Gene identifier mapping

**Issue**
In Part 2 of the tutorial, gene identifier mapping is not explicitly mentioned, but there are instances where gene mapping is needed. Especially, the tutorial involves various steps related to gene expression data and the selection of specific genes like selecting, intersecting, and manipulating gene sets based on their relevance to the analysis.

**Example Scenario:**
Consider Task 2 of the BuDDI analysis tutorial, where gene IDs are formatted as 'ENSG00000000003', 'ENSG00000000005', 'ENSG00000000419', 'ENSG00000000457', and the goal is to transform these gene IDs into a different format like 'MIR1302-2HG', 'FAM138A', 'OR4F5', 'AL627309.1', 'AL627309.3'.

For your specific situation, the single-cell matched tissue has the mapping of the genes.

**Suggested Approach:**
In certain scenarios, the gene identifier mapping may not be available for all genes when transitioning from Ensembl IDs to ontology-based names. To address this, it is recommended to leverage the gene mapping from the single-cell matched tissue, as it likely contains a more comprehensive set of mappings.

In the provided example, a gene mapping is demonstrated using a Pandas DataFrame. The mapping includes columns for gene names ("Name") and Ensembl identifiers ("Ens"). The mapping can be done follow:

### Create an empty DataFrame with columns for gene names and Ensembl IDs
gene_maps = pd.DataFrame(columns=["Name", "Ens"])
### Populate the "Name" column with gene names from the single cell AnnData object
gene_maps["Name"] = adata.var.index
### Populate the "Ens" column with Ensembl IDs from the AnnData object
gene_maps["Ens"] = adata.var["gene_ids"].values
### Save the gene mapping DataFrame to a CSV file
gene_maps.to_csv(f'{data_path}/gene_maps.csv')
### Extract the gene names for later use
gene_ids = gene_maps["Name"]

Start in Part 2 of the tutorial, especially Task 2, and review where gene expression data and gene sets are selected or manipulated. Document the need for gene identifier mapping, include the provided pandas example using adata.var, and make the mapping workflow clear for cases where Ensembl mappings are incomplete.

Written by the indexing model from the issue text.