A census of human soluble protein complexes.

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.

Evaluation of single-cell RNAseq labelling algorithms using cancer datasets.

Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance. Here, we assess 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive large-scale assessment of such methods in a cancer-specific context. Using several performance metrics, we show that cell-based methods generally achieved higher performance and were faster compared to cluster-based methods. Cluster-based methods more successfully labelled non-malignant cell types, likely because of a lack of gene signatures for relevant malignant cell subpopulations. Larger cell numbers present in some cell types in training data positively impacted prediction scores for cell-based methods. Finally, we examined which methods performed favorably when trained and tested on separate patient cohorts in scenarios similar to clinical applications, and which were able to accurately label particularly small or under-represented cell populations in the given datasets. We conclude that scPred and SVM show the best overall performances with cancer-specific data and provide further suggestions for algorithm selection. Our analysis pipeline for assessing the performance of cell type labelling algorithms is available in https://github.com/shooshtarilab/scRNAseq-Automated-Cell-Type-Labelling.

Anatomy of DNA methylation signatures: Emerging insights and applications.

DNA methylation (DNAm) signatures are unique patterns of DNAm alterations defined for rare disorders caused by pathogenic variants in epigenetic regulatory genes. The potential of DNAm signatures (also known as "episignatures") is just beginning to emerge as there are >300 known epigenetic regulatory genes, ∼100 of which are linked to neurodevelopmental disorders. To date, approximately 50 signatures have been identified, which have proven unexpectedly successful as predictive tools for classifying variants of uncertain significance as pathogenic or benign. The molecular basis of these signatures is poorly understood. Furthermore, their relationships to primary disease pathophysiology have yet to be adequately investigated, despite clear demonstrations of potential connections. There are currently no published guidelines for signature development. As signatures are highly dependent on the samples and methods used to derive them, we propose a framework for consideration in signature development including sample size, statistical parameters, cell type of origin, and the value of detailed clinical and molecular information. We illustrate the relationship between signature output/efficacy and sample size by generating and testing 837 DNAm signatures of Kleefstra syndrome using downsampling analysis. Our findings highlight that no single DNAm signature encompasses all DNAm alterations present in a rare disorder, and that a substandard study design can generate a DNAm signature that misclassifies variants. Finally, we discuss the importance of further investigating DNAm signatures to inform disease pathophysiology and broaden their scope as a functional assay.

DNA Methylation Signature for EZH2 Functionally Classifies Sequence Variants in Three PRC2 Complex Genes.

Weaver syndrome (WS), an overgrowth/intellectual disability syndrome (OGID), is caused by pathogenic variants in the histone methyltransferase EZH2, which encodes a core component of the Polycomb repressive complex-2 (PRC2). Using genome-wide DNA methylation (DNAm) data for 187 individuals with OGID and 969 control subjects, we show that pathogenic variants in EZH2 generate a highly specific and sensitive DNAm signature reflecting the phenotype of WS. This signature can be used to distinguish loss-of-function from gain-of-function missense variants and to detect somatic mosaicism. We also show that the signature can accurately classify sequence variants in EED and SUZ12, which encode two other core components of PRC2, and predict the presence of pathogenic variants in undiagnosed individuals with OGID. The discovery of a functionally relevant signature with utility for diagnostic classification of sequence variants in EZH2, EED, and SUZ12 supports the emerging paradigm shift for implementation of DNAm signatures into diagnostics and translational research.

Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery.

Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R-SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data. Here, we introduce Genomics4RD, an integrated web-accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs. Genomics4RD has been designed to standardize data collection and processing, and to help users systematically collect, prioritize, and visualize participant information. Data storage, authorization, and access procedures have been developed in collaboration with policy experts and stakeholders to ensure the trusted and secure access of data by external researchers. The breadth and standardization of data offered by Genomics4RD allows researchers to compare candidate disease genes and variants between participants (i.e., matchmaking) for discovery purposes, while facilitating the development of computational approaches for multiomic data analyses and enabling clinical translation efforts for new genetic technologies in the future.

MetaFusion: a high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates.

MOTIVATION: Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. RESULTS: MetaFusion is a flexible metacalling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format. Calls are annotated, merged using graph clustering, filtered and ranked to provide a final output of high-confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high-confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases and is provided with a Benchmarking Toolkit to calibrate new callers. AVAILABILITY AND IMPLEMENTATION: MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Impact of assisted reproduction, infertility, sex and paternal factors on the placental DNA methylome.

Children conceived using Assisted Reproductive Technologies (ART) have a higher incidence of growth and birth defects, attributable in part to epigenetic perturbations. Both ART and germline defects associated with parental infertility could interfere with epigenetic reprogramming events in germ cells or early embryos. Mouse models indicate that the placenta is more susceptible to the induction of epigenetic abnormalities than the embryo, and thus the placental methylome may provide a sensitive indicator of 'at risk' conceptuses. Our goal was to use genome-wide profiling to examine the extent of epigenetic abnormalities in matched placentas from an ART/infertility group and control singleton pregnancies (n = 44/group) from a human prospective longitudinal birth cohort, the Design, Develop, Discover (3D) Study. Principal component analysis revealed a group of ART outliers. The ART outlier group was enriched for females and a subset of placentas showing loss of methylation of several imprinted genes including GNAS, SGCE, KCNQT1OT1 and BLCAP/NNAT. Within the ART group, placentas from pregnancies conceived with in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) showed distinct epigenetic profiles as compared to those conceived with less invasive procedures (ovulation induction, intrauterine insemination). Male factor infertility and paternal age further differentiated the IVF/ICSI group, suggesting an interaction of infertility and techniques in perturbing the placental epigenome. Together, the results suggest that the human placenta is sensitive to the induction of epigenetic defects by ART and/or infertility, and we stress the importance of considering both sex and paternal factors and that some but not all ART conceptuses will be susceptible.

Prevalence and Clinical Features of Inflammatory Bowel Diseases Associated With Monogenic Variants, Identified by Whole-Exome Sequencing in 1000 Children at a Single Center.

BACKGROUND & AIMS: A proportion of infants and young children with inflammatory bowel diseases (IBDs) have subtypes associated with a single gene variant (monogenic IBD). We aimed to determine the prevalence of monogenic disease in a cohort of pediatric patients with IBD. METHODS: We performed whole-exome sequencing analyses of blood samples from an unselected cohort of 1005 children with IBD, aged 0-18 years (median age at diagnosis, 11.96 years) at a single center in Canada and their family members (2305 samples total). Variants believed to cause IBD were validated using Sanger sequencing. Biopsies from patients were analyzed by immunofluorescence and histochemical analyses. RESULTS: We identified 40 rare variants associated with 21 monogenic genes among 31 of the 1005 children with IBD (including 5 variants in XIAP, 3 in DOCK8, and 2 each in FOXP3, GUCY2C, and LRBA). These variants occurred in 7.8% of children younger than 6 years and 2.3% of children aged 6-18 years. Of the 17 patients with monogenic Crohn's disease, 35% had abdominal pain, 24% had nonbloody loose stool, 18% had vomiting, 18% had weight loss, and 5% had intermittent bloody loose stool. The 14 patients with monogenic ulcerative colitis or IBD-unclassified received their diagnosis at a younger age, and their most predominant feature was bloody loose stool (78%). Features associated with monogenic IBD, compared to cases of IBD not associated with a single variant, were age of onset younger than 2 years (odds ratio [OR], 6.30; P = .020), family history of autoimmune disease (OR, 5.12; P = .002), extra-intestinal manifestations (OR, 15.36; P < .0001), and surgery (OR, 3.42; P = .042). Seventeen patients had variants in genes that could be corrected with allogeneic hematopoietic stem cell transplantation. CONCLUSIONS: In whole-exome sequencing analyses of more than 1000 children with IBD at a single center, we found that 3% had rare variants in genes previously associated with pediatric IBD. These were associated with different IBD phenotypes, and 1% of the patients had variants that could be potentially corrected with allogeneic hematopoietic stem cell transplantation. Monogenic IBD is rare, but should be considered in analysis of all patients with pediatric onset of IBD.

EpigenCentral: Portal for DNA methylation data analysis and classification in rare diseases.

Epigenetic processes play a key role in regulating gene expression. Genetic variants that disrupt chromatin-modifying proteins are associated with a broad range of diseases, some of which have specific epigenetic patterns, such as aberrant DNA methylation (DNAm), which may be used as disease biomarkers. While much of the epigenetic research has focused on cancer, there is a paucity of resources devoted to neurodevelopmental disorders (NDDs), which include autism spectrum disorder and many rare, clinically overlapping syndromes. To address this challenge, we created EpigenCentral, a free web resource for biomedical researchers, molecular diagnostic laboratories, and clinical practitioners to perform the interactive classification and analysis of DNAm data related to NDDs. It allows users to search for known disease-associated patterns in their DNAm data, classify genetic variants as pathogenic or benign to assist in molecular diagnostics, or analyze patterns of differential methylation in their data through a simple web form. EpigenCentral is freely available at http://epigen.ccm.sickkids.ca/.

CHARGE and Kabuki Syndromes: Gene-Specific DNA Methylation Signatures Identify Epigenetic Mechanisms Linking These Clinically Overlapping Conditions.

Epigenetic dysregulation has emerged as a recurring mechanism in the etiology of neurodevelopmental disorders. Two such disorders, CHARGE and Kabuki syndromes, result from loss of function mutations in chromodomain helicase DNA-binding protein 7 (CHD7LOF) and lysine (K) methyltransferase 2D (KMT2DLOF), respectively. Although these two syndromes are clinically distinct, there is significant phenotypic overlap. We therefore expected that epigenetically driven developmental pathways regulated by CHD7 and KMT2D would overlap and that DNA methylation (DNAm) alterations downstream of the mutations in these genes would identify common target genes, elucidating a mechanistic link between these two conditions, as well as specific target genes for each disorder. Genome-wide DNAm profiles in individuals with CHARGE and Kabuki syndromes with CHD7LOF or KMT2DLOF identified distinct sets of DNAm differences in each of the disorders, which were used to generate two unique, highly specific and sensitive DNAm signatures. These DNAm signatures were able to differentiate pathogenic mutations in these two genes from controls and from each other. Analysis of the DNAm targets in each gene-specific signature identified both common gene targets, including homeobox A5 (HOXA5), which could account for some of the clinical overlap in CHARGE and Kabuki syndromes, as well as distinct gene targets. Our findings demonstrate how characterization of the epigenome can contribute to our understanding of disease pathophysiology for epigenetic disorders, paving the way for explorations of novel therapeutics.

The missing indels: an estimate of indel variation in a human genome and analysis of factors that impede detection.

With the development of High-Throughput Sequencing (HTS) thousands of human genomes have now been sequenced. Whenever different studies analyze the same genome they usually agree on the amount of single-nucleotide polymorphisms, but differ dramatically on the number of insertion and deletion variants (indels). Furthermore, there is evidence that indels are often severely under-reported. In this manuscript we derive the total number of indel variants in a human genome by combining data from different sequencing technologies, while assessing the indel detection accuracy. Our estimate of approximately 1 million indels in a Yoruban genome is much higher than the results reported in several recent HTS studies. We identify two key sources of difficulties in indel detection: the insufficient coverage, read length or alignment quality; and the presence of repeats, including short interspersed elements and homopolymers/dimers. We quantify the effect of these factors on indel detection. The quality of sequencing data plays a major role in improving indel detection by HTS methods. However, many indels exist in long homopolymers and repeats, where their detection is severely impeded. The true number of indel events is likely even higher than our current estimates, and new techniques and technologies will be required to detect them.

Novel protein interactions with endoglin and activin receptor-like kinase 1: potential role in vascular networks.

Endoglin and activin receptor-like kinase 1 are specialized transforming growth factor-beta (TGF-ß) superfamily receptors, primarily expressed in endothelial cells. Mutations in the corresponding ENG or ACVRL1 genes lead to hereditary hemorrhagic telangiectasia (HHT1 and HHT2 respectively). To discover proteins interacting with endoglin, ACVRL1 and TGF-ß receptor type 2 and involved in TGF-ß signaling, we applied LUMIER, a high-throughput mammalian interactome mapping technology. Using stringent criteria, we identified 181 novel unique and shared interactions with ACVRL1, TGF-ß receptor type 2, and endoglin, defining potential novel important vascular networks. In particular, the regulatory subunit B-beta of the protein phosphatase PP2A (PPP2R2B) interacted with all three receptors. Interestingly, the PPP2R2B gene lies in an interval in linkage disequilibrium with HHT3, for which the gene remains unidentified. We show that PPP2R2B protein interacts with the ACVRL1/TGFBR2/endoglin complex and recruits PP2A to nitric oxide synthase 3 (NOS3). Endoglin overexpression in endothelial cells inhibits the association of PPP2R2B with NOS3, whereas endoglin-deficient cells show enhanced PP2A-NOS3 interaction and lower levels of endogenous NOS3 Serine 1177 phosphorylation. Our data suggest that endoglin regulates NOS3 activation status by regulating PPP2R2B access to NOS3, and that PPP2R2B might be the HHT3 gene. Furthermore, endoglin and ACVRL1 contribute to several novel networks, including TGF-ß dependent and independent ones, critical for vascular function and potentially defective in HHT.

Intercellular network structure and regulatory motifs in the human hematopoietic system.

The hematopoietic system is a distributed tissue that consists of functionally distinct cell types continuously produced through hematopoietic stem cell (HSC) differentiation. Combining genomic and phenotypic data with high-content experiments, we have built a directional cell-cell communication network between 12 cell types isolated from human umbilical cord blood. Network structure analysis revealed that ligand production is cell type dependent, whereas ligand binding is promiscuous. Consequently, additional control strategies such as cell frequency modulation and compartmentalization were needed to achieve specificity in HSC fate regulation. Incorporating the in vitro effects (quiescence, self-renewal, proliferation, or differentiation) of 27 HSC binding ligands into the topology of the cell-cell communication network allowed coding of cell type-dependent feedback regulation of HSC fate. Pathway enrichment analysis identified intracellular regulatory motifs enriched in these cell type- and ligand-coupled responses. This study uncovers cellular mechanisms of hematopoietic cell feedback in HSC fate regulation, provides insight into the design principles of the human hematopoietic system, and serves as a foundation for the analysis of intercellular regulation in multicellular systems.

Role of STAT5 and epigenetics in lactation-associated upregulation of multidrug transporter ABCG2 in the mammary gland.

The multidrug resistance efflux transporter ATP-binding cassette subfamily G member 2 (ABCG2) is not only overexpressed in certain drug-resistant cancers but is also highly expressed in the mammary gland during lactation, carrying xenobiotics and nutrients into milk. We sought to investigate the molecular mechanisms involved in the upregulation of ABCG2 during lactation. Expression profiling of different mouse Abcg2 mRNA isoforms (E1a, E1b, and E1c) revealed that E1b is predominantly expressed and induced in the lactating mouse mammary gland. Despite this induction, analyses of CpG methylation status and published ChIP-seq datasets reveal that E1b promoter sequences in the virgin gland are already hypomethylated and marked with the open chromatin histone mark H3K4me2. Using a forced-weaning model to shut down lactation, we found that within 24 h there was a significant reduction in Abcg2 mRNA expression and a loss of signal transducer and activator of transcription-5 (STAT5) occupancy at the mouse Abcg2 gene. Luciferase reporter assays further showed that some of these STAT5-binding regions that contained interferon-γ-activated sequence (GAS) motifs function as an enhancer after prolactin treatment. We conclude that Abcg2 is already poised for expression in the virgin mammary gland and that STAT5 plays an important role in Abcg2 expression during lactation.

DAnCER: disease-annotated chromatin epigenetics resource.

Chromatin modification (CM) is a set of epigenetic processes that govern many aspects of DNA replication, transcription and repair. CM is carried out by groups of physically interacting proteins, and their disruption has been linked to a number of complex human diseases. CM remains largely unexplored, however, especially in higher eukaryotes such as human. Here we present the DAnCER resource, which integrates information on genes with CM function from five model organisms, including human. Currently integrated are gene functional annotations, Pfam domain architecture, protein interaction networks and associated human diseases. Additional supporting evidence includes orthology relationships across organisms, membership in protein complexes, and information on protein 3D structure. These data are available for 962 experimentally confirmed and manually curated CM genes and for over 5000 genes with predicted CM function on the basis of orthology and domain composition. DAnCER allows visual explorations of the integrated data and flexible query capabilities using a variety of data filters. In particular, disease information and functional annotations are mapped onto the protein interaction networks, enabling the user to formulate new hypotheses on the function and disease associations of a given gene based on those of its interaction partners. DAnCER is freely available at http://wodaklab.org/dancer/.

Clinical Implementation of MetaFusion for Accurate Cancer-Driving Fusion Detection from RNA Sequencing.

Oncogenic fusion genes may be identified from next-generation sequencing data, typically RNA-sequencing. However, in a clinical setting, identifying these alterations is challenging against a background of nonrelevant fusion calls that reduce workflow precision and specificity. Furthermore, although numerous algorithms have been developed to detect fusions in RNA-sequencing, there are variations in their individual sensitivities. Here this problem was addressed by introducing MetaFusion into clinical use. Its utility was illustrated when applied to both whole-transcriptome and targeted sequencing data sets. MetaFusion combines ensemble fusion calls from eight individual fusion-calling algorithms with practice-informed identification of gene fusions that are known to be clinically relevant. In doing so, it allows oncogenic fusions to be identified with near-perfect sensitivity and high precision and specificity, significantly outperforming the individual fusion callers it uses as well as existing clinical-grade software. MetaFusion enhances clinical yield over existing methods and is able to identify fusions that have patient relevance for the purposes of diagnosis, prognosis, and treatment.

OrthoNets: simultaneous visual analysis of orthologs and their interaction neighborhoods across different organisms.

MOTIVATION: Protein interaction networks contain a wealth of biological information, but their large size often hinders cross-organism comparisons. We present OrthoNets, a Cytoscape plugin that displays protein-protein interaction (PPI) networks from two organisms simultaneously, highlighting orthology relationships and aggregating several types of biomedical annotations. OrthoNets also allows PPI networks derived from experiments to be overlaid on networks extracted from public databases, supporting the identification and verification of new interactors. Any newly identified PPIs can be validated by checking whether their orthologs interact in another organism. AVAILABILITY: OrthoNets is freely available at http://wodaklab.org/orthonets/.

Objective sequence-based subfamily classifications of mouse homeodomains reflect their in vitro DNA-binding preferences.

Classifying proteins into subgroups with similar molecular function on the basis of sequence is an important step in deriving reliable functional annotations computationally. So far, however, available classification procedures have been evaluated against protein subgroups that are defined by experts using mainly qualitative descriptions of molecular function. Recently, in vitro DNA-binding preferences to all possible 8-nt DNA sequences have been measured for 178 mouse homeodomains using protein-binding microarrays, offering the unprecedented opportunity of evaluating the classification methods against quantitative measures of molecular function. To this end, we automatically derive homeodomain subtypes from the DNA-binding data and independently group the same domains using sequence information alone. We test five sequence-based methods, which use different sequence-similarity measures and algorithms to group sequences. Results show that methods that optimize the classification robustness reflect well the detailed functional specificity revealed by the experimental data. In some of these classifications, 73-83% of the subfamilies exactly correspond to, or are completely contained in, the function-based subtypes. Our findings demonstrate that certain sequence-based classifications are capable of yielding very specific molecular function annotations. The availability of quantitative descriptions of molecular function, such as DNA-binding data, will be a key factor in exploiting this potential in the future.

Generation of DNA Methylation Signatures and Classification of Variants in Rare Neurodevelopmental Disorders Using EpigenCentral.

There are more than 700 genes that encode proteins that function in epigenetic regulation and chromatin modification. Germline variants in these genes (typically heterozygous) are associated with rare neurodevelopmental disorders (NDDs) characterized by growth abnormalities and intellectual and developmental delay. Advancements in next-generation sequencing have dramatically increased the detection of pathogenic sequence variants in genes encoding epigenetic machinery associated with NDDs and, concurrently, the number of clinically uninterpretable variants classified as variants of uncertain significance (VUS). Recently, DNA methylation (DNAm) signatures, disorder-specific patterns of DNAm change, have emerged as a functional tool that provides insights into disorder pathophysiology and can classify pathogenicity of variants in NDDs. To date, our group and others have identified DNAm signatures for more than 60 Mendelian neurodevelopmental disorders caused by variants in genes encoding epigenetic machinery. There is broad interest in both the research and clinical communities to develop and catalog DNAm signatures in rare NDDs, but there are challenges in optimizing study design considerations and availability of platforms that integrate bioinformatics tools with the appropriate statistical framework required to analyze genome-wide DNAm data. We previously published EpigenCentral, a platform for analysis of DNAm data in rare NDDs. In this article, we utilize the published Weaver syndrome dataset to provide step-by-step protocols for using EpigenCentral for exploratory analysis to identify DNAm signatures and for classification of NDD variants. We also provide important considerations for experimental design and interpretation of DNAm results. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Exploratory analysis to identify disorder-specific DNAm signatures Basic Protocol 2: Classification of variants associated with neurodevelopmental disorders.

Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets.

Tumors are complex biological entities that comprise cell types of different origins, with different mutational profiles and different patterns of transcriptional dysregulation. The exploration of data related to cancer biology requires careful analytical methods to reflect the heterogeneity of cell populations in cancer samples. Single-cell techniques are now able to capture the transcriptional profiles of individual cells. However, the complexity of RNA-seq data, especially in cancer samples, makes it challenging to cluster single-cell profiles into groups that reflect the underlying cell types. We have developed a framework for a systematic examination of single-cell RNA-seq clustering algorithms for cancer data, which uses a range of well-established metrics to generate a unified quality score and algorithm ranking. To demonstrate this framework, we examined clustering performance of 15 different single-cell RNA-seq clustering algorithms on eight different cancer datasets. Our results suggest that the single-cell RNA-seq clustering algorithms fall into distinct groups by performance, with the highest clustering quality on non-malignant cells achieved by three algorithms: Seurat, bigSCale and Cell Ranger. However, for malignant cells, two additional algorithms often reach a better performance, namely Monocle and SC3. Their ability to detect known rare cell types was also among the best, along with Seurat. Our approach and results can be used by a broad audience of practitioners who analyze single-cell transcriptomic data in cancer research.