SEARCH · Search bioRxiv

Results for “single cell RNA sequencing”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10Linked to original sources

bioRxiv2019

Automated identification of Cell Types in Single Cell RNA Sequencing

Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

bioinformatics↗

bioRxiv2020

Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing to Define Immune Cell Chimerism in the Rejecting Kidney Transplant

In solid organ transplantation, donor derived immune cells are assumed to decline with time after surgery. Whether donor leukocytes persist within kidney transplants or play any role in rejection is unknown, however, in part because of limited techniques for distinguishing recipient and donor cells. To address this question, we performed paired whole exome sequencing of donor and recipient DNA and single cell RNA sequencing (scRNA-seq) of 5 human kidney transplant biopsy cores. Exome sequences were used to define single nucleotide variations (SNV) across all samples. By analyzing expressed SNVs in the scRNA-seq dataset we could define recipient vs. donor cell origin for all 81,139 cells. The leukocyte donor to recipient ratio varied with rejection status for macrophages and with time post-transplant for lymphocytes. Recipient macrophages were characterized by inflammatory activation and donor macrophages by antigen presentation and complement signaling. Recipient origin T cells expressed cytotoxic and pro-inflammatory genes consistent with an effector cell phenotype whereas donor origin T cells are likely quiescent expressing oxidative phosphorylation genes relative to recipient T cells. Finally, both donor and recipient T cell clones were present within the rejecting kidney, suggesting lymphoid aggregation. Our results indicate that donor origin macrophages and T cells have distinct transcriptional profiles compared to their recipient counterparts and donor macrophages can persist for years post transplantation. This study demonstrates the power of this approach to accurately define leukocyte chimerism in a complex tissue such as the kidney transplant coupled with the ability to examine transcriptional profiles at single cell resolution.

genomics↗

bioRxiv2023

Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data

Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single-cell field lacks a formal discussion on how to design the neural network and choose the hyperparameters. Here, we conducted an empirical study to answer this question. Our study used many real and simulated scRNA-seq datasets to examine the impacts of the neural network architecture, the activation function, and the regularization strategy on imputation accuracy and downstream analyses. Our results show that (i) deeper and narrower autoencoders generally lead to better imputation performance; (ii) the sigmoid and tanh activation functions consistently outperform other commonly used functions including ReLU; (iii) regularization improves the accuracy of imputation and downstream cell clustering and DE gene analyses. Notably, our results differ from common practices in the computer vision field regarding the activation function and the regularization strategy. Overall, our study offers practical guidance on how to optimize the autoencoder design for scRNA-seq data imputation.

bioinformatics↗

bioRxiv2021

Joint Gene Network Construction by Single-Cell RNA Sequencing Data

SO_SCPLOWUMMARYC_SCPLOWIn contrast to differential gene expression analysis at single gene level, gene regulatory networks (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases and traits. Recently, single-cell RNA sequencing (scRNA-seq) data has started to be used for constructing GRNs at a much finer resolution than bulk RNA-seq data and microarray data. However, scRNA-seq data are inherently sparse which hinders direct application of the popular Gaussian graphical models (GGMs). Furthermore, most existing approaches for constructing GRNs with scRNA-seq data only consider gene networks under one condition. To better understand GRNs under different but related conditions with single-cell resolution, we propose to construct Joint Gene Networks with scRNA-seq data (JGNsc) using the GGMs framework. To facilitate the use of GGMs, JGNsc first proposes a hybrid imputation procedure that combines a Bayesian zero-inflated Poisson (ZIP) model with an iterative low-rank matrix completion step to efficiently impute zero-inflated counts resulted from technical artifacts. JGNsc then transforms the imputed data via a nonparanormal transformation, based on which joint GGMs are constructed. We demonstrate JGNsc and assess its performance using synthetic data. The application of JGNsc on two cancer clinical studies of medulloblastoma and glioblastoma identifies novel findings in addition to confirming well-known biological results.

genetics↗

bioRxiv2026

A novel proliferative candidate genes panel for idiopathic pulmonary fibrosis: insights from integrated bulk and single-cell RNA sequencing

BackgroundIdiopathic pulmonary fibrosis (IPF) remains a fatal interstitial lung disease with limited diagnostic specificity and therapeutic options. This study integrates bulk and single-cell RNA sequencing (RNA-seq) to identify novel biomarkers and elucidate molecular mechanisms underlying IPF pathogenesis. MethodsWe prospectively enrolled 14 treatment-naive IPF patients and 6 controls. Bulk RNA-seq was performed on bronchoalveolar lavage fluid (BALF), while single-cell RNA-seq analyzed lung tissues from 4 IPF patients and 3 controls. Differentially expressed genes (DEGs) were identified (|log2FC| >1, FDR <0.05), followed by functional enrichment, protein-protein interaction (PPI) network analysis, and cell-type-specific expression profiling. Results1. DEG Identification: Bulk RNA-seq revealed 108 DEGs (24 upregulated, 84 downregulated). KEGG enrichment analysis of DEGs revealed that upregulated genes were mainly enriched in inflammation and immune pathways (such as NF-{kappa}B signaling pathway, Fc epsilon RI signaling pathway, B cell receptor signaling pathway, phagosome, Fc gamma R-mediated phagocytosis), pyrimidine metabolism, cell cycle, and PI3K-Akt signaling pathway. 2. PPI Network: Module analysis identified a proliferative gene module 1 (NUF2, CEP55, ANLN, TTK, TK1, MYBL2, CCNA2, RRM2, CDT1) linked to cell division and cycle regulation. 3. Single-Cell Insights: scRNA-seq of 30,477 cells delineated 11 populations. Module 1 genes exhibited predominant expression in proliferating cells, Module 1 signature score of proliferating cells was significantly higher in IPF than in control group. 4. Pathogenic Links: Key genes (e.g., CEP55, TTK) were associated with PI3K/AKT signaling, epithelial-mesenchymal transition (EMT), and anti-apoptotic pathways, mirroring oncogenic mechanisms. ConclusionThis multi-omics approach uncovers a proliferation-centric gene module in IPF, revealing shared molecular pathways with tumorigenesis. Our findings highlight novel diagnostic biomarkers and suggest repurposing cell cycle inhibitors as potential therapies. Future studies should validate these targets in preclinical models to advance precision medicine for IPF.

genetics↗

bioRxiv2022

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing for personalized oncology

Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 are novel. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-{beta}/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

cancer biology↗

bioRxiv2017

Accounting For Technical Noise In Single-Cell RNA Sequencing Analysis

Recent technological breakthroughs have made it possible to measure RNA expression at the single-cell level, thus paving the way for exploring expression heterogeneity among individual cells. Current single-cell RNA sequencing (scRNA-seq) protocols are complex and introduce technical biases that vary across cells, which can bias downstream analysis without proper adjustment. To account for cell-to-cell technical differences, we propose a statistical framework, TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. More importantly, TASC is able to adjust for covariates to further eliminate confounding that may originate from cell size and cell cycle differences. In simulation and real scRNA-seq data, TASC achieves accurate Type I error control and displays competitive sensitivity and improved robustness to batch effects in differential expression analysis, compared to existing methods. TASC is programmed to be computationally efficient, taking advantage of multi-threaded parallelization. We believe that TASC will provide a robust platform for researchers to leverage the power of scRNA-seq.

bioinformatics↗

bioRxiv2017

Deciphering cell lineage specification during male sex determination with single-cell RNA sequencing

The gonad is a unique biological system for studying cell fate decisions. However, major questions remain regarding the identity of somatic progenitor cells and the transcriptional events driving cell differentiation. Using time course single cell RNA sequencing on XY mouse gonads during sex determination, we identified a single population of somatic progenitor cells prior sex determination. A subset of these progenitors differentiate into Sertoli cells, a process characterized by a highly dynamic genetic program consisting of sequential waves of gene expression. Another subset of multipotent cells maintains their progenitor state but undergo significant transcriptional changes that restrict their competence towards a steroidogenic fate required for the differentiation of fetal Leydig cells. These results question the dogma of the existence of two distinct somatic cell lineages at the onset of sex determination and propose a new model of lineage specification from a unique progenitor cell population.

developmental biology↗

bioRxiv2020

Fixed single-cell RNA sequencing for understanding virus infection and host response

Single-cell transcriptomic studies that require intracellular protein staining, rare cell sorting, or inactivation of infectious pathogens are severely limited because current high-throughput RNA sequencing methods are incompatible with paraformaldehyde treatment, a common tissue and cell fixation and preservation technique. Here we present FD-seq, a high-throughput method for droplet-based RNA sequencing of paraformaldehyde-fixed, stained and sorted single-cells. We show that FD-seq preserves the mRNA integrity and relative abundances during fixation and subsequent cell retrieval. Furthermore, FD-seq detects a higher number of genes and transcripts than methanol fixation. We applied FD-seq to investigate two important questions in Virology. First, by analyzing a rare population of cells supporting lytic reactivation of the human tumor virus KSHV, we identified TMEM119 as a host factor that mediates viral reactivation. Second, we found that upon infection with the betacoronavirus OC43, which causes the common cold and is a close relative of SARS-CoV-2, pro-inflammatory pathways are primarily upregulated in lowly-infected cells that are exposed to the virus but fail to express high levels of viral genes. FD-seq thus enables integrating phenotypic with transcriptomic information in rare cell populations, and preserving and inactivating pathogenic samples that cannot be handled under regular biosafety measures.

genomics↗

bioRxiv2024

Single cell RNA sequencing of nc886, a non-coding RNA transcribed by RNA polymerase III, with a primer spike-in strategy

Single cell RNA sequencing (scRNA-seq) has emerged as a versatile tool in biology, enabling comprehensive genomic-level characterization of individual cells. Currently, most scRNA-seq methods generate barcoded cDNAs by capturing polyA tails of mRNAs, which excludes many non-coding RNAs (ncRNAs), especially those transcribed by RNA polymerase III (Pol III). Although previously thought to be expressed constitutively, Pol III-transcribed ncRNAs are expressed variably in healthy and disease states and play important roles therein, necessitating their profiling at the single cell level. In this study, we have developed a measurement protocol for nc886 as a model case, as an initial step for scRNA-seq for Pol III-transcribed ncRNAs. Specifically, we spiked in an oligo-tagged nc886-specific primer during the polyA tail capture process for the 5-reading in scRNA-seq. We then produced sequencing libraries for standard 5 gene expression and oligo-tagged nc886 separately, to accommodate different cDNA sizes and ensure undisturbed transcriptome analysis. We applied this protocol in three cell lines which express high, low, and zero levels of nc886, respectively. Our results show that the identification of oligo tags exhibited limited target specificity, and sequencing reads of nc886 enabled the correction of non-specific priming. These findings suggest that gene-specific primers (GSPs) can be employed to capture RNAs lacking a polyA tail, with subsequent sequence verification ensuring accurate gene expression counting. Moreover, we embarked on an analysis of differentially expressed genes in cell line sub-clusters with differential nc886 expression, demonstrating variations in gene expression phenotypes. Collectively, the primer spike-in strategy allows us for a combined analysis of ncRNAs and gene expression phenotype.

genomics↗

bioRxiv2025

Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs

Bone marrow mononuclear cells (BMMCs) are a heterogeneous pool of hematopoietic progenitors and mature immune cells that collectively sustain hematopoiesis and coordinate immune responses. The bone marrow serves not only as the primary site for blood cell production but also as a niche for various disorders, including blood cancers. Advances in single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have significantly enhanced our understanding of the cellular biology and molecular dynamics within this complex microenvironment. The choice between these two approaches, however, is often shaped or constrained by the study design, such as research objectives, sample type, and preservation conditions. Consequently, methodological differences in library preparation and transcript capture efficiency can introduce systematic biases that complicate downstream analyses and interpretation, underscoring the need to identify and account for method-specific features. In this study, we conducted a comparative analysis of matched snRNA-seq and scRNA-seq datasets from 11 pairs of healthy donor bone marrow mononuclear cell samples, generated using the popular 10x Genomics platform. We evaluated method-specific biases using multiple quality metrics and compared cell type proportions and transcriptomic signatures captured by each approach. Integrative analysis of these datasets is feasible but not advisable due to systematic gene length biases that were observed between these approaches. Our results showed that despite inherent differences in library complexity, both protocols reliably captured all major cell types. This comparative analysis highlights intrinsic differences between snRNA-seq and scRNA-seq data, providing valuable insights into their respective advantages, limitations, and trade-offs. These findings can assist researchers in selecting the optimal method tailored to specific biological questions and sample characteristics, and also enable more method-aware data analysis and interpretation.

bioinformatics↗

bioRxiv2019

Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets

BackgroundRead coverage of RNA sequencing data reflects gene expression and RNA processing events. Single-cell RNA sequencing (scRNA-seq) methods, particularly "full-length" ones, provide read coverage of many individual cells and have the potential to reveal cellular heterogeneity in RNA transcription and processing. However, visualization tools suited to highlighting cell-to-cell heterogeneity in read coverage are still lacking. ResultsHere, we have developed Millefy, a tool for visualizing read coverage of scRNA-seq data in genomic contexts. Millefy is designed to show read coverage of all individual cells at once in genomic contexts and to highlight cell-to-cell heterogeneity in read coverage. By visualizing read coverage of all cells as a heat map and dynamically reordering cells based on diffusion maps, Millefy facilitates discovery of "local" region-specific, cell-to-cell heterogeneity in read coverage, including variability of transcribed regions. ConclusionsMillefy simplifies the examination of cellular heterogeneity in RNA transcription and processing events using scRNA-seq data. Millefy is available as an R package (https://github.com/yuifu/millefy) and a Docker image to help use Millefy on the Jupyter notebook (https://hub.docker.com/r/yuifu/datascience-notebook-millefy).

bioinformatics↗

bioRxiv2022

Accurate genotype-based demultiplexing of single cell RNA sequencing samples from non-human animals

Single cell sequencing technologies (scRNA-seq, scATAC-seq, etc.) have revolutionized the study of complex tissues and unique organisms, providing researchers with a much needed species agnostic tool to study biological processes at the cellular level. To date, scRNA-seq technologies are expensive, require sufficient cell quantities, and need biological replicates to avoid batch effects or artifactual results. Pooling cells from multiple individuals into a single scRNA-seq library can address these problems. However, sample labeling protocols for facilitating the computational separation of pooled scRNA-seq samples, termed demultiplexing, have undesirable limitations, particularly in resource-limited organisms. One promising solution developed for use in humans exploits the genetic diversity between individuals (i.e., single nucleotide polymorphisms (SNP)) to demultiplex pooled scRNA-seq samples. The use of SNP-based demultiplexing methods has not been validated for use in non-human species, but the widespread use of SNP-based demuxers would greatly facilitate research in commonly used, emerging, and more obscure species. In this study we applied SNP-based demultiplexing algorithms to pooled scRNA-seq datasets from numerous species and applied diverse ground truth confirmation assays to validate genetic demultiplexing results. SNP-based demultiplexers were found to accurately demultiplex pooled scRNA-seq data from species including zebrafish, African green monkey, Xenopus laevis, axolotl, Pleurodeles waltl, and Notophthalmus viridescens. Our results demonstrate that SNP-based demultiplexing of unlabeled, pooled scRNA-seq samples can be used with confidence in all of the species studied in this work. Further, we show that the only genomic resource required for this approach is the single-cell sequencing data and a de novo transcriptome. The incorporation of pooling and SNP-demultiplexing into scRNA-seq study designs will greatly increase the reproducibility and experimental options for studying species previously limited by technical uncertainties, computational hurdles, or limited cell quantities.

bioinformatics↗

bioRxiv2025

Determining gene specificity from multivariate single-cell RNA sequencing data

An important application of single-cell genomics experiments is to identify genes specific to biological categories or experimental conditions. Although numerous approaches have been proposed to identify such genes, we consider an axiomatic approach based on defining properties that a specificity measure should have. This leads us to develop ember (Entropy Metrics for Biological ExploRation), which we show is the only method satisfying four key desired properties for a specificity measure. Applying ember to eight tissues from eight founder mouse strains, we find that gene specificity is often unintuitive: canonical markers can be supplanted, housekeeping genes are context-dependent, and mouse strain can drive unexpected cell type switching. Unsupervised learning on entropy metrics uncovers shared genes specialized to male gonads and kidney, as well as genes specific to non-consecutive developmental stages in the kidney. To facilitate further exploration of gene specificity in mice, we have also developed a comprehensive specificity database, along with a web interface, API and MCP server. Extending ember to a human PBMC dataset collected from 255 diverse individuals, we find that variation in PBMCs is largely localized to classical monocytes. We also find genes with unique specificity by sex, age and ancestral background. Together, these applications establish ember as a powerful tool and provide a roadmap for elucidating the impact of human genetic variation using the murine model.

bioinformatics↗

bioRxiv2020

SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation

A main challenge in analyzing single-cell RNA sequencing (scRNASeq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called "dropout"), the gene expression matrix has substantial zero read counts. Existing imputation methods either treat each cell or each gene identically and independently, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs, which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering. Optionally, SIMPLEs can integrate bulk RNASeq data for estimating dropout rates. In simulations, SIMPLEs performed significantly better than prevailing scRNASeq imputation methods by various metrics. By applying SIMPLEs to several real data sets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.

bioinformatics↗

bioRxiv2019

Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to simultaneously measure cellular composition and state which is impossible with bulk profiling approaches. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power.

bioinformatics↗

bioRxiv2019

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. While single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems, application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance, lack of mRNA polyadenylation, and thick cell walls. Here, we present Prokaryotic Expression-profiling by Tagging RNA In Situ and sequencing (PETRI-seq), a low-cost, high-throughput, prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates >200 mRNAs/cell for exponentially growing E. coli. These characteristics enable robust discrimination of cell-states corresponding to different phases of growth. When applied to wild-type S. aureus, PETRI-seq revealed a rare sub-population of cells undergoing prophage induction. We anticipate broad utility of PETRI-seq in defining single-cell states and their dynamics in complex microbial communities.

systems biology↗

bioRxiv2023

A brand-new clustering method and analysis system designed for revealing the truth of the high-dimension large data deciphered the complex composition structure of human brain endothelial cells from single- cell RNA sequence data

The clustering method is the key to high dimensional large data analysis, especially for single-cell NGS data in biological science and biomedicine sectors. Those data require a hierarchical clustering method to unveil important biological features including differentiation patterns, stem cell identifications, cell sub-type discovery, and so on. Traditional hierarchical clustering has several issues to be applied to large high-dimension data. There are a few new approaches invented recently trying to fill in the blank. However, these approaches were either based on low-dimension or down-sampled data after dimension reduction (Anibal et al., 2022) from methods like PCA or consumed an enormous amount of computing resources to get a massive number of layer levels with highly limited interpretable information. In order to create a practically available solution, I invented an entirely new hierarchical clustering method called the BW method which can be directly applied to high-dimension large data without a requirement for dimension reduction or massive computing resources. I applied BW clustering to six single-cell RNA sequence sample data. BW clustering brought deep insight into these sample data including sub-type, differentiation branch, cell state changes (development, aging process), and gene expression instability. BW-generated layers were very concise. For almost nineteen thousand cells, BW clustering only yielded 9 layers. An analysis system was created based on the BW clustering method which can unprecedentedly display the true form of high dimensional data space. The resource BW required is also very low as all the work done in this paper used a 16GB memory laptop only, making it easily accessible to researchers with limited computing resources. Overall, the BW clustering method represents a major advancement in high-dimensional large data analysis for biological and biomedical applications.

bioinformatics↗