Complete Analysis Tutorial

Introduction

What is RNA-seq?

RNA sequencing (RNA-seq) is a powerful technology that captures a snapshot of all the genes actively being expressed in your cells at a given moment. Unlike older microarray methods, RNA-seq can detect novel transcripts, alternative splicing events, and provide highly accurate quantification across a wide dynamic range.

In a typical RNA-seq experiment, messenger RNA is extracted from samples, converted to cDNA, and sequenced using high-throughput platforms. The resulting data—millions of short sequence reads—must then be processed through a series of computational steps to extract biological meaning.

What is TransXplorer?

TransXplorer is an automated end-to-end web server for translational RNA-seq analysis and therapeutic discovery. It bridges the gap between raw sequencing data and clinical application by semi-automating complex bioinformatic tasks—enabling researchers without extensive computational expertise to perform publication-quality analysis from start to finish.

The platform covers the complete analytical journey: from optional FASTQ file processing through differential expression analysis, batch effect correction, pathway enrichment, network analysis, drug-target discovery, and clinical validation with TCGA data integration.

Tutorial Dataset: GSE151427

In this tutorial, we'll analyze GSE151427—a study of human iPSC-derived endothelial cells comparing two developmental lineages:

What You'll Learn

Data Input

Supported Input Formats

TransXplorer accepts gene expression count matrices in multiple formats: CSV, TXT, TSV, and XLSX. The matrix should have genes as rows and samples as columns. Importantly, you should upload raw counts (integers)—not normalized values like FPKM or TPM.

Sample Metadata

The metadata file defines your experimental design—which samples belong to which groups, and any batch or covariate information. This is critical for proper statistical modeling of your comparison.

Normalization Methods

Raw counts must be normalized to account for differences in sequencing depth between samples. TransXplorer offers several methods:

• TMM (Trimmed Mean of M-values): Recommended default. Calculates scaling factors by removing genes with extreme expression, providing robust normalization.
• RLE (Relative Log Expression): Used by DESeq2. Calculates a pseudo-reference sample and derives scaling factors from median ratios.
• VST (Variance Stabilizing Transformation): DESeq2-specific transformation that stabilizes variance across the mean—ideal for visualization and clustering.
• CPM/TPM: Counts/Transcripts per million. Simple normalization useful for comparing expression levels across genes.

Exploratory Analysis

Before statistical testing, exploratory analysis reveals patterns in your data and identifies potential confounding factors. This step is crucial for ensuring biological signals aren't obscured by technical artifacts.

Principal Component Analysis (PCA)

PCA reduces the high-dimensional gene expression data (thousands of genes) into a few principal components that capture the most variance. Each point represents a sample, and samples with similar expression profiles will cluster together.

UMAP Visualization

Uniform Manifold Approximation and Projection (UMAP) is a non-linear dimensionality reduction technique that often reveals cluster structures that linear methods like PCA miss. UMAP preserves both local and global data structure, making it excellent for identifying subpopulations.

Batch Effect Detection

Before vs After Correction

Differential Expression

Parameters

Volcano Plot

DEG Summary

Top DEGs

Pathway Enrichment

Results Summary

Enrichment Plot

Pathway Bubble Plot

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Gene-Pathway Network

Drug Target Discovery

TransXplorer integrates with DGIdb and OpenTargets to identify potential therapeutic targets.

Analysis Modes

Drug Prioritization

Cell Type Deconvolution

Estimate cellular composition using reference-based deconvolution algorithms.

Cell Type Proportions

Group Comparison

WGCNA Co-expression

Identify modules of highly correlated genes and relate them to experimental conditions.

Gene Dendrogram

Gene Clustering

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Module-Trait Correlation

Module-Trait Relationships

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Gene Regulatory Networks

Identify transcription factors controlling your DEGs and their regulatory relationships.

Master Regulators

TF Activity Heatmap

TF Activities

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PPI Network Analysis

Map DEGs onto protein interaction databases to identify hub proteins.

Analysis Modes

Network Statistics

Interactive Network

Hub Proteins

Network Metrics

PPI Network Metrics Summary

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Frequently Asked Questions