Biology subjects

Sankaran, K.

Publications and source records attributed to Sankaran, K..

7 recordsLinked to original sources

bioRxiv2026

scSketch: Interactive Sketch-based Trajectory Exploration and Pathway-Aware Analysis of Single-Cell Data

Interactively exploring gene expression gradients across low-dimensional cell embeddings is central to single-cell RNA sequencing analysis, yet there arent tools that allow users to sketch trajectories and interactively compute pathway-level interpretation. We present scSketch, a tool that enables users to iteratively explore and test trajectory hypotheses in single-cell data while maintaining statistical validity and biological interpretability. Specifically, users apply interactive directional sketching to draw trajectories across embeddings and probe continuous processes such as cellular differentiation and cell state transitions. scSketch automatically computes gene-trajectory correlations and applies online false discovery rate (FDR) control to maintain statistical validity during iterative exploration. Significant genes are grouped into Reactome pathways for contextual interpretation. Applied to human oral keratinocytes infected with human cytomegalovirus, scSketch revealed infection-associated gradients involving interferon responses, metabolic remodeling and autophagy. Together, these features position scSketch as a bridge between exploratory visualization and mechanistic insight in single-cell biology.

bioinformatics↗

bioRxiv2025

phylobar: an R package for multiresolution compositional barplots in omics studies

SummaryStacked barplots, though widely used in microbiome studies, can obscure important patterns in microbiome data. They omit rare taxa and can mask shifts that emerge at finer taxonomic levels. To address this issue, we introduce phylobar, an R package that interactively links stacked barplots with overview phylogenetic or taxonomic hierarchies. The interface allows users to collapse or expand subtrees, paint color palettes interactively, and search for specific taxa. This allows comparison across taxonomic resolutions that are hidden in static overviews. phylobar works with any omics data with hierarchical organization, including cell type hierarchies, as we demonstrate in a case study of immune cell composition in COVID-19 patients. Availability and Implementationphylobar is available as an R package on GitHub. It uses the htmlwidgets library to link interactive D3 visualizations with R. The interactive plots can be embedded within R Markdown or Quarto notebooks, and views can be exported as vector graphics files. The package is open source and documented at https://mkdiro-O.github.io/phylobar.

bioinformatics↗

bioRxiv2025

Interactive Visualization of Metric Distortion in Nonlinear Data Embeddings using the distortions Package

Nonlinear dimensionality reduction methods like UMAP and t-SNE can help to organize high-dimensional genomics data into manageable low-dimensional representations, like cell types or differentiation trajectories. Such reductions can be powerful, but inevitably introduce distortion. A growing body of work has demonstrated that this distortion can have serious consequences for downstream interpretation, for example, suggesting clusters that do not exist in the original data. Motivated by these developments, we implemented a software package, distortions, which builds on state-of-the-art methods for measuring local distortions and displays them in an intuitive and interactive way. Through case studies on simulated and real data, we find that the visualizations can help flag fragmented neighborhoods, support hyperparameter tuning, and enable method selection. We believe that this extra layer of information will help practitioners use nonlinear dimensionality reduction methods more confidently. The package documentation and notebooks reproducing all case studies are available online at https://krisrs1128.github.io/distortions/site/.

bioinformatics↗

bioRxiv2024

Semisynthetic Simulation for Microbiome Data Analysis

High-throughput sequencing data lie at the heart of modern microbiome research. Effective analysis of these data requires careful preprocessing, modeling, and interpretation to detect subtle signals and avoid spurious associations. In this review, we discuss how simulation can serve as a sandbox to test candidate approaches, creating a setting that mimics real data while providing ground truth. This is particularly valuable for power analysis, methods benchmarking, and reliability analysis. We explain the probability, multivariate analysis, and regression concepts behind modern simulators and how different implementations make trade-offs between generality, faithfulness, and controllability. Recognizing that all simulators only approximate reality, we review methods to evaluate how accurately they reflect key properties. We also present case studies demonstrating the value of simulation in differential abundance testing, dimensionality reduction, network analysis, and data integration. Code for these examples is available in an online tutorial (https://go.wisc.edu/8994yz) that can be easily adapted to new problem settings.

bioinformatics↗

bioRxiv2024

multimedia: Multimodal Mediation Analysis of Microbiome Data

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing softwares rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110. IMPORTANCEMicrobiome studies routinely gather complementary data to capture different aspects of a microbiomes response to a change, such as the introduction of a therapeutic. Mediation analysis clarifies the extent to which responses occur sequentially via mediators, thereby supporting causal, rather than purely descriptive, interpretation. multimedia is a modular R package with close ties to the wider microbiome software ecosystem that makes statistically rigorous, flexible mediation analysis easily accessible, setting the stage for precise and causally informed microbiome engineering.

bioinformatics↗

bioRxiv2023

MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics

The R-Shiny package MolPad provides an interactive dashboard for understanding the dynamics of longitudinal molecular co-expression in microbiomics. The main idea for addressing the issue is first to use a network to overview major patterns among their predictive relationships and then zoom into specific clusters of interest. It is designed with a focus-plus-context analysis strategy and automatically generates links to online curated annotations. The dashboard consists of a cluster-level network, a bar plot of taxonomic composition, a line plot of data modalities, and a table for each pathway. Further, the package includes functions that handle the data processing for creating the dashboard. This makes it beginner-friendly for users with less R programming experience. We illustrate these methods with a case study on a longitudinal metagenomics analysis of the cheese microbiome.

microbiology↗

bioRxiv2019

Strategies for understanding dynamic, personalized profiles of host-derived proteins and microbes from human stool

Measuring host proteins through noninvasive stool-based assays opens new avenues for characterizing states of gastrointestinal health. However, the extent to which these proteins vary over time and between healthy subjects is poorly characterized. Here, we characterize technical and biological sources of variability in mass spectrometry-based measurements of host proteins in stool. We identify the proteins that most vary over time within an individual, and among different individuals. Finally, we examine and compare temporal and inter-individual variation in host protein and bacterial taxonomic profiles of the same fecal specimens. To address these issues, five self-reported healthy individuals were each sampled eight times over four weeks. First, we demonstrate that mass spectrometry-based identification and label-free quantification of stool proteins exhibit non-significant variability (p>0.05) between both technical and preparative replicates for a subset of 78 proteins, supporting the utility of this method for biomarker measurement. Second, although 13 human stool proteins varied significantly in relative abundance over time within individuals, 58 proteins varied significantly (at least four-fold) between subjects. The average pair-wise difference between individuals was greater than the average within-subject difference for both the proteome and microbiome datasets (p<0.0001). Fecal host proteins, like the traditional fecal protein marker, calprotectin, unambiguously pointed to innate and adaptive immune responses. For example, one subjects fecal protein profile suggested a sub-clinical inflammatory state. From these data, we conclude that host-centric protein measurements in stool reveal a wide range of variation during states of apparent health, and add a valuable complementary insight into host-microbiota relationships. IMPORTANCEHuman proteins in stool hold untapped potential for characterizing gastrointestinal health. To fully harness this potential and create a baseline of healthy stool protein abundances and identifications, it will be important to establish the extent to which these proteins might vary in the absence of disease. This study quantifies the major sources of variation in stool protein abundance data. We assessed technical, preparative, temporal, and inter-subject variability of human protein abundances in stool and found that among these sources, differences between subjects accounted for the greatest amount of variation, followed by temporal differences, and then technical factors. Our paired microbiome analysis found matching patterns of temporal and inter-subject variability. By characterizing multiple variance parameters in host stool protein abundances, our analysis helps to contextualize a wide range of future disease-focused stool studies as well as elucidate host-microbe interactions.

molecular biology↗