Transcriptomic Analysis Approaches

What is Transcriptomics and Where Do You Start? 🧬 If genomics gives us the blueprint of life, transcriptomics tells us the story being told right now: which genes are active, when, and how they respond to the world around them. Transcriptomics helps us answer questions like: 🧠 Which genes are switched on during disease? 💊 How do cells respond to drugs? 🌿 What makes one tissue different from another? So… how can you start exploring transcriptomic data (even as a beginner)? 🔹 Step 1: Understand the Basics Start with clear, beginner friendly explanations: 📘 PubMed Article: https://lnkd.in/duE72nbv 📗 PMC Review: https://lnkd.in/dSgqPzXW 🎥 StatQuest — Intro to RNA-Seq: https://lnkd.in/dcwKXBYy 🎥 StatQuest — RNA-seq Data Analysis Overview: https://lnkd.in/dzwuxkvs 🔹 Step 2: Quality Control (QC) Matters — a Lot! Before analysis, make sure your data is clean and reliable. Tools like FastQC, MultiQC, and Trimmomatic help you assess read quality, detect adapter contamination, and trim poor-quality reads. 🎥 FastQC Tutorial — YouTube: https://lnkd.in/dQ5Mvyry 🔹 Step 3: Get Real Data (for Free!) Practice on publicly available datasets: 📂 NCBI GEO — thousands of curated transcriptomic studies: https://lnkd.in/dSh2wNjv 📂 SRA (Sequence Read Archive) — raw sequencing data: https://lnkd.in/d5qwZMTm 📂 EMBL-EBI ENA — global sequence repository: https://lnkd.in/dtPp3tpS You can also find processed expression matrices ready for downstream analysis. 🔹 Step 4: Start Analyzing! If you’re new, start with visual or no-code workflows before jumping into command-line pipelines: 💻 Galaxy RNA-Seq Training: https://lnkd.in/dGSgFNgP 💻 nf-core RNA-seq pipeline: https://nf-co.re/rnaseq Or, if you know a bit of R: try DESeq2 or edgeR in RStudio. Transcriptomics isn’t just data it’s how we listen to the voice of the cell. Each dataset is a conversation between biology and computation waiting to be decoded. 💬 If you share your topic ideas in the comments, I can suggest a real example dataset for one of them in the next post! #Bioinformatics #Transcriptomics #RNASeq #Genomics #ComputationalBiology #Research #DataScience #STEM #LearningJourney #OpenScience

When I first started analyzing transcriptomic data, I thought “Pathway analysis = GSEA.” Turns out, they are not the same thing. And confusing them (along with “gene sets” vs “pathways”) can completely change how you interpret your results. Here’s the difference, simply put: 🔹 Gene sets ≠ Pathways - Gene sets are just groups of genes collected around a theme (ex: “hypoxia signature genes,” “genes upregulated in stem cells”). They don’t have to represent a biological mechanism. - Pathways are curated maps of biological processes, with interactions and relationships (ex: KEGG Glycolysis, Reactome Cell Cycle). They’re more structured and mechanistic. 🔹 Pathway analysis (Overrepresentation Analysis, ORA) You take your list of “significant” genes and ask: are these overrepresented in specific gene sets or pathways compared to chance? Simple, but it depends on where you draw your cutoff for “significant.” 🔹 GSEA (Gene Set Enrichment Analysis) No cutoff needed. Instead, you rank all genes by expression change and test whether gene sets or pathways cluster toward the top or bottom of the ranking. This lets you see subtle but coordinated changes that ORA might miss. 👉🏻 In practice: - Pathway analysis = quick check of which processes dominate among your strongest hits. - GSEA = a deeper scan for consistent biological themes across the whole transcriptome. Neither is “better.” They answer different questions. And often, the most insight comes from using both together. Let me know if you could be interested about knowing some useful tools for performing those types of analysis… But also I’m curious: in your work, do you rely more on GSEA or on pathway enrichment tests? — #bioinformatics #lablife #researchlife #postdoc #phd #tools #coding #genomics #RNAseq #genesexpression image_source: https://lnkd.in/e7Bmp7wm

Spatial transcriptomics technologies such as 10x Visium, SLIDE-seqV2 and seqFISH allow for the measurement of gene expression within the original tissue context at distinct levels of spatial resolution and gene coverage.  To overcome some limitations of these technologies, at team led by @Daiwei Zhang at the University of Pennsylvania developed  iStar, a method for predicting spatial gene expression to near-single-cell resolution by integrating spatial transcriptomics (ST) data and high-resolution histology images. The method enables gene expression prediction in tissue sections where only histology images are available and is able to generate intricate molecular maps of cells within tissues. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. https://lnkd.in/eHeN-9Rr Methods overview: The authors provide a detailed description of iStar's algorithm, which consists of three components: the histology feature extractor, super-resolution gene expression predictor, and tissue architecture annotator. It explains the process of rescaling and padding histology images to ensure consistent processing across different resolutions. The section describes the hierarchical partitioning of images into tiles and the use of a hierarchical vision transformer to extract features at different scales. It details the super-resolution gene expression predictor, which uses a weakly supervised learning framework to model gene expression at the superpixel level. The methods outline the tissue architecture annotator, which segments tissue by clustering superpixels using their gene expression information and assigns biological interpretations to tissue regions through cell type inference. Results overview: The authors evaluate iStar's accuracy in super-resolution gene expression prediction using a simulated dataset derived from a Xenium breast cancer dataset. It compares iStar's performance to that of XFuse, another state-of-the-art method, showing that iStar's predictions more closely match the ground truth. This  section continues by assessing iStar's capability for high-resolution annotation of tissue architecture across multiple tissue sections. It showcases iStar's ability to bypass challenging image registration tasks and perform multi-sample tissue segmentation. The results highlight iStar's strong agreement with manual annotations while providing increased granularity in tissue segmentation. The section then describes iStar's application to another HER2+ breast cancer dataset, demonstrating its generalizability in super-resolution tissue segmentation and annotation. It explains how iStar can conduct cell-type annotation at the superpixel level and infer cell types based on predicted gene expressions of marker genes. 10x Genomics

Standard spatial transcriptomics platforms can analyze tissue samples up to about 25 square millimeters. But what if you need to study an entire tumor or organ section? The context: Spatial transcriptomics has emerged as a powerful tool for understanding how gene expression varies across tissue space, providing insights into cell-cell interactions, tissue organization, and disease mechanisms. However, current commercial platforms face significant constraints: high costs, lengthy processing times, limited gene coverage, and crucially, small capture areas that restrict analysis to tissue fragments rather than whole organs or large anatomical structures. This size limitation is particularly problematic for studying complex diseases like multiple sclerosis, where pathological changes occur across vast brain regions with heterogeneous patterns that can't be captured in small tissue sections. Amelia Schroeder et al. developed iSCALE (inferring Spatially resolved Cellular Architectures in Large-sized tissue Environments), a computational framework that addresses this scale problem by leveraging the relationship between gene expression patterns and histological features visible in standard H&E stained slides. Key technical approach: - "Daughter capture" integration: Combines multiple small spatial transcriptomics data from different tissue regions - H&E-guided prediction: Uses machine learning to predict gene expression patterns across entire large tissue sections based on histological features - Semi-automatic alignment: Develops methods to computationally stitch together data from different tissue sections - Cellular-resolution inference: Predicts super-resolution gene expression at near single-cell level Validation and applications: The method was tested on multiple sclerosis brain samples, where iSCALE uncovered lesion-associated cellular characteristics that were undetectable by conventional ST experiments. The approach enables analysis of tissue areas far exceeding the physical constraints of current platforms while maintaining spatial resolution. Broader implications: This work demonstrates a shift from hardware-limited to computationally-enabled spatial transcriptomics. Rather than requiring ever-larger capture arrays, iSCALE shows how intelligent integration of limited experimental data with computational inference can overcome physical platform constraints. The approach could enable spatial transcriptomics studies of whole organs, developmental processes requiring large-scale analysis, and disease contexts where pathology spans anatomical regions. How might computational approaches like this change the scale of questions we can address in spatial biology? paper: https://lnkd.in/e2fmn7UC code: https://lnkd.in/eiGsh2HD #SpatialTranscriptomics #ComputationalBiology #MachineLearning #Neuroscience #MultipleSclerosis #DigitalPathology

RNA-seq vs. Single-Cell RNA-seq: Which is Right for Your Research? When it comes to transcriptomics, RNA-seq and single-cell RNA-seq (scRNA-seq) are the two powerhouse technologies. But how do you decide which is right for your research? 🤔 Let’s break it down. 🍵 RNA-seq: Ideal for analysing bulk tissue or cell populations. It gives you: - Deep sequencing coverage for detecting low-abundance transcripts. - Information on isoform diversity and splicing events. - Cost-effective profiling of hundreds of samples. Perfect for: ✔️ Differential gene expression between conditions. ✔️ Identifying splicing or fusion events. ✔️ Studies where single-cell resolution isn’t critical. 🍅🫑🫛 scRNA-seq: Shines when you need to dive into the cellular heterogeneity within tissues. It offers: - High-resolution profiling of individual cells. - Identification of rare cell populations. - Insights into cellular states and trajectories. Perfect for: ✔️ Mapping cell populations in complex tissues (e.g., immune cells). ✔️ Developmental biology and disease progression studies. ✔️ Capturing dynamic processes like cell differentiation. 🧪 Key Considerations - Budget: Bulk RNA-seq is more cost-effective, while scRNA-seq requires deeper pockets. Not only generating data is more expensive for scRNA-seq, but also the analysis: more resources are required, both computational and working-hours. - Goals: Bulk RNA-seq is great for tissue-level understanding, while scRNA-seq zooms in at the single-cell level. Bulk RNA-seq captures low-abundance transcript, whereas scRNA-seq often does not. On the bright side: You don’t always have to choose! These technologies can be integrated to complement each other, offering both breadth and depth in your research. Which technology do you prefer and why? Does it always have to be single-cell level? #RNAseq #SingleCell #Transcriptomics #Genomics #Bioinformatics #Biology #Research #Immunology #Neuroscience #DataScience #Biomedicine