Project Title

Gene Expression Analysis Project

Description

This project is built to facilitate the analysis of liver cancer gene expression data by performing a series of statistical computations. The code is modular, consisting of several classes that handle specific tasks such as loading data, calculating statistical values, and generating reports.

The key components are:

• GeneExpressionData Class: Handles reading liver cancer gene expression data and storing it in an easily accessible format.

• StatisticalAnalysis Class: Performs statistical analyses on the gene expression data, including calculating means, standard deviations, and other differential values.

• AnalysisReport Class: Generates formatted reports to display the statistical analysis results, either on the screen or in a file.

Getting Started

Getting the data

• Download the liver cancer data GSE 14520_U133A.csv (approx. 135MB) from the CuMiDa website

Dependencies

• Python 3.x

Libraries:

• statistics (standard Python library)
• collections (standard Python library)
• argparse (standard Python library)
• heapq (standard Python library)

Ensure that your Python environment is set up with the necessary modules before running the program.

Installing

• Clone this repository to your local machine:

  git clone https://github.com/Mahsa-Zf/gene_expression_analysis

• Navigate to the cloned directory

Executing program

• Make sure all the required .py files (gene_expression_data.py, statistical_analysis.py, report.py, and main.py) are in the same directory.
• Run the main script using Python
• Replace data/liver_cancer_gene_expression.csv with the path to your input data file.
• Example of commandline execution

python3 main.py --data_file Liver_GSE14520_U133A.csv --threshold 6 --genes_above_threshold 121_at  --statistics 121_at --differential 121_at --output sample

AVAILABLE PARAMETERS:

• --data_file: Path to the CSV file containing liver cancer gene expression data.
• --gene_name: Gene name for which you want the expression values.
• --get_all_gene_names: If set True, all gene names are returned.
• --statistics: List of gene names to calculate statistics for.
• --differential: List of gene names to calculate differential expression for.
• --top_n: Number of top differentially expressed genes to analyze.
• --threshold: Threshold value for filtering gene expression.
• --genes_above_threshold: List of gene names to find values above the threshold (optional).
• --output: Output destination(s) for the report (e.g., 'screen', 'output.txt').
• --add: Add a new output destination.
• --remove: Remove an existing output destination.
• --check: Check if a specific destination is in the output destinations list.

Help

In case you need help regarding command-line arguments, you can use the following command to view available options:

python main.py --help

Author

Mahsa Zamanifard