Author: Lei
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How to Analyze RNAseq Data for Absolute Beginners Part 8: Alternative Splicing Analysis
Introduction Alternative splicing (AS) stands as one of the most fascinating mechanisms in molecular biology, allowing a single gene to produce multiple protein variants. This process dramatically expands the complexity of our proteome, enabling cells to fine-tune their protein repertoire in response to various conditions and developmental stages. Through RNA sequencing (RNA-seq), we can now…
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How to Analyze RNAseq Data for Absolute Beginners Part 7: Unlocking Cell-Type Resolution from Bulk RNA-seq Data With Deconvolution Analysis
Introduction Bulk RNA sequencing has become a cornerstone technology in molecular biology, providing comprehensive insights into gene expression patterns across tissues. However, the complexity of tissue samples, containing multiple cell types, presents unique challenges in data interpretation. This tutorial, Part 7 in our RNA-seq analysis series, focuses on deconvolution analysis – a powerful computational approach…
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How to Analyze RNAseq Data for Absolute Beginners Part 6: A Comprehensive Guide for Cancer Subtype Prediction
Meta Description: Learn how to predict cancer subtypes using RNA-seq data through practical implementations of PAM50, genefu, and GSVA methods. Perfect for bioinformaticians and computational biologists working with gene expression data. Introduction Cancer subtype prediction from RNA-seq data is crucial for personalized medicine and treatment optimization. This tutorial, part 6 in our RNA-seq analysis series,…
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How to Analyze RNAseq Data for Absolute Beginners Part 5: From DEGs to Pathways – Best Practices
Introduction After completing the data preparation, statistical testing, and visualization steps, we’re finally ready to explore the biological significance of our RNA sequencing data. As biologists, this is the moment we’ve been waiting for – but how do we make sense of the hundreds or thousands of differentially expressed genes (DEGs) we’ve identified? Living organisms…
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How to Analyze RNAseq Data for Absolute Beginners Part 4: A Complete Guide to Creating Publication-Ready Figures
Introduction After identifying differentially expressed genes (DEGs), the next crucial step is visualizing your results effectively. Strong visualizations don’t just make your data look pretty – they transform complex genomic information into clear, interpretable insights that can reveal hidden patterns and biological stories within your data. In this tutorial, you’ll learn how to create publication-quality…
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How to Analyze RNAseq Data for Absolute Beginners Part 3: From Count Table to DEGs – Best Practices
As we move forward in our RNAseq analysis journey, we’ll be transitioning from the Linux environment to R, a powerful and versatile statistical analysis tool. R is not only a programming language but also a platform widely used in data science, statistical computing, and predictive modeling. Tech giants like Microsoft, Meta, Google, Amazon, and Netflix…
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How to Analyze RNAseq Data for Absolute Beginners Part 2: From Fastq to Counts – Best Practices
Introduction The most straightforward way to obtain a count table is to request it directly from your sequencing company or your institution’s sequencing core. This option may involve an additional fee. However, for those eager to learn or save money, let’s walk through the process together. Before we dive in, a quick reminder: If you…
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How to analyze RNAseq Data for Absolute Beginners Part 1: Environment setup
Introduction RNA sequencing (RNAseq) has revolutionized the field of transcriptomics, offering unprecedented insights into gene expression patterns across entire genomes. This powerful technique allows researchers to quantify RNA levels, discover novel transcripts, and identify differentially expressed genes under various conditions. Whether you’re studying cancer progression, developmental biology, or environmental responses in organisms, RNAseq is an…
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Recent Posts
- How to Analyze RNAseq Data for Absolute Beginners Part 8: Alternative Splicing Analysis
- How to Analyze RNAseq Data for Absolute Beginners Part 7: Unlocking Cell-Type Resolution from Bulk RNA-seq Data With Deconvolution Analysis
- How to Analyze RNAseq Data for Absolute Beginners Part 6: A Comprehensive Guide for Cancer Subtype Prediction
- How to Analyze RNAseq Data for Absolute Beginners Part 5: From DEGs to Pathways – Best Practices
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Adapter Trimming Alternative Splicing Analysis BAM bar plot breast cancer classification cancer subtypes cell-type composition CIBERSORTx Conda Environment Setting Count Differential Expression dotplot Enrichment FASTQ gene expression Gene Expression Quantification ggplot2 GO GSEA immune cell profiling KEGG molecular subtypes MSigDB PAM50 Pathway R Reads Mapping RNA-seq analysis RNAseq analysis for beginners violin plot