# Define function to download gzipped file from url, unzip it, and load it into pandas DF

def download_and_load_data(url, output_filename, sep="\t", column_filter=None):

# Step 1: Download the file using wget

print(f"Downloading {output_filename} from {url}...")

subprocess.run(["wget", "-O", output_filename + ".gz", url], check=True)

# Step 2: Gunzip the file

print(f"Unzipping {output_filename}.gz...")

with gzip.open(output_filename + ".gz", "rb") as gz_file:

with open(output_filename, "wb") as out_file:

out_file.write(gz_file.read())

# Step 3: Load the data into a pandas DataFrame

print(f"Loading {output_filename} into a pandas DataFrame...")

df = pd.read_csv(output_filename, sep=sep, index_col=0)

# Optional: Filter columns based on the keyword

if column_filter:

print(f"Filtering columns with keyword '{column_filter}'...")

filtered_columns = [col for col in df.columns if column_filter in col]

df = df[filtered_columns]

return df

# Load data for 5xFAD mouse model (8mo only)

url = "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE168137&format=file&file=GSE168137%5FcountList%2Etxt%2Egz"

output_filename = "GSE168137_countList.txt"

column_keyword = "cortex_4mon"

countlist_5xFAD = download_and_load_data(url, output_filename, column_filter=column_keyword)

# Rename columns

new_column_names = ["5xFAD_cortex_4mo_Female_1", "5xFAD_cortex_4mo_Female_2","5xFAD_cortex_4mo_Female_3", "5xFAD_cortex_4mo_Female_4", "5xFAD_cortex_4mo_Female_5", "5xFAD_cortex_4mon_Male_1", "5xFAD_cortex_4mon_Male_2", "5xFAD_cortex_4mon_Male_3", "5xFAD_cortex_4mon_Male_4", "5xFAD_cortex_4mon_Male_5", "BL6_cortex_4mon_Female_1", "BL6_cortex_4mon_Female_2", "BL6_cortex_4mon_Female_3", "BL6_cortex_4mon_Female_4", "BL6_cortex_4mon_Female_5", "BL6_cortex_4mon_Male_1", "BL6_cortex_4mon_Male_2", "BL6_cortex_4mon_Male_3", "BL6_cortex_4mon_Male_4", "BL6_cortex_4mon_Male_5"]

countlist_5xFAD.columns = new_column_names

# Drop ensemble version ID from gene_id's

countlist_5xFAD.index = countlist_5xFAD.index.str.split('.').str[0]

# View first 5 rows of data

countlist_5xFAD.head()

# create MyGeneInfo object

mg = mygene.MyGeneInfo()

# get the ensembl id from index

ensembl_ids = countlist_5xFAD.index.tolist()

# query the gene symbols for the ensemble ids and onvert result to dataframe

gene_info = mg.querymany(ensembl_ids, scopes='ensembl.gene', fields='symbol', species='mouse')

gene_df = pd.DataFrame(gene_info)

# remove duplicate ensemble ids and rows where symbol is missing or duplicated

gene_df = gene_df.dropna(subset=['symbol']).drop_duplicates(subset='query')

# map gene symbols back to original dataframe and move gene_name column to front column

countlist_5xFAD['Gene_Name'] = countlist_5xFAD.index.map(gene_df.set_index('query')['symbol'])

cols = ['Gene_Name'] + [col for col in countlist_5xFAD.columns if col != 'Gene_Name']

countlist_5xFAD = countlist_5xFAD[cols]

# view first 5 rows of data

countlist_5xFAD.head()

# rename countlist_5xFAD for use in tutorial

data = countlist_5xFAD