Documentation for utils module

def _fetchFromServer(
    ensemble_server: str, attributes: list, database: str = "hsapiens_gene_ensembl"
):
    """
    Fetches data from the specified ensemble server.

    Args:
        ensemble_server (str): The URL of the ensemble server to fetch data from.
        attributes (list): The list of attributes to fetch from the server.
        database (str): The database to fetch data from.

    Returns:
        pd.DataFrame: A pandas DataFrame containing the fetched data.
    """
    server = BiomartServer(ensemble_server)
    ensmbl = server.datasets[database]
    print(attributes)
    res = pd.read_csv(
        io.StringIO(
            ensmbl.search({"attributes": attributes}, header=1).content.decode()
        ),
        sep="\t",
    )
    return res

def createFoldersFor(filepath: str):
    """
    will recursively create folders if needed until having all the folders required to save the file in this filepath
    """
    prevval = ""
    for val in os.path.expanduser(filepath).split("/")[:-1]:
        prevval += val + "/"
        if not os.path.exists(prevval):
            os.mkdir(prevval)

def downsample_profile(mat: Tensor, dropout: float):
    """
    This function downsamples the expression profile of a given single cell RNA matrix.

    The noise is applied based on the renoise parameter,
    the total counts of the matrix, and the number of genes. The function first calculates the noise
    threshold (scaler) based on the renoise parameter. It then generates an initial matrix count by
    applying a Poisson distribution to a random tensor scaled by the total counts and the number of genes.
    The function then models the sampling zeros by applying a Poisson distribution to a random tensor
    scaled by the noise threshold, the total counts, and the number of genes. The function also models
    the technical zeros by generating a random tensor and comparing it to the noise threshold. The final
    matrix count is calculated by subtracting the sampling zeros from the initial matrix count and
    multiplying by the technical zeros. The function ensures that the final matrix count is not less
    than zero by taking the maximum of the final matrix count and a tensor of zeros. The function
    returns the final matrix count.

    Args:
        mat (torch.Tensor): The input matrix.
        dropout (float): The renoise parameter.

    Returns:
        torch.Tensor: The matrix count after applying noise.
    """
    batch = mat.shape[0]
    ngenes = mat.shape[1]
    dropout = dropout * 1.1
    # we model the sampling zeros (dropping 30% of the reads)
    res = torch.poisson((mat * (dropout / 2))).int()
    # we model the technical zeros (dropping 50% of the genes)
    notdrop = (torch.rand((batch, ngenes), device=mat.device) >= (dropout / 2)).int()
    mat = (mat - res) * notdrop
    return torch.maximum(mat, torch.zeros((1, 1), device=mat.device, dtype=torch.int))

def getBiomartTable(
    ensemble_server: str = "http://may2024.archive.ensembl.org/biomart",
    useCache: bool = False,
    cache_folder: str = "/tmp/biomart/",
    attributes: List[str] = [],
    bypass_attributes: bool = False,
    database: str = "hsapiens_gene_ensembl",
):
    """generate a genelist dataframe from ensembl's biomart

    Args:
        ensemble_server (str, optional): the biomart server. Defaults to "http://may2023.archive.ensembl.org/biomart".
        useCache (bool, optional): whether to use the cache or not. Defaults to False.
        cache_folder (str, optional): the cache folder. Defaults to "/tmp/biomart/".
        attributes (List[str], optional): the attributes to fetch. Defaults to [].
        bypass_attributes (bool, optional): whether to bypass the attributes or not. Defaults to False.
        database (str, optional): the database to fetch from. Defaults to "hsapiens_gene_ensembl".

    Raises:
        ValueError: should be a dataframe (when the result from the server is something else)

    Returns:
        pd.DataFrame: the dataframe
    """
    attr = (
        [
            "ensembl_gene_id",
            "hgnc_symbol",
            "gene_biotype",
            "entrezgene_id",
        ]
        if not bypass_attributes
        else []
    )
    assert cache_folder[-1] == "/"

    cache_folder = os.path.expanduser(cache_folder)
    createFoldersFor(cache_folder)
    cachefile = os.path.join(cache_folder, ".biomart.parquet")
    if useCache & os.path.isfile(cachefile):
        print("fetching gene names from biomart cache")
        res = pd.read_parquet(cachefile)
    else:
        print("downloading gene names from biomart")

        res = _fetchFromServer(ensemble_server, attr + attributes, database=database)
        res.to_parquet(cachefile, index=False)
    res.columns = attr + attributes
    if type(res) is not type(pd.DataFrame()):
        raise ValueError("should be a dataframe")
    res = res[~(res["ensembl_gene_id"].isna())]
    if "hgnc_symbol" in res.columns:
        res.loc[res[res.hgnc_symbol.isna()].index, "hgnc_symbol"] = res[
            res.hgnc_symbol.isna()
        ]["ensembl_gene_id"]
    return res

def get_ancestry_mapping(all_elem: list, onto_df: pd.DataFrame):
    """
    This function generates a mapping of all elements to their ancestors in the ontology dataframe.

    Args:
        all_elem (list): A list of all elements.
        onto_df (DataFrame): The ontology dataframe.

    Returns:
        dict: A dictionary mapping each element to its ancestors.
    """
    ancestors = {}
    full_ancestors = set()
    for val in all_elem:
        ancestors[val] = get_all_ancestors(val, onto_df) - set([val])

    for val in ancestors.values():
        full_ancestors |= set(val)
    # removing ancestors that are not in our datasets
    full_ancestors = full_ancestors & set(ancestors.keys())
    leafs = set(all_elem) - full_ancestors
    full_ancestors = full_ancestors - leafs

    groupings = {}
    for val in full_ancestors:
        groupings[val] = set()
    for leaf in leafs:
        for ancestor in ancestors[leaf]:
            if ancestor in full_ancestors:
                groupings[ancestor].add(leaf)

    return groupings, full_ancestors, leafs

def is_outlier(adata: AnnData, metric: str, nmads: int):
    """
    is_outlier detects outliers in adata.obs[metric]

    Args:
        adata (annData): the anndata object
        metric (str): the metric column to use
        nmads (int): the number of median absolute deviations to use as a threshold

    Returns:
        pd.Series: a boolean series indicating whether a cell is an outlier or not
    """
    M = adata.obs[metric]
    outlier = (M < np.median(M) - nmads * median_abs_deviation(M)) | (
        np.median(M) + nmads * median_abs_deviation(M) < M
    )
    return outlier

def length_normalize(adata: AnnData, gene_lengths: list):
    """
    length_normalize normalizes the counts by the gene length

    Args:
        adata (anndata): the anndata object
        gene_lengths (list): the gene lengths

    Returns:
        anndata: the anndata object
    """
    adata.X = csr_matrix((adata.X.T / gene_lengths).T)
    return adata

def load_dataset_local(
    remote_dataset: ln.Collection,
    download_folder: str,
    name: str,
    description: str,
    use_cache: bool = True,
    only: Optional[List[int]] = None,
):
    """
    This function loads a remote lamindb dataset to local.

    Args:
        lb (lamindb): The lamindb instance.
        remote_dataset (lamindb.Dataset): The remote Dataset.
        download_folder (str): The path to the download folder.
        name (str): The name of the dataset.
        description (str): The description of the dataset.
        use_cache (bool, optional): Whether to use cache. Defaults to True.
        only (list, optional): A list of indices to specify which files to download. Defaults to None.

    Returns:
        lamindb.Dataset: The local dataset.
    """
    saved_files = []
    default_storage = ln.Storage.filter(root=ln.settings.storage.as_posix()).one()
    files = (
        remote_dataset.artifacts.all()
        if not only
        else remote_dataset.artifacts.all()[only[0] : only[1]]
    )
    for file in files:
        organism = list(set([i.ontology_id for i in file.organism.all()]))
        if len(organism) > 1:
            print(organism)
            print("Multiple organisms detected")
            continue
        if len(organism) == 0:
            print("No organism detected")
            continue
        organism = bt.Organism.filter(ontology_id=organism[0]).one().name
        # bt.settings.organism = organism
        path = file.path
        try:
            file.save()
        except IntegrityError:
            print(f"File {file.key} already exists in storage")
        # if location already has a file, don't save again
        if use_cache and os.path.exists(os.path.expanduser(download_folder + file.key)):
            print(f"File {file.key} already exists in storage")
        else:
            path.download_to(download_folder + file.key)
        file.storage = default_storage
        try:
            file.save()
        except IntegrityError:
            print(f"File {file.key} already exists in storage")
        saved_files.append(file)
    dataset = ln.Collection(saved_files, name=name, description=description)
    dataset.save()
    return dataset

def load_genes(organisms: Union[str, list] = "NCBITaxon:9606"):  # "NCBITaxon:10090",
    """
    Loads genes from the given organisms.

    Args:
        organisms (Union[str, list]): The organisms to load genes from.

    Returns:
        pd.DataFrame: The genes dataframe.
    """
    organismdf = []
    if type(organisms) is str:
        organisms = [organisms]
    for organism in organisms:
        genesdf = bt.Gene.filter(
            organism_id=bt.Organism.filter(ontology_id=organism).first().id
        ).df()
        genesdf = genesdf.drop_duplicates(subset="ensembl_gene_id")
        genesdf = genesdf.set_index("ensembl_gene_id").sort_index()
        # mitochondrial genes
        genesdf["mt"] = genesdf.symbol.astype(str).str.startswith("MT-")
        # ribosomal genes
        genesdf["ribo"] = genesdf.symbol.astype(str).str.startswith(("RPS", "RPL"))
        # hemoglobin genes.
        genesdf["hb"] = genesdf.symbol.astype(str).str.contains(("^HB[^(P)]"))
        genesdf["organism"] = organism
        organismdf.append(genesdf)
    organismdf = pd.concat(organismdf)
    for col in [
        "source_id",
        "run_id",
        "created_by_id",
        "updated_at",
        "stable_id",
        "created_at",
        "_aux",
        "_branch_code",
        "space_id",
    ]:
        if col in organismdf.columns:
            organismdf.drop(columns=[col], inplace=True)
    # temp fix
    drop = {
        "ENSG00000112096",
        "ENSG00000137808",
        "ENSG00000161149",
        "ENSG00000182230",
        "ENSG00000203812",
        "ENSG00000204092",
        "ENSG00000205485",
        "ENSG00000212951",
        "ENSG00000215271",
        "ENSG00000221995",
        "ENSG00000224739",
        "ENSG00000224745",
        "ENSG00000225178",
        "ENSG00000225932",
        "ENSG00000226377",
        "ENSG00000226380",
        "ENSG00000226403",
        "ENSG00000227021",
        "ENSG00000227220",
        "ENSG00000227902",
        "ENSG00000228139",
        "ENSG00000228206",
        "ENSG00000228906",
        "ENSG00000229352",
        "ENSG00000231575",
        "ENSG00000232196",
        "ENSG00000232295",
        "ENSG00000233776",
        "ENSG00000236166",
        "ENSG00000236673",
        "ENSG00000236740",
        "ENSG00000236886",
        "ENSG00000236996",
        "ENSG00000237133",
        "ENSG00000237513",
        "ENSG00000237548",
        "ENSG00000237838",
        "ENSG00000239446",
        "ENSG00000239467",
        "ENSG00000239665",
        "ENSG00000244693",
        "ENSG00000244952",
        "ENSG00000249860",
        "ENSG00000251044",
        "ENSG00000253878",
        "ENSG00000254561",
        "ENSG00000254740",
        "ENSG00000255633",
        "ENSG00000255823",
        "ENSG00000256045",
        "ENSG00000256222",
        "ENSG00000256374",
        "ENSG00000256427",
        "ENSG00000256618",
        "ENSG00000256863",
        "ENSG00000256892",
        "ENSG00000258414",
        "ENSG00000258808",
        "ENSG00000258861",
        "ENSG00000259444",
        "ENSG00000259820",
        "ENSG00000259834",
        "ENSG00000259855",
        "ENSG00000260461",
        "ENSG00000261068",
        "ENSG00000261438",
        "ENSG00000261490",
        "ENSG00000261534",
        "ENSG00000261737",
        "ENSG00000261773",
        "ENSG00000261963",
        "ENSG00000262668",
        "ENSG00000263464",
        "ENSG00000267637",
        "ENSG00000268955",
        "ENSG00000269028",
        "ENSG00000269900",
        "ENSG00000269933",
        "ENSG00000269966",
        "ENSG00000270188",
        "ENSG00000270394",
        "ENSG00000270672",
        "ENSG00000271043",
        "ENSG00000271409",
        "ENSG00000271734",
        "ENSG00000271870",
        "ENSG00000272040",
        "ENSG00000272196",
        "ENSG00000272267",
        "ENSG00000272354",
        "ENSG00000272370",
        "ENSG00000272551",
        "ENSG00000272567",
        "ENSG00000272880",
        "ENSG00000272904",
        "ENSG00000272934",
        "ENSG00000273301",
        "ENSG00000273370",
        "ENSG00000273496",
        "ENSG00000273576",
        "ENSG00000273614",
        "ENSG00000273837",
        "ENSG00000273888",
        "ENSG00000273923",
        "ENSG00000276612",
        "ENSG00000276814",
        "ENSG00000277050",
        "ENSG00000277077",
        "ENSG00000277352",
        "ENSG00000277666",
        "ENSG00000277761",
        "ENSG00000278198",
        "ENSG00000278782",
        "ENSG00000278927",
        "ENSG00000278955",
        "ENSG00000279226",
        "ENSG00000279765",
        "ENSG00000279769",
        "ENSG00000279948",
        "ENSG00000280058",
        "ENSG00000280095",
        "ENSG00000280250",
        "ENSG00000280346",
        "ENSG00000280374",
        "ENSG00000280710",
        "ENSG00000282080",
        "ENSG00000282246",
        "ENSG00000282965",
        "ENSG00000283486",
        "ENSG00000284299",
        "ENSG00000284741",
        "ENSG00000285106",
        "ENSG00000285162",
        "ENSG00000285476",
        "ENSG00000285762",
        "ENSG00000286065",
        "ENSG00000286228",
        "ENSG00000286601",
        "ENSG00000286699",
        "ENSG00000286949",
        "ENSG00000286996",
        "ENSG00000287116",
        "ENSG00000287388",
        "ENSG00000288541",
        "ENSG00000288546",
        "ENSG00000288630",
        "ENSG00000288639",
        "ENSMUSG00000069518",
        "ENSMUSG00000073682",
        "ENSMUSG00000075014",
        "ENSMUSG00000075015",
        "ENSMUSG00000078091",
        "ENSMUSG00000094958",
        "ENSMUSG00000095547",
        "ENSMUSG00000095891",
        "ENSMUSG00000096385",
        "ENSMUSG00000096519",
        "ENSMUSG00000096923",
        "ENSMUSG00000097078",
    }
    organismdf = organismdf[~organismdf.index.isin(drop)]
    return organismdf

def populate_my_ontology(
    organisms: List[str] = ["NCBITaxon:10090", "NCBITaxon:9606"],
    sex: List[str] = ["PATO:0000384", "PATO:0000383"],
    celltypes: List[str] = [],
    ethnicities: List[str] = [],
    assays: List[str] = [],
    tissues: List[str] = [],
    diseases: List[str] = [],
    dev_stages: List[str] = [],
    organism_clade: str = "vertebrates",
):
    """
    creates a local version of the lamin ontologies and add the required missing values in base ontologies

    run this function just one for each new lamin storage

    erase everything with bt.$ontology.filter().delete()

    add whatever value you need afterward like it is done here with:

    `bt.$ontology(name="ddd", ontolbogy_id="ddddd").save()`

    `df["assay_ontology_term_id"].unique()`

    Args:
        lb (lamindb): lamindb instance.
        organisms (list, optional): List of organisms. Defaults to ["NCBITaxon:10090", "NCBITaxon:9606"].
        sex (list, optional): List of sexes. Defaults to ["PATO:0000384", "PATO:0000383"].
        celltypes (list, optional): List of cell types. Defaults to [].
        ethnicities (list, optional): List of ethnicities. Defaults to [].
        assays (list, optional): List of assays. Defaults to [].
        tissues (list, optional): List of tissues. Defaults to [].
        diseases (list, optional): List of diseases. Defaults to [].
        dev_stages (list, optional): List of developmental stages. Defaults to [].
    """
    # cell type
    if celltypes is not None:
        if len(celltypes) == 0:
            bt.CellType.import_source()
        else:
            names = bt.CellType.public().df().index if not celltypes else celltypes
            records = bt.CellType.from_values(names, field="ontology_id")
            ln.save(records)
        bt.CellType(name="unknown", ontology_id="unknown").save()
    # Organism
    if organisms is not None:
        names = (
            bt.Organism.public(organism=organism_clade).df().index
            if not organisms
            else organisms
        )
        source = bt.PublicSource.filter(name="ensembl", organism=organism_clade).last()
        records = [
            organism_or_organismlist
            if isinstance(organism_or_organismlist, bt.Organism)
            else organism_or_organismlist[0]
            for organism_or_organismlist in [
                bt.Organism.from_source(ontology_id=name, source=source)
                for name in names
            ]
        ]
        ln.save(records)
        bt.Organism(name="unknown", ontology_id="unknown").save()
    # Phenotype
    if sex is not None:
        names = bt.Phenotype.public().df().index if not sex else sex
        source = bt.PublicSource.filter(name="pato").first()
        records = [
            bt.Phenotype.from_source(ontology_id=i, source=source) for i in names
        ]
        ln.save(records)
        bt.Phenotype(name="unknown", ontology_id="unknown").save()
    # ethnicity
    if ethnicities is not None:
        if len(ethnicities) == 0:
            bt.Ethnicity.import_source()
        else:
            names = bt.Ethnicity.public().df().index if not ethnicities else ethnicities
            records = bt.Ethnicity.from_values(names, field="ontology_id")
            ln.save(records)
        bt.Ethnicity(
            name="unknown", ontology_id="unknown"
        ).save()  # multi ethnic will have to get renamed
    # ExperimentalFactor
    if assays is not None:
        if len(assays) == 0:
            bt.ExperimentalFactor.import_source()
        else:
            names = bt.ExperimentalFactor.public().df().index if not assays else assays
            records = bt.ExperimentalFactor.from_values(names, field="ontology_id")
            ln.save(records)
        bt.ExperimentalFactor(name="unknown", ontology_id="unknown").save()
        # lookup = bt.ExperimentalFactor.lookup()
        # lookup.smart_seq_v4.parents.add(lookup.smart_like)
    # Tissue
    if tissues is not None:
        if len(tissues) == 0:
            bt.Tissue.import_source()
        else:
            names = bt.Tissue.public().df().index if not tissues else tissues
            records = bt.Tissue.from_values(names, field="ontology_id")
            ln.save(records)
        bt.Tissue(name="unknown", ontology_id="unknown").save()
    # DevelopmentalStage
    if dev_stages is not None:
        if len(dev_stages) == 0:
            bt.DevelopmentalStage.import_source()
            source = bt.PublicSource.filter(organism="mouse", name="mmusdv").last()
            bt.DevelopmentalStage.import_source(source=source)
        else:
            names = (
                bt.DevelopmentalStage.public().df().index
                if not dev_stages
                else dev_stages
            )
            records = bt.DevelopmentalStage.from_values(names, field="ontology_id")
            ln.save(records)
        bt.DevelopmentalStage(name="unknown", ontology_id="unknown").save()

    # Disease
    if diseases is not None:
        if len(diseases) == 0:
            bt.Disease.import_source()
        else:
            names = bt.Disease.public().df().index if not diseases else diseases
            records = bt.Disease.from_values(names, field="ontology_id")
            ln.save(records)
        bt.Disease(name="normal", ontology_id="PATO:0000461").save()
        bt.Disease(name="unknown", ontology_id="unknown").save()
    # genes
    for organism in ["NCBITaxon:10090", "NCBITaxon:9606"]:
        # convert onto to name
        organism = bt.Organism.filter(ontology_id=organism).one().name
        names = bt.Gene.public(organism=organism).df()["ensembl_gene_id"]

        # Process names in blocks of 10,000 elements
        block_size = 10000
        for i in range(0, len(names), block_size):
            block = names[i : i + block_size]
            records = bt.Gene.from_values(
                block,
                field="ensembl_gene_id",
                organism=organism,
            )
            ln.save(records)

def translate(
    val: Union[str, list, set, Counter, dict], t: str = "cell_type_ontology_term_id"
):
    """
    translate translates the ontology term id to the name

    Args:
        val (str, dict, set, list, dict): the object to translate
        t (flat, optional): the type of ontology terms.
            one of cell_type_ontology_term_id, assay_ontology_term_id, tissue_ontology_term_id.
            Defaults to "cell_type_ontology_term_id".

    Returns:
        dict: the mapping for the translation
    """
    if t == "cell_type_ontology_term_id":
        obj = bt.CellType
    elif t == "assay_ontology_term_id":
        obj = bt.ExperimentalFactor
    elif t == "tissue_ontology_term_id":
        obj = bt.Tissue
    elif t in [
        "development_stage_ontology_term_id",
        "simplified_dev_stage",
        "age_group",
    ]:
        obj = bt.DevelopmentalStage
    elif t == "disease_ontology_term_id":
        obj = bt.Disease
    elif t == "self_reported_ethnicity_ontology_term_id":
        obj = bt.Ethnicity
    else:
        return None
    if type(val) is str:
        return {val: obj.filter(ontology_id=val).one().name}
    elif type(val) is list or type(val) is set:
        return {i: obj.filter(ontology_id=i).one().name for i in set(val)}
    elif type(val) is dict or type(val) is Counter:
        return {obj.filter(ontology_id=k).one().name: v for k, v in val.items()}

def validate(adata: AnnData, organism: str, need_all=False):
    """
    validate checks if the adata object is valid for lamindb

    Args:
        adata (anndata): the anndata object
        lb (lamindb): the lamindb instance
        organism (str): the organism

    Raises:
        ValueError: if the adata object is not valid
        ValueError: if the anndata contains invalid ethnicity ontology term id according to the lb instance
        ValueError: if the anndata contains invalid organism ontology term id according to the lb instance
        ValueError: if the anndata contains invalid sex ontology term id according to the lb instance
        ValueError: if the anndata contains invalid disease ontology term id according to the lb instance
        ValueError: if the anndata contains invalid cell_type ontology term id according to the lb instance
        ValueError: if the anndata contains invalid development_stage ontology term id according to the lb instance
        ValueError: if the anndata contains invalid tissue ontology term id according to the lb instance
        ValueError: if the anndata contains invalid assay ontology term id according to the lb instance

    Returns:
        bool: True if the adata object is valid
    """
    organism = bt.Organism.filter(ontology_id=organism).one().name

    if adata.var.index.duplicated().any():
        raise ValueError("Duplicate gene names found in adata.var.index")
    if adata.obs.index.duplicated().any():
        raise ValueError("Duplicate cell names found in adata.obs.index")
    for val in [
        "self_reported_ethnicity_ontology_term_id",
        "organism_ontology_term_id",
        "disease_ontology_term_id",
        "cell_type_ontology_term_id",
        "development_stage_ontology_term_id",
        "tissue_ontology_term_id",
        "assay_ontology_term_id",
    ]:
        if val not in adata.obs.columns and need_all:
            raise ValueError(
                f"Column '{val}' is missing in the provided anndata object."
            )

    if not bt.Ethnicity.validate(
        adata.obs["self_reported_ethnicity_ontology_term_id"],
        field="ontology_id",
    ).all() and not set(adata.obs["self_reported_ethnicity_ontology_term_id"]) == set(
        ["unknown"]
    ):
        raise ValueError("Invalid ethnicity ontology term id found")
    if not bt.Organism.validate(
        adata.obs["organism_ontology_term_id"], field="ontology_id"
    ).all():
        raise ValueError("Invalid organism ontology term id found")
    if not bt.Phenotype.validate(
        adata.obs["sex_ontology_term_id"], field="ontology_id"
    ).all() and not set(adata.obs["self_reported_ethnicity_ontology_term_id"]) == set(
        ["unknown"]
    ):
        raise ValueError("Invalid sex ontology term id found")
    if not bt.Disease.validate(
        adata.obs["disease_ontology_term_id"], field="ontology_id"
    ).all() and not set(adata.obs["self_reported_ethnicity_ontology_term_id"]) == set(
        ["unknown"]
    ):
        raise ValueError("Invalid disease ontology term id found")
    if not bt.CellType.validate(
        adata.obs["cell_type_ontology_term_id"], field="ontology_id"
    ).all() and not set(adata.obs["self_reported_ethnicity_ontology_term_id"]) == set(
        ["unknown"]
    ):
        raise ValueError("Invalid cell type ontology term id found")
    if not bt.DevelopmentalStage.validate(
        adata.obs["development_stage_ontology_term_id"],
        field="ontology_id",
    ).all() and not set(adata.obs["self_reported_ethnicity_ontology_term_id"]) == set(
        ["unknown"]
    ):
        raise ValueError("Invalid dev stage ontology term id found")
    if not bt.Tissue.validate(
        adata.obs["tissue_ontology_term_id"], field="ontology_id"
    ).all() and not set(adata.obs["self_reported_ethnicity_ontology_term_id"]) == set(
        ["unknown"]
    ):
        raise ValueError("Invalid tissue ontology term id found")
    if not bt.ExperimentalFactor.validate(
        adata.obs["assay_ontology_term_id"], field="ontology_id"
    ).all() and not set(adata.obs["self_reported_ethnicity_ontology_term_id"]) == set(
        ["unknown"]
    ):
        raise ValueError("Invalid assay ontology term id found")
    if not bt.Gene.validate(
        adata.var.index, field="ensembl_gene_id", organism=organism
    ).all():
        raise ValueError("Invalid gene ensembl id found")
    return True