Biology subjects

Selvarajoo, K.

Publications and source records attributed to Selvarajoo, K..

11 recordsLinked to original sources

bioRxiv2026

Platform Biological Divergence: quantifying gene-level differences between bulk and single-cell transcriptomics in breast cancer

Bulk and single-cell RNA-seq offer complementary views of tissue transcriptomes. Nevertheless, pseudo-bulk (PB) profiles derived from single-cell data often differ systematically from true bulk measurements. These discrepancies are rarely quantified at the gene level, despite their impact on downstream analyses. To investigate the PB-bulk disagreement, here we introduce, based on linear mixture models, a gene-centric platform biological divergence (PBD) score. Across matched breast cancer, human adipose, and healthy retinal datasets, PBD stratifies genes into low-PBD "stable proxies" that retain true biological signals and high-PBD "divergent drivers" that shape PB-bulk differences. High-PBD (15-25%) genes show platform-specific signatures: bulk-enriched drivers highlight stromal, immune, and metabolic programs (breast cancer, adipose) and housekeeping/translational pathways (retina), whereas PB-enriched drivers reflect cell-intrinsic and regulatory processes, including immune, endothelial, progenitor, ECM, developmental, and photoreceptor programs. In contrast, low-PBD genes reliably preserve biology-driven variation and improve cross-platform concordance. Overall, the PBD framework reveals systematic gene-level biases between PB and bulk profiles, enabling more accurate comparison and integration of bulk and single-cell transcriptomes across tissues and disease contexts.

bioinformatics↗

bioRxiv2026

Synthetic Data to Explore Transcriptional Regulation of Differentially Expressed Genes in Ovarian Cancer

In major diseases like cancer, many genes are dysregulated. While differential expression (DE) analyses help identify these genes, they do not reveal the transcriptional regulatory landscape between control and disease states. Here, we developed a process-driven model based on generalized stochastic transcriptional regulation (TR) to study DE genes in ovarian cancer. We generated over 39,000 synthetic gene expression profiles based on RNA-seq dataset of 11 samples (5 control FTE and 6 ovarian cancer), and fitted the model parameters to real data achieving between 82% and 99% transcriptome-wide similarity through various data analytics. Through the model we investigated the top 100 DE genes transcriptional regulation. Notably, GSTA3, SNTN, DNAI2 and KIF19, which have been associated with cancer, possess significantly larger transcriptional quantal and RNA degradation rates, leading to their greater variability in cancer. This mechanistic understanding could potentially guide gene-targeted interventions for cancer or other diseases in the future.

systems biology↗

bioRxiv2025

Machine learning differentiates between bulk and pseudo-bulk RNA-seq datasets

Modern synthetic data generators and deconvolution methods rely heavily on single-cell (sc) RNA- seq data. Aggregated scRNA-seq (pseudo-bulk) is commonly assumed to closely match true bulk RNA-seq, making it a dependable benchmark for developing and evaluating new bioinformatics methods. Here, we investigated paired bulk and scRNA-seq datasets using machine learning techniques to assess the fidelity of pseudo-bulk profiles. Our results demonstrate that pseudo-bulks differ substantially from bulk RNA-seq in both analytic metrics and biological processes.

bioinformatics↗

bioRxiv2025

Understanding the Role of Toggle Genes in Chronic Lymphocytic Leukemia Proliferation

Cancer cell populations, such as chronic lymphocytic leukemia (CLL), are characterized by aberrant proliferation and plasticity: cells may switch between states so increasing population heterogeneity. Previous works have shown that gene expression noise can impact cell-state transition. To gain better insights into transcriptome-wide expression dynamics and the effect of noise on state transition, here we investigate RNA-Seq data of proliferative (PC) and non-proliferative (NPC) CLL cells. Various data analytics were applied to the whole transcriptome, switch-like toggle (ON/OFF) genes, temporal differentially expression (DE) genes, and randomly selected genes. Collectively, we identified 2713 temporal DE genes (DESeq2 with 4-fold, p < 0.05) and 1704 toggle genes shaping the differentiation process over a period of 96h, with 604 overlapping genes between them. Despite their lower numbers compared to DE, toggle genes contributed significantly to gene expression noise in both cell types. Toggle gene analyses revealed the enrichment of genes involved in processes such as G-alpha signaling and muscle contraction as proliferation related RHO-GTPase, interleukin and chemokine signaling, and lymphoid cell communication. Thus, toggle genes, although being random (at single cell level) ON/OFF genes, can show (at population level) a symmetry breaking favoring one of the two ON/OFF states so contributing to gene expression functional variability. These results suggest that toggle genes play an important role in shaping cell population plasticity.

genomics↗

bioRxiv2024

Identifying effective evolutionary strategies for uncovering reaction kinetic parameters under the effect of measurement noises

The transition from explanative modelling of fitted data to the predictive modelling of unseen data for systems biology endeavors necessitates the effective recovery of reaction parameters. Yet, the relative efficacy of optimization algorithms in doing so remains under-studied, as to the specific reaction kinetics and the effect of measurement noises. To this end, we simulate the reactions of an artificial pathway using 4 kinetic formulations: generalized mass action (GMA), Michaelis-Menten, linear-logarithmic, and convenience kinetics. We then compare the effectiveness of 5 evolutionary algorithms (CMAES, DE, SRES, ISRES, G3PCX) for objective function optimization in kinetic parameter hyperspace to determine the corresponding estimated parameters. We quickly dropped the DE algorithm due to its poor performance. Baring measurement noise, we find CMAES algorithm to only require a fraction of the computational cost incurred by other EAs for both GMA and linear-logarithmic kinetics yet performing as well by other criteria. However, with increasing noise, SRES and ISRES perform more reliably for GMA kinetics, but at considerably higher computational cost. Conversely, G3PCX is among the most efficacious for estimating Michaelis-Menten parameters regardless of noise, while achieving numerous folds saving in computational cost. Cost aside, we find SRES to be versatilely applicable across GMA, Michaelis-Menten, and linear-logarithmic kinetics, with good resilience to noise. On the other hand, we could not identify the parameters of convenience kinetics using any algorithm. Together, we find algorithms that are effective under marked measurement noise for specific reaction kinetics, as a step towards predictive modelling for systems biology endeavors.

systems biology↗

bioRxiv2023

The impact of short-lived controls on the interpretation of lifespan experiments and progress in geroscience

Although lifespan extension remains the gold standard for assessing interventions hypothesized to impact the biology of aging, there are important limitations to this approach. Our reanalysis of lifespan studies from multiple sources suggests that the use of short-lived control cohorts tends to exaggerate the relative efficacy of putative longevity interventions. Moreover, due to the high cost and long timeframes of mouse studies, it is rare that a particular longevity intervention will be independently replicated by multiple groups. To facilitate identification of successful interventions, we propose an alternative approach. The level of confidence we can have in an intervention is proportional to the degree of lifespan extension above the strain- and species-specific upper limit of lifespan, which we can estimate from comparison to historical controls. In the absence of independent replication, a putative mouse longevity intervention should only be considered with high confidence when control lifespans are close to 900 days or if the final lifespan of the treated group is considerably above 900 days. Using this "900-day rule" we identified several candidate interventions from the literature that merit follow-up studies.

physiology↗

bioRxiv2022

A concerted increase in readthrough and intron retention drives transposon expression during aging and senescence

Aging and senescence are characterized by pervasive transcriptional dysfunction, including increased expression of transposons and introns. Our aim was to elucidate mechanisms behind this increased expression. Most transposons are found within genes and introns, with a large minority being close to genes. This raises the possibility that transcriptional readthrough and intron retention are responsible for age-related changes in transposon expression rather than expression of autonomous transposons. To test this, we compiled public RNA-seq datasets from aged human fibroblasts, replicative and drug-induced senescence in human cells and RNA-seq from aging mice and senescent mouse cells. Indeed, our reanalysis revealed a correlation between transposons expression, intron retention and transcriptional readthrough across samples and within samples. Both intron retention and readthrough increased with aging or cellular senescence and these transcriptional defects were more pronounced in human samples as compared to those of mice. In support of a causal connection between readthrough and transposon expression, analysis of models showing induced transcriptional readthrough confirmed that they also show elevated transposon expression. Taken together, our data suggests that elevated transposon reads during aging seen in various RNA-seq dataset are concomitant with multiple transcriptional defects. Intron retention and transcriptional readthrough are the most likely explanation for the expression of transposable elements that lack a functional promoter.

bioinformatics↗

bioRxiv2019

Searching for Simple Rules in Pseudomonas aeruginosa Biofilm Formation

Living cells display complex and non-linear behaviors, especially when posed to environmental threats. Here, to understand the self-organizing cooperative behavior of a microorganism Pseudomonas aeruginosa, we developed a discrete spatiotemporal cellular automata model based on simple physical rules, similar to Conways game of life. The time evolution model simulations were experimentally verified for P. aeroginosa biofilm for both control and antibiotic azithromycin (AZM) treated condition. Our model suggests that AZM regulates the single cell motility, thereby resulting in delayed, but not abolished, biofilm formation. In addition, the model highlights the importance of reproduction by cell to cell interaction is key for biofilm formation. Overall, this work highlights another example where biological evolutionary complexity may be interpreted using rules taken from theoretical disciplines.

systems biology↗

bioRxiv2019

Large Scale-Free Network Organization is Likely Key for Biofilm Phase Transition

Non-linear Kuramoto model has been used to study synchronized or sync behavior in numerous fields, however, its application in biology is scare. Here, I introduce the basic model and provide examples where large scale small-world or scale-free networks are crucial for spontaneous sync even for low coupling strength. This information was next checked for relevance in living systems where it is now well-known that biological networks are scale-free. Our recent transcriptome-wide data analysis of Saccharomyces cerevisiae biofilm showed that low to middle expressed genes are key for scale invariance in biology. Together, the current data indicate that biological network connectivity structure with low coupling strength, or expression levels, is sufficient for sync behavior. For biofilm regulation, it may, therefore, be necessary to investigate large scale low expression genes rather than small scale high expression genes.

systems biology↗

bioRxiv2019

ABioTrans: A Biostatistical tool for Transcriptomics Analysis

Here we report a bio-statistical/informatics tool, ABioTrans, developed in R for gene expression analysis. The tool allows the user to directly read RNA-Seq data files deposited in the Gene Expression Omnibus or GEO database. Operated using any web browser application, ABioTrans provides easy options for multiple statistical distribution fitting, Pearson and Spearman rank correlations, PCA, k-means and hierarchical clustering, differential expression analysis, Shannon entropy and noise (square of coefficient of variation) analyses, as well as Gene ontology classifications.\n\nAvailability and implementationABioTrans is available at https://github.com/buithuytien/ABioTrans\n\nOperating system(s): Platform independent (web browser)\n\nProgramming language: R (R studio)\n\nOther requirements: Bioconductor genome wide annotation databases, R-packages (shiny, LSD, fitdistrplus, actuar, entropy, moments, RUVSeq, edgeR, DESeq2, NOISeq, AnnotationDbi, ComplexHeatmap, circlize, clusterProfiler, reshape2, DT, plotly, shinycssloaders, dplyr, ggplot2). These packages will automatically be installed when the ABioTrans.R is executed in R studio.\n\nNo restriction of usage for non-academic.

bioinformatics↗

bioRxiv2018

Long Range Order and Short Range Disorder in Saccharomyces cerevisiae Biofilm

AbstractBiofilm, a colony forming cooperative response of microorganisms under environmental stress, is a major concern for food safety, water safety and drug resistance. Most current works focus on controlling biofilm growth by targeting single genes. Here, we investigated transcriptome-wide expressions of the biofilm yeast Saccharomyces cerevisiae in wildtype, and 6 previously identified biofilm regulating overexpression strains (DIG1, SAN1, TOS8, ROF1, SFL1, HEK2). When tested across various statistical distributions, all transcriptome-wide data fitted well with lognormal distribution above TPM value of 5. Using this threshold as a low expression filter, Pearson auto-and cross-correlation reveal a strong transcriptome-wide invariance among all genotypes, which is also reflected by the random selection of 50 gene expressions. Focusing on the 50 highly expressed genes, however, they differ significantly between the genotypes. Principal components analysis (PCA) shows global similarity between DIG1, SAN1, ROF1, SFL1 and HEK2. Thus, although single overexpression strains may show significant favourable local and acute expression changes (short range disorder), the almost unperturbed global and collective structure between the genotypes indicate gradual adaptive response converging to original stable biofilm states (long range order). Hierarchical clustering and Gene Ontology show 11 groups of local (e.g. mitochondria processes, amine & nucleotide metabolic processes) and 6 groups of global (e.g. transcription, translation & cell cycle) processes for all genotypes. These data indicate that there is a strong global regulatory structure that keeps the overall biofilm stable in all investigated strains.

systems biology↗