Performing mutational and survival analysis on chronic myeloid leukemia (CML) differentially expressed genes (DEGs) from microarray data involves several steps. Here’s a structured approach:

Step 1: Data Acquisition and Preprocessing

Step 2: Mutational Analysis

Step 3: Survival Analysis

Step 4: Functional and Pathway Analysis

Step 5: Validation and Interpretation

Thank you Idowu Goodnews Oluwaseyi for your prompt reply.

I have performed all of the step 1 techniques. For step 2 mutational analysis i have searched cbioportal for chronic myeloid leukemia mutational data but its not available there. I am not familiar with COSMIC so its a little difficult to understand its output after providing my gene of interest as the input. For step 3 there is no survival data that corresponds to the microarray dataset that i have selected so i was hoping to perform survival analysis from a generalized repository specific to CML.

I am still not sure how to go about doing mutational and survival for CML dataset. I would be grateful if you would guide me properly.

Thank you.

Performing mutational and survival analysis on chronic myeloid leukemia (CML) data involves several key steps. Here’s a detailed guide to help you through the process:

Step-by-Step Guide

1. Data Acquisition and Preprocessing

a. Obtain Microarray Data:

• Download the raw microarray data from repositories like GEO (Gene Expression Omnibus) or ArrayExpress.

b. Quality Control and Normalization:

• Use tools like R/Bioconductor packages (affy, limma, oligo) for quality control, normalization, and preprocessing of the microarray data.
• Perform background correction, normalization (e.g., RMA normalization), and probe summarization.

c. Identify Differentially Expressed Genes (DEGs):

• Apply statistical methods to identify DEGs between CML and normal/control samples using the limma package in R.
• Adjust p-values for multiple testing (e.g., using Benjamini-Hochberg correction).

2. Mutational Analysis

a. Integration with Mutation Data:

• Obtain mutation data for the samples if available from databases like COSMIC or cBioPortal.

b. Annotation of DEGs with Mutations:

• Use tools like ANNOVAR or Ensembl VEP to annotate the identified DEGs with known mutations.
• Investigate the functional impact of these mutations using databases like SIFT, PolyPhen, or MutationTaster.

3. Survival Analysis

a. Clinical Data Integration:

• Acquire clinical data, including survival information, from the same cohort or linked repositories.

b. Survival Analysis:

• Perform survival analysis using the survival package in R.
• Fit Kaplan-Meier survival curves and use the log-rank test to compare survival between different groups (e.g., high vs. low expression of a DEG).
• Perform Cox proportional hazards regression to identify DEGs significantly associated with survival.

4. Functional and Pathway Analysis

a. Gene Ontology (GO) and Pathway Enrichment Analysis:

• Use tools like DAVID, Enrichr, or the clusterProfiler package in R to perform GO and pathway enrichment analysis on the DEGs.

b. Network Analysis:

• Use STRING or Cytoscape to visualize and analyze protein-protein interaction networks of the DEGs.
• Identify key regulatory genes or hubs within the network.