Box Plot Controls
Linear Mixed Model Analysis
User Manual
Table of Contents
Introduction
This dashboard is designed as a comprehensive tool for the analysis and visualization of transcription factor binding site (TFBS) motif enrichment across various cell types and disease states within the human pancreas. It integrates a suite of bioinformatics tools to facilitate dynamic data exploration and hypothesis testing.
Getting Started
Upon initialization, the dashboard presents a multi-module interface each tailored to specific analyses of motif enrichment, enabling users to navigate through diverse datasets and visualization tools effectively.
Sidebar Menu
The sidebar provides access to multiple analytical modules, each dedicated to different aspects of motif analysis, from basic data tabulation to complex statistical modeling.
Detailed Views
Below are the detailed functionalities provided in each module of the dashboard:
Table (Cell Types)
Explore comprehensive tabulated data detailing TFBS motif and their deviation scores (ChromVar) across various cell types. This module supports sorting, filtering, and downloading of the dataset for external analysis.
Boxplot (Cell Types)
Generate boxplots to visualize the distribution of selected motifs' enrichment scores across cell types. This visual tool helps identify outliers and assess overall variability in motif presence between different cellular environments.
Heatmap (Cell Types)
Create heatmaps to depict the variability of motifs across cell types that can be filtered by TF class or top variabile motifs . This visualization is particularly useful for identifying patterns of motif enrichment and clustering similar cell types based on motif expression profiles.
Box Plot (Disease States)
Visualize how disease states affect motif variability with box plots, allowing for comparisons across different pathological conditions to understand their impact on transcriptional regulation in pancreatic cells.
Linear Mixed Models (Disease States)
Utilize Linear Mixed Models to statistically evaluate the effects of disease states on motif expression, integrating fixed and random effects to account for inter-sample variability and experimental design.