AltAnalyze

How to Use AltAnalyze for Comprehensive Single-Cell RNA-Seq Visualizations

Single-cell RNA sequencing (scRNA-Seq) yields massive, complex datasets. Extracting meaningful biological insights requires powerful visualization tools. AltAnalyze is a comprehensive, open-source application designed to analyze and visualize guide RNAs, splicing variants, and gene expression from scRNA-Seq data.

Here is a step-by-step guide to generating publication-ready visualizations using AltAnalyze. 1. Prepare Your Input Data

Before loading data into AltAnalyze, format your files correctly.

Expression Matrix: Use a filtered counts matrix (e.g., from Cell Ranger). Rows should be genes, and columns should be cell barcodes.

Metadata File: Create a text file linking cell barcodes to experimental conditions, time points, or batches.

File Format: Save files as tab-delimited text (.txt) or comma-separated values (.csv). 2. Set Up the AltAnalyze Workflow

Initialize your project to ensure correct annotation mapping.

Select Species: Choose your organism (e.g., Human, Mouse) to download the matching Ensembl genome databases.

Select Platform: Choose the “RNA-Seq” or “Single-Cell RNA-Seq” option depending on your specific protocol.

Run ICGS: Select the Iterative Clustering and Guide-gene Selection (ICGS) workflow. This algorithm automatically identifies distinct cell populations without user bias. 3. Generate Dimensionality Reduction Plots Visualize cell clustering and global heterogeneity.

UMAP and t-SNE: AltAnalyze automatically calculates these embeddings after ICGS clustering.

Customization: Use the graphical interface to color code cells by cluster, sample origin, or specific gene expression levels.

Exporting: Save these plots directly as high-resolution PDFs or PNGs for publications. 4. Create Expression Heatmaps Identify marker genes that define your cell clusters.

Hierarchical Clustering: AltAnalyze groups both cells and genes automatically to reveal coordinated expression modules.

Visual Anchors: Use the software settings to display z-score normalized values, making subtle expression differences highly visible.

Annotation Bars: Add color strips to the top of the heatmap to align clusters with your experimental metadata. 5. Analyze Splicing and Isoform Variation

Uncover cellular diversity beyond standard gene-level expression.

Splicing Visualizations: AltAnalyze specializes in detecting alternative splicing events (e.g., exon skipping) within single cells.

Sashimi Plots: Generate Sashimi plots directly through the interface to visualize junction read coverage across specific genomic loci. 6. Perform Pathway and Network Enrichment Connect visual clusters to biological functions.

Lineage Visualizations: Use the integrated NetX toolkit within AltAnalyze to build gene regulatory networks.

Pathway Activation Maps: Project cell-specific expression data onto WikiPathways or KEGG diagrams to visualize which biological pathways are active in specific clusters. To tailor this guide further, let me know: What format your raw data is currently in?

Which specific plots (UMAP, Heatmaps, or Splicing) you need most?