Methods for Cell Analysis

🧮 Optimal Transport & Wasserstein Distance in Single-Cell Biology: A Mathematical Framework I've just published a comprehensive guide covering the mathematical foundations and computational applications of optimal transport theory for single-cell data analysis. 📚 Key Method Categories: 📈 Trajectory Inference: Waddington-OT 🔗 Multi-Omics Alignment: SCOT, Pamona 🧬 Perturbation Prediction: CellOT 🌊 Stochastic Modeling: GENOT, PRESCIENT 🗺️ Atlas-Scale Integration: moscot 📈 Evolution Timeline: 2019: Waddington-OT (trajectory inference paradigm) 2020-2021: SCOT, PRESCIENT (multi-omics + generative approaches) 2022: Pamona, OT-scOmics (partial alignment + similarity metrics) 2023: CellOT, TIGON (neural OT, separating growth from transport) 2024: GENOT, GRouNdGAN (conditional flows, causal GAN) 2025: Labeled GWOT, moscot (label constraints, atlas-scale mapping) 📊 Resources Included: Comparison table: 11 methods from trajectory inference to 1.7M-scale integration Mathematical foundations: Monge-Kantorovich theory, entropic regularization, Gromov-Wasserstein Software ecosystem guide (POT, moscot, Waddington-OT, SCOT, Pamona, CellOT) Common pitfalls: data normalization, hyperparameter tuning (ε: 0.001-0.1), ground cost selection, validation strategies This is part of my AI4Bio Learning Hub (https://lnkd.in/gS3ivaR5) where I share technical deep dives as a Computational Immunologist working at the intersection of single-cell genomics, AI, and therapeutic development. 📖 Full guide: https://lnkd.in/gMnaEZGA 💬 Spot an error? Have suggestions? Working on OT methods? I'd love your feedback to keep this resource accurate and comprehensive!

🟥 Lineage Mapping via Single-Cell ATAC-Seq and Methylome A deeper understanding of how stem cells differentiate into different cell types requires tools that can capture the molecular mechanisms that guide these transitions. Because traditional lineage tracing methods rely primarily on genetic markers, their understanding of the underlying molecular mechanisms is limited. Combining single-cell ATAC sequencing and single-cell DNA methylation analysis, two powerful epigenomic technologies, can provide complementary perspectives on chromatin accessibility and DNA methylation at single-cell resolution. Single-cell ATAC sequencing primarily reveals open chromatin regions and can identify active enhancers, promoters, and transcription factor binding sites that drive lineage-specific gene expression. At the same time, because DNA methylation patterns are inherited during cell division and are often lineage-specific, single-cell methylome analysis can also provide a stable record of cell identity and lineage history. Combining these technologies can provide a multi-layered approach to track cell fate trajectories and map differentiation hierarchies. This integrated strategy has been shown to be particularly effective in dynamic systems such as hematopoiesis, where multipotent progenitor cells differentiate into a range of blood and immune cells. By analyzing thousands of single cells at different developmental stages, researchers can reconstruct branching lineage trees and pinpoint key regulatory events associated with fate decisions. In addition, this dual-omics approach can reveal intermediate cell states and rare cell populations that transcriptomics cannot reveal. In addition to developmental biology, this method is currently being applied to disease areas such as leukemia and solid tumors, where abnormal lineage selection leads to pathological changes. In addition, it also provides a valuable benchmark for evaluating stem cell differentiation in regenerative medicine and cell therapy development. In summary, the combination of single-cell ATAC-seq and methylome profiling provides a powerful toolkit for analyzing epigenetic control of lineage specification, allowing scientists to gain a deeper understanding of cell identity, memory, and fate transitions in health and disease states. References [1] Leif Ludwig et al., Cell 2019 (DOI: 10.1016/j.cell.2019.01.022) [2] Hanqing Liu et al., Nature 2023 (https://lnkd.in/e77cmT3s) #SingleCellEpigenomics #ATACseq #Methylome #LineageTracing #CellFateMapping #StemCellBiology #DevelopmentalBiology #Hematopoiesis #EpigeneticMemory #ChromatinAccessibility #RegenerativeMedicine #SingleCellOmics #PrecisionBiology #Epigenetics #CancerResearch #CSTEAMBiotech

New @NatureProtocols: A comprehensive guide to using CellRank for single-cell fate mapping 🧬 Philipp Weiler (now @Memorial Sloan Kettering) has done an outstanding job creating this detailed protocol for our CellRank framework, as a hands-on walkthrough of how to efficiently use it. The paper walks through: • Integrating complementary data views (velocity, pseudotime, time points) • Inferring cell-cell transition probabilities • Computing terminal states and fate probabilities • Analyzing lineage priming at scale CellRank addresses a key challenge in single-cell biology: reconstructing cellular trajectories from destructive snapshots. By combining multiple information sources consistently, we can better understand developmental dynamics and disease progression. This protocol paper complements our CellRank 2 methods paper (Nature Methods, 2024) and provides the practical guidance needed to apply these methods at scale. Great work by Philipp and thanks to the entire community using and contributing to CellRank! 📖 Protocol: https://lnkd.in/egBx2RGq 💻 Code: https://lnkd.in/efTefxxq #scRNAseq #DevelopmentalBiology #MachineLearning #OpenScience

🔬 Introducing Milo: A Game-Changer for Single-Cell Data Analysis I'm excited to share insights about Milo, a powerful computational framework published in Nature Biotechnology that's transforming how we analyze differential abundance in single-cell datasets. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗠𝗶𝗹𝗼 𝘂𝗻𝗶𝗾𝘂𝗲? Unlike traditional clustering-based approaches, Milo performs differential abundance testing by assigning cells to 𝘱𝘢𝘳𝘵𝘪𝘢𝘭𝘭𝘺 𝘰𝘷𝘦𝘳𝘭𝘢𝘱𝘱𝘪𝘯𝘨 𝘯𝘦𝘪𝘨𝘩𝘣𝘰𝘳𝘩𝘰𝘰𝘥𝘴 on k-nearest neighbor graphs. This innovative approach overcomes key limitations: ✅ No need for discrete clustering ✅ Captures continuous trajectories ✅ Handles complex experimental designs ✅ Controls for batch effects ✅ Maintains FDR control 𝗞𝗲𝘆 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀: • Scalable & Fast: Analyzes 200,000+ cells in under 60 minutes • Flexible: Works with complex designs including continuous covariates • Powerful: Identifies subtle perturbations obscured by clustering • Open Source: Available as an R package via Bioconductor 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗶𝗺𝗽𝗮𝗰𝘁: The paper demonstrates Milo's capabilities through compelling applications: • Identified a fate-biased epithelial precursor declining with age in mouse thymus • Revealed compositional changes in human cirrhotic liver • Detected perturbations across multiple cell lineages 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: As single-cell datasets grow larger and more complex, methods like Milo enable us to detect disease-relevant cell state changes that would otherwise go unnoticed. This has profound implications for understanding development, disease, and aging. 📊 The method leverages generalized linear models and a weighted FDR procedure, making it applicable to diverse experimental settings and single-cell modalities beyond RNA-seq. 🔗 Get started: https://lnkd.in/e833k5_4 Published in Nature Biotechnology (2022) by Dann et al. from Wellcome Sanger Institute, University of Edinburgh, and collaborators. #SingleCell #Bioinformatics #ComputationalBiology #DataScience #GenomicAnalysis #OpenSource #Research

Honestly I did not know that… Q: Are there any alternatives to PCA for finding patterns in single-cell RNA-seq data? A: Yes — and they can often reveal more biologically meaningful insights than principal components alone. While PCA is widely used, it has limits: it assumes linearity, enforces orthogonality, and doesn't model dropout or noise. That's why many researchers now use alternative decomposition methods to uncover hidden structure in single-cell data: ⸻ 1. Non-negative Matrix Factorization (NMF) • What it does: Decomposes gene expression into additive, parts-based patterns (like topics) • Why it's used: Outputs are positive, making co-expression programs easy to interpret • Tools: NMF in R, RunNMF() in Seurat • Used for: Cell identity, gene modules, transcriptional programs ⸻ 2. Independent Component Analysis (ICA) • What it does: Finds components that are statistically independent (not just uncorrelated) • Why it's used: Can separate overlapping signals like stress and cell cycle • Tools: fastICA (R), scICA (Python) • Used for: Gene module discovery, deconfounding sources of variation ⸻ 3. Topic Modeling / LDA • What it does: Models each cell as a mixture of gene expression "topics" (like NLP) • Why it's used: Captures soft clustering and overlapping programs • Tools: cisTopic, celda, scLDA • Used for: Cell states, transcriptional programs, fuzzy identities ⸻ 4. Variational Autoencoders (VAEs) • What it does: Learns a nonlinear, probabilistic latent space • Why it's used: Handles dropout, noise, and complex manifolds • Tools: scVI, totalVI, destVI • Used for: Data denoising, batch correction, multimodal analysis ⸻ 5. Factor Analysis (FA) • What it does: Like PCA but models noise explicitly • Why it's used: More realistic handling of uncertainty in gene expression • Tools: MOFA+ • Used for: Latent factor discovery and multi-omic integration ⸻ Bottom line: PCA is fast and useful, but not the only game in town. For deeper insights, especially when signals overlap or noise dominates, these alternative methods can give you more interpretable and powerful decompositions. Which one have you tried — or want to try next? #scRNAseq #Bioinformatics #DimensionalityReduction #NMF #scVI #MOFA #LDA #DeepLearning #SingleCell #LatentFactors #PCAAlternatives #AlWahhaab

Researchers developed an “in-capillary” method to analyze carbohydrates that cover individual human cells, which hold biomarkers for a variety of deadly diseases. Northeastern University, Boston. July 23, 2024. Key: The Ivanov Lab is paving the way to a variety of diagnostic tests that are possible by way of a single blood draw, including — someday — cancer. Excerpt: Every cell in the human body has a thin covering of glycoconjugates referred to as "conjugates” formed from the binding of carbohydrates to proteins, lipids and other cell surface molecules. The carbohydrates — called glycans — play a significant role in cell communication and the cell’s ability to respond to disease. Note: "Common blood type tests are based on glycan analysis,” said Alexander Ivanov, associate professor of chemistry and chemical biology. In addition, “We see glycans reflect cell types and cell states,” he continued, which are “potentially diagnostic of different diseases.” “Cancer is definitely among those.” Abnormalities in the presentation of glycans on the cell surface, their types and composition, their chemical linkages and quantities, all provide potentially useful information to the researcher or diagnostician, including biomarkers for a variety of diseases. A team of researchers led by Ivanov has developed a method by which they can analyze the surface glycans of an individual, still-living human cell, as well as those found in minuscule volumes of plasma and other blood isolates. The overall representation of glycans in a cellular environment, especially on the cell surface — “abnormalities represent an overt source of potential biomarkers for diagnostic, prognostic, and treatment monitoring of various human diseases,” was published in Nature Communications. Publication: Nature Communications 08 May 2024 Native N-glycome profiling of single cells and ng-level blood isolates using label-free capillary electrophoresis-mass spectrometry Anne-Lise Marie, Yunfan Gao & Alexander R. Ivanov https://lnkd.in/eeqPffKB

This image compares three types of blotting techniques used to detect specific molecules: Southern Blot, Northern Blot, and Western Blot. These methods help analyze DNA, RNA, and protein, respectively. Key Points: 1. Southern Blot (DNA) Detects specific DNA fragments. Steps: DNA is cut using restriction enzymes. Gel electrophoresis separates fragments on agarose gel. Transferred to a membrane and probed with complementary single-stranded DNA or RNA. Results: Allows detection of specific DNA sequences and measurement of fragment size and quantity. 2. Northern Blot (RNA) Detects specific RNA transcripts (e.g., mRNA). Steps: RNA is separated using gel electrophoresis on agarose gel. Transferred to a membrane and probed with single-stranded DNA or RNA complementary to the transcript. Results: Identifies RNA fragments, their size, and the level of expression. 3. Western Blot (Protein) Detects specific proteins. Steps: Proteins are denatured and separated using electrophoresis on acrylamide gel. Transferred to a membrane. Primary antibodies bind to the target protein, and secondary antibodies amplify detection. Results: Measures protein size and expression levels. Summary of Differences: Target Molecule: DNA (Southern), RNA (Northern), Protein (Western). Gel Type: Agarose for DNA/RNA; Acrylamide for protein. Probes: Southern/Northern: Single-stranded complementary sequences. Western: Antibodies. Each blotting method provides unique insights into gene expression, genetic sequences, and protein analysis, making them essential tools in molecular biology.

Single-cell mRNA sequencing for biologists part 3: Cellular heterogeneity? Dimensionality reduction & Clustering! 🧬 And now the fun begins: making plots and assessing cellular heterogeneity. Two key techniques are most commonly used: dimensionality reduction and clustering. Here’s an overview of both techniques including pitfalls and critical analysis parameters. 📊 Identify cellular landscapes with UMAP: Dimensionality reduction transforms high-dimensional gene expression data into a lower-dimensional space, ideally preserving both local and global structures. UMAPs are often used for this which are usually based on principal components (PCs). • Critical: Include enough PCs to capture biological variation, while excluding PCs that represent technical noise. Use an Elbow plot to help decide. • Keep in mind: UMAP distances are not a perfect measure of cell differences as relationships are not linear. There is a fine balance between preserving local- and global relationships. 🧩 Explore heterogeneity and cell (sub)populations using clustering: Clustering is used to group cells with similar gene expression profiles into discrete clusters. These clusters can help identify and characterize diverse cell types, states, and developmental trajectories. • Critical: The resolution parameter determines the number of clusters. Use biological expertise to identify meaningful clusters. A stable number of clusters across multiple resolutions may indicate a good resolution. • Keep in mind: Clusters can still contain a mix of cell subsets. Manually annotation and possibly adjusting clusters may be necessary in some circumstances. ✅ Cluster quality: Assessing cluster quality is crucial. Clusters can form based on irrelevant factors, such as low-quality cells with high mitochondrial reads or cell cycle stage (also see part 2). • Critical: Use violin plots to check if clusters formed due to unwanted variation. If so, revisit preprocessing to correct for it. • Keep in mind: Reliable clustering is essential for downstream analysis. 📦 Batch correction: Batch effects can drive unwanted cluster formation. Batch correction algorithms mitigate these effects. • Critical: Various batch correction algorithms are available and easy to apply, e.g. harmony. • Keep in mind: Computational correction for batches can lead to overcorrection resulting in a loss of relevant biological information. 🔍 Conclusion: Dimensionality reduction, clustering, and quality assurance are integral to scRNA-seq analysis. Good clusters are essential for downstream analysis, but clusters don’t always perfectly reflect cell subsets. Interested in more details? Let me know! ⏭ Next up: The wide world of scRNAseq downstream analyses. Other parts: Part 1: https://lkdin.io/4IF5 Part 2: https://lkdin.io/4IJh #scRNAseq #Genomics #Research #Biotechnology #Immunology #Neuroscience #DevelopmentalBiology #SingleCell #Transcriptomics #Biotech #StartUps #Academia