Data analysis of open access colo-rectal patient data from the TCGA database.
The initial focus is on the Differential Expression analysis of
the RNAseq data from the TCGA Database and the aim is to identify all statistically significant differences
between the two groups highlighted below. The samples are Human
from the colo-rectal patients of the TCGA database with two locations; Rectum and Left Colon.
This repository contains code to analyse colon and rectum cancer datasets from the TCGA database. The STAR
Counts were downloaded and the analysis was performed using the packages cited in the script. The analysis includes pre-processing of the TCGA data, Principal Component Analysis (PCA), Differential Gene Expression Analysis, and data visualization of results.
Sample info:
the READ (rectum) cohort: 172 unique case submitter IDs
the COAD (colon) cohort: 461 unique case submitter IDs further stratified into sub-localizations
localization 'descending colon',
localization 'sigmoid colon',
localization 'splenic flexure'
From the TCGA database; the READ (rectum) cohort and the COAD (colon) cohorts were stratified. The
"rectum" group was compared to with the "left colon" group which includes the followinig localizations
'descending colon', 'sigmoid colon', and 'splenic flexure'. The data included gene expression
quantification with STAR counts.
The data underwent pre-processing to prepare the data for downstream analysis, inluding data
wrangling, and data normalization. This step included variance stabilizing transformation (vst)
and Deseq2 package normalization to ensure data comparability across samples.
Several PCA plots were generated to visualize the variation in the data. Samples that are similar
to each other will cluster together in the PCA plot. PCA transforms a large set of variables into a
smaller one that still contains most of the information in the large set. It does this by identifying
the directions (principal components) in which the data varies the most. The data was first normalized (vst)
so that the variance becomes independent of the mean. The axes of a PCA plot represent the principal
components. Typically, the first two principal components (PC1 and PC2) are plotted, as they capture the
most variance. Additionally, a 3D PCA plot was also produced to visualize the data with the
third Principal Component.
A volcano plot was generated to visualize the differential expression results. This plot displays
-> Log2 Fold Change (x-axis): Indicates the magnitude of expression change between colon and rectum samples.
-> -Log10 Adjusted p-value (y-axis): Represents the statistical significance of the expression change.
-> Significance Thresholds: Horizontal and vertical lines on the plot indicate thresholds for significance
and fold change, helping to highlight the most differentially expressed genes.
A heatmap was created to show the expression levels of the top differentially expressed genes. Key features
of the heatmap include
-> Expression Patterns: It visualizes the expression patterns of these genes across samples, facilitating the
identification of clusters or patterns in gene expression.
-> Clustering: Both genes and samples are clustered to reveal similarities and differences in expression profiles.
This R Markdown file contains code for analyzing TCGA data and to perform PCA analysis,
Differential Gene Expression Analysis, and other bioinformatics visualization. The code provided is released
under the MIT License and is intended for use in research and educational projects.
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