Campylobacter and Neisseria species. Current methods for identifying the expression states of phase-variable genes involve taking a high number of single colonies. This approach is subject to bias, sampling effects and high workloads that reduce the ability to perform intermediary sampling. The use of high concentration colony sweeps provides a work around but reduces the resolution of combinatorial expression profiles (termed phasotypes). A parsimonious approach combining both single colony and sweep data was developed to overcome these limitations. The critical methodological advance is the use of an algorithm that utilises the experimental data from the two sample types and a parsimonious, iterative mathematical analysis that outputs the phasotype distribution with the highest likelihood of underpinning the experimental data sets. The advantages of this unified method are increased resolution and accuracy of gene expression state combinations as compared to conventional single colony sampling, reduced requirement for sampling large numbers of colonies leading to reduced costs, and a higher capacity for collecting samples and replicates.•Inputting of sweep and single colony data into an algorithm for a rapid determination of the combinatorial phase variation states (phasotypes) for repeat-mediated phase-variable bacterial genes•This method reduces the number of single colony samples required to produce accurate estimates of phasotypes•This method will reduce the costs of phasotype analyses and increase potential to analyse more time points or sample sites leading to an improved understanding of how phase variation contributes to bacterial host persistence and the ability to cause diseaseShort sequence repeat mediated phase variation results in diverse phenotype presentation in many bacteria including   Specifications tableMicrobial population dynamics are subject to change due to environmental pressures and stochastic events that result in bottlenecks. A bottleneck refers to a rapid reduction in population size either due to selection, by the presence of specific environmental pressures that act against certain phenotypes, or non-selectively through random elimination of portions of the population regardless of phenotype Phase variation (PV) is mediated by heritable genetic mechanisms that generate intra-clonal diversity in asexually reproducing bacteria and is one outcome of localised hypermutation within contingency loci. One mechanism of PV is mediated by slip strand mispairing of hypermutable DNA regions containing simple sequence repeats (SSR). Expansion or contraction of the number of repeats in the SSR due to slip strand mispairing affects phenotypic expression of the contingency locus ,5. ChangNeisseria meningitidis and Campylobacter jejuni). Current methods for detecting repeat tract changes include, but are not limited to, whole genome or gene sequencing across single colonies or pooled isolates [Experimental analyses of PV are important for improving our understanding of microbial adaptability and determinants of microbial survival and replication of a number of important bacterial pathogens  and fragment length calling, relative to a size standard ladder, from output files with a software package such as PeakScanner. The most abundant PCR product produces a signature peak reflecting the PCR product length and is termed the major peak. Several smaller peaks, termed minor peaks, may surround the major peak but only the latter is considered for determining the repeat number of the colony. This fragment is then compared to a control PCR product of known repeat number and expression state.Contrastingly, the sweep data set may contain a combination of multiple populations with varying fragment lengths due to differences in the repeat tract lengths. PCR amplification and GeneScan of this population produces a series of peaks whose heights/areas are proportional to the repeat numbers in the population. In order to estimate the expression state(s) for the sweep, all peaks are considered and sub-divided into repeat numbers that produce ON and OFF states. Following this division, the combined total for the peak heights (or areas) of the ON peaks is divided by the combined total for all peaks. For example, if the total peak height showing an ON state is 21,253 while the total peak height for the OFF state is 7,271, then the ON % for that sweep would be ∼75 % . To generate realistic populations, a simple switching algorithm was developed. For a population of 64 phasotypes, an individual phasotype was selected through weighted random selection acting on its proportion within the total population. The phasotype was then subject to a single gene switch (i.e. 000000 to 10000) and then re-added to the population. This produces a normal like distribution around a defined starting population. This process was repeated 20 times to produce 20 unique pseudo-populations with known proportions of each phasotype. The final phasotype distributions were utilised to calculate the ON % for each gene.To test whether sweep data can be utilised in combination with single colony analyses to accurately predict phasotype distributions, the algorithm was tested To mimic the single colony sampling method of experimental protocols, phasotypes were selected randomly from each of the 20 pseudo-populations using a random number generator and the proportion of each phasotype in the whole population. Multiple cumulative samples were generated that contained between 1 and 100 phasotypes. This method simulates picking single colonies from dilution plates of a large bacterial population with the number of samples being equivalent to the number of colonies. The next step was to utilise the algorithm to correct the single colony sampling data. The input data for the algorithm is the ON % for each gene, as calculated for the whole pseudo-population, and the proportions of each phasotype, as calculated from the 1 to 100 sample populations . This correction was performed for each of the sample sizes and all of the 20 pseudo-populations.In order to assess the effectiveness of the algorithm in making improved predictions about the phasotype distribution of a sampled population compared to the real distribution, the Jensen-Shannon divergence equation was selected to determine the similarity of two distributions. A normalised distribution of phasotypes was used to seed each run of the algorithm. This distribution can be assumed to be a probability distribution, where each phasotype is represented as a proportion of the population. Two identical distributions of phasotypes  will produce a Jensen-Shannon divergence of 0 whereas a maximum value of 1 shows the highest possible divergence between the two distributions (e.g. phasotype proportions of 0.5:0.5:0:0 versus 0:0:0.5:0.5). Two scenarios were selected to test the robustness of the sweep corrected algorithm, a highly diverse input population where each phasotype is selected randomly (as outlined above), and a low diversity input population were a single phasotype is selected. This latter approach results in the selected phasotype forming an increased fraction of the population (i.e. 50 % of the population). These scenarios represents two possible situations often encountered during PV testing: A) a population under neutral selection; or B) one under positive selection.in silico scenarios . Thus the sweep corrected data has a tendency to overestimate some rarer phasotypes while underestimating the major phasotype. Conversely, the single colony sampling method overestimates the dominant phasotypes and in general underestimates the rare phasotypes but with occasions where one or a few rare phasotypes are overestimated due to chance picking of a rare phasotype. Overall, both methods provide a very close match to the true distribution at 100 samples with the over and underestimated values not producing a significant variation in phasotype proportion. Importantly, when sample size is reduced to 20 colonies, the single colony method produces significant over and under estimates of rare phasotypes .As a further test, a bimodal example was examined wherein two phasotypes, 000000 and 111111, were chosen to each form ∼33 % of the total population with the remaining phasotypes being randomly distributed by application of the switching method described previously. This scenario represents a rare situation where a population is split between two major divergent phasotypes. In this case, sweep correction only marginally outcompetes the single colony sampling method with sample sizes of >1 . This fiin silico generated data, the sample size would need to identical to the total size of the population. More of less samples will always lead to some phasotypes being over-sampled. For example, if a population has 4 members across 4 phasotypes , a divergence score of 0 can only be reached with samples sizes of 4, 8, 12, etc. and these samples must have identical proportions for each phasotype (i.e. 3 single colonies for each phasotype with a sample size of 12). A sample size of 30 (non-divisible by 4) cannot produce a divergence of 0. In the case of the in silico data in In all cases, the Jensen Shannon divergence score did not reach 0 by 100 samples. This is in part due to the randomness of sampling. In order to achieve a divergence score of 0, for the Multiple SSR phase-variable genes are present in a diverse range of bacterial pathogens and commensals. Stochastic, independent switches in expression of these genes can give rise to high levels of variants, termed phasotypes, with combinatorial differences in expression of these genes. These phasotypes are potential determinants of important bacterial phenotypes and measures of the impact of population modulators such as bottlenecks. An accurate method for analysis of phasotype distributions is therefore critical for assessing changes in phase-variable populations during evolution both within and between hosts. Phasotype analyses have relied on sampling of small numbers of colonies with consequent sampling and resolution issues. The method outlined here describes a novel algorithmic approach to eliminating random selection bias or insufficient sampling and an overall improved resolution. Our tool combines both experimental sweep and single colony data to provide a more accurate picture of the phasotype distributions than currently achieved by the current approach of using low numbers of single colonies alone. The use of sweep data has been under utilised to date due to the risks associated with slippage in SSRs occurring during PCR reactions that create biases in the phasotype distributions. A PCR correction is applied to the sweep data prior to input into the sweep correction algorithm leading to a further enhancement in the accuracy of this approach.in-silico modelling of various scenarios showed that sweep correction of the distribution of phasotypes leads to more accurate determination of the phasotype population distribution than colony samples alone  and OFF (0) state stochastically, reversibly and independently. As such, the probability of a single gene switch is significantly greater than the probability of 2 or more gene switches for a given phasotype.The probability (p) of n gene switches occurring simultaneously for a total of N genes within a phasotype is:For the simplest case of a single bacterium, and hence a single phasotype, this means that only a limited number of phasotypes are likely to be occupied by the majority of bacteria within the population. Additionally, the phasotype with the greatest number of gene switches, as compared to the starting phasotype, will comprise the lowest proportion of the population. This mutational pattern applies limitations to the potential mutational space occupied by a specific expression pattern for individual genes and hence provides a framework for predicting the phasotypes. This framework is the basis for the method elaborated herein. It should be noted that growth of a population through ‘enough’ generations, and in the absence of any selective or non-selective bottlenecks, will eventually lead to generation of a population containing all possible phasotypes. This latter occurrence is likely to be a rare observation in natural populations.Our approach is to combine the minimum parsimony described above with ON % data, generated through a GeneScan analysis of sweep data, and phasotype distributions calculated from a small number of single colonies. This process involves application of a series of iterative steps starting with data from single colonies to seed the system and generation of a new phasotype distribution for each iteration of the algorithm. The phasotype data for N genes is normalised based on the total number of phasotypes within the distribution such that the total population will sum to 1. The input data are, therefore, the ON % for each PV gene in a phasotype derived from analyses of sweep data and the proportions of each phasotype derived from the single colony analysis.The assumed starting point for this method is multiplex PCR and GeneScan data for multiple phase-variable genes as performed on a sweep and multiple single colonies derived from a specific bacterial population. The sweep and single colonies should be obtained from the same serial dilution of this bacterial population. The sweep should be obtained from a plate where there is either confluent growth or 1000+ colonies and the single colonies from plates where there are isolated single colonies. To compute the sweep adjusted phasotypes for this population, the following information must be provided, the number of genes, the ON % for each gene as determined from the sweep data see  and the Step 1Determine ON % from sweep data1.Import the autosequencer file for the sweep data into the PeakScanner software (or other appropriate software) and analyse with the relevant size standard for the samples (e.g. GS600LIZ).2.Examine the peaks for the sample and identify the major and minor peaks for each repeat tract.3.Output the areas for the major and minor peaks.4.Assign repeat numbers to major and minor peaks relative to a control of known repeat number.5.Assign ON and OFF states to peaks based on known associations between the repeat number and in frame translation of the gene sequence.Step 2Analyse single colony data for proportions of slippage1.Analyse the GeneScan data for multiple single colonies as described in 2.For each gene, determine the areas of all major and minor peaks.3.Calculate the total area for all the combined minor peaks and divide by the total area of all peaks to obtain the % area for the minor peaks.4.For each gene, determine the % area of the minor peaks across 10 or more single colonies for samples where the major peak has the same number of repeats.Step 3Correct ON % for slippage1.Calculate the average % area for the minor peaks from multiple single colonies (see Step 2) as a measure of the degree of slippage occurring during the PCR reactions (note that the correction is applied utilizing peaks of the same repeat number and for the same gene).2.Subtract the % area for the minor peaks obtained from single colonies from the % area for the minor peaks in the sweep data.3.Add the % area for the minor peaks obtained from single colonies to the area for the major peaks in the sweep data.4.The output data has now been subject to sweep correction and can be utilized as input for Step 4Input data into the algorithm1.Open program.2.Input phasotypes of single colonies.3.Input ON % values for each gene as derived from sweep data.4.Indicate number of iterations.5.Run algorithm.The algorithm is run in Python v3.9.5 and a suitable computer is required for utilizing this program. The program can produce data for phasotypes of 2 or more genes. The expression states of each gene are coded as 1 for ON and 0 for OFF.Step 5Run algorithm1.The distribution of phasotypes derived from the experimental single colony analyses is converted into a series of ON % for each gene in the phasotype.2.Divergence between the values for ON % for each gene in the system derived from the sweep data versus the single colony phasotype distribution is calculated and the largest variance in ON % between the sweep and colony is used to identify the gene requiring the largest correction.3.All phasotypes present in the distribution are considered for potential changes. This process leads to identification and selection of those phasotypes whose ON or OFF states could be switched to reduce the divergence in the ON % of the identified gene.4.The percent change required to align the ON % for a specific gene from the phasotype data to the sweep data is proportionally distributed between all selected phasotypes. This process simultaneously decreases the relative representation of the most divergent phasotypes in the population while increasing those phasotypes most closely aligned with the observed expression state.5.This new population then becomes the new phasotype distribution. The algorithm goes through multiple cycles adjusting each gene individually until either there is convergence - where the difference in gene ON % is the same between the new phasotype distribution and sweep data - or a user defined number of cycles is completed.6.The final output file contains the corrected gene ON % for the population and estimates of the proportions for each possible phasotype within the population.Each iteration of the algorithm goes through the following steps:-For each cycle of the algorithm only a single PV gene state is considered for each phasotype in the system, therefore producing the minimum number of state changes required to mirror the percentage of gene ON % between the phasotype and sweep data.Step 6Perform check of distributions of the phasotype output of the algorithm1.Utilise the single colony data to derive the phasotypes .6.Where such differences are observed, examine the single colony data for evidence of a bimodal distribution of two phasotypes that have non-overlapping combinations of expression states.7.Flag these samples as potentially requiring further investigation (e.g. sampling of more single colonies) or low confidence.Serial PassageC. jejuni strain NCTC 11168ca was grown for 24 h in Brucella broth (6 mL). After this initial incubation, a sample of 600 µL was transferred to a new liquid culture (6 mL: i.e. a 1 in 10 dilution) and regrown for another 24 h. This cycle was repeated across five days in order to increase diversity within the bacterial population. After the last cycle, a serial 10-fold dilution was performed in triplicate with Brucella broth as diluent. A 10 uL aliquot from each dilution was spread with a sterile cotton swab onto a blood-agar plate and incubated for 2–3 days at 42 °C under microaerophilic conditions:- 83 % N2, 10 % CO2 and 7 % O2 in a VAIN cabinet . Multiple single colonies (n = 287) were picked and individually re-suspended in deionised water in non-skirted PCR plates. Bacterial sweeps were taken from 10°, 101 and 102 dilution plates, re-suspended in deionised water and aliquoted into non-skirted PCR plates. The single colonies and sweep samples were then denatured by heating to 94 °C for 5 min in a PCR machine.GeneScan of multiple phase variable genesA total of 6 phase-variable genes were selected and the repeat tracts of these genes were amplified from all single colony and sweep samples using multiplex PCR reactions, as described by Lango-Scholey et al. Following confirmation of the expected PCR products, an A-tailing reaction was performed on the PCR products as previously described The ‘sweep’ GeneScan analyses were utilised for determining the relative proportions of repeat numbers that correlate with the ON and OFF states by determining the areas under the major and minor peaks observed in the ThermoFisher dashboard. The ON % was determined by dividing the area of the peak representing the ON number of repeats by the area of all combined peaks and multiplying by 100. Finally, a correction for PCR slippage was applied to the sweep gene ON percentage. Slippage in the repeat tracts occurs during the PCR reactions and can alter the estimated size of the major and minor peaks. The slippage correction was calculated utilising the PCR data from the single colony GeneScan data as the majority of single colonies will contain a low number of variants due to derivation from a single cell and to the low number of generations. For each colony, the ratio of the area of the minor to the major peak was selected from the data in the PSAnalyse output file, and an estimate of the average level of slippage across multiple colonies was generated for each gene. A value for slippage rate (as a % from the main peak) was estimated by taking the average major to minor peak ratio and dividing it across the total number of single colonies to gain an average combined −1 and +1 repeat peak error rate per colony for each gene (ranging from 0.3 to 17 %). This was then applied to the sweep data by correcting the values for the major and minor peaks to give a new ON %. For example, if all single colonies for a given gene show a secondary peak of 10 % of the height or area of the major peak, then this is an indication that slippage has occurred within the single colony PCR reactions and will also have occurred in the sweep PCR reactions. Therefore, before estimating the ON % for a given sweep set, 10 % of the major peak data should be subtracted from the minor peak data and added to the major peak data  peak means that the true values for these major and minor peaks are 7700 and 1300, respectively).C. jejuni population in triplicate. The ON % states for six genes were calculated from either the phasotypes of the 288 single colony analyses or from the sweep peak ratios. Three sweep duplicates were combined for each sweep data set while all colonies were combined to build the gene ON % values. The values for the ON % for each of the colony datapoints were corrected for slippage against the single colony values , sweep corrected sampling showed a large reduction in the divergence score with the sweep corrected divergence score for a sample size of 10 colonies being 2-fold lower than for the uncorrected score. Across the corrected sampled populations the divergence scores are similar across all samples sizes ranging from a peak of 0.324 with 10 colonies to the lowest value of 0.14 with 100 colonies whereas the scores for the uncorrected samples range from 0.81 to 0.11.Commonly a range of between 18 and 30 The mean divergence score for the uncorrected population was approximately 0.3 as compared to 0.2 for the corrected population. The Welch's T Test returned a P value of <0.0035 indicating a significant change in the mean for the population from the sweep corrected distribution as compared to the uncorrected population for a sample size of 20.All the comparisons described above were predicated on the assumption that ON % values derived from sweeps had a high accuracy relative to the actual population. However, the sweep PCRs may have inaccuracies due to the quality of the PCR, difficulties in determining slippage rates, peak height/size cut-off effects in the PeakScanner software or a bias in the sweep sampling. These effects may result in major differences between the sweep and actual populations in gene ON % values. The role of these potential error rates on estimating population phasotype proportions was assessed by simulating generic error rates. Twenty sets of 16 single colonies were randomly sampled from the in vivo data set. These samples were compared to the known phasotype distribution using the Jensen Shannon divergence. An additional 7 single colonies were randomly selected across 20 replicates and corrected using the sweep derived ON % states. These samples were also compared to the known phasotype distribution. Finally, the gene ON % values, as derived from the sweep data, were randomly altered by a fixed percentage error change  that ranged from 1 to 100 % . For errn = 288) of single colonies and correcting for PCR slippage using single colony data, we have shown that the ON % of a gene can be estimated accurately for a population through taking sweeps as shown in Both the new model and the current single colony sampling strategies were assessed for accuracy through a PV analysis of experimental data of 6 phase-variable genes. By combining known sweep data for a set of genes with a large volume (The application of the algorithm across a range of sample sizes was compared to uncorrected distributions. As shown in No ethical issues identifiedJonathan Holmes: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Lickson Munjoma: Validation, Investigation, Data curation, Writing – original draft. Christopher D. Bayliss: Conceptualization, Writing – original draft, Writing – review & editing, Supervision.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Cancer is a molecular complex and heterogeneous disease. Each type of cancer is usually composed of several subtypes with different treatment responses and clinical outcomes. Therefore, subtyping is a crucial step in cancer diagnosis and therapy. The rapid advances in high-throughput sequencing technologies provide an increasing amount of multi-omics data, which benefits our understanding of cancer genetic architecture, and yet poses new challenges in multi-omics data integration.P-values in survival analysis over many typical integrative methods.We propose a graph convolutional network model, called MRGCN for multi-omics data integrative representation. MRGCN simultaneously encodes and reconstructs multiple omics expression and similarity relationships into a shared latent embedding space. In addition, MRGCN adopts an indicator matrix to denote the situation of missing values in partial omics, so that the full and partial multi-omics processing procedures are combined in a unified framework. Experimental results on 11 multi-omics datasets show that cancer subtypes obtained by MRGCN with superior enriched clinical parameters and log-rank test https://github.com/Polytech-bioinf/MRGCN.githttps://figshare.com/articles/software/MRGCN/23058503. Cancer is a large family of diseases that can originate in almost any organ or tissue of the human body when abnormal cells grow uncontrollably, that is, beyond the usual boundaries, invade adjacent areas of the body, and/or spread to other organs . The traThe progress in high-throughput sequencing technologies has provided the collection of various types of omics data with unprecedented details. Some large national and international consortia, such as The Cancer Genome Atlas (TCGA), have collected thousands of biological tumor samples data from multiple molecular events. Integrating and analyzing these multi-omics data representing information from different molecular processes could improve holistic view of understanding of the complex biology. Specifically, cancer is accumulation of mutations and epimutations  and its K-means  to identify cancer subtypes. First, MRGCN constructs one graph for each omics data using neighborhood relationships and encodes each omics data to obtain individual embedding representation. Second, MRGCN builds an indicator matrix to express the data missing situation and integrates each individual embedding into a consensus representation. Third, MRGCN decodes the consensus representation to reconstruct graph structures and node attribute simultaneously. Furthermore, MRGCN adopts self-supervised learning mechanism to enhance the discriminability of consensus representation. Finally, all aforementioned parts are incorporated into a joint optimization problem and solved by the deep learning framework. The cancer subtyping is carried out via spectral clustering based on the obtained consensus representation.To our best knowledge, MRGCN is the first attempt at using GCN to simultaneously reconstruct graph structures and node attribute obtaining the latent representation in both full and partial multi-omics data. Extensive computational experiments on 11 datasets demonstrate the superiority of MRGCN in cancer subtyping capability over corresponding solutions to multi-omics data integration.MRGCN includes four modules, i.e. individual encoder, consensus representation, node attribute reconstruction, and graph structures reconstruction. Cancer subtyping is carried out on the consensus representation via spectral clustering algorithm. Each module and step will be detailed in the following sections.V is the number of omics. vth omics measurements, where d is the ultimate dimension of consensus embedding space. N, Let H. Finally, the node attribute reconstruction module and graph structure reconstruction module achieve multi-reconstruction conducted on H.As shown in vth omics measurements. The individual encoder of each omics is a nonlinear function vth omics data attribute mth encoder layer are computed as follows:where M is the layer number of encoder. I is the identity matrix. mth layer need to be determined by training. m = 1, let vth omics data, and when m = M, let vth omics data. It is noteworthy that the individual embedding of each omics data should has the same feature dimension, that is, the numbers of columns for each d.One similarity graph is constructed for each omics data, as follows:where G to demonstrate the data missing situation, as follows:The measurements from different omics reflect different aspects of the same disease, but they also with the consensus sematic information, such as the same cluster label distribution or consensus representation , breast invasive carcinoma (BIC), colon adenocarcinoma (COAD), glioblastoma multiforme (GBM), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), skin cutaneous melanoma (SKCM), and sarcoma (SARC). Three omics levels are adopted for integration, including DNA methylation, mRNA and miRNA expression. In addition, the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer dataset , 2019, aThe proposed method MRGCN is compared with 11 integrative methods on full datasets and 3 related methods on partial datasets, respectively.K-means and spectral clustering, which conduct clustering operation on the concatenated multi-omics data, as well as nine integrating methods, i.e. LRAcluster, CC, PINS, MCCA, iClusterBayes, SNF, SNFCC, MSNE, and NEMO. The survival analysis and enrichment analysis of clinical labels are utilized to assess the performance of subtyping , cancer in lymph nodes (pathologic N), metastases (pathologic M), and total progression (pathologic stage). The number of clusters within comparison methods is set to be the same value reported in the original papers, as suggested by Several computational experiments were performed to evaluate the effectiveness of cancer subtyping via multi-omics data. We compare our MRGCN to 11 methods on full multi-omics datasets, including two directly clustering methods, i.e. ubtyping . For surα and β are both set to 1. If the lowest dimension in each omics expression for given dataset is <2000, the dimension of consensus representation d is set to be z-score (Competitive methods are realized using the publicly available code. The details of hyper-parameters in codes are described in P-value of MRGCN reaches to 3.0. iClusterBayes is the second with 2.9. None of the methods found clusters with significantly different survival for COAD and OV datasets. MRGCN found at least one enriched clinical parameter in all datasets. LRAcluster, CC, iClusterBayes, SNF, SNFCC and MSNE are tied for second with 10. Furthermore, the average number of enriched clinical parameters of MRGCN is 2.0, while LRAcluster is the second with 1.7. These results demonstrate MRGCN could identify significant coherent and clinically relevant patient subtypes.P-values than other comparison methods, which indicates the MRGCN is better than the other existing methods.The visualization of the consensus representation from MRGCN on BIC dataset by t-SNE and UMAP is shown in In order to compare the subtypes obtained by MRGCN and the existing subtypes, meanwhile to display the differential expression among different subtypes, we designed the experiments with following processes. First, subtyping results of PAM50 classification (α and β. Their values are selected from α and β could be always set to 1. In addition, P-value for each cluster of METABRIC. In the second fold, in order to guarantee the training and validation data have the same multi-omics, we randomly set aside 80% of BIC data for training, 20% for validation. The experimental procedures follow the first fold study, and its results are shown in The generalization capability of the proposed method is carried out in 2-fold validation study. The first fold is on BIC and METABRIC datasets. METABRIC has two omics, but BIC has three omics. The overlapped expression between them is only mRNA. Thus, the four clusters of BIC are already obtained via the proposed MRGCN trained on three omics. Then, each sample of METABRIC is classified into the four clusters of BIC using shrunken centroid classifier  score anθ of the patients and removed their mRNA expression. Consequently, for TCGA datasets miRNA and DNA methylation expression are full, and for METABRIC dataset CNV expression is full. The survival analysis and enrichment of clinical labels are still adopted to evaluate the quality of methods. The performance is presented in In order to evaluate the performance of methods on partial multi-omics datasets, we simulate some patients loss omics measurements. Specifically, we randomly sampled a fraction Cancer subtyping plays an important role in targeted treatment and precision medicine, and ultimately helps to increase survival chances of cancer patients. Cancer is a phenotypic end-point of event accumulated by multiple levels of biological system from genome to proteome. Multi-omics data integration can improve understanding of underlying biological mechanisms and improves clinical outcome. An effective cancer subtyping framework, namely MRGCN is presented for multi-omics integration and clustering. Different from existing integrative approaches, MRGCN aims at preserving omics expression and similarity relationships simultaneously. Also, MRGCN aims to deal with the tackle of some samples missing values on partial omics. To solve these problems efficiently, the reconstruction GCN and indicator matrix are designed and applied. Based on 10 TCGA and 1 METABRIC multi-omics datasets, computational experimental results indicate that MRGCN can provide better integrative performance. Although two or three levels omics are used in experiments, MRGCN is an open framework and could easily be utilized in more omics scenarios. We believe that MRGCN will ultimately lay the foundations for refined representation and understanding of diseases. Another important future work is to involve protein–protein interaction networks to improve the interpretability of integrative embedding.btad353_Supplementary_DataClick here for additional data file.


International consortia, including ENCODE, Roadmap Epigenomics, Genomics of Gene Regulation and Blueprint Epigenome have made large-scale datasets of open chromatin regions publicly available. While these datasets are extremely useful for studying mechanisms of gene regulation in disease and cell development, they only identify open chromatin regions in individual samples. A uniform comparison of accessibility of the same regulatory sites across multiple samples is necessary to correlate open chromatin accessibility and expression of target genes across matched cell types. Additionally, although replicate samples are available for majority of cell types, a comprehensive replication-based quality checking of individual regulatory sites is still lacking. We have integrated 828 DNase-I hypersensitive sequencing samples, which we have uniformly processed and then clustered their regulatory regions across all samples. We checked the quality of open-chromatin regions using our replication test. This has resulted in a comprehensive, quality-checked database of Open CHROmatin (OCHROdb) regions for 194 unique human cell types and cell lines which can serve as a reference for gene regulatory studies involving open chromatin. We have made this resource publicly available: users can download the whole database, or query it for their genomic regions of interest and visualize the results in an interactive genome browser. ENCODE, GGR and REMC datasets contain DNase-I sequencing for hundreds of primary cell types, stem cells, cell lines and tissues. Blueprint Epigenome project has also made open chromatin data available for hundreds of haematopoietic cell types. These datasets are extremely useful for studying mechanisms of gene regulation in different contexts, including disease and cell development. However, current regulatory region identification pipelines process samples within these datasets individually, making it difficult to annotate and analyze DNase-I Hypersensitive Sites (DHS) across multiple samples in a study, and across studies. Accessibility of regions of open chromatin varies across cell types, and may even vary within biological replicates of the same cell type. The absence of a well-defined, reference map for genomic locations of accessible chromatin that is also consistent across replicates frequently poses challenges to many downstream analyses, such as investigations into the association between accessibility state of specific regulatory elements and the expression of the genes which they control.In recent years, multiple international consortia-based projects, such as ENCODE6; however, they do not assess the quality of individual regulatory sites across multiple samples. It is already well-recognized that several peaks identified in a sample by peak calling algorithms may not be supported in replicate samples8. Quality checking methods such as jMOSAiCS7, MSPC8 and irreproducibility discovery rate (IDR)9 have been developed to compare peaks identified in pairs of replicates in order to identify replicable peaks in sequencing experiments with a main focus on ChIP-seq experiments. IDR particularly has been used in the ENCODE Project, and has been applied to various sequencing-based experiments such as ATAC-seq, DNase-seq and ChIP-seq data. However, QC methods are limited by the fact that they compare replicates from one cell type only, and subsequently can be quite stringent at filtering true peaks in the absence of orthogonal information.Although most epigenomics datasets such as ENCODE, GGR, REMC and Blueprint Epigenome data contain multiple biological and/or technical replicates of the same cell type, a comprehensive replication-based quality check of individual regulatory regions identified by peak calling algorithms is still lacking. Quality checking measurements that are used in the majority of other open chromatin databases check quality of individual samples and filter unreliable samples10 and filters unreliable DHS sites10. This has resulted in a quality-checked database of regions of open chromatin comprising 1,455,046 reliable DHS for 194 unique cell types across the autosomal genome.In order to address the above problems we designed and implemented a pipeline that obtained DNase-I Hypersensitive (DHS) peaks of 828 data samples obtained from multiple resources , aligned them across samples, assessed replicability of each DHS site across several cell types using our recently developed replication test. has developed a high-resolution map of DNase-I hypersensitive sites using the 733 biosamples from ENCODE and REMC projects. Here, we have incorporated the samples from two additional consortium-based projects (NHGRI GGR and Blueprint Epigenome), increasing the number of immune-related cell types and states. We have also incorporated a replication test to remove false positive DHS sites and improve quality and reliability of the detected DHS sites. Additionally, in our analysis, DHS sites are aligned across different cell types and tissues, facilitating correlating DHS sites with matched gene expression data.In a related study, Meuleman et alhttps://dhs.ccm.sickkids.ca/), where users can query and download the data either fully or at specific genomic regions of interest. We have also prepared an interactive genome browser for effective visualization of DHS sites across several cell types.In summary, OCHROdb is a comprehensive DHS database obtained by (a) aligning the same regulatory sites across more than 800 samples from multiple public datasets and (b) checking quality of individual DHS sites, and it can serve as a reference for studies concerned with DHS activities and their role in gene regulation. This database can be accessed through our website (https://www.encodeproject.org). Blueprint DHS data samples from Haematopoietic Epigenomes are available at (http://www.blueprint-epigenome.eu). In August 2017 we collected from these resources the following types of the data:11. This was available for 362 samples from ENCODE, 318 samples from REMC, 51 samples from GGR and 97 samples from Blueprint Epigenome project 12 to get access to and query metadata for ENCODE, REMC and GGR data files. The metadata of Blueprint Epigenome data was downloaded from ftp://ftp.ebi.ac.uk/pub/databases/blueprint/data_index/homo_sapiens/data.index.Metadata describing file accession ID, cell type, project, assembly and file download URL . Briefly, first a bwa index and a mappability file were produced by using a reference (e.g. GCRh38) fasta file and a DNase mappability file specific to a read size. Then BWA was used to generate bam files containing all reads mapped to the reference genome, and the bam files were merged and filtered to include only high quality mapped reads. At the last step, hotspot peak calling algorithm11 was applied to the filtered bam files to identify enriched regions (i.e. peaks and hotspots). DHS data samples released through Blueprint Epigenome project were also analyzed following a similar procedure. First, the reads were mapped to human genome GCRh38 reference using BWA 0.7.7. Bam files were sorted and duplicates were marked using Picard Tools13. Then, the output bam files were filtered and reads with Mapping Quality of less than 15 were removed. Finally, the Hotspot peak calling algorithm was applied to identify peaks and hotspots.All DHS samples released from ENCODE project website  had previously gone through the ENCODE uniform processing pipeline for DNase-I Hypersensitive experiments 16 with its default parameters to cluster the peaks across multiple samples , where N is the number of nodes (i.e. peaks), and k is the number of resources allocated per node . The fact that MCL complexity is linear with the number of peaks makes it quite fast for our application. Using MCL we were able to group 117,488,197 DHS peaks across 828 samples into 4,020,940 DHS clusters across the autosomal chromosomes.In the 828 samples, we found a total of 117,488,197 DHS peaks on the autosomes (i.e. excluding X and Y chromosomes). Peak calling algorithms such as Hotspot10 to assess consistency across replicates  distribution17. We selected DHS clusters that pass a nominal significance threshold of p ≤ 0.05 for the combined replication test and called them replicable DHS. In order to examine how many runs of replication test is required in order to obtain a stable set of replicable DHS, we applied the replication test 20 times. Then for any N between 1 and 19, we found a list of replication tests obtained by combining N replication tests. Our results indicated that the Area Under the Curve (AUC) obtained for sensitivity/specificity analysis remained above 0.95 after nine iterations, confirming that running the replication test ten times is appropriate , so we can check DHS cluster quality by looking for evidence that DHS peaks are replicated in this analysis. We expect that for a DHS cluster that represents a true regulatory site, the DHS accessibility is consistent across replicates. We therefore employed our previously designed replication testtes Fig. , we repeate Fig. . To veri18 in each tissue for (1) all DHS peaks called across the sample replicates; and (2) the subset of the 1,460,986 replicable DHS active in that tissue. For these analyses, we used publicly available summary statistics data from genome-wide association studies for multiple sclerosis19 as a test case. We found that autosomal genome covered by all DHS peaks and by the replicable DHS explained effectively the same disease heritability enrichment, indicating that our approach identifies most DHS relevant to disease pathogenesis  explained by all DHS-detected peaks in a tissue, suggesting the non-replicating peaks are spurious. In addition, we overlapped our replicable DHS with the list of experimentally validated enhancers (from ENdb and VISTA databases) and promotors (from hsEPDnew database). We found that 80.2% and 93.7% of known enhancers from the ENdb and VISTA overlap replicable DHS peaks, respectively. In addition, 67.2% of known promotors from the hsEPDnew database overlap replicable DHS peaks. These results confirm that our replicable DHS peaks contain a good portion of experimentally tested enhancers and promotors from public databases; The replicable DHS peaks in our dataset that do not overlap the list of known promotors and enhancer  could be the promotors and enhancers which have not been experimentally validated yet in these databases. Also, these DHS peaks are likely to be specific to cell types not considered in the previous studies.Figure sis Fig. . This isp value) of the most significant DHS peak of a sample to that replicable DHS in the sample of interest.Chromatin accessibility as a quantitative measure is the density of mapped DNase-I cleavage at different genomic locations. Chromatin accessibility varies across replicable DHS sites of the same sample. Also different samples have different accessibilities at the same replicable DHS site. Since DHS accessibility can be an indicator of how active a DHS site is in a particular sample, we incorporated DHS accessibility information to our data. To measure significance of accessibility of each replicable DHS in each sample, we first identified all DHS peaks that belong to a replicable DHS, and assigned − log10) due to the fact that DHS data were generated and processed in multiple centres   Fig. A. As theGR) Fig. B. We appFinally, we collapsed DHS intensities of samples from the same cell types by measuring median intensities over these samples. 1,455,046 out of 1,460,986 (99.5%) replicable DHS have median intensities higher than 0.25 in at least one cell type. We considered these 1,455,046 sites as the final replicable DHS sites to build our database of regions of open chromatin for 194 cell types.We have prepared a comprehensive and quality-checked database of regions of open chromatin using DNase-I Hypersensitive datasets generated by REMC, ENCODE, GGR and Blueprint Epigenome Projects. OCHROdb contains DHS intensities for 1,455,046 DHS sites aligned over 828 samples. These 828 samples comprise of 194 distinct cell types, including 27 immune cell types, 18 CNS, 9 muscle, 57 cell lines, 10 stem cells, 6 digestive system cell types in addition to lung, cardiovascular, fibroblasts, kidney, extra embryo, male and female reproductive system and endothelial cell types . After submitting coordinates of a region of interest, an exportable table of the results matching the user input is generated by a JavaScript library called DataTables.net26 Fig. A.27, an embeddable genome browser, by specifying cell types of interest, and the coordinates of the genomic region or the name of the genes of interest  and make these results available in our database. Additionally, we aim to process non-human (e.g. mouse) open chromatin datasets and release them separately in our open chromatin database.28, we plan to apply our processing pipeline to these samples and release the processed data as part of our open chromatin database. Making a comprehensive database of regions of open chromatin using the data released by IHEC project aligns with IHEC goals and will benefit the scientific community. We will maintain our database and release new updates every six months.Recently, the International Human Epigenome Consortium (IHEC) have collected and processed individual epigenome data samples from multiple large-scale international projects, such as ENCODE, REMC and Blueprint Epigenome. It is expected that more epigenome samples from other projects become available through IHEC portal. This includes the data from Deutsches Epigenome Program (DEEP), McGill Epigenomics Mapping Center, 4D Nucleome, Hong Kong Epigenomes Project (EpiHK), Multiple MS and SYSCID. As more open chromatin data samples are generated and released by IHEC and other projectsMultiple large-scale consortia-based projects, including ENCODE, REMC, Blueprint and GGR have generated thousands of sequencing-based data samples that capture regions of open chromatin for the whole genome in hundreds of cell types. Building on this immensely informative DHS datasets, we have developed an analysis pipeline that gets hundreds of pre-processed DHS data samples (i.e. in narrow peaks format) as the input, aligns regions of open chromatin across samples, checks quality of each region using a replication-based test, and outputs a database of open chromatin accessibility across the whole genome.In the publicly available datasets, the DHS peaks are identified for each individual sample separately, while our method incorporates peaks from multiple samples together, aligns them across samples, and releases them as a coherent DHS database consisting of replicable peaks from multiple samples. Since in the previous datasets, each sample is analyzed separately, annotation of the same DHS sites across several samples is not available.Additionally, the batch effects that exist across samples due to the fact that multiple centers generate and analyze the data are not adjusted in the previous DHS datasets. In our database, we removed the batch effects that exist in the data samples collected from multiple centers.9.We have employed our replication test that considers multiple samples from a diverse range of cell types in order to check replicability of each DHS site. This results in identifying replicable DHS that present true regulatory sites Fig. , while pThrough applying our processing pipeline to 828 DHS data samples obtained from multiple public datasets, we have built a database of 1,455,046 regions across the whole genome for 828 samples consisting of 194 cell types. The main advantages of OCHROdb compared to previous datasets that are available publicly  are as follows.In summary, we believe that our open chromatin database, which contains a wide range of cell types can serve as a reference map for regions of open chromatin and will find many applications in studies concerned with uncovering gene regulatory mechanisms of disease and cell development.Supplementary Information.Supplementary Table S1.


Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advances in profiling chromatin accessibility at single-cell resolution (scATAC-seq) have brought new insights to the understanding of epigenetic heterogeneity. With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. Here we develop Cellcano, a computational method based on a two-round supervised learning algorithm to identify cell types from scATAC-seq data. The method alleviates the distributional shift between reference and target data and improves the prediction performance. After systematically benchmarking Cellcano on 50 well-designed celltyping tasks from various datasets, we show that Cellcano is accurate, robust, and computationally efficient. Cellcano is well-documented and freely available at  Accurately annotating cell types is a fundamental step in single-cell omics data analysis. Here, the authors develop a computational method called Cellcano based on a two-round supervised learning algorithm to identify cell types for scATAC-seq data and perform benchmarking to demonstrate its accuracy, robustness and computational efficiency. In scRNA-seq data analysis, computational cell type identification based on gene expression values of individual cells (referred to as “celltyping” hereafter) is one of the most fundamental questions. Many celltyping methods are currently available9 and several benchmark papers have been published12. These methods can be roughly categorized as supervised and unsupervised. According to benchmark studies, supervised celltyping methods have advantages over unsupervised ones in accuracy, robustness and scalability14.The developments of single cell sequencing technologies have greatly enhanced the understanding of biological mechanisms in complex tissues. Among all single cell assays, single-cell RNA-sequencing (scRNA-seq) has been the most popular with over 1200 analytical tools developed15. Recent years have also witnessed the shift from measuring chromatin accessibility in bulk samples to single-cell level by single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq)16. Like in scRNA-seq, celltyping is also an important question in scATAC-seq data analysis. However, scATAC-seq data have certain characteristics that make the celltyping more difficult. First of all, scATAC-seq data are much sparser due to low read counts17, which results in weaker signals for distinguishing cell types. Secondly, unlike scRNA-seq, feature space is not well-defined in scATAC-seq data, which poses difficulties in extracting useful information. The raw scATAC-seq data can be summarized to counts on genome-wide fixed-size bins, peaks representing the accessible regions, or genes18. Thus, the determination of feature space is an additional important step in scATAC-seq celltyping. Although it is possible to do celltyping through experimental procedures such as Fluorescence-activated cell sorting (FACS)19 or leveraging information from multi-omics sequencing techniques such as SNARE-seq20, these datasets are expensive and limited. Therefore, method specifically developed for scATAC-seq celltyping is in urgent need.Gene expression can be regulated by several factors. Among them, chromatin accessibility is essential for the interaction between DNA and regulatory elements, and provides important information for understanding the transcriptional regulatory mechanism24. As of now, many methods have been developed for single-cell omics data integration while very limited methods have been specifically developed for scATAC-seq celltyping. Seurat24 and scJoint25 use scRNA-seq as reference to transfer cell labels to scATAC-seq. Due to the strong data distributional shift between different measurements, the two methods can significantly underperform. Signac26, a recently developed end-to-end scATAC-seq data analysis pipeline, provides functions for scATAC-seq data integration and label transfer. EpiAnno was also published very recently to perform supervised celltyping in scATAC-seq using scATAC-seq as reference27. A major problem of Signac and EpiAnno is that they use read counts from called peaks as input, where the peaks are highly data dependent. Due to technical and biological artifacts, concordance of peaks can be low between reference and target28, which would result in a loss of information and undesirable celltyping results. Additionally, EpiAnno is not computationally scalable for large datasets.Most existing computational scATAC-seq celltyping methods are unsupervised and based on prior knowledge29 on anchors using the predicted pseudo labels, and then apply the trained KD model to predict cell types for remaining non-anchor cells. Through extensive real data analyses, we demonstrate that Cellcano is significantly more accurate, computationally efficient, and scalable compared to existing methods. Cellcano is well-documented and freely available at https://marvinquiet.github.io/Cellcano/.In this work, we develop a computational celltyping method for scATAC-seq, named Cellcano. Cellcano implements a two-round supervised learning algorithm. It first trains a multi-layer perceptron (MLP) on the reference dataset and predicts cell types in target data. From the prediction results, Cellcano selects some target cells that are considered well-predicted (referred to as anchors) to form a new training set. Next, Cellcano trains a self-Knowledge Distiller model (KD model)30 pipeline to process the raw data and obtain gene scores for both reference and target datasets (details in Methods section). The choice of the input is carefully investigated, and the results show that using gene scores provides good prediction accuracy and computational efficiency (details in later section). Then Cellcano applies F-test on reference gene scores to select cell-type-specific genes as features for model construction31. After obtaining the reference and target gene scores for the selected features, Cellcano adopts a two-round supervised celltyping strategy, shown in Fig. Cellcano uses gene-level summaries from the raw scATAC-seq data as inputs. Given the raw data, Cellcano incorporates ArchR32. For the six datasets, we design 50 celltyping tasks  datasets and two mouse brain datasets  and uses them as reference to predict cell types for non-anchors in the second round. The number of anchors is specified by user as a cutoff for the quantiles of entropies. For example, when using 0.3 entropy quantile cutoff, 30% of the cells in the target dataset will be selected as anchor cells. As an exploration, we first compare the performance between anchors and non-anchors under different quantile cutoffs (0.1 to 0.6 with step size 0.1) in human PBMCs celltyping tasks and mouse brain celltyping tasks. Results . We put all the results together to make direct comparisons on prediction performances. We evaluate the prediction performances from all methods by different metrics, including overall accuracy (Acc), adjusted rand index (ARI), macro F1 score (macroF1), Cohen’s kappa (κ), median F1 score (medianF1), median precision, and median recall.After deciding the input data and the anchor numbers for Cellcano, we compare Cellcano with other supervised scATAC-seq celltyping methods. We benchmark Cellcano against six competing supervised celltyping methods: SeuratWe first focus on the celltyping methods and compare the performances where we have one fixed gold standard target data Fig. . In totat-test on Accuracy, ARI and macroF1 in three comparisons: (1) Cellcano and ACTINN, (2) Cellcano and scJoint, and (3) Cellcano and Signac. The test statistics show that Cellcano performs significantly better than ACTINN , scJoint  and Signac  in Accuracy. Results hold for all comparisons in ARI. For macroF1, Cellcano slightly outperforms ACTINN while largely outperforms scJoint and Signac. In summary, Cellcano outperforms all other methods considering all scenarios: two systems (human PBMCs and mouse brain), 50 celltyping tasks, and seven metrics.We then evaluate the performances in all other 22 human PBMCs celltyping tasks Fig. . Since t33 PBMCs dataset as reference) as an example to visualize the prediction results after each round by tSNE and UMAP. Figure To further demonstrate how the two-round procedure in Cellcano outperforms, we use one celltyping task . We evaluate the prediction performances by Acc, ARI and macroF1.A key advantage of the two-round approach in Cellcano is that training a model using anchors in target data alleviates the distributional shift problem between the reference and target data. The distributional shift is often caused by batch effect in high-throughput data. This leads to a question whether our two-round strategy is better than the one where we first remove batch effect and then apply a direct prediction. According to a recent benchmark studyAs mentioned earlier, we put all prediction results from celltyping and integration with label transfer into boxplots Fig.  for a diWe evaluate the computational performance of Cellcano and show all celltyping methods’ runtime for all celltyping tasks Fig. . For faiComputational celltyping for single cell omics data is an important problem. Such methods are under-developed for scATAC-seq data. In this work, we develop Cellcano, a two-round supervised scATAC-seq celltyping method. Due to distributional shift, the first-round prediction can be inaccurate, and the anchors can be noisy. The KD model in the second round is thus used to distill the knowledge from a noisily labeled input. We have shown in 50 celltyping tasks with data from two systems (human PBMCs and mouse brain) that Cellcano significantly outperforms other celltyping methods and integration with label transfer methods both in prediction and computational performances. Cellcano is also robust against the anchor selection procedure and batch effects in the data.Cellcano has several advantages and methodological features. First, Cellcano uses gene scores as input, which has many advantages compared to using bin or peak counts: (1) genes have a much smaller feature space, which significantly improve the computational performance; (2) genes are shared among datasets, which provides potential to be further connected to other modalities, such as gene expression data. We show that using gene scores works as equally well or even better than using bin counts as input. Secondly, Cellcano implements strategies in selecting and using anchors. The MLP in Cellcano can better capture the non-linear relationship between the gene scores and the corresponding cell types. In addition, the KD model is robust to anchors with noisy labels. Moreover, Cellcano does not need to jointly operate on the reference and target datasets, like Seurat, Signac, and scJoint does. This allows Cellcano to be trained on a compendium of reference datasets and provide a pre-trained model.There are some further developments for Cellcano we plan to work on. First, Cellcano can be adapted to other celltyping scenarios, for example, cross-modality predictions (using scRNA-seq as reference for scATAC-seq celltyping), celltyping in single-cell DNA methylation, etc. Another interesting question is to use multimodal reference data, for example, to jointly use scRNA- and scATAC-seq data as reference to improve celltyping results for either scRNA- and scATAC-seq data. Such an approach can potentially further improve prediction performance.https://timoast.github.io/sinto/installation.html).Data collection and genome unification: We collected four datasets from human PBMCs and two datasets from mouse brains and then downloaded the raw scATAC-seq data (10X fragment files or bam files). The original raw data sometimes are aligned to different versions of genomes, we therefore use liftOver to unify the human PBMCs data to hg19 genome and the mouse brains data to mm10 genome. After unification, we use ArchR to process the raw scATAC-seq data. Note that for Signac, we need an additional step to convert bam files into fragment files with sinto  will be. We set T = 1 in the first-round MLP model. During training, we use cross-entropy as the loss function to minimize the distributional difference between the one-hot encoded cell type label p and the predicted cell type probabilities σ(Z):In Cellcano’s first-round prediction, we first train an MLP model with a After training the MLP model, we apply the trained MLP model to the target data to obtain the probabilities for each cell being in each cell type.qik for cell i being in cell type k, we calculate the entropy EM×1 for all M cells asWhen the target data size is small, Cellcano takes the class with the largest probability as the final predicted cell type for each cell and stops. When the target size is large (over 1000 cells by default), we perform a second-round prediction. We first select anchors from the target, and we aim at selecting accurate anchors, which can also capture the full scope of target distribution to guide the second-round prediction. With the first-round predicted probabilities, denoted as When a cell label is more confidently assigned, its entropy over the predicted probabilities is lower, and the prediction is in general more accurate Fig. –8. Once 41. Inspired by this and several recent works42, we propose to use the teacher-student interaction to alleviate the noisy label problem. Specifically, the teacher model distills knowledge from both clean supervision and noisy supervision by producing “soft labels” as the training targets of the student model. Compared to the “hard labels” that only contain over-confident 1’s and 0’s, “soft labels” are smoothed and thus more noise-tolerated43. Also, there are cell types sharing similar profiles during celltyping which fits the fine-grained classification setting in the KD model. In Cellcano, we apply a “self-KD model” where we have the exact same structure for the teacher model and the student model. We set them to be vanilla MLPs of two hidden layers with 64 and 16 nodes, respectively. To let the model be more generalizable, we put the dropout layer right after the input layer. We use ReLU as the activation function.Although the anchors cannot be perfectly predicted from the first round, they are important complementary training data for improving prediction, since these cells are from the exact same target domain where we previously lack supervision. To deal with training data with noisy labels, we implement a self-Knowledge Distiller (KD) model in the second-round training. The KD technique was originally proposed to transfer the knowledge learned from a sophisticated teacher model to a light-weighted student model, by treating the prediction results produced from the teach model as the “soft labels” for training the student modelT of the softmax function to be larger. We use cross-entropy loss for the teacher model, then train the student model with the teacher’s “soft labels” as well as the one-hot encoded “hard labels”. The idea is to learn a label smoothing regularization so that the label distribution can be better captured. The KD loss function for the student model is a weighted average of two losses, which is shown in the equation below:We first train the teacher model with the anchors as input. To make the label “softer”, we set the temperature T1 and T2 are temperatures in the softmax functions, and α is a hyperparameter for balancing the two losses. The first part of the KD loss is a cross-entropy loss where the student prediction qs is guided by “hard labels” (anchor cell types from first-round prediction), and we set the T1 as 1. The second part represents the Kullback–Leibler (KL) divergence loss which measures the probability distribution distances between the soft teacher prediction qt and the soft student prediction qs, where T2 can be adjusted. We set T2 = 3 for the second part to soften the label distribution. Overall, we set α as 0.1 to value more on the teacher model’s “soft labels”. The KD model is trained for 30 epochs.Here, 34. For Signac, we use raw fragment files as input and follow the vignettes provided by Signac on scATAC-seq data integration. We notice that overlapping genomics regions to obtain peak count matrices for target datasets takes a much longer time than generating gene scores with ArchR. For EpiAnno, we use peak counts matrices as input. To accommodate the memory limitation of EpiAnno, we set the hyper parameter peak rate as 0.08 or 0.05 for large input matrices, while keeping the original 0.03 peak rate for remaining matrices.We benchmark Cellcano to six competing methods: Seurat, scJoint, Signac, EpiAnno, ACTINN, and SingleR. For all methods except EpiAnno and Signac, we use the scATAC-seq gene score matrix before feature selection in the place of scRNA-seq gene expression matrix as the reference and follow their default procedures for celltyping. In Seurat, we choose reciprocal principal component analysis (RPCA) to calculate the joint embedding of reference and target datasets as it is proved to have better integration performance in a benchmark paperWe benchmark Cellcano to six combinations of batch-effect removal methods with different feature-level inputs: LIGER with peaks as input, LIGER with genome-wide bins as input, ComBat with peaks as input, ComBat with genome-wide bins as input, Harmony with gene scores as input and Portal with gene scores as input. We use corresponding feature matrices derived from ArchR as input for each combination and use the default procedures to remove batch effect between the reference and target datasets. ComBat returns corrected counts which has the same dimension as the original data while LIGER, Harmony, and Portal return joint embeddings with size as 20, 50, and 20, respectively. We then split the corrected counts or joint embeddings into reference and target datasets according to the batch information before integration and perform celltyping with MLP. Here, the MLP model has the same structure and settings with the MLP model in Cellcano’s first-round prediction.We choose overall accuracy, ARI and macroF1 for performance evaluation. Accuracy describes the number of correctly assigned cells divided by total number of cells. This can be used as an indicator to show how well most cells are assigned. ARI measures the cluster concordance between the true labels and predicted labels. MacroF1 treats all cell types equally, which puts more emphasis on the accuracies of smaller clusters compared to other metrics. For a fair comparison, we also include median F1 score (medianF1), median precision, median recall, and Cohen’s kappa (κ), which were used in the EpiAnno paper. The medianF1, median prediction and median recall regard predicting each cell type as a binary classification task and calculate the median performance for each cell type. Cohen’s kappa measures the agreement between labels from the ground truth and the predictor. In summary, these metrics measure different perspectives and can be used to fairly reflect prediction performance among different performers.In this work, we set random seed as 2022 for Python random package along with tensorflow package to ensure the reproducibility of our results. No statistical method was used to predetermine sample size. Cells with low-quality were excluded based on standard scATAC-seq preprocessing procedures. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Reporting Summary


Properly integrating spatially resolved transcriptomics (SRT) generated from different batches into a unified gene-spatial coordinate system could enable the construction of a comprehensive spatial transcriptome atlas. Here, we propose SPIRAL, consisting of two consecutive modules: SPIRAL-integration, with graph domain adaptation-based data integration, and SPIRAL-alignment, with cluster-aware optimal transport-based coordination alignment. We verify SPIRAL with both synthetic and real SRT datasets. By encoding spatial correlations to gene expressions, SPIRAL-integration surpasses state-of-the-art methods in both batch effect removal and joint spatial domain identification. By aligning spots cluster-wise, SPIRAL-alignment achieves more accurate coordinate alignments than existing methods.The online version contains supplementary material available at 10.1186/s13059-023-03078-6. Recent years have witnessed the great success of single cell technologies in characterizing cell state dynamics in complex biological systems, which reveals the cellular mechanisms of development and disease at an unprecedented resolution. However, the lack of spatial dimension hindered the exploration of how cell identity and cell fate are influenced by their surrounding environment. Advances in spatially resolved transcriptomics (SRT) technologies have supplied the spatial dimension to achieve joint measurement of the gene expression profiles and spatial coordinates. Such spatial technologies include imaging-based –4 and neIn the traditional single cell community, integrating single cell non-spatial transcriptomes from various sources enables the comparisons of distinct tissues, organs, or individuals in one shared space, e.g., the Human Cell Atlas (HCA) . With thUnlike scRNA-seq technologies, in which cells’ representations are classically defined by their own transcriptome profiles only, SRT technologies add spatial information, thus providing the opportunity to analyze the gene expression variations with both cell identity and the surrounding microenvironment . HoweverRecently, several methods have been proposed to address the integration of SRT datasets containing multiple slices for joint clustering –20. One Besides these, some other methods , 23, focBesides the gene expressions, the spatial coordinates of different samples are also required to be aligned to make them comparable in the physical space. A few methods have tried to align coordinates of different SRT data recently. PASTE  attemptsTo overcome the limitations of all these methods, we proposed a new method SPIRAL to effectively integrate data in both feature space, including low-dimensional embeddings, high-dimensional gene expressions, and physical space. SPIRAL consists of two seamless modules, i.e., SPIRAL-integration for batch effect removal and SPIRAL-alignment for spatial coordinate alignment. The first module (SPIRAL-integration) incorporates GraphSAGE  and domaWe demonstrated the advantages of SPIRAL on several synthetic and recently published SRT datasets , 28–31. In this work, we designed a method SPIRAL, which combines gene expressions and spatial relationships in the consecutive processes of batch effect removal and coordinate alignment by employing graph-based domain adaption and cluster-aware Gromov-Wasserstein optimal transport. The graph-based domain adaption is able to consider gene expressions of neighbors during removing batch effects, and the cluster-aware alignment can avoid mistakenly mixing up different clusters.SPIRAL consists of two seamlessly connected modules, i.e., SPIRAL-integration and SPIRAL-alignment. SPIRAL-integration corrects batch effects via integrating expression profiles and spatial relationships with a unified framework combining GraphSAGE network  and domaTo quantitatively evaluate the performance of SPIRAL on removing batch effects of the same cell types and discerning cells with similar expression profiling but distinct spatial neighbors, we generated three types of simulated datasets simulate1-3) with the same cell-type compositions but different spatial arrangements, in which two cell types (Group1 and Group2) are adjacent in feature space and distant in physical space  containing Group1-4 as batch-shared cell types and Group5-6 as batch-specific cell types, and another three-batch dataset (simulate5) containing Group1-3 in Batch1, Group1,3,4 in Batch2, and Group2,3,5,6 in Batch3  than the other clusters  of the cortex. Combining the DEGs and the mouse brain sagittal anatomy diagram  and Geary’s C (mean: 0.424 vs 0.665) than SPIRAL  as the reference coordinate map on account of its intact structure and high-quality gene expressions  sequenced by 10X Visium, Stereo-seq , and SliAfter integrating Stereo-seq and Slide-seq V2, we wanted to test the abilities of prediction and generalization of our model. To this end, we used the trained model to predict the 10X Visium data. The spatial distributions of the predicted clusters were well consistent with the anatomic structures  is a deep neural network-based inductive graph node embedding framework, whose inductive learning mode superiorities general graph convolution network (GCN)-based methods on predictions of unseen data, which is the HCA intends to do for more and more unseen scRNA and SRT data using current data. GraphSAGE leverages node features and graph structures to learn an embedding function to embed nodes to low-dimensional spaces. In our framework, GraphSAGE functions as an encoder to generate low-dimensional embeddings by nonlinearly aggregating gene expressions from its physical neighbors. We first convert SRT data into an undirected graph, The loss function of GraphSAGE depicts the ability of learned embeddings to reconstruct original graph, which is expressed as maximizing the similarities of embeddings between nearby nodes and minimizing them between disparate nodes. For embedding The decoder takes the latent embedding as input to reconstruct the processed gene expression. The output of layer In which the summation operates on The discriminator extracts a smaller dimension of er (GRL) . The outIn which The classifier takes another portion ,d=1,⋯,D . The outThe total loss of SPIRAL-integration is weighted summation of the four losses in one minibatch, where In all experiments, we set the layer of GraphSAGE as 512–32, the decoder as 32–512, the classifier as 4, and the discriminator as 32–16. The Firstly, we selected one of samples as reference, on to which all other samples are aligned. There are no strict conditions for reference selection, which can be the one with complete spatial domains, for example, 10X Visium has larger priority than Slide-seq as it has larger views. Although, do not worry about this problem too much, because we can extend the region of reference coordinate system by aligning new clusters. Next, we registered the shared clusters between the reference sample and the remaining samples using biological embedding. Specifically, for shared cluster age of R , which rage of R , 54.There are two key parameters to be considered, parameter ll types . For cluFor SPIRAL embeddings, we used Louvain algorithm implemented by “scanpy.pp.neighbors” and “scanpy.tl.louvain” of scanpy package on simulated and sagittal mouse brain data, Louvain algorithm implemented by “FindNeighbors” and “FindClusters” of Seurat package on coronal mouse brain and mouse olfactory bulb data, and “mclust_R” of STAGATE package on DLPFC data. For other compared methods, we adopted the provided clustering methods by them.For DLPFC and mouse olfactory bulb data, we refined the cluster label of each spot by its surrounding spots as did in spaGCN .We evaluated the extent of integrations of same domains and separations of different domains by the local inverse Simpson index (LISI) of batches in each domain (LISI-batch) and the LISI of domains in all data (LISI-domain) . The comWe evaluated the spatial coherence of predicted cluster labels on original coordinates and annotated labels on aligned coordinates by z-scaled spatial coherence scores, which was proposed by PASTE  by calcuI and Geary’s C, which are used to measure spatial autocorrelations of genes in SRT data analysis  [C are converted by subtracting by 1 to make sure lager value means lager spatial autocorrelations.We evaluated the spatial autocorrelations by Moran’s nalysis  , 38. We The pseudo-time is calculated by sc.tl.dpt of “scanpy” package, which is used to calculate spearman correlations between laminar labels from 1 to 7, representing from L1 to WM.We adopted Splatter to simulate gene expressions. We simulated four two-batch datasets and one three-batch dataset, all of which contain 500 genes. In simulate1-simulate3, we simulated the same cell type compositions and two similar cell types: Group1 and Group2; in simulate4 and simulate 5, we generated different cell type compositions. For spatial positions, we randomly assigned cells of each cell type to a restricted regular region. In simulate1, the spatial distributions of cell types are the same; in simulate2, the spatial ordering of cell types among two batches are reversed; and in simulate3, the coordinates of one batch are rotated on the basis of simulate2. In simulate4 and simulate5, we assumed the same spatial arrangements of shared cell types.The DLPFC data consists of 12 sections for three donors, each donor has two pairs of https://support.10xgenomics.com/spatial-gene-expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Anteriorhttps://support.10xgenomics.com/spatial-gene-expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Anterior_Section_2https://support.10xgenomics.com/spatial-gene-expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Posteriorhttps://support.10xgenomics.com/spatial-gene-expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Posterior_Section_2The sagittal mouse brain is divided along the anterior-posterior axis, which have one pair of replicates respectively. The number of spots ranges from 2696 to 3353. We annotated domains by domain-marker genes and H&E staining images. The links of these data are as follows:There three coronal mouse brain sections, which are formalin-fixed paraffin-embedded (FFPE) tissue with H&E staining, fresh frozen tissue with H&E staining, and fresh frozen tissue with immunofluorescence (IF) staining. The number of spots is 2264, 2702, 2903. We labeled these tissues by marker genes and Allen brain atlas.https://www.10xgenomics.com/resources/datasets/mouse-brain-section-coronal-1-standard-1-1-0https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-section-1-coronal-stains-dapi-anti-neu-n-1-standard-1-1-0https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0The links of these data are as follows:https://www.10xgenomics.com/resources/datasets/adult-mouse-olfactory-bulb-1-standard.We used mouse olfactory bulb data of 10X Visium, Stereo-seq and Slide-seq V2, whose resolutions are Additional file 1: Figure S1. Simulating. A. Spatial arrangements of cell types on original and aligned coordinates by SPIRAL and PASTE for simulate 1-3 datasets. Colors represent cell types and shapes represent batches. B-C UMAP visualizations of simulate2-3 (B) and simulate4-5 (C) datasets, each dataset occupies two rows, in which colors represent cell types (upper) and batches (bottom). Figure S2. DLPFC. A. UMAP visualizations of four-section integrations for each donor . B. Spatial arrangements of annotated layers on aligned coordinates by SPIRAL and PASTE  and spatial distributions of clusters  on SPIRAL-aligned coordinates. C. Spatial distributions of raw, Seurat and SPIRAL-integrated domain-marker gene expressions, RORB, KRT17 and PCP4 in 151507, 151669, 151673 and SPIRAL-aligned coordinates. Figure S3. Sagittal mouse brain. A. H&E staining images of both anterior and posterior mouse brains. B. The anatomy diagram of sagittal mouse brain, where black line divides mouse brain into anterior and posterior parts. C. Spatial visualizations of annotated domain labels for four samples. D. UMAP visualizations of raw and integrated embeddings by five methods in integrating four samples. Each method occupies two columns, where domains are colored in left and batches are colored in right. E. Spatial arrangements of annotated domains on samples with one of replicates were rotated 60 degree. F. Spatial arrangements of annotated domains on aligned coordinates of anterior1&2 (left) and posterior1&2 (right) by SPIRAL (upper) and PASTE (bottom). G. Spatial disposition of mclust-derived clusters by SPIRAL on four samples. H. Comparison of the expressions of DEGs from cluster10 and DEGs from cluster 2,3,15,21 in mouse olfactory bulb domains. Figure S4. Coronal mouse brain. A. Annotations of domains generated by marker genes and Allen mouse brain atlas in three coordinates. B. UMAP visualizations of integrations of embeddings from origin and five methods. C. The anatomy diagram of coronal mouse brain for hippocampus structures (left) and the distributions of the corresponding gene expressions (right). D. The arrangements of derived clusters by for spatial based methods on the region of hippocampus. E. Statistics of sequence depth of three protocols. F. Spatial arrangements of annotated domains on aligned coordinates by SPIRAL (left) and PASTE (right). G. Spatial patterns of domain-marker gene expressions from raw data and SPIRAL integrated data in the cortex region of 10X Normal data. Figure S5. Mouse olfactory bulb. A. Spatial distributions of clusters derived by SPIRAL in aligned coordinates by SPIRAL (left) and PASTE right). B&C. Spatial distributions of Louvain-derived clusters on the embeddings of harmony_STAGATE (B) and STAligner (C). D. Spatial distributions of predicted clusters of 10X Visium data using model trained by Stereo seq data and Slide V2 data. E. Spatial distributions of of predicted clusters on aligned coordinates by SPIRAL. F. The percentages of cell types in each cluster. G. Spatial autocorrelations of raw and integrated gene expressions in original and aligned coordinates. H. Spatial distributions of raw, SPIRAL-integrated and Seurat-integrated layer-marker expressions, Nrgn, Gabra1, Slc6a11 and Kctd12 on three samples. Each method occupies one row.Additional file 2. Supplementary Table.Additional file 3. Review History.