Bioinfospace Blog
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SigXTalk: Dissecting Cell-Cell Communication Crosstalk with Hypergraph Learning
SigXTalk is a new Nature Communications 2025 method that quantifies pathway crosstalk in cell-cell communication using hypergraph neural networks and Random Forest. Introduces fidelity and specificity as measures of signaling selectivity from scRNA-seq data.
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Learning Single-Cell Analysis with Theis Lab: Measuring RNA Velocity
RNA velocity reconstructs cellular trajectories by analyzing the balance between unspliced and spliced RNA, revealing developmental directionality that static snapshots alone cannot show. **(177 characters)**
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Learning Single-Cell with Theis Lab: Not All Features Are Created Equal
Not all 30,000 genes detected in single-cell RNA-seq are created equal. Feature selection using scry's deviance-based approach separates biologically meaningful genes from noise.
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Learning Single-Cell Analysis with Theis Lab: The Mathematics Behind Normalization
Normalization corrects sequencing biases in single-cell data. Explores Gamma-Poisson distributions and hands-on practice with scanpy normalization for accurate downstream analysis.
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Single-Cell Analysis with Theis Lab: Taming High-Dimensional Data with Dimensionality Reduction
Understanding the "curse of dimensionality" and its impact on single-cell analysis 2. Grasping the principles and characteristics of PCA, t-SNE, and UMAP
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Learning Single-Cell Analysis with Theis Lab: Bridging the Batch Gap (Part 2)
How does BBKNN achieve lightning-fast integration through its clever "neighbor graph modification" strategy?
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Learning Single-Cell Analysis with Theis Lab: Bridging the Batch Effect Divide (Part 1)
**Batch effects are unavoidable in single-cell RNA-seq, but scVI and scANVI enable robust data integration. Learn to merge datasets and select informative genes while preserving biological variation.** (198 characters)
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Learning Single-Cell with Theis Lab: How to Read a QC Report
Decode FastQC reports for single-cell RNA-seq data in this Theis Lab guide. Master the preprocessing quality metrics that shape every downstream analysis decision.
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Learn Single-Cell Analysis with Theis Lab: What Does Quality Control Actually Control?
Quality control in single-cell RNA-seq controls technical artifacts—ambient contamination via SoupX and doublets via scDblFinder—while preserving biological variation hidden in dropout events.
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Learning Single-Cell Analysis with Theis Lab: Confused About Clustering vs. Dimensionality Reduction?
Understand the biological significance and technical principles of single-cell clustering 2. Grasp the core concepts of KNN graph construction and the Leiden algorithm
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Learn Single-Cell Analysis with Theis Lab: Tracing the \"Fate\" Trajectory of Cells
Pseudotime reconstructs cell fate trajectories from snapshots. Compare DPT and Palantir algorithms, and master root cell selection—the foundation of reliable pseudotime trajectory inference.
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Single-Cell Analysis with Theis Lab: How Many Cell Annotation Strategies Do You Know?
Master four cell annotation strategies for single-cell sequencing: manual marker genes, automated tools, reference mapping, and ensemble methods for identifying cell types in complex datasets. (192 characters)
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Learning Single-Cell Analysis with Theis Lab: Format Conversion — Understanding the Underlying Logic (Part 2)
Using Seurat's built-in functions to convert between SingleCellExperiment and Seurat objects 2. Using the rpy2 and reticulate packages for mutual interoperability between R and Python
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Learning Single-Cell Analysis with Theis Lab: Format Conversion — Understanding the Underlying Logic (Part 1)
Why cross-ecosystem interoperability is necessary, what challenges it poses, and how to achieve it 2\. Understanding disk-based data interoperability methods, including HDF5 and related formats
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Single-Cell Analysis with Theis Lab: Alignment and Mapping Done Right
Alignment strategies for single-cell RNA-seq: compare genome-level vs. transcriptome-level mapping approaches and select optimal reference genomes for accurate single-cell analysis.
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Learning Single-Cell Analysis with Theis Lab: All About Quantification
Cell barcode correction and UMI resolution enable accurate single-cell gene quantification from RNA-seq data. Extract, correct, and deduplicate molecular tags to convert alignments into cell-level expression matrices.
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Learning Single-Cell Analysis with Theis Lab: An Overview of Single-Cell Sequencing
Single-cell sequencing enables transcriptome analysis at individual cell resolution. Covers cell theory, sequencing history from first to third generation, and foundational concepts.
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Single-Cell Analysis with Theis Lab: From Quality Control to the Count Matrix
From raw reads to count matrix: Cell Ranger output structure, QC filtering decisions, and what happens between sequencing and downstream single-cell analysis.
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Learning Single-Cell Analysis with Theis Lab: scanpy + muon, Your Single-Cell Swiss Army Knife
Understand the API design and functional modules of scanpy and muon 2\. Master the basic single-modal data analysis workflow with scanpy
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Learning Single-Cell Analysis with Theis Lab: alevin-fry in Practice
Hands-on Upstream Data Processing 1\. Get to know alevin-fry, a lightweight single-cell data processing tool
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Learning Single-Cell Analysis with Theis Lab: MuData — Your Companion for Multimodal Data
Best Practices in Single-Cell Analysis 1. Understand the characteristics and storage requirements of multimodal data
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Learning Single-Cell Analysis with Theis Lab: An Introduction to AnnData
Best Practices in Single-Cell Analysis 1. Understand the major single-cell analysis ecosystems and their differences
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Learning Single-Cell Analysis with Theis Lab: 01. Preface
Introducing the "Learning Single-Cell with Theis Lab" series — a guided walkthrough of the Single-Cell Best Practices book, from core concepts to hands-on Python code.
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Statistics Self-Help Guide 5: It's Time to See Through the Bar Chart
The editors were blunt: bar charts are severely overused, and in many situations they simply cannot accurately convey the true nature of statistical data. So what should you use instead?
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Statistics Self-Help Guide 4: Statistical Power — Don't Let Your Experiments Be Blind
Statistical power explained: why underpowered studies miss real effects, how to calculate required sample size, and whether your negative result proves no difference.
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Statistics Self-Help Guide 3: The p-value and t-test You've Probably Never Fully Understood
The p-value and t-test explained from first principles: what the number actually means, how to interpret it correctly, and the five most common misconceptions.
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Statistical Self-Help Guide 2: The Deceptive Error Bar
Error bars decoded: the critical difference between SD, SEM, and 95% CI — and why mixing them up leads to systematically misinterpreted data in publications.
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Statistics Self-Help Guide 1: Embracing Uncertainty
Why statistical uncertainty is the foundation of experimental biology — and how to stop conflating p-values, effect sizes, and confidence intervals in your own analysis.
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I Finally Couldn't Resist Fixing SeuratDisk
Practical Seurat V5 ↔ AnnData conversion strategies now that SeuratDisk is broken — updated methods compatible with the new V5 data structure.
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Mastering LLMs: What's It Like to Have an All-Purpose AI Assistant?
A legendary tool with 65k GitHub stars — Claude Code on steroids. Have you tried this all-purpose assistant yet?
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Mastering Large Language Models: A First Look at APIs
Master practical LLM skills and integrate them into bioinformatics research. A hands-on introduction to APIs for leveraging large language models in computational biology workflows.
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The Future Is Now: The Era of LLM × Single-Cell Has Begun
The era of LLM-powered single-cell analysis: how foundation models like scGPT and GPT-4 are transforming cell annotation and biological discovery in 2024–2025.
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Bring AI Home: A Beginner's Guide to Running LLMs Locally
Run powerful LLMs like DeepSeek R1 locally on a personal laptop using LM Studio — no server, no cloud, no deep technical knowledge required.
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The New Azimuth Might Just Be the Best Cell Annotation Tool Out There
The Satija Lab's new Pan-Human Azimuth enables reference-based cell annotation across tissues — a hands-on guide to running it on your own single-cell dataset.
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Which Tool Is Best for Single-Cell Format Conversion? The Ultimate 2025 Roundup
Systematic comparison of single-cell format conversion tools in 2025: which Seurat ↔ AnnData/MuData methods remain reliable with the V5 data structure.
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Stop Using Only RDS for Single-Cell Data — Give qs a Try!
Today, let's talk about a topic that sounds boring but can genuinely be a lifesaver — how to efficiently store your single-cell data. Single-cell datasets are getting massive. It's now routine to work with hundreds of thousands, even millions of cells. Even if…
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Downstream Note 3: Interactive Visualization in Three Minutes
ShinyCell converts analyzed Seurat, AnnData, or Loom objects into an interactive Shiny app — explore gene expression, clusters, and metadata without writing a line of code.
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Downstream Notes 2: Can You Recover the Original Counts from Normalized Data? Here's the Trick
Back-calculate the original raw count matrix from normalized single-cell data — essential when reanalyzing public datasets that only provide normalized counts.
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Downstream Note 1: The Ultimate pySCENIC Troubleshooting Guide
Complete pySCENIC installation guide and troubleshooting reference. SCENIC infers gene regulatory networks and transcription factor activity from single-cell RNA-seq data.
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Upstream Notes 4: A Quick Guide to BD Single-Cell Upstream Analysis
BD Rhapsody upstream analysis: microwell-based single-cell capture, Seven Bridges Cloud pipeline configuration, and how BD's approach differs from 10x Genomics.
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Upstream Notes 3: Multiplexed Single-Cell Sequencing? Meet cellranger multi
Cell Ranger multi for multiplexed 10x experiments: TCR/BCR + Gene Expression processing, CellPlex hashtag demultiplexing, and multi-sample config files explained.
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Upstream Notes 2: Cell Ranger count Isn't That Hard
Cell Ranger count walkthrough: Linux setup, reference genome configuration, FASTQ input, key output files, and the parameters that actually matter.
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Upstream Notes 1: Let's Download Some Data!
Download raw sequencing data from GEO and SRA using sra-toolkit: prefetch, fasterq-dump, and the right flags for 10x single-cell FASTQ files.
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A Simple qPCR Analysis Tool
A lightweight Shiny app for one-click qPCR analysis: upload CSV/Excel, auto-compute ΔΔCt and fold changes, and generate publication-style bar charts with statistics.
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tidyplots in Practice: Publication-Quality Figures for RNA-seq
Imagine spending three days tweaking ggplot2 parameters, only to end up with a figure so ugly it makes your reviewers question their life choices. The arrival of tidyplots is like a godsend for those of us who are artistically challenged! Three big advantages of tidyplots:
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qPCR Tool Update!
Updated qPCR Shiny app with improved statistical methods, paired t-test support, and ANOVA alternatives — based on community feedback since the original release.
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T Cell Nomenclature Gets Its Long-Overdue Standardization! (Plus: scRNA-seq T Cell Annotation Tool Recommendations)
A landmark 2025 Nature paper standardizes T cell nomenclature — and what the new unified taxonomy means for single-cell annotation of T cell subtypes.
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STAR Has a Bug?! A Complete Debugging Walkthrough
The popular alignment tool STAR has now reached version 2.7.11b. However, after updating the other day, I hit this error when trying to run it: Transcriptome.cpp:18:Transcriptome: exiting because of INPUT FILE error: could not open input file /geneInfo.tab
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Is STARsolo Really 10× Faster Than CellRanger?
Benchmarking STARsolo against Cell Ranger for 10x scRNA-seq quantification: speed, output consistency, and when each tool is the right choice.
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SCIPAC: Discovering Clinically Relevant Cell Populations in Single-Cell Data
SCIPAC (Genome Biology, 2024) quantifies each cell's association with clinical phenotypes — identifying the most disease-relevant cell populations in your single-cell dataset.