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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2021

Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing

The majority of variants associated with complex traits and common diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown regulatory effects in cis and trans. By leveraging biobank-scale GWAS data, massively parallel CRISPR screens and single cell transcriptome sequencing, we discovered target genes of noncoding variants for blood trait loci. The closest gene was often the target gene, but this was not always the case. We also identified trans-effects networks of noncoding variants when cis target genes encoded transcription factors, such as GFI1B and NFE2. We observed that GFI1B trans-target genes were enriched for GFI1B binding sites and fine-mapped GWAS variants, and expressed in human bone marrow progenitor cells, suggesting that GFI1B acts as a master regulator of blood traits. This platform will enable massively parallel assays to catalog the target genes of human noncoding variants in both cis and trans.

genomics↗

bioRxiv2025

Unveiling the Immune Landscape of COVID-19 and prolonged Long-COVID through Single-Cell RNA Sequencing

Long-COVID affects at least 10% of COVID-19 survivors, displaying debilitating symptoms across multiple organ systems. Despite the increasing prevalence, the underlying causes remain unclear. This study presents a unique analysis of the PBMC transcriptomic landscape of COVID-19 and Long-COVID patients at a single-cell resolution. We reconstructed the cell state and communication using differentially expressed gene profiling and ligand-receptor interaction analyses. Our results reveal altered T and NK cell subset proportions, diminished proliferating lymphocyte and B cell signalling capacity, and the expression of exhaustion and cytotoxicity associated genes 1.5 - 2 years post-infection, suggesting incomplete immune recovery. Collectively, these findings provide insights into the immune processes underlying the progression of COVID-19 into a chronic Long-COVID state.

immunology↗

bioRxiv2024

Subtypes and proliferation patterns of small intestine neuroendocrine tumors revealed by single cell RNA sequencing

Neuroendocrine tumors (NETs) occur primarily in the small intestine, lung and pancreas. Due to their rarity compared to other malignancies in these organs, their complex biology remains poorly understood, including their oncogenesis, tumor composition and the intriguing phenomena of mixed neuroendocrine non-neuroendocrine neoplasms (MiNEN). Here we profiled ten low-grade small intestine NET (SiNET) samples as well as one mixed lung tumor by single-cell or single-nuclei RNA-seq. We find that SiNETs are largely separated into two distinct subtypes, in which the neuroendocrine cells upregulate epithelial or neuronal markers, respectively. Surprisingly, in both subtypes the neuroendocrine cells are largely non-proliferative while higher proliferation is observed in multiple non-malignant cell types. Specifically, B and plasma cells are highly proliferative in the epithelial-like SiNET subtype, potentially reflecting the outcome of high Migration Inhibitory Factor (MIF) expression in those tumors, which may constitute a relevant target. Finally, our analysis of a mixed lung neuroendocrine tumor identifies a population of putative progenitor cells that may give rise to both neuroendocrine and non-neuroendocrine (squamous) cells, potentially explaining the origin of the mixed histology. Taken together, our results provide important insights and hypotheses regarding the biology of neuroendocrine neoplasms.

cancer biology↗

bioRxiv2019

Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains

Technical advances have enabled the identification of high-resolution cell types within tissues based on single-cell transcriptomics. However, such analyses are restricted in human brain tissue due to the limited number of brain donors. In this study, we integrate mouse and human data to predict cell-type proportions in human brain tissue, and spatially map the resulting cellular composition. By applying feature selection and linear modeling, combinations of human and mouse brain single-cell transcriptomics profiles can be integrated to \"fill in\" missing information. These combined \"in silico chimeric\" datasets are used to model the composition of nine cell types in 3,702 human brain samples in six Allen Human Brain Atlas (AHBA) donors. Cell types were spatially consistent regardless of the scRNA-Seq dataset (91% significantly correlated) or AHBA donor (p-value = 4.43E-20 by t-test) used in the model. Importantly, neuron nuclei location and neuron mRNA location were correlated only after accounting for neural connectivity (p-value = 1.26E-10), which supports the notion that gene expression is a better indicator than nuclei location of cellular localization for cells with large and irregularly shaped cell bodies, such as neurons. These results advocate for the integration of mouse and human data in models of brain tissue heterogeneity.

bioinformatics↗

bioRxiv2026

Single cell Correlation Analysis (SCA): Identifying self-renewing subpopulation of human acute myeloid leukemia stem cells using single cell RNA sequencing analysis

Leukemia stem cells (LSCs), a rare and self-renewing subpopulation, drive Acute myeloid leukemia (AML) relapse and therapy resistance. While single-cell gene expression profiling (scGEP) offers high-resolution insights into LSC biology, existing computational tools rely on relative and arbitrary similarity metrics. This limits their ability to definitively identify true biological cell identities or compare rare cell populations across independent datasets. To overcome these limitations, we developed Single cell Correlation Analysis (SCA), a novel computational method that utilizes a permutation-based false discovery rate (FDR) framework and common background datasets to establish standardized, statistically rigorous thresholds of similarity. We applied SCA to query human AML scRNA-seq datasets using an experimentally validated murine scGEP of LSC self-renewal as our reference. SCA demonstrated superior specificity and precision, maintaining low false positive rates compared to existing reference-based annotation tools. Using SCA, we successfully identified cells expressing a conserved self-renewal program (scaLSC-SR) in both adult and pediatric human AML samples. These scaLSC-SR cells share classical LSC immunophenotypic markers (e.g., CD34, CD96, CD200) and exhibit transcriptomic hallmarks of LSC biology, including evasion of apoptosis and immune responses. Furthermore, this self-renewal program is significantly enriched in poor-risk genetic subtypes, specifically TP53 and NRAS mutated AML. Finally, we derived a 28-gene signature (LSC-SR28) from human AML patients that accurately captures LSC stemness and is highly prognostic across multiple independent clinical cohorts. SCA provides a robust, statistically principled framework for identifying rare and biologically meaningful cell populations across heterogeneous single-cell datasets. By successfully identifying a functional self-renewal profile to human AML, SCA reveals conserved, clinically relevant LSC populations that drive relapse and therapy resistance across different age groups.

cancer biology↗

bioRxiv2018

Virus-inclusive single cell RNA sequencing reveals molecular signature predictive of progression to severe dengue infection

Dengue virus (DENV) infection can result in severe complications. Yet, the understanding of the molecular correlates of severity is limited, partly due to difficulties in defining the peripheral blood mononuclear cells (PBMCs) that are associated with DENV in vivo. Additionally, there are currently no biomarkers predictive of progression to severe dengue (SD). Bulk transcriptomics data are difficult to interpret because blood consists of multiple cell types that may react differently to infection. Here we applied virus-inclusive single cell RNA-seq approach (viscRNA-Seq) to profile transcriptomes of thousands of single PBMCs derived early in the course of disease from six dengue patients and four healthy controls, and to characterize distinct DENV-associated leukocytes. Multiple genes, particularly interferon response genes, were upregulated in a cell-specific manner prior to progression to SD. Expression of MX2 in naive B cells and CD163 in CD14+ CD16+ monocytes was predictive of SD. The majority of DENV-associated cells in the blood of two patients who progressed to SD were naive IgM B cells expressing the CD69 and CXCR4 receptors and antiviral genes, followed by monocytes. Bystander uninfected B cells also demonstrated immune activation, and plasmablasts from two patients exhibited antibody lineages with convergently hypermutated heavy chain sequences. Lastly, assembly of the DENV genome revealed diversity at unexpected genomic sites. This study presents a multi-faceted molecular elucidation of natural dengue infection in humans and proposes biomarkers for prediction of SD, with implications for profiling any tissue and viral infection, and for the development of a dengue prognostic assay.\n\nSignificanceA fraction of the 400 million people infected with dengue annually progresses to severe dengue (SD). Yet, there are currently no biomarkers to effectively predict disease progression. We profiled the landscape of host transcripts and viral RNA in thousands of single blood cells from dengue patients prior to progressing to SD. We discovered cell-type specific immune activation and candidate predictive biomarkers. We also revealed preferential virus association with specific cell populations, particularly naive B cells and monocytes. We then explored immune activation of bystander cells, clonality and somatic evolution of adaptive immune repertoires, and viral genomics. This multi-faceted approach could advance understanding of pathogenesis of any viral infection, map an atlas of infected cells and promote the development of prognostics.

immunology↗

bioRxiv2026

Single-cell RNA sequencing reveals collagen interactions between detached osteoclasts and activated fibroblasts in granulation tissue surrounding sequestra in medication-related osteonecrosis of the jaw

ObjectivesMedication-related osteonecrosis of the jaw (MRONJ) is a rare but serious complication of anti-resorptive agents used for osteoporosis and bone metastasis. As the global population ages, the number of patients with MRONJ continues to rise. Resorptive osteoclasts firmly attached to bone surfaces cannot be detected using single-cell RNA-Seq (scRNA-Seq); however, we hypothesized that multinucleated giant and detached osteoclasts (DetOCs) can be detected by this method. This study aimed to characterize DetOCs with scRNA-seq. Materials and MethodsscRNA-Seq and immunohistochemistry staining was performed on granulation tissues surrounding sequestra from four patients with MRONJ and radicular cysts, a common odontogenic infectious disease with normal bone metabolism. ResultsscRNA-Seq and immunohistochemistry staining revealed that DetOCs were detected exclusively in the granulation tissues of MRONJ. These DetOCs expressed well-known osteoclast markers as well as COL27A1. Subcluster analysis revealed that SPP1+ TREM2+ macrophages differentiated into DetOCs and expressed several types of collagens. Immunostaining confirmed the expression of COL27A1 not only in DetOCs in MRONJ but also in osteoclasts in mandibular cancer. CellChat analysis revealed interactions mediated by collagen signaling pathways between DetOCs, myeloid cells, and activated fibroblasts. ConclusionsWe successfully characterized DetOCs and identified a distinctive microenvironment involving DetOCs and activated fibroblasts in the granulation tissues surrounding sequestra in MRONJ. Clinical RelevanceIdentification of DetOCs and their unique interaction with activated fibroblasts in MRONJ lesions provides novel insight into the pathophysiology of sequestrum formation, suggesting potential therapeutic targets to ameliorate MRONJ.

immunology↗

bioRxiv2023

Combining Bulk and Single-Cell RNA Sequencing Data to Identify RNA methylation and Autophagy-Related Signatures in Patients with Chronic Obstructive Pulmonary Disease

BackgroundChronic Obstructive Pulmonary Disease (COPD) is a heterogeneous lung condition associated with RNA methylation and autophagy. However, the specific autophagy-related genes and RNA methylation regulators involved in COPD development remain unknown. MethodsWe analyzed COPD and non-COPD patients datasets obtained from the Gene Expression Omnibus database, including Tissue Sequencing Transcriptome (bulk-seq) and single-cell sequencing (scRNA-seq) data. Differentially expressed genes (DEGs) were identified through differential genetic analysis using non-COPD bulk-seq data as the control group and COPD samples were used as the experimental group. Animal experiments were conducted to validate the expression of key genes. COPD model mice were exposed to smoke for four months, and lung function and histopathological changes were assessed. The mRNA and protein expression levels of FTO, IGF2BP2, DDIT3, DNAJB1, and YTHDF3 were measured using RT-qPCR and Western blotting, respectively. ResultsWe identified FTO, IGF2BP2, and YTHDF3 as key methylation genes, along with autophagy hub genes DDIT3 and DNAJB1. Animal experiments showed significantly increased mRNA and protein levels of FTO, YTHDF3 and DNAJB1 and significantly decreased levels of IGF2BP2 in lung tissue of COPD mice compared to the control group. ConclusionOur findings suggest that DDIT3 and DNAJB1 as autophagy hub genes, along with FTO, IGF2BP2 and YTHDF3 as RNA methylation genes, may play crucial roles in the development of COPD. These findings, supported by bulk-seq and scRNA-seq data, contribute novel genetic evidence for understanding the epigenetics of COPD.

cell biology↗

bioRxiv2023

cellsnake: a user-friendly tool for single cell RNA sequencing analysis

BackgroundSingle-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, non-expert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. ResultsWe have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. ConclusionAs an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.

bioinformatics↗

bioRxiv2026

Scaling recipes for single-cell RNA sequencing foundation models: when do scaling laws hold?

Deep learning models exhibit empirical scaling laws whereby performance changes predictably with model size, dataset size, and training compute. Although these relationships are well established in domains such as language and image modelling, their applicability to biological data remains unclear. Here, we investigate scaling behaviour in foundation models trained on large collec tions of single-cell transcriptomes. We show that pre-training loss decreases systematically with model capacity and training compute, exhibiting a power law dependence on model size. The strength and regularity of these trends differ between model formulations. We identify and quantify empirical relationships linking the optimal learning rate and depth-to-width ratio to model size and depth or compute. These results demonstrate that scaling principles extend to transcriptomic modelling. More broadly, they provide a quantitative framework for estimating the expected returns from additional resources and selecting suit able hyperparameters and architectures, thereby supporting the development of increasingly capable foundation models for omics data.

bioinformatics↗

bioRxiv2021

Single-cell RNA sequencing reveals micro-evolution of the stickleback immune system

Pathogenic infection is an important driver of many ecological processes. Furthermore, variability in immune function is an important driver of differential infection outcomes. New evidence would suggest that immune variation extends to broad cellular structure of immune systems. However, variability at such broad levels is traditionally difficult to detect in non-model systems. Here we leverage single cell transcriptomic approaches to document signatures of microevolution of immune system structure in a natural system, the three-spined stickleback (Gasterosteus aculeatus). We sampled nine adult fish from three populations with variability in resistance to a cestode parasite, Schistocephalus solidus, to create the first comprehensive immune cell atlas for G. aculeatus. Eight major immune cell types, corresponding to major vertebrate immune cells, were identified. We were also able to document significant variation in both abundance and expression profiles of the individual immune cell types, among the three populations of fish. This variability may contribute to observed variability in parasite susceptibility. Finally, we demonstrate that identified cell type markers can be used to reinterpret traditional transcriptomic data. Combined our study demonstrates the power of single cell sequencing to not only document evolutionary phenomena (i.e. microevolution of immune cells), but also increase the power of traditional transcriptomic datasets.

immunology↗

bioRxiv2024

The primary sclerosing cholangitis and ulcerative colitis colonic mucosa defined through paired microbial and single-cell RNA sequencing

Primary sclerosing cholangitis (PSC) is a chronic progressing cholestatic disease that often co-occurs with inflammatory bowel disease (PSC-IBD). PSC-IBD affecting the colon (PSC-UC) is likened clinically to ulcerative colitis (UC), however differences include a right colon dominance, less severe inflammatory presentation and a greater lifetime risk of colorectal cancer. To understand the basis of clinical differences, we combine single-cell mRNA and antigen receptor sequencing, 16S ribosomal DNA analysis and spatial transcriptomics on biopsies from multiple colon regions of both PSC-UC and UC patients in remission or at the time of relapse. We discover disease-specific cell and microbial profiles between these cohorts, highlighting a distinct landscape in the right colon of PSC-UC patients and an epithelial-endothelial cell state that may contribute to intestinal permeability in UC. We show the expansion of an activated mast cell state in both diseases during flare, and demonstrate the requirement of TMEM176B in sustaining this activated state. Together this work demonstrates that PSC-UC and UC are distinct diseases with common cell mechanisms during inflammation, providing cellular and microbial insights to improve treatment of both patient cohorts.

cell biology↗

bioRxiv2023

Correlation of myeloid-derived suppressor cell expansion with upregulated transposable elements in severe COVID-19 unveiled in single-cell RNA sequencing reanalysis

Some studies investigated the potential role of transposable elements (TEs) in COVID-19 pathogenesis and complications. However, to the best of our knowledge, there is no study to examine the possible association of TEs expression in cell functions and its potential role in COVID-19 immune response at the single-cell level. In this study, we reanalyzed single-cell RNA seq data of bronchoalveolar lavage (BAL) samples obtained from six severe COVID-19 patients and three healthy donors to assess the probable correlation of TE expression with the immune responses induced by the SARS-CoV-2 virus in COVID-19 patients. Our findings indicated that the expansion of myeloid-derived suppressor cells (MDSCs) may be a characteristic feature of COVID-19. Additionally, a significant increase in TEs expression in MDSCs was observed. This upregulation of TEs in COVID-19 may be linked to the adaptability of these cells in response to their microenvironments. Furthermore, it appears that the identification of overexpressed TEs by Pattern recognition receptors (PRRs) in MDSCs may enhance the suppressive capacity of these cells. Thus, this study emphasizes the crucial role of TEs in the functionality of MDSCs during COVID-19.

systems biology↗

bioRxiv2025

Immunomodulatory Effects of a Cell Processing Device: Insights from Single Cell RNA Sequencing and Gene Set Enrichment Analysis

The Selective Cytopheretic Device (SCD), marketed as QUELimmuneTM, is an autologous leukocyte processing extracorporeal device which promotes immunomodulation of a systemic hyperinflammatory states. To date, interrogation of the mechanism of action (MoA) of this cell-directed therapy has provided insight into therapeutic impact on neutrophils. However, characterization of monocytes has proven to be more difficult due to their dynamic plasticity, requiring more sophisticated analysis methods. Single cell ribonucleic acid sequencing (scRNAseq) methodology using 10x Flex is reported to further elucidate MO MoA in SCD therapy. Analysis strategies of unsupervised clustering and interrogation of specific genes of interest based on previous mechanistic insights from cell surface marker analysis and bulk RNA sequencing were utilized. SCD-treated monocytes were demonstrated to have lower expression of several proinflammatory gene products including TNF and IL-6 and enrichment in anti-inflammatory gene markers, suggesting that SCD monocyte processing results in the release of monocytes with a less inflammatory phenotype. Calcium-dependent gene pathways are also downregulated, consistent with the low iCa environment of SCD therapy. Furthermore, analysis points to the involvement of key transcription factors such as AP-1 and NFkB. Taken together with preclinical and clinical data, scRNAseq confirms several aspects of the suspected SCD MoA on monocytes to transform them to a lesser pro-inflammatory phenotype.

immunology↗

bioRxiv2025

Deciphering Tumor Microenvironment Dynamics in Tumorigenesis and Lymph Node Metastasis of Esophageal Squamous Cell Carcinoma using Single-Cell RNA Sequencing

Esophageal squamous cell carcinoma (ESCC) exhibits profound inter-tissue heterogeneity, yet how immune and stromal ecosystems diverge between primary tumors and metastatic lymph nodes remains poorly understood. Here, we generated a single-cell atlas of 344,790 cells and paired T cell receptor profiles from primary tumor mucosa, adjacent mucosa, non-metastatic lymph nodes, and metastatic lymph nodes from 18 patients with treatment-naive ESCC. We identify compartment-specific immune and stromal architectures that shape distinct antitumor responses and suppressive programs. Primary tumors were characterized by marked enrichment of regulatory T cells (TREG) and activated dendritic cells coordinated through CTLA4-associated circuits, accompanied by accelerated CD8+ T cell differentiation toward intermediate and terminal exhaustion with loss of cytotoxicity. In contrast, metastatic lymph nodes preserved substantial pools of pre-exhausted CD8+ T cells with retained effector potential and strong clonal connectivity to the primary tumor, suggesting sustained antigen-driven trafficking. However, these reinvigoration-competent populations were embedded within a niche dominated by TREM2high macrophages that delivered SPP1-dependent suppressive signals to exhausted and regulatory T cells. Stromal lineages also displayed niche-specific specialization: tumor mucosa contained chemokine-rich inflammatory fibroblasts, whereas metastatic lymph nodes upregulated extracellular matrix remodeling programs. Together, our findings demonstrate that ESCC progression is governed by anatomically distinct exhaustion trajectories, clonal behaviors, stromal states, and suppressive circuits. This framework provides mechanistic insight into why lymph node response is a key determinant of clinical outcome and highlights the need for site-tailored immunomodulation strategies that target TREG-mediated suppression in primary tumors and TREM2high macrophage programs in metastatic nodes.

cancer biology↗

bioRxiv2026

Pan-cancer analysis of single-cell RNA sequencing data from 304 human tumors sheds light on the aneuploidy paradox

Aneuploidy poses a central paradox in cancer biology: it impairs cellular fitness in normal cells but drives cancer progression. To resolve this, we analyzed single cell transcriptomes from >665,000 cells - including [~]288,000 malignant cells - across 304 tumors and 15 cancer types. Integrating transcriptomics with inferred aneuploidy profiles, we characterized cell-intrinsic programs and interactions with the tumor microenvironment. Unexpectedly, highly aneuploid single cells exhibited reduced proliferation and metabolism, contrasting sharply with tumor-bulk profiles. We show this divergence is driven by karyotypic heterogeneity: in highly heterogeneous tumors, aneuploid cells display signatures of acute stress and negative selection. Conversely, in clonally aneuploid tumors, these detrimental signatures are lost and replaced by signatures of increased proliferation and enhanced metabolism, reflecting adaptation. Additionally, we identified consistent transcriptional programs driven by recurrent chromosome-arm alterations across both single cells and bulk tumors. These findings illuminate the selective forces shaping tumor evolution and the aneuploidy paradox. Statement Of SignificanceBy jointly evaluating gene expression and aneuploidy at single-cell resolution, we demonstrate that karyotypic heterogeneity underlies the transcriptional impact of aneuploidy, reveal distinct cellular responses to emerging versus stable states, and identify recurrent chromosomal alterations that drive conserved transcriptional programs. Our findings capture the dynamics of aneuploidy evolution in human tumors, providing novel insights into the aneuploidy paradox.

cancer biology↗

bioRxiv2024

Temporally resolved single-cell RNA sequencing reveals protective and pathological responses during herpes simplex virus 1 CNS infection

Herpes Simplex Virus 1 (HSV-1) is a common human neurotropic virus with the majority of adults harboring latent-recurrent infections. In rare cases, HSV-1 infection can access the central nervous system through the neuronal route and develop into life-threatening encephalitis. Here, we used a mouse model for HSV-1 infection to describe the transcriptomic profile of the infected brain stem at the single-cell level and with temporal resolution. Among resident brain cells, microglia increased in proportion during the course of infection, while astrocytes, pericytes, and endothelial cell levels decrease. At the levels of peripheral immune cells, we found notably monocytes to strongly influx the infected brain. Large dynamic changes were found in the abundance of subpopulations of the different cell types following virus infection. For instance, we identify one subpopulation of microglia exhibiting very high type I interferon and chemokine expression early during infection. This population was also enriched for viral transcripts, suggesting localization at foci of infection, and orchestrating recruitment of other immune cells. In contrast, for the infiltrating monocytes, we identified a larger panel of unique subpopulations with antiviral and inflammatory phenotypes, and found not all of these being highly positive for viral transcripts, thus indicating monocyte activities beyond the infected brain areas. Finally, investigation of endothelial cell cross-talk with other cell types revealed that cytokines derived from microglia and monocyte, but also T cells, contribute to disturbance of the blood brain barrier. Our work thus reveals for the first time the complex nature of the cellular response in the virus-infected brain, which seeks to eliminate infection but can also prime for pathological changes.

immunology↗

bioRxiv2022

Consensus Label Propagation with Graph Convolutional Networks for Single-Cell RNA Sequencing Cell Type Annotation

MotivationSingle-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective. ResultsWe present a Graph Convolutional Network (GCN) based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq data sets from different tissues, we show that our method improves accuracy over a simple consensus algorithm and the average of the underlying tools. We also compare our method to a non-parametric neighbor majority approach, showing comparable results. We then demonstrate that our GCN method allows for feature interpretation, identifying important genes for cell type classification. We present our completed pipeline, written in PyTorch, as an end-to-end tool for automating and interpreting the classification of scRNA-seq data. AvailabilityOur code for conducting the experiments in this paper and using our model is available at https://github.com/lewinsohndp/scSHARP Contactd_lewinsohn@coloradocollege.edu Supplementary informationSupplementary data are available at Journal Name online.

bioinformatics↗