Biology subjects

Jun, S.-H.

Publications and source records attributed to Jun, S.-H..

9 recordsLinked to original sources

bioRxiv2026

An assessment of normalization and differential expression methods for miRNA-seq analysis using a realistic benchmark dataset

MicroRNAs are short noncoding RNAs that regulate gene expression and are commonly profiled by small RNA sequencing (miRNA-seq). Despite the widespread use of miRNA-seq, datasets are often analyzed with RNA-seq method such as DESeq2 or edgeR, which do not take into account the specific characteristics of miRNA-seq data. Here, we present a benchmark study of normalization and differential expression approaches using a realistic ground-truth dataset. By mixing mouse RNA of two organs, we generated expression trends while capturing biological and technical variability. Using monotonicity across the dataset and expected fold changes from the mixture design, we assessed normalization and differential expression methods. Normalization benchmarking showed that within-sample scaling, particularly Read Per Million (RPM), best preserved the expected monotonic trends, outperforming cross-sample methods such as TMM, rlog, and VST. These approaches sometimes recovered apparent monotonicity among abundant miRNAs, but inspection of individual profiles suggested likely over-correction. Regarding differential expression, edgeR consistently ranked among the best-performing methods across several metrics, including log2 fold-change estimation, with performance comparable to miRNA-seq-specific tools such as miRglmm and NBSR. DESeq2, edgeR-v4, and limma-based approaches tended to systematically underestimate log2 fold changes. Applying a common RPM-based normalization substantially improved the performance of cross-sample methods, highlighting the strong influence of normalization on differential expression analysis. Overall, our findings support within-sample scaling methods such as RPM for normalization, and edgeR, miRglmm, or NBSR for differential expression. The dataset has been made publicly available, providing a valuable resource for objective method comparison and future miRNA-seq software development.

bioinformatics↗

bioRxiv2026

PARiS: Probabilistic Assignment and Repartitioning of isomiR Sequences: A data-driven method for denoising isomiR read count data

MicroRNAs (miRNAs) are non-coding RNAs, approximately 18-24 nucleotides in length, with important gene regulatory functions. In small RNA sequencing (sRNA-seq), observed isoforms of miRNA, called isomiRs, arise from my biological and technical processes. Alterations in isomiR expression has been linked to a wide variety of human diseases, from cancers to neurological diseases. However, it is difficult to distinguish between technical and biological isomiRs. We present PARiS, an algorithm for the Probabilistic Assignment and Repartitioning of isomiR Sequences, that identifies technical error isomiRs in sRNA-seq data and reassigns them to their most likely biological source. We assess the ability of PARiS to identify and remove error isomiR sequences in a realistic simulation study. Additionally, we compare PARiS to alternative approaches, focusing on downstream miRNA-level differential expression analysis in a variety of settings, including a set of simulated datasets, an experimental benchmark dataset, and three colorectal adenocarcinoma cell lines.

bioinformatics↗

bioRxiv2025

LN's t-Test: A Principled Approach to t-Testing in Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity, yet differential gene expression (DGE) analysis remains hindered by inconsistencies in log fold change (LFC) estimation. Existing methods, such as those implemented in Scanpy and Seurat, rely on log-transformed count data with a pseudocount, introducing a bias that compromises the reliability of the statistical inference. In this work, we propose LNs t-test, a novel approach to DGE testing that circumvents these biases by employing a log-Normal (LN) distribution-based LFC estimator. Our method jointly estimates the probability of non-zero expression and the mean of positive expression values, enabling an asymptotically unbiased and normally distributed LFC estimator with corresponding confidence intervals. Through extensive simulation studies, we demonstrate that LNs t-test outperforms competing methods by reducing false discovery rates and providing more accurate effect size estimates. Notably, we leverage stochastic ordering theory to explain why conventional t-tests systematically mis-classify non-differentially expressed genes under realistic variance conditions. Our approach offers a theoretically principled and computationally efficient alternative for DGE analysis in scRNA-seq, with implications for improving the reliability and interpretability of single-cell transcriptomics studies. Code that implements the results is available on GitHub: https://github.com/okviman/DE-ZILN.

bioinformatics↗

bioRxiv2024

Statistical Modeling for MicroRNA Sequencing Data

MicroRNAs play a central role in regulating gene expression and modulating diseases. Despite the importance of microRNAs, statistical methods for analyzing them have received far less attention compared to messenger RNAs. Commonly, messenger RNA-seq methods are applied to microRNA-seq data, which may produce erroneous results due to the highly competitive nature of microRNA sequencing. This study critically examines and challenges the assumptions of messenger RNA-seq methods when applied to microRNA-seq data. We propose a Negative Binomial Softmax Regression (NBSR) method to model the unique characteristics of microRNA-seq data. On both simulated and experimental datasets, NBSR outperforms existing methods and offers a new perspective for analyzing microRNA-seq data. NBSR is implemented in Python and freely available as open-source software.

bioinformatics↗

bioRxiv2022

Distinct immune responses associated with vaccination status and protection outcomes after malaria challenge

Understanding immune mechanisms that mediate malaria protection is critical for improving vaccine development. Vaccination with radiation-attenuated Plasmodium falciparum sporozoites (PfRAS) induces high level of sterilizing immunity against malaria and serves as a valuable tool for the study of protective mechanisms. To identify vaccine-induced and protection-associated responses during malaria infection, we performed transcriptome profiling of whole blood and in-depth cellular profiling of PBMCs from volunteers who received either PfRAS or noninfectious mosquito bites, followed by controlled human malaria infection (CHMI) challenge. In-depth single-cell profiling of cell subsets that respond to CHMI in mock-vaccinated individuals showed a predominantly inflammatory transcriptome response. Whole blood transcriptome analysis revealed that gene sets associated with interferon responses and T and B cell signatures were increased and decreased, respectively, in protected vaccinees as early as one day following CHMI. In contrast, non-protected vaccinees and mock-vaccinated individuals exhibited shared transcriptome changes after CHMI characterized by decreased innate cell signatures and inflammatory responses. Additionally, immunophenotyping data showed different induction profiles of v{delta}2+{gamma}{delta} T cells, CD56+ CD8+ T effector memory (Tem) cells, and non-classical monocytes between protected vaccinees and individuals developing blood-stage parasitemia, following treatment and resolution of infection. Our data provide key insights in understanding immune mechanistic pathways of PfRAS-induced protection and infective CHMI. We demonstrate that protective immunity by PfRAS is associated with early changes in interferon and adaptive immune responses. Author summaryMalaria poses a significant global health threat, causing over half a million deaths annually. Effective vaccines are critically needed to prevent malaria disease. Our incomplete understanding of immune mechanisms that mediate malaria protection is hampering the development of effective vaccines. Irradiated sporozoite vaccines can induce highly sterilizing protection against malaria and are a valuable tool for the analysis of immune protection. Here, we aimed to characterize correlates of immune protection in individuals vaccinated with a suboptimal dose of irradiated sporozoite and subsequently challenged with live malaria parasite. Blood samples were taken before and after malaria challenge, and gene expression and cell type profiles were measured. We observed that the trajectories of immune response after malaria challenge is highly distinct between protected and non-protected vaccinees. We observed early perturbations in interferon response and adaptive immune cells in protected vaccinees whereas inflammatory and innate cell response were unique to non-protected vaccinees. We also observed that the immune profile after malaria challenge was distinctly similar between non-protected vaccinees and mock-vaccinated individuals. Our study sheds light on the dynamics of vaccine-induced immune responses that are associated with protection from malaria after CHMI.

immunology↗

bioRxiv2022

Robust Normalization and Integration of Single-cell Protein Expression across CITE-seq Datasets

CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.

bioinformatics↗

bioRxiv2021

PhylEx: Accurate reconstruction of clonal structure via integrated analysis of bulk DNA-seq and single cell RNA-seq data

We propose PhylEx: a clonal-tree reconstruction method that integrates bulk genomics and single-cell transcriptomics data. In addition to the clonal-tree, PhylEx also assigns single-cells to clones, which effectively produce clonal expression profiles, and generates clonal genotypes. By analyzing scRNA-seq integrated with bulk DNA-seq, PhylEx can take advantage of co-occurrences of the mutations found in the cells. In the probabilistic model underlying PhylEx, the raw read counts from scRNA-seq follow a mixture of Beta-Binomial distributions, which accounts for the sparse nature of single-cell gene expression data; the mixture lessens the penalty caused by mutations not observed due to mono-allelic expression. We rigorously evaluated PhylEx on simulated datasets as well as a biological dataset consisting of a previously well-characterized high-grade serous ovarian cancer (HGSOC) cell line. PhylEx outperformed the state-of-the-art methods by a wide margin both when comparing capacity for clonal-tree reconstruction and capacity for correctly clustering mutations. By analyzing HGSOC and HER2+ breast cancer data, we also show that PhylEx clears the way for phylo-phenotypic analysis of cancer, i.e., that the clonal expression profiles, induced by the cell-to-clone assignments, can be exploited in a manner beyond what is possible with only expression-based clustering.

bioinformatics↗

bioRxiv2020

Inferring Tumor Progression in Large Datasets

Identification of mutations of the genes that give cancer a selective advantage is an important step towards research and clinical objectives. As such, there has been a growing interest in developing methods for identification of driver genes and their temporal order within a single patient (intra-tumor) as well as across a cohort of patients (inter-tumor). In this paper, we develop a probabilistic model for tumor progression, in which the driver genes are clustered into several ordered driver pathways. We develop an efficient inference algorithm that exhibits favorable scalability to the number of genes and samples compared to a previously introduced ILP-based method. Adopting a probabilistic approach also allows principled approaches to model selection and uncertainty quantification. Using a large set of experiments on synthetic datasets, we demonstrate our superior performance compared to the ILP-based method. We also analyze two biological datasets of colorectal and glioblastoma cancers. We emphasize that while the ILP-based method puts many seemingly passenger genes in the driver pathways, our algorithm keeps focused on truly driver genes and outputs more accurate models for cancer progression. Author summaryCancer is a disease caused by the accumulation of somatic mutations in the genome. This process is mainly driven by mutations in certain genes that give the harboring cells some selective advantage. The rather few driver genes are usually masked amongst an abundance of so-called passenger mutations. Identification of the driver genes and the temporal order in which the mutations occur is of great importance towards research and clinical objectives. In this paper, we introduce a probabilistic model for cancer progression and devise an efficient inference algorithm to train the model. We show that our method scales favorably to large datasets and provides superior performance compared to an ILP-based counterpart on a wide set of synthetic data simulations. Our Bayesian approach also allows for systematic model selection and confidence quantification procedures in contrast to the previous non-probabilistic progression models. We also study two large datasets on colorectal and glioblastoma cancers and validate our inferred model in comparison to the ILP-based method.

cancer biology↗

bioRxiv2018

SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction

Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The methods robustness is investigated using various synthetic datasets and a biological dataset of 18 cells. Author summaryCell lineage tree reconstruction carries a significant potential for studies of development and medicine. The lineage tree reconstruction task is especially challenging for cells taken from healthy tissue due to the scarcity of mutations. In addition, the single-cell whole-genome sequencing technology introduces artifacts such as amplification errors, allelic dropouts, and sequencing errors. We propose Scuphr, a probabilistic framework to reconstruct cell lineage trees. We designed Scuphr for single-cell DNA sequencing data; it accounts for technological artifacts in its graphical model and uses germline heterozygous sites to improve its accuracy. Scuphr is embarrassingly parallel; the speed of the computational analysis is inversely proportional to the number of available computational nodes. We demonstrated that Scuphr is fast, robust, and more accurate than the state-of-the-art method with the synthetic data experiments. Moreover, in the biological data experiment, we showed Scuphr successfully identifies different clones and further obtains more support on closely related cells within clones.

bioinformatics↗