""" .. _differential-gene-exp: Differential gene expression analysis ===================================== This tutorial showcases one of the basic ways to perform differential gene expression analysis using the atlasapprox-disease API. You will use the ``metadata`` and ``differential_gene_expression`` functions to identify datasets for a specific cell type, analyze gene expression changes in a disease context, and identify frequently occurring differentially expressed genes across datasets. The tutorial uses memory B cells as an example, but you can apply the code to any cell type, disease, or tissue of interest using the API's many features. # sphinx_gallery_thumbnail_path = '_static/differential_gene_exp.png' """ # %% # Contents # -------- # # - Overview metadata with filters # - Perform differential gene expression analysis for a specific cell type across datasets # - Find frequently occurring differentially expressed genes # - Tips for further exploration # %% # Installation # ------------ # # Install the required packages using `pip`: # # .. code-block:: bash # # pip install atlasapprox-disease pandas # %% # Import libraries and initialize the API # --------------------------------------- # # Import the necessary libraries import atlasapprox_disease as aad import pandas as pd # Initialize the API api = aad.API() # %% # Overview datasets with cell type-specific data # --------------------------------------------- # # One way to start is to use the ``metadata`` function to get an overview of all the data relevant to what you want to explore, such as a cell type, disease, or tissue. # In this example, we will focus on datasets related to memory B cells as a simple starting point: cell_metadata = api.metadata(cell_type="memory B cell") # Display the result cell_metadata #%% # The DataFrame contains 186 rows, each representing a unique combination of metadata attributes (e.g. tissue, disease, sex, and development stage) involving memory B cells. # %% # To see the full list of unique diseases without truncation: #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ cell_metadata.disease.unique() #%% # As shown, there is a variety of diseases involving memory B cell data, e.g., COVID-19, post-COVID-19 disorder, breast carcinoma, and Crohn disease, which you can explore further. # For example, you can select a disease like COVID-19 to perform differential gene expression analysis on memory B cells, as demonstrated in the following sections: # %% # Perform differential gene expression analysis for memory B cells in COVID-19 # ---------------------------------------------------------------------------- # # To understand how memory B cells respond to COVID-19, query the top 10 up- and down-regulated genes (20 in total) across all datasets with diseased and normal conditions. # This analysis identifies genes with the most significant expression changes in COVID-19 compared to healthy samples. df_genes = api.differential_gene_expression( differential_axis = "disease", disease="covid", cell_type="memory B cell", top_n=10 # Top 10 up and down-regulated genes to query ) # Display the results df_genes # %% # The resulting DataFrame lists the top 10 up- and down-regulated genes for memory B cells in COVID-19 across all relevant datasets. # Key columns include gene, regulation, expression and metric (fold change). # Up-regulated genes may indicate activation of immune memory or antibody production pathways in response to COVID-19, while down-regulated genes could suggest suppression of other functions. # Since the query includes multiple datasets and tissues, variations in gene expression may reflect dataset-specific or tissue-specific differences. # %% # Find frequently occurring differentially expressed genes # -------------------------------------------------------- # # Since memory B cells are present in multiple datasets, identify which genes appear most frequently as top differentially expressed genes across these datasets. # This analysis highlights genes consistently affected by COVID-19 in memory B cells. # Count the frequency of up-regulated genes across datasets up_gene_counts = df_genes[df_genes["regulation"] == "up"]["gene"].value_counts() # Display the results print("Frequency of up-regulated genes across datasets:") print(up_gene_counts) # %% # The output shows the frequency of up-regulated genes across datasets, for example, XAF1 appearing 4 times, NFKBID 3 times, MX1 3 times, and so on. # This is how you can use the API to identify genes that frequently appear as top differentially expressed genes in your analysis. # You can also explore down-regulated genes or analyze other diseases to compare results across different conditions. # %% # Examples for further exploration # -------------------------------- # # 1. Analyze down-regulated genes: Repeat the frequency analysis for down-regulated genes to identify consistently suppressed pathways. # # .. code-block:: python # # down_gene_counts = df_genes[df_genes["regulation"] == "down"]["gene"].value_counts() # print(down_gene_counts) # # 2. Explore other diseases: Use the diseases from the metadata (e.g., influenza) to compare memory B cell responses across conditions. # # .. code-block:: python # # df_influenza = api.differential_gene_expression( # disease="influenza", # cell_type="memory B cell", # top_n=10 # ) # # 3. Explore specific tissues: Query differential gene expression for a specific tissue (e.g., kidney) to analyze expression changes across all diseases and cell types in that tissue. # # .. code-block:: python # # df_kidney = api.differential_gene_expression( # tissue="kidney", # top_n=10 # ) # print(df_kidney) # %% # Next steps # ---------- # # This tutorial introduced differential gene expression analysis with the atlasapprox-disease API. # To learn more, explore additional functions like average to retrieve gene expression levels, or dotplot for visualizing expression patterns. # # Visit the official documentation # for further details.