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Abstract Single-cell RNA-seq (scRNA-seq) is widely used for transcriptome profiling, but most analyses focus on gene-level events, with less attention devoted to alternative splicing. Here, we present scASfind, a novel computational method to allow for quantitative analysis of cell type-specific splicing events using full-length scRNA-seq data. ScASfind utilizes an efficient data structure to store the percent spliced-in value for each splicing event. This makes it possible to exhaustively search for patterns among all differential splicing events, allowing us to identify marker events, mutually exclusive events, and events involving large blocks of exons that are specific to one or more cell types.

Abstract Background Uveal melanoma (UM), the most common adult intraocular tumor, is characterized by high malignancy and poor prognosis in advanced stages. Angiogenesis is critical for UM development, however, not only the role of vascular endothelial dysfunction in UM remains unknown, but also their analysis at the single-cell level has been lacking. A comprehensive analysis is essential to clarify the role of the endothelium in the development of UM. Methods By using single-cell RNA transcriptomics data of 11 cases of primary and liver metastasis UM, we analyzed the endothelial cell status. In addition, we analyzed and validated ECs in the in vitro model and collected clinical specimens. Subsequently, we explored the impact of endothelial dysfunction on UM cell migration and explored the mechanisms responsible for the endothelial cell abnormalities and the reasons for their peripheral effects. Results UM metastasis has a significantly higher percentage of vascular endothelial cells compared to in situ tumors, and endothelial cells in metastasis show significant senescence. Senescent endothelial cells in metastatic tumors showed significant Krüppel-like factor 4 (KLF4) upregulation, overexpression of KLF4 in normal endothelial cells induced senescence, and knockdown of KLF4 in senescent endothelium inhibited senescence, suggesting that KLF4 is a driver gene for endothelial senescence. KLF4-induced endothelial senescence drove tumor cell migration through a senescence-associated secretory phenotype (SASP), of which the most important component of the effector was CXCL12 (C-X-C motif chemokine ligand 12), and participated in the composition of the immunosuppressive microenvironment. Conclusion This study provides an undesirable insight of senescent endothelial cells in promoting UM metastasis.

Abstract Objective Proliferative nodular formation represents a characteristic pathological feature of benign prostatic hyperplasia (BPH) and serves as the primary cause for prostate volume enlargement and consequent lower urinary tract symptoms (LUTS). Its specific mechanism is largely unknown, although several cellular processes have been reported to be involved in BPH initiation and development and highlighted the crucial role of epithelial cells in proliferative nodular formation. However, the technological limitations hinder the in vivo investigation of BPH patients. Methods The robust cell type decomposition (RCTD) method was employed to integrate spatial transcriptomics and single cell RNA sequencing profiles, enabling the elucidation of epithelial cell alterations during nodular formation. Immunofluorescent and immunohistochemical staining was performed for verification. Results The alterations of epithelial cells during the formation of nodules in BPH was observed, and a distinct subgroup of basal epithelial (BE) cells, referred to as BE5, was identified to play a crucial role in driving this progression through the hypoxia-induced epithelial-mesenchymal transition (EMT) signaling pathway. BE5 served as both the initiating cell during nodular formation and the transitional cell during the transformation from luminal epithelial (LE) to BE cells. A distinguishing characteristic of the BE5 cell subgroup in patients with BPH was its heightened hypoxia and upregulated expression of FOS. Histological verification results confirmed a significant association between c-Fos expression and key biological processes such as hypoxia and cell proliferation, as well as the close relationship between hypoxia and EMT in BPH tissues. Furthermore, a strong link between c-Fos expression and the progression of BPH was also been validated. Additionally, notable functional differences were observed in glandular and stromal nodules regarding BE5 cells, with BE5 in glandular nodules exhibiting enhanced capacities for EMT and cell proliferation characterized by club-like cell markers. Conclusions This study elucidated the comprehensive landscape of epithelial cells during in vivo nodular formation in patients, thereby offering novel insights into the initiation and progression of BPH.

Abstract Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.

Abstract Background Infectious Salmon Anaemia Virus (ISAV) is an Orthomixovirus that represents a large problem for salmonid aquaculture worldwide. Current prevention and treatment methods are only partially effective. Genetic selection and genome engineering have the potential to develop ISAV resistant salmon stocks. Both strategies can benefit from an improved understanding of the genomic regulation of ISAV pathogenesis. Here, we used single-cell RNA sequencing of an Atlantic salmon cell line to provide the first high dimensional insight into the transcriptional landscape that underpins host-virus interaction during early ISAV infection. Results Salmon head kidney (SHK-1) cells were single-cell RNA sequenced at 24, 48 and 96 h post-ISAV challenge. At 24 h post infection, cells showed expression signatures consistent with viral entry, with genes such as PI3K, FAK or JNK being upregulated relative to uninfected cells. At 48 and 96 h, infected cells showed a clear anti-viral response, characterised by the expression of IFNA2 or IRF2. Uninfected bystander cells at 48 and 96 h also showed clear transcriptional differences, potentially suggesting paracrine signalling from infected cells. These bystander cells expressed pathways such as mRNA sensing, RNA degradation, ubiquitination or proteasome; and up-regulation of mitochondrial ribosome genes also seemed to play a role in the host response to the infection. Correlation between viral and host genes revealed novel genes potentially key for this fish-virus interaction. Conclusions This study has increased our understanding of the cellular response of Atlantic salmon during ISAV infection and revealed host-virus interactions at the cellular level. Our results highlight various potential key genes in this host-virus interaction, which can be manipulated in future functional studies to increase the resistance of Atlantic salmon to ISAV.

Abstract Ovarian cancer is a highly heterogeneous gynecological malignancy that seriously affects the survival and prognosis of female patients. Single-cell sequencing and transcriptome analysis can effectively characterize tumor heterogeneity to better study the mechanism of occurrence and development. In this study, we identified differentially expressed genes with different differentiation outcomes of tumor cells by analyzing a single-cell dataset. Based on the differentially expressed genes, we explored the differences in function and tumor microenvironment among clusters via consensus clustering. Meanwhile, WGCNA was employed to obtain key genes related to ovarian cancer. On the basis of the TCGA and GEO datasets, we constructed a risk model consisting of 7 genes using the LASSO regression model, and successfully verified that the model was characterized as an independent prognostic factor, efficiently predicting the survival prognosis of patients. In addition, immune signature analysis showed that patients in the high-risk group exhibited lower anti-tumor immune cell infiltration and immunosuppressive status, and had poorer responsiveness to chemotherapeutic drugs and immunotherapy. In conclusion, our study provided a 7-gene prognostic model based on the heterogeneity of OC cells for ovarian cancer patients, which could effectively predict the prognosis of patients and identify the immune microenvironment status of patients.

Abstract Background Glioma is the most prevalent primary tumor of the central nervous system. Glioblastoma multiforme (GBM) is the most malignant form of glioma with an extremely poor prognosis. A novel, regulated cell death form of copper-induced cell death called “cuproptosis” provides a new prospect for cancer treatment by regulating cuproptosis. Methods Data from bulk RNA sequencing (RNA-seq) analysis (The Cancer Genome Atlas cohort and Chinese Glioma Genome Atlas cohort) and single cell RNA-seq (scRNA-seq) analysis were integrated to reveal their relationships. A scoring system was constructed according to the cuproptosis-related gene expression, and core genes were experimentally verified using real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR), Western blot (WB), immunohistochemistry (IHC), and immunofluorescence (IF). Moreover, cell counting kit-8 (CCK8), colony formation, 5-ethynyl-2’-deoxyuridine (EdU) incorporation, transwell, and flow cytometry cell cycle were performed to evaluate cell proliferation, invasion, and migration. Results The Cuproptosis Activation Scoring (CuAS) Model has stable and independent prognostic efficacy, as verified by two CGGA datasets. Epiregulin (EREG), the gene of the model has the most contributions in the principal component analysis (PCA), is an onco-immunological gene that can affect immunity by influencing the expression of programmed death-ligand 1 (PD-L1) and mediate the process of cuproptosis by influencing the expression of ferredoxin 1 (FDX1). Single cell transcriptome analysis revealed that high CuAS GBM cells are found in vascular endothelial growth factor A (VEGFA) + malignant cells. Oligodendrocyte transcription factor 1 (OLIG1) + malignant is the original clone, and VEGF and CD99 are the differential pathways of specific cell communication between the high and low CuAS groups. This was also demonstrated by immunofluorescence in the tissue sections. Furthermore, CuAS has therapeutic potential for immunotherapy, and we predict that many drugs (methotrexate, NU7441, KU -0063794, GDC-0941, cabozantinib, and NVP-BEZ235) may be used in patients with high CuAS. Conclusion EREG is the core onco-immunological biomarker of CuAS and modulates the cross-talk between VEGF and CD99 signaling in glioblastoma, and CuAS may provide support for immunotherapy and chemotherapy.

Abstract Background Single-cell RNA sequencing (scRNA-seq) allows the detection of rare cell types in complex tissues. The detection of markers for rare cell types is useful for further biological analysis of, for example, flow cytometry and imaging data sets for either physical isolation or spatial characterization of these cells. However, only a few computational approaches consider the problem of selecting specific marker genes from scRNA-seq data. Results Here, we propose sc2marker, which is based on the maximum margin index and a database of proteins with antibodies, to select markers for flow cytometry or imaging. We evaluated the performances of sc2marker and competing methods in ranking known markers in scRNA-seq data of immune and stromal cells. The results showed that sc2marker performed better than the competing methods in accuracy, while having a competitive running time.

Abstract Background The development of multiple myeloma (MM) is considered to involve a multistep transformation process, but the role of cytogenetic abnormalities and molecular alterations in determining the cell fate of multiple myeloma (MM) remains unclear. Here, we have analyzed single cell RNA-seq data and bulk gene profiles to reveal a novel signature associated with MM development. Methods The scRNA-seq data from GSE118900 was used to profile the transcriptomes of cells from MM patients at different stages. Pseudotemporal ordering of the single cells was performed using Monocle package to feature distinct transcriptomic states of the developing MM cells. The bulk microarray profiles from GSE24080 and GSE9782 were applied to identify a signature associated with MM development. Results The 597 cells were divided into 7 clusters according to different risk levels. They were initiated mainly from monoclonal gammopathy of undetermined significance (MGUS), newly diagnosed MM (NDMM), or relapsed and/or refractory myeloma (RRMM) with cytogenetically favorable t(11;14), moved towards the cells from smoldering MM (SMM) or NDMM without t(11;14) or t(4;14), and then finally to cells from SMM or RRMM with t(4;14). Based on the markers identified in the late stage, the bulk data was used to develop a 20-gene signature stratifying patients into high and low-risk groups (GSE24080: HR = 3.759, 95% CI 2.746–5.145; GSE9782: HR = 2.612, 95% CI 1.894–3.603), which was better than the previously published gene signatures (EMC92, UAMS70, and UAMS17) and International Staging System. This signature also succeeded in predicting the clinical outcome of patients treated with bortezomib (HR = 2.884, 95% CI 1.994–4.172, P = 1.89e−8). The 20 genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients with MM. Conclusion Our comprehensive analyses offered new insights in MM development, and established a 20-gene signature as an independent biomarker for MM.

Abstract Background Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-sEq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome. Results We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result. Conclusions Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs.

Abstract Background Whole lung tissue transcriptomic profiling studies in chronic obstructive pulmonary disease (COPD) have led to the identification of several genes associated with the severity of airflow limitation and/or the presence of emphysema, however, the cell types driving these gene expression signatures remain unidentified. Methods To determine cell specific transcriptomic changes in severe COPD, we conducted single-cell RNA sequencing (scRNA seq) on n = 29,961 cells from the peripheral lung parenchymal tissue of nonsmoking subjects without underlying lung disease (n = 3) and patients with severe COPD (n = 3). The cell type composition and cell specific gene expression signature was assessed. Gene set enrichment analysis (GSEA) was used to identify the specific cell types contributing to the previously reported transcriptomic signatures. Results T-distributed stochastic neighbor embedding and clustering of scRNA seq data revealed a total of 17 distinct populations. Among them, the populations with more differentially expressed genes in cases vs. controls (log fold change >|0.4| and FDR = 0.05) were: monocytes (n = 1499); macrophages (n = 868) and ciliated epithelial cells (n = 590), respectively. Using GSEA, we found that only ciliated and cytotoxic T cells manifested a trend towards enrichment of the previously reported 127 regional emphysema gene signatures (normalized enrichment score [NES] = 1.28 and = 1.33, FDR = 0.085 and = 0.092 respectively). Among the significantly altered genes present in ciliated epithelial cells of the COPD lungs, QKI and IGFBP5 protein levels were also found to be altered in the COPD lungs. Conclusions scRNA seq is useful for identifying transcriptional changes and possibly individual protein levels that may contribute to the development of emphysema in a cell-type specific manner.

Abstract Background JUNO and IZUMO1 are the first receptor-ligand protein pairs discovered to be essential for sperm-oocyte fusion; their interaction is indispensable for fertilization. Methods PCR was used to clone the full-length DNA sequence of the Juno gene in sheep. The single nucleotide polymorphism (SNP) loci of Juno were genotyped by Sequenom MassARRAY®. PCR combined with rapid amplification of cDNA Ends were used to clone the full-length cDNA sequence of Juno and Izumo1. Reverse transcriptase-PCR (RT-PCR) and real time-quantitative-PCR (RT-qPCR) were used to analyze the genes’ expression in tissues of sheep, and single cell RNA-seq was used to analyze the genes’ expression in oocytes, granulosa cells and follicular theca of polytocous and monotocous Small Tail Han ewes. Bioinformatics was used to analyze advanced structure and phylogeny of JUNO and IZUMO1 proteins. Results The full-length DNA sequence of the Juno gene in sheep was cloned and nine SNPs were screened. We found a significant association between the g.848253 C > A locus of Juno and litter size of Small Tail Han sheep (P A locus of Juno results in a nonconservative missense point mutation leading to a change from Phe to Leu at position 219 in the amino acid sequence. Conclusions For the first time, this study systematically analyzed the expression, structure and function of Juno and Izumo1 genes and their encoded proteins in Small Tail Han sheep, providing the basis for future studies of the regulatory mechanisms of Juno and Izumo1 genes.

Abstract Background High throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner. Results We introduce RankCorr, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RankCorr proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RankCorr to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells. Conclusions According to the metrics introduced in this work, RankCorr is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RankCorr is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RankCorr software is available for download at https://github.com/ahsv/RankCorr with extensive documentation.

Abstract Cell clustering is one of the most common routines in single cell RNA-seq data analyses, for which a number of specialized methods are available. The evaluation of these methods ignores an important biological characteristic that the structure for a population of cells is hierarchical, which could result in misleading evaluation results. In this work, we develop two new metrics that take into account the hierarchical structure of cell types. We illustrate the application of the new metrics in constructed examples as well as several real single cell datasets and show that they provide more biologically plausible results.

Abstract Background With the rapid development of single-cell RNA sequencing technology, it is possible to dissect cell-type composition at high resolution. A number of methods have been developed with the purpose to identify rare cell types. However, existing methods are still not scalable to large datasets, limiting their utility. To overcome this limitation, we present a new software package, called GiniClust3, which is an extension of GiniClust2 and significantly faster and memory-efficient than previous versions. Results Using GiniClust3, it only takes about 7 h to identify both common and rare cell clusters from a dataset that contains more than one million cells. Cell type mapping and perturbation analyses show that GiniClust3 could robustly identify cell clusters. Conclusions Taken together, these results suggest that GiniClust3 is a powerful tool to identify both common and rare cell population and can handle large dataset. GiniCluster3 is implemented in the open-source python package and available at https://github.com/rdong08/GiniClust3.

Abstract Background Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

Abstract Background CCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation remains unclear. Results We knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating that gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, implying tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explains why knockdown of CTCF leads to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found that cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes. Conclusions To our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

Abstract Background Classic dendritic cells (cDCs) play a central role in the immune system by processing and presenting antigens to activate T cells, and consist of two major subsets: CD141+ cDC (cDC1) and CD1c+ cDC (cDC2). A population of migratory precursor cells, the pre-cDCs, is the immediate precursors to both cDC subsets. Previous studies showed that there were two pre-committed pre-cDC subpopulations. However, the key molecular drivers of pre-commitment in human pre-cDCs were not investigated. Results To identify the key molecular drivers for pre-commitment in human pre-cDCs, we performed single cell RNA sequencing (RNA-Seq) of two cDC subsets and pre-cDCs, and bulk RNA-Seq of pre-cDCs and cDCs from human peripheral blood. We found that pre-DC subpopulations cannot be separated by either variable genes within pre-cDCs or differentially expressed genes between cDC1 and cDC2. In contrast, they were separated by 16 transcription factors that are themselves differentially expressed or have regulated targets enriched in the differentially expressed genes between bulk cDC1 and cDC2, with one subpopulation close to cDC1 and the other close to cDC2. More importantly, these two pre-cDC sub-populations are correlated with ratio of IRF8 to IRF4 expression level more than their individual expression level. We also verified these findings using three recently published datasets. Conclusions In this study, we demonstrate that single cell transcriptome profiling can reveal pre-cDCs differentiation map, and our results suggest the concept that combinatorial dose of transcription factors determines cell differentiation fate.

Abstract Background The advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification. Results We have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment. Conclusions Based on our study, we found that when marker genes are expressed at fold change of 4 or more, either Seurat or SIMLR algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fold change of 2, choice of the single cell algorithm is dependent on the number of single cells isolated and rarity of cell types to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in the design of single cell experiments.

Abstract Background Single cell transcriptomics is critical for understanding cellular heterogeneity and identification of novel cell types. Leveraging the recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel unsupervised clustering algorithms that are robust to high levels of technical and biological noise and scale to datasets of millions of cells. Results We present novel computational approaches for clustering scRNA-seq data based on the Term Frequency - Inverse Document Frequency (TF-IDF) transformation that has been successfully used in the field of text analysis. Conclusions Empirical experimental results show that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches.

Abstract High-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individual genes and provide an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on simulated data, a single-cell RNA-seq haematopoiesis study and mouse embryonic stem cells generated using droplet barcoding. The method is robust to high levels of technical variation and dropout, which are common in single-cell data.

Abstract Single cell experimental techniques reveal transcriptomic and epigenetic heterogeneity among cells, but how these are related is unclear. We present MATCHER, an approach for integrating multiple types of single cell measurements. MATCHER uses manifold alignment to infer single cell multi-omic profiles from transcriptomic and epigenetic measurements performed on different cells of the same type. Using scM&T-seq and sc-GEM data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondences. MATCHER also reveals new insights into the dynamic interplay between the transcriptome and epigenome in single embryonic stem cells and induced pluripotent stem cells.