l in gzip.open(os.path.join(matrix_folder, "barcodes.tsv.gz"), "r").readlines()]

	accession = np.array([x.split("\t")[0] for x in genelines]).astype("str")
	gene = np.array([x.split("\t")[1].strip() for x in genelines]).astype("str")
	cellids = np.array([sampleid + ":" + x.strip() for x in bclines]).astype("str")

	col_attrs = {"CellID": cellids}
	row_attrs = {"Accession": accession, "Gene": gene}

	tsne_file = os.path.join(indir, "outs", "analysis", "tsne", "projection.csv")
	# In cellranger V2 the file moved one level deeper
	if not os.path.exists(tsne_file):
		tsne_file = os.path.join(indir, "outs", "analysis", "tsne", "2_components", "projection.csv")
	if os.path.exists(tsne_file):
		tsne = np.loadtxt(tsne_file, usecols=(1, 2), delimiter=',', skiprows=1)
		col_attrs["X"] = tsne[:, 0].astype('float32')
		col_attrs["Y"] = tsne[:, 1].astype('float32')

	clusters_file = os.path.join(indir, "outs", "analysis", "clustering", "graphclust", "clusters.csv")
	if os.path.exists(clusters_file):
		labels = np.loadtxt(clusters_file, usecols=(1, ), delimiter=',', skiprows=1)
		col_attrs["ClusterID"] = labels.astype('int') - 1

	path = os.path.join(outdir, sampleid + ".loom")
	create(path, matrix, row_attrs, col_attrs, file_attrs={"Genome": genome})
	return path 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import create [as 別名]
def create_from_matrix_market(out_file: str, sample_id: str, layer_paths: Dict[str, str], row_metadata_path: str, column_metadata_path: str, delim: str = "\t", skip_row_headers: bool = False, skip_colums_headers: bool = False, file_attrs: Dict[str, str] = None, matrix_transposed: bool = False) -> None:
	"""
	Create a .loom file from .mtx matrix market format

	Args:
		out_file:				path to the newly created .loom file (will be overwritten if it exists)
		sample_id:				string to use as prefix for cell IDs
		layer_paths:			dict mapping layer names to paths to the corresponding matrix file (usually with .mtx extension)
		row_metadata_path:		path to the row (usually genes) metadata file
		column_metadata_path:	path to the column (usually cells) metadata file
		delim:					delimiter used for metadata (default: "\t")
		skip_row_headers:		if true, skip first line in rows metadata file
		skip_column_headers: 	if true, skip first line in columns metadata file
		file_attrs:				dict of global file attributes, or None
		matrix_transposed:		if true, the main matrix is transposed
	
	Remarks:
		layer_paths should typically map the empty string to a matrix market file: {"": "path/to/filename.mtx"}.
		To create a multilayer loom file, map multiple named layers {"": "path/to/layer1.mtx", "layer2": "path/to/layer2.mtx"}
		Note: the created file MUST have a main layer named "". If no such layer is given, BUT all given layers are the same
		datatype, then a main layer will be created as the sum of the other layers. For example, {"spliced": "spliced.mtx", "unspliced": "unspliced.mtx"}
		will create three layers, "", "spliced", and "unspliced", where "" is the sum of the other two.
	"""
	layers: Dict[str, Union[np.ndarray, scipy.sparse.coo_matrix]] = {}

	for name, path in layer_paths.items():
		matrix = mmread(path)
		if matrix_transposed:
			matrix = matrix.T
		layers[name] = matrix
	if "" not in layers:
		main_matrix = None
		for name, matrix in layers.items():
			if main_matrix is None:
				main_matrix = matrix.copy()
			else:
				main_matrix = main_matrix + matrix
		layers[""] = main_matrix

	genelines = open(row_metadata_path, "r").readlines()
	bclines = open(column_metadata_path, "r").readlines()

	accession = np.array([x.split("\t")[0] for x in genelines]).astype("str")
	if(len(genelines[0].split("\t")) > 1):
		gene = np.array([x.split("\t")[1].strip() for x in genelines]).astype("str")
		row_attrs = {"Accession": accession, "Gene": gene}
	else:
		row_attrs = {"Accession": accession}

	cellids = np.array([sample_id + ":" + x.strip() for x in bclines]).astype("str")
	col_attrs = {"CellID": cellids}

	create(out_file, layers[""], row_attrs, col_attrs, file_attrs=file_attrs)

	if len(layers) > 1:
		with loompy.connect(out_file) as ds:
			for name, layer in layers.items():
				if name == "":
					continue
				ds[name] = layer