Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer’s proliferation and relapse mechanisms. Single-cell RNA sequencing data provide grounds for understanding the functional state of cancer as a whole; however, much research remains to identify and reconstruct clonal relationships toward characterizing the changes in functions of individual clones. We present PhylEx that integrates bulk genomics data with co-occurrences of mutations from single-cell RNA sequencing data to reconstruct high-fidelity clonal trees. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We analyze high-grade serous ovarian cancer and breast cancer data to show that PhylEx exploits clonal expression profiles beyond what is possible with expression-based clustering methods and clear the way for accurate inference of clonal trees and robust phylo-phenotypic analysis of cancer. The functional changes of individual clones in single cell RNA sequencing (scRNA-seq) data remain elusive. Here, the authors develop PhylEx that integrates bulk genomics data with co-occurrences of mutations revealed by scRNA-seq data and apply it to high-grade serous ovarian cancer cell line and breast cancer datasets. The evolutionary relationship between clones can be represented by a phylogenetic tree or clonal tree. Inferring clonal population structure, genotypes, and trees from sequence data has been an active area of research in the past decade with implications for cancer treatment4. Early approaches used bulk sequence data coupled with computational deconvolution to address the admixed nature of bulk data9. The limitations of clonal analysis using only the bulk method is well documented in the literature . Recent advances in single-cell DNA sequencing (scDNA-seq) technologies have prompted the development of approaches better tailored to these data types14 as well as methods that integrate scDNA-seq data with the bulk data for joint analysis for improved accuracy16.Cancer is an evolutionary process with ongoing mutational processes coupled with selection and drift leading to genetic diversity within the tumor cell populations. Though each cell is fundamentally distinct in cancer, there typically exist groups of cells that are genomically nearly identical, so-called clonal populations18. However, the two-step approach does not fully utilize the available data as information in the scRNA data cannot be used to improve clonal population structure. Hence, there is an unmet need for an integrative approach to simultaneously identify clonal population structure and the associated clonal genotypes from bulk DNA- and scRNA-seq data towards identifying intra-tumor heterogeneity in clonal gene expression profiles.Though the aforementioned methods can resolve clonal population structure, they cannot identify functional differences that result from the genomic heterogeneity. The increasing availability of single-cell RNA sequencing (scRNA-seq) data provides an approach to partially address this problem. Recent methods that seek to assign gene expression profiles to clones have treated the problem as a two-step procedure whereby the clonal population structure is identified and then scRNA data is aligned to clonal genotypesPhylo Expression) that integrates bulk DNA- and scRNA-seq data to meet this need. PhylEx leverages information about the single-nucleotide variants (SNVs) observed within a single cell to identify clones, improve clonal tree reconstruction, and facilitate highly accurate mapping of RNA expression profiles to clones. Thus, PhylEx unlocks the potential for phylo-phenotypic analysis, to discover and characterize tumor’s progression and relapse mechanisms at gene and functional (pathway) levels of individual clones in relation to clonal genotypes, within an evolutionary context. We systematically benchmark PhylEx using synthetic data and compare it to existing state-of-the-art clone reconstruction methods. We then evaluate the performance of PhylEx on high-grade serous ovarian cancer (HGSOC) cell lines, which were thoroughly investigated using the direct library preparation (DLP) scDNA approach in ref. 19. The experimental results demonstrate that integration of bulk DNA and scRNA allows for the identification of clonal population structure with high fidelity. Finally, we apply PhylEx to breast cancer data along with HGSOC cell line to characterize patterns of cancer progression using the clonal expression profiles.In this work, we introduce a Bayesian probabilistic method called PhylEx  zero expression (2) mono-allelic expression of the reference allele; (3) mono-allelic expression of the variant allele; (4) bi-allelic expression; and (5) no mutation and provided examples of each of the five categories in Supplementary Fig. 21, for a recent discussion on zeros in scRNA-seq data). The mono-allelic expression may arise due to bursty expression22, where a cell may express only the reference allele or the variant allele but not both. The bi-allelic expression arises when both alleles are expressed. Finally, expression of variant allele depends on the clonality of the cell. If a cell does not harbor a mutation at a locus, then a variant will only be observed in error be it at the stage of sequencing or bioinformatics processing. Our exploratory analyses of the real sequencing data shown in Supplementary Fig. The underlying statistical model is based on the tree-structured stick breaking (TSSB) process, a flexible prior distribution over the clonal tree structure23 and Metropolis-Hastings for exploring the clone fractions7. PhylEx marginalizes over all possible cell-to-clone assignments to evaluate the likelihood of the single-cell data as marginalization has the positive effect of removing uncertainty in scoring the clonal tree due to latent cell-to-clone membership variables. PhylEx generates samples from the posterior distribution over the clonal tree as well as a maximum a posteriori (MAP) tree. The output also includes clonal genotypes and cell-to-clone assignments. The clone analysis conducted by PhylEx then facilitates a range of differential expression investigations on the otherwise inaccessible tumor clones.The inference machinery takes advantage of slice sampling to explore the space of clonal trees15. In contrast to our method, ddClone uses scDNA-seq data and as such, ddClone cannot infer clonal gene expression profiles; furthermore, ddClone does not infer a clonal tree. B-SCITE also performs integrated analysis of bulk DNA- and scDNA-seq data but it targets mutation trees16; a post-processing step needs to be applied to convert mutation trees to (sub)clonal trees. As the two methods are tailored to work with scDNA-seq, they are not well suited to handle the stochasticity in scRNA-seq data, necessitating the development of a specialized method tailored for scRNA-seq. Namely, PhylEx is better equipped to account for sparse nature of the scRNA-seq data due to both biological and technical reasons and elevated false negative rates due to mono-allelic expression , given an initial clonal configuration matrix obtained from bulk DNA-seq data. Specifically, Cardelino represents clonal configuration by a binary matrix C ∈ {0, 1}N×K, where N denotes the number of SNVs, K denotes the number of clones and the entries cnk ∈ {0, 1} indicates presence of mutation n = 1, ..., N in clone k = 1, ..., K. Cardelino gains some flexibility from a poorly constructed initial clonal configuration matrix by re-assigning SNVS to clones; nonetheless, the mapping of cells to clones is sensitive to the initial clonal configuration matrix, which is noisy if inferred purely from bulk DNA-seq data as we demonstrate in this paper. In contrast, the primary goal of PhylEx is to infer a clonal tree to reveal an evolutionary process underlying cancer progression. As the clonal tree yields clonal configuration matrix, PhylEx also provides high fidelity clonal configuration matrix to facilitate the mapping of cells to the clones. Note that workflow presented by Cardelino represents a two-step approach to mapping scRNA-seq data: first step involves inferring the clonal structure using bulk DNA-seq, followed by the second step of mapping scRNA-seq data to the discovered clones. We show that inaccuracies from the first step propagate to the second step, resulting in an inaccurate mapping : for a given number of clones, K, Cardelino-free reconstructs the clonal configuration matrix by assigning each of N SNVs and single cells to to one of the K pre-specified clones. This procedure does not involve bulk data likelihood nor an initial clonal configuration matrix inferred from bulk DNA-seq data. The authors only recommend using Cardelino-free when bulk data is missing as the performance of Cardelino-free is found to be inferior to Cardelino (p. 416 of ref. 18). It is important to note that (i) Cardelino-free serves a different use case than that of PhylEx, and (ii) the target of inference is a clonal configuration matrix as opposed to a clonal tree. DENDRO also differs from PhylEx in two ways. First, it reconstructs a single cell phylogeny. Second, it uses only scRNA-seq data . De novo reconstruction of single-cell phylogeny from scRNA-seq data is inherently challenging due to high levels of sparsity and missingness. By directly targetting clonal trees and integrating bulk DNA-seq data likelihood, PhylEx aims to alleviate these concerns.Finally, some methods perform 7. Figure We begin with an illustrative example to test the strength of the co-occurrence signal in single-cell data. We simulated bulk and scRNA-seq data for 100 SNVs and 20 single-cells over a cherry shaped tree . We used V-measure and the ancestral reconstruction error given in Eq. . Hence, we compared expected loss, which roughly translates as an average number of SNVs incorrectly predicted for each cell . The ex26. For solid tumors, spatial samples are taken as statistical replicates with common evolutionary history but possibly with different cellular prevalences. However, depending on the type of tumor, spatial sampling may not be feasible. In particular, multi-regional sampling is difficult to perform without prior surgical tumor removal, preventing it from impacting pre-surgical treatment decisions. We evaluated the performance of PhylEx on simulated data consisting of a single-region bulk DNA-seq combined with scRNA-seq data against bulk methods supplied with multi-region DNA data.Multi-region sequencing is a standard approach to improve the accuracy of the clonal tree reconstruction, e.g., to resolve branchingWe used a multifurcating tree and simulated the bulk DNA data with and without copy number variation. Devoid of copy number evolution and given multi-region data, the bulk methods achieved high accuracy  and false-negative (FN) rates, especially when applied to scRNA-seq data as the expression profile is inherently sparse, bursty, with frequent mono-allelic expression. One of the key features of PhylEx is that it works with the raw read counts and does not require cell genotyping.Next, we compared PhylEx to two methods that integrate bulk genomics data with scDNA-seq data, B-SCITE and ddCloneWe found that PhylEx outperformed ddClone and B-SCITE on synthetic data generated from both binary and multifurcating trees, under evolutionary models with and without copy numbers aberrations  and two from relapse specimens (OV2295R2 and TOV2295R). These cell-lines have been assayed using the direct library preparation (DLP) scDNA-seq technology28 and analyzed by some of the leading experts in the field of computational oncology using multiple genomic characteristics, e.g., copy-numbers, breakpoints, and SNVs, to reconstruct a clonal tree, which we call DLP clonal tree . Since their evidence-based analysis was supported by multiple genomic characteristics, DLP clonal tree must be considered very solid, providing a fertile opportunity to assess performance of PhylEx on real sequencing data. To that end, we performed Smart-Seq3 scRNA-seq29 on OV2295 and OV2295R2 (TOV2295R was difficult to grow and we could not use it).To assess the performance of PhylEx on real data, we analyzed a related set of high-grade serous ovarian cancer cell-lines19. To elaborate, an SNV is removed from evaluation only based on whether the authors of the original publication made consistent annotation with the tree that they inferred ; inferred PhylEx tree was not used in determining which SNVs to remove. The annotation for each of the SNVs from ref. 19 is given in Supplementary Data We constructed a single-region pseudo-bulk data by combining the scDNA-seq data from the two cell-lines OV2295 and OV2295R2. We obtained 360 scRNA-seq cells passing quality control and identified 67 SNVs with coverage in the scRNA data. Of the 67 SNVs, 21 SNVs were removed from evaluation, but not the PhylEx analysis, due to incompatible annotation in the original publicationEFGHI in Fig. ABCD clone was assigned to the CD clone. The clones EF and EFGHI were clumped together, thereby also clustering the lone SNV in EFGHI clone with the SNVs in EF clone. We compared the results of PhylEx to those inferred with Canopy, TSSB, ddClone, and B-SCITE16 on three clustering metrics and ancestral reconstruction metric. PhylEx significantly outperformed all of the other methods  and the primary ABCD clade (OV2295) 32, but is increasingly being used in cancer studies33. An open question in the cancer context is whether pseudo-time trajectories reflect evolutionary history. As pseudo-time analysis is based purely on gene expression, this is not guaranteed. We applied the pseudo-time method Slingshot34 on the 2-dimensional representation obtained by ZINB-WaVE with the cells clustered using (i) PhylEx and (ii) mclust based on gene expression data35. Trajectories inferred by slingshot did not reflect the evolutionary histories: (i) the parent-child clones ABCD and CD appear as siblings , and the EF clone (relapse tumor). The ancestral clone is represented by the cells assigned to the root node (161 cells), the ABCD clone is represented by the cells assigned to the left child of the root and its descendants (152 cells), and the EF clone is represented by the cells assigned to the right child of the root (47 cells) in the tree given in Fig. 39 on the set MSigDB C5 (gene ontology)40. Several pathways related to the immune system were significantly down-regulated in the EF clone compared to the ABCD clone  using edgeRndants 15 cells, aTaken together, these results demonstrate that insights obtained by comparing expression profiles in the context of PhylEx clones provide the capacity for phylo-phenotypic analysis which can be used to dissect the tumor gene expression patterns beyond what is possible with current single-cell expression analysis methods.29. 10X on the other hand is a droplet based technology, which allows sequencing of large number of cells. While a comprehensive study comparing Smart-Seq3 to 10X is unavailable, the general understanding is that Smart-Seq3 offers better coverage and possibly depth on a smaller number of cells and 10X allows sequencing of a much larger number of cells at lower coverage42.Next, we investigate the applicability of PhylEx on widely available 10X Genomics scRNA-seq data (referred to as 10X for brevity). Smart-Seq3, like its predecessor Smart-Seq2, is a plate-based, full-length transcript sequencing technology offering improved sensitivity to detect transcripts over its predecessorWe obtained a total of 6616 cells sequenced using 10X 3′ sequencing of the HGSOC cell-lines. We computed sample statistics on the coverage of mutations across cells where we define a cell to cover a mutation at a loci if the read count at the loci contains at least one variant read. On average, cells sequenced using 10X platform had coverage of mutations for 0.4527 loci with the median of 0; in comparison, a cell acquired using Smart-Seq3 had coverage of mutations for 3.253 loci with median of 3 . PhylEx outperformed the bulk deconvolution methods , implicating changes to the cell replication mechanism, and identified a mutation in TP53 and MAP3K8 in Clone 2, hinting at the proliferation of cancer beginning at Clone 2. In Clone 3, we noted mutations to genes involved in PI3K and MAPK pathways  and to MDC1 (DNA repair). Clone 4 appears to be characterized by changes to the RAS pathway as evidenced by mutations to ETS2. Overall, the clonal tree provides a vital context in which to analyze and inspect mutations in cancer.We retrieved the NanoString PanCancer human pathway panel gene list of 770 curated genes  for the downstream analysis. Focusing on this set of genes helps to identify driver mutations for each clone. Among the SNVs used in our analysis, 24 overlapped the NanoString list . Clone 1 had a mutation in CDC6 and only two other mutations, perhaps indicating that its cells more closely resemble normal cells than the cancer cells.We performed gene set enrichment analysis on the MSigDB Hallmark gene sets to compare the parent-child clones Fig. c. GSEA rOverall, the PhylEx analysis identifies the driver mutations Fig. b, elucidIn this work, we have presented PhylEx, for integrating bulk genomic and single-cell transcriptomic data to reconstruct clonal trees, which paves the road for characterizing the functional state of individual clones via phylo-phenotypic analysis. We have shown how PhylEx enhances downstream analysis by providing a clonal tree and the opportunity to compare the clones’ functional states – revealing the interplay between the evolutionary process and the clones’ phenotypes.We established that specialized methods for integrating bulk with single-cell transcriptomics are necessary. By modeling read counts, PhylEx bypasses the need for performing cell genotyping and hence, avoids compounding of errors stemming from dichotomizing counts into binary values. PhylEx, using only a single region bulk sequencing combined with scRNA-seq, outperforms state-of-the-art bulk-based methods supplied with multi-region data. We expect these findings to shift the paradigm from multi-region sequencing to single-region sequencing accompanied by scRNA-seq. This approach will simultaneously reduce the effort required for data acquisition, improve the accuracy of the clonal reconstruction, and allow for functional analysis of individual clones. Moreover, many researchers will realize that the single-cell RNA data they already possess should be exploited for clonal analysis or, even, to perform supplementary single-cell RNA sequencing for this purpose. With the prevalence of bulk DNA sequencing and rapidly growing studies conducting scRNA-seq, we expect that PhylEx will prove profitable to cancer researchers studying the functional implications of cancer evolution.44. There exist methods that perform copy number inference from scRNA-seq data such as InferCNV, HoneyBADGER, and CopyKAT47; however, these methods do not consider copy number variation in the context of evolution. For example, CopyKAT, relies on hierarchical clustering on the expression data. While InferCNV and HoneyBADGER allow subclonal copy number inference, they are limited to bifurcating trees. With evidence for multifurcation in cancer evolution  as well as linear evolution48, coupled with a lack of resolution to detecting binary branching from scRNA-seq data, this is potentially a severe limitation. The performances of these methods also depend on having a set of normal reference cells as CNV inference from scRNA-seq data require reference expression levels of the normal cells. As such, CNV clones did not have high concordance with SNV clones when applied to HER2+ scRNA-seq data  in Stockholm, with reference numbers 2016/957-31 and 2017/742-32. Biobank approval was obtained from the Stockholm medical biobank. Before surgery, informed consent in accordance with the Declaration of Helsinki was given to the patient for signature. The patient was not compensated since our study did not include any extra steps other than the standard treatment procedures for the disease.Tumor resections and matching dermal biopsies from 4 individual breast cancer patients were freshly collected. Tissues were manually homogenized and genomic DNA samples were isolated by using the QIAamp DNA mini kit (QIAGEN). The library was prepared by using Twist Bioscience Human Core Exome kit (Twist Bioscience) according to the manufacture protocol. The bulk DNA samples were then sequenced in a S4 flow cell lane by the NovaSeq 6000 platform (Illumina) at the National Genomics Infrastructure, Science for Life Laboratory, Uppsala.Tissues were homogenized and cells were released by using the gentleMACS™ Octo Dissociator with Heaters and the human tumor dissociation kit (both from Miltenyi Biotec), according to the manufacturer protocols. Afterwards, the cells were washed two times with F12-DMEM medium (Gibco) and collected by centrifugation at 300g for 5 minutes. The single-cell suspensions were further generated by passing the resuspended cells through the 70 mm cell strainers. The single cell suspensions were then further stained with the Zombie Aqua Fixable viability dye  at room temperature for 20 min, then washed with phosphate-buffered saline (PBS). The cells were incubated with Human TruStain Fc block  for 10 min to limit unspecific antibody binding, then stained for 20 min with anti-EPCAM  and anti-CD45  in FACS buffer (PBS + 0.5% Bovine Serum Albumin). The cells were subsequently washed and resuspended in FACS buffer. Fluorescence-activated cell sorting (FACS) using an influx flow cytometer (BD Biosciences) was performed to sort live EPCAM+CD45- single-cells into 384 well plates for Smart-Seq3 analysis. The list of antibodies is provided in Supplementary Data 27.Culture of ovarian cancer cell lines OV2295, TOV2295, and OV2295R cells were cultured in a 1:1 mix of Media 199 (Sigma Aldrich) and MCDB 105 (Sigma Aldrich) supplemented with 10% FBS in a humidified environment at 37C. For single-cell RNA sequencing, all cells used in this study were sorted on a BD Influx into 384 well plates using index-sorting and single-cell purity mode directly into lysis buffer, dNTPs (0.67 mM/each), and Oligo-dT (0.67uM)). Sorted plates were stored at −80 °C and thawed immediately prior to library generation. The cell line originates from ref. For single-cell RNAseq libraries, the Smart-Seq3 method was used according to the published protocol (PMID: 32518404). In brief, plates were quickly centrifuged before reverse transcription (25 mM Tris-HCl pH 8.3 (Sigma), 30 mM NaCl (ThermoFisher), 2.5 mM MgCl2 (ThermoFisher), 1 mM GTP (ThermoFisher), 8 mM DTT (ThermoFisher), 0.5 μ/μl RNase inhibitor (Takara), 2 μM TSO (IDT), 2 μ/μl Maxima H-minus reverse transcriptase (ThermoFisher)), and amplified using KAPA HiFI Hotstart polymerase (Roche) to generate full-length cDNA libraries (22 cycles PCR). Final library concentrations were determined and normalized for each cell using Picogreen. Diluted cDNA of 100 pg per sample was used for tagmentation . The final samples were analyzed using a Bioanalyzer  and sent for sequencing on a Novaseq S Prime lane, PE 2x150bp (Illumina). Library quality was compared to index sorting results to confirm that negative wells yielded low complexity libraries. The list of oligonucleotides are available at 10.17504/protocols.io.bcq4ivyw and provided as Supplementary Data PhylEx performs Bayesian posterior inference over the clonal tree, assignment of SNVs to clones, cellular prevalences by integrating bulk DNA- and scRNA-seq data. The graphical model for PhylEx is provided in Supplementary Fig. T as a rooted tree with the nodeset denoted by V. The nodeset represents the set of clones; we will use the term node and clone interchangeably. The root node r represents healthy cells and has exactly one child; the lone child of the root represents the cancer progenitor clone. Each non-root node v has one parent, denoted ρ(v) but each non-root node may have any number of children (zero or more), the set of children of v is denoted κ(v). We achieve flexibility in modeling the number of children by using tree-structured stick-breaking process (TSSB) prior20, which is a prior over arbitrary tree depth and width, particularly useful in modeling clonal trees where the number of branching events is unknown in advance. We denote the number of SNVs under consideration by N. The clonal membership of SNVs is represented by z = , where zn ∈ V for n = 1, …, N. The aforementioned TSSB defines a joint distribution over T, z, which we denote P0.We define the latent clonal tree n = 1, ..., N as follows: T, z, first introduced in PhyloSub and PhyloWGS7, which we denote as P0. The hierarhical priors enforce the following restrictions on the cellular prevalence parameters: (1) ∑u∈κ(v) ϕu ≤ ϕv and (2) 0 ≤ ϕv ≤ 1. The cellular prevalence of a clone represents the proportion of cell population that inherit the genomic profiles of the clone . Therefore, one important property that must be satisfied is the sum of the cellular prevalence of the descendants of a clone v to not exceed its own cellular prevalence ϕv. The first restriction enforces this property. The second restriction enforces the fact that we are dealing with the proportion of cell population .Each node of the tree is associated with cellular prevalence parameter denoted 52); we use TitanCNA51, from which we obtain major and minor copy numbers, , for each SNV, n = 1, ..., N. Hence, we denote the bulk data by bn, dn denote the variant reads and read depth at locus n. The scRNA-seq data is denoted by C denotes the number of cells and bc,n, dc,n denote the variant reads and read depth at locus n for cell c.From the bulk data, we assume clonal copy number information is available along with the number of reads mapping to the variant and reference alleles. The clonal copy number can be obtained using a wide range of public software  is given by tree-structured stick-breaking process (TSSB) prior20 and P0 is adopted from ref. 6.The likelihood of the bulk and single-cell data is assumed to be conditionally independent given 8. In PhylEx, TSSB is used as a prior distribution over the SNV assignment and the tree topology, P0 with hyperparameters λ0 > 0, λ ∈  process is a Bayesian non-parametric prior defined on infinite trees where a unit length stick is recursively partitioned by nodes of the tree. The TSSB process has proven to be useful in cancer phylogenetics, e.g., refs. ν-sticks and ψ-sticks. The ν sticks are used to allocate the size of the stick assigned to the nodes while the ψ sticks determine the size of the sticks to be allocated to the children. Let υu denote the portion of the stick available to be broken up by node u and πu denote the portion of the unit length stick assigned to node u. To determine πu, we first sample νu ~ Beta, where ∣u∣ denotes the height of node u in the tree. Then, we set πu = νuυu to determine the portion of the unit-stick assigned to node u. The remaining stick, (1 − πu)υu, is allocated to the children of u in the following manner: sample ψu,k ~ Beta for children k = 1, 2, ... of u and then setting υu,k = (1−πu)υuψk ∏j<k.TSSB prior defines partition of unit length stick to the nodes of the tree, where this partitioning is determined by u is proportional to πu, hence, the height of the tree is closely related to the size of the ν-stick. The larger the ν-sticks broken by an ancestral nodes, smaller the stick length available for the descendant nodes. Therefore, the hyperparameters λ0, λ govern the height of the tree. It is straightforward to see that the width of the tree depends on γ. We found that setting γ ≤ 1 makes the most sense for cancer phylogenetics applications since the number of branches in a clonal tree is relatively small; note that γ = 1 ⇒ ψu,k ~ Uniform. We provide details on setting the hyperparameters to control the TSSB parameters in Supplementary Section The prior probability of an SNV being assigned to node P0, is adopted from ref. 6. In essence, this amounts to converting the cellular prevalences to clone fractions,uηu = 1 for a fixed tree T and hence, we can place a Dirichlet distribution on ηu as a prior distribution, conditioned on tree T.The prior distribution on the cellular prevalences, T, z:5, under section heading The PyClone model description; we also provide an example to illustrate the marginalization process in the Supplementary Section zn, and sequencing error probability, ϵ. Note that n-th SNV is assigned since zn ∈ V denotes the assignment of SNV n to a clone . Letting v(g), c(g) be the number of variant copies and total copy numbers for a genotype g, the success probability is given by,The bulk data likelihood assumes site independence conditional on T, z and cell-to-clone membership, u, it inherits all of the SNVs assigned to ancestral nodes of u. We denote the mutation status of cell c for locus n by μc,n ∈ {0, 1}, which can be seen as a function of T, z, ζc .We assume that the scRNA-seq likelihood is conditionally independent over cell and locus given bc,n/dc,n for HGSOC data  or mono-allelic  expression, and χc,n as the probability of expressing the variant allele for cell c at locus n. The parameters of the Beta distribution, α0, β0, αn, βn are hyperparameters of the model. For small values of α0, β0, the Beta distribution places most of the probability mass at the two ends as shown in Supplementary Fig. α0 = β0 makes the distribution symmetric. We use α0 = β0 = 0.01 for HGSOC and HER2+ analysis and set n = 1, ..., N. The parameters αn, βn determine the levels of bi-allelic expression and are estimated as part of data pre-processing step . One iteration of the slice sampler for PhylEx requires computation of bulk data likelihood as well as the single cell data likelihood. As we marginalize over the clone assignment of the single cells, the computational cost requires O(C ⋅ ∣V∣).Inference is performed using slice sampling as described in ref. 54. To evaluate the reconstruction accuracy, we use an ancestral reconstruction error, defined on a pair of SNVs as follows. For two nodes u, v in the tree, we say u < v to mean that u is ancestral to v. We can extend this definition to SNVs i, j. We will say i < j if and only if i is assigned to node u and j to v such that u < v. We formulate an ancestral matrix of dimension N × N, where the -th is set to 1 if i < j. We denote the ancestral matrix for the ground truth SNV-to-clone assignment by A*, then we can compute the absolute error (AE) of an ancestral matrix A by summing over unique pairs:N denoting the true mutation status for cell c, N denoting the predicted mutation status for cell c, and #[a ≠ b] denotes the number of entries where vectors a, b disagree. The expected loss marginalizing over the cell-to-clone assignment is then defined as,We used V-measure, adjusted rand index, and adjusted mutual information as implemented in scikit-learn (version 0.23.1)55. For variant calling, we used Strelka v2.9.2 and Mutect2 as part of GATK v4.1.4.057. We processed the VCF file using vcfR v1.12.058 to obtain for each SNV (i) position in the genome (loci), (ii) the variant and reference alleles, (iii) the number of reads mapping to variant and reference alleles. We used the PASS filter to select the high-confidence SNVs. PhylEx requires major and minor copy number profiles for the SNVs; we used TitanCNA v1.24.051. Given these input data, we provide functionalities to prepare the input data for running PhylEx in the code repository: https://github.com/junseonghwan/PhylExAnalysis. We used Falcon v0.252 to obtain copy number profiles needed for running Canopy9.The bulk tumor and matching normal samples are to be pre-processed following standard guidelines as per GATK standard practice59 with GRCh37 version of Human Genome and Ensembl version 75 annotations were used to align the reads60. UMI-tools v1.1.161 was then used to correct the UMI and group UMI reads. An in-house script was used to intersect the reads with bam files and obtain read and UMI counts for each gene; we used Rsamtools v2.2.362 to obtain the reads mapping to the variant and the reference alleles from the BAM file needed for running PhylEx and used Rsubread v2.0.163 to generate feature counts for downstream gene expression analysis. For 10X Chromium data 10X Genomics CellRanger v6.0.1 was used to generate BAM files and the count matrix64.Individual fastq files for the cells are obtained using Illumina bcl2fq tool, then converted to ubam format with cell and UMI tags using a script that detect Smart-Seq 3 specific pattern at the beginning of reads with UMI. STAR v2.7.319 using samtools v1.965. The copy number profiles are obtained from the bulk samples using TitanCNA and the scRNA-seq is aligned using STAR aligner. A list of SNVs are provided in the data repository provided in ref. 19; therefore, we did not need to make variant calls. Among the list of SNVs provided in ref. 19, 634 SNVs were found to be exonic. We further filtered these set of SNVs using scRNA-seq data. For each loci n = 1, …, 634, we selected it for analysis if there were at least two cells such that bc,n ≥ 2, which resulted in 67 SNVs. Including SNVs that do not have sufficient single-cell coverage does not help to evaluating different methods and their capacities for inferring the branching events and the ancestral relationship. We retrieved the reads at each of these 67 loci for each cell using Rsamtools v2.2.3. A similar approach was adopted for 10X analysis, however, due to shallow depth, we used bc,n ≥ 1 as using a more stringent condition resulted in dropping most of the SNVs from the analysis.The pseudobulk DNA-seq data are obtained by combining scDNA data analyzed in18. The downstream gene expression analyis was performed in R v3.6.3 66. We used biomaRt v2.46.3 for converting and unifying gene names67. We used SingleCellExperiment v1.12.0 for filtering and processing of feature counts matrix from scRNA-seq data68. We used edgeR v.3.32.1 and limma v3.46.0 for differential gene expression and gene set enrichment analysis39. We used zinbwave v1.12.030 and Rtsne v0.1569 for dimension reduction, slingshot v1.8.034 for trajectory analysis, and mclust v5.4.735 for cluster analysis in the reduced dimensions. The plots were generated using ggplot v3.3.370.We ran ddClone v0.2, B-SCITE v2.0, PhyloWGS v1.0, Canopy v1.3.0, and Cardelino v0.6.4 for benchmarkingFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We present a method for interpreting new transcriptomic datasets through instant comparison to public datasets without high-performance computing requirements. We apply Principal Component Analysis on 536 studies comprising 44,890 human RNA sequencing profiles and aggregate sufficiently similar loading vectors to form Replicable Axes of Variation (RAV). RAVs are annotated with metadata of originating studies and by gene set enrichment analysis. Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package. We demonstrate the efficient and coherent database search, robustness to batch effects and heterogeneous training data, and transfer learning capacity of our method using TCGA and rare diseases datasets. GenomicSuperSignature aids in analyzing new gene expression data in the context of existing databases using minimal computing resources. Many transcriptomic profiles have been deposited in public archives but are underused for the interpretation of experiments. Here the authors report GenomicSuperSignature for interpreting new transcriptomic datasets through comparison to public archives, without high-performance computing requirements. The computational infrastructure and skills currently required to leverage the vast quantities of publicly available transcriptomic data render such analyses infeasible for most basic, translational, and clinical researchers. Those who wish to do so must often turn to well-funded computational collaborators with access to significant compute resources in order to provide context and aid in interpreting new experiments. Yet, as public data resources grow, there is a critical need to reduce computational burdens to their application while increasing the breadth of data resources being integrated and analyzed.1 and there have been multiple attempts to detect biological and technical signals through these lower-dimensional, latent variable representations. Gene co-expression can result from shared function or regulation2, association with tissue composition or cell type3, and technical batch effects4. In the confluence of these factors, dimensionality reduction can assist interpretability and reduce the burden of multiple hypothesis testing, but can also lead to incomplete or misleading interpretation. The valid interpretation would be improved by comparison of latent variables in new datasets to those also present in public transcriptome databases.Dimensionality reduction has been broadly adopted to transform large transcriptomes onto a smaller number of latent variables representing co-expressed transcripts. Many dimensionality reduction approaches, differing in the optimization and constraint criteria, are available5. scCoGAPS recovers features in a source dataset and then projects a new dataset onto this learned latent space through projectR6. This approach requires users to train their own model and mostly focuses on single-cell RNA sequencing datasets with similar biology. Pathway-Level Information Extractor (PLIER) aims to extract biologically meaningful and interpretable signatures from high dimensional molecular data by identifying latent variables that map to a single gene set or a group of highly related gene sets with positive correlations7. MultiPLIER applies the PLIER approach to transfer learned patterns from a large public dataset to rare diseases8. Other tools focus on recovering consistent signals from multiple datasets across distinct platforms10, increasing interpretability11, simple database search12, or transfer learning between datasets of a specific type14. However, none of these tools enable a routine exploratory analysis of new studies through comparison to large public transcriptome databases  and Non-negative Matrix Factorization (NMF) remain widely used in their original form and as bases for newer methods. For example, Single Cell Coordinated Gene Activity in Pattern Sets (scCoGAPS) is an NMF method optimized for large, sparse single-cell RNA sequencing datasets15, utilizing RAVs as well-defined and replicable latent variables defined by multiple previous studies in place of de novo latent variables. The interpretability of RAVs is enhanced through annotations by MEdical Subject Headings (MeSH) and Gene Set Enrichment Analysis (GSEA). Through the use of pre-built, pre-annotated, dimension-reduced RAVs, GenomicSuperSignature leverages knowledge from tens of thousands of samples and from PubMed and MSigDB16, to the dataset at hand within seconds on an ordinary laptop. We demonstrate these functionalities in colorectal carcinoma, breast invasive carcinoma, systemic lupus erythematosus, and rare inflammatory disease. GenomicSuperSignature is implemented as an R/Bioconductor package for straightforward incorporation into popular RNA-seq analysis pipelines.Here, we introduce GenomicSuperSignature, a toolkit for interpreting new RNA-seq datasets in the context of a large-scale database of previously published and annotated results. As an exploratory data analysis tool, GenomicSuperSignature matches PCA axes in a new dataset to an annotated index of Replicable Axes of Variation (RAV) that are represented in previously published independent datasets. GenomicSuperSignature also can be used as a tool for transfer learningThe current RAVmodel is trained on 536 studies containing 44,890 human RNA sequencing profiles. This RAVmodel is associated with 18,798  MeSH terms and 70,687  MSigDB curated C2) gene sets. This integration of data resources Fig.  is accom gene set17. We analyzed TPM count data using PCA following log-transformation, then identified clusters of similar principal components (PCs) from independent datasets using hierarchical clustering on Spearman distance and ward.D agglomeration. This approach was compared to alternatives based on (1) ability to group synthetic true-positive PCs, (2) separation of synthetic true-negative PCs added to training data, (3) magnitude of changes in the results compared to the simplest method, and (4) maintenance of RAVs identified from a focused training dataset when adding unrelated datasets. Alternative approaches considered but not selected for training the final model included NMF, Independent Component Analysis (ICA), PLIER7 and MultiPLIER8, Variance-Stabilizing Transformation (VST)18, combining training datasets into a single dataset instead of analyzing them independently, increasing the number of PCs included per dataset, and alternative clustering algorithms including graph-based clustering. These assessments are described in the “Build RAVmodels” section of the Methods.Model training methods were optimized for robustness, simplicity, computational cost, and validity (see Methods). Briefly, the RAVmodel was trained on the RNA-seq Sample Compendia of refine.bioA RAVmodel is composed of an index of RAVs (RAVindex), model metadata, and annotation modules linked through RAVs . About 40% of RAVs in RAVmodel_C2 and 50% of RAVs in RAVmodel_PLIERpriors do not have any enriched pathway and the majority of them are one- or two- element clusters  consist of PCs from unique studies. 622 non-single-element RAVs are composed of only one study and 80% of them have no or only one MSigDB C2 pathway enriched.To guide the interpretation, GenomicSuperSignature gives a message when the output includes any of the following RAVs: (1) single-element RAVs, (2) RAVs with no or too-many enriched pathways, where ‘too-many’ is defined as 5% of input gene sets , (3) non-single-element RAVs constructed from a single study. These criteria together include 2557 RAVs.To demonstrate the ability to match datasets under new analysis to relevant published datasets, we applied RAVmodel to five TCGA datasets Fig. . Based o20. Ma et al. subsequently proposed a continuous scoring system called PC Cluster Subtype Scores (PCSS) based on an analysis of 8 CRC microarray datasets comprising 1,867 samples and found it was more closely correlated to microsatellite instability (MSI)22, grade, stage, and tumor location20. Importantly, these previous efforts both employed curated databases of only CRC transcriptomes, whereas the training set of the current RAVmodel consists of less than 2% CRC studies  observed across numerous patient cohorts19. A p-value near 1  means that no additional information is provided by a full model composed of two subtype definitions compared to a model with only one. CMS-associated RAVs performed better than discrete CMS on all four phenotypes and also outperformed PCSSs except for tumor location  to compare the different subtype models for association with clinicopathological variablesion Fig. . Interesion Fig. . This suion Fig. . In conc8 - systemic lupus erythematosus whole blood (SLE-WB)23 and nasal brushing (NARES)24 datasets. We searched for the SLE pathology-relevant RAV in three different ways using the SLE-WB dataset23. First, we identified RAV1551 based on the highest validation score with the positive average silhouette width. Second, we searched for the keyword, neutrophil, in the top three enriched pathways of all RAVs. Thirteen RAVs, including RAV1551, had two keyword-containing enriched pathways. Lastly, we used the neutrophil count of the SLE-WB dataset to find the metadata-associated RAV. For the continuous variables like neutrophil count, we compared the R2 between the target variable and all RAVs, where RAV1551 showed the maximum R2, 0.395  Exclude studies with more than 1,000 samples because they are more likely to be single-cell RNA sequencing datasets. (2) Exclude studies assigned with a MeSH term, “Single-Cell Analysis”. (3) Exclude studies with fewer than 50 successfully downloaded and imported samples  using the average transcript length across samples and the library size 30, followed by the log2 transformation. Ensembl transcript names were converted into gene symbols using the AnnotationDbi package31. Row normalization was done on all samples together, not at the individual study level, because correcting variability at the study level could remove the signals we want to capture32. For model building, we used 13,934 common genes among 536 studies’ top 90% varying genes, where the variation cutoff was based on their study-level standard deviation  were clustered via hierarchical clustering. For hierarchical clustering, we calculated the distances between loadings using Spearman’s correlation coefficient and clustered them with the ward.D agglomeration method. The number of clusters was set to the minimum that can separate up to 50 negative controls , megaPCA , perPLIER ), and megaPLIER . One of the downsides of the megaPLIER approach was that the direct link between LVs and the training data was not available. Also, the annotation database was inseparable from the model building, making it harder to scale. The perPLIER approach blended LVs in each cluster and lost distinct signatures. Like megaPLIER, megaPCA did not maintain the links between signature and its source data. Additionally, megaPCA picked up only a handful of strong signatures in top PCs, which we can still capture through the perPCA approach without losing weaker signatures. Overall, we choose the perPCA approach for our model building because it is more scalable, keeps the link between signature and its source data, and captures both pan-dataset and per-dataset signatures.We assessed multiple dimensionality reduction methods for RAVindex building. Non-negative Matrix Factorization (NMF) was excluded because there is no clear criterion to select representative components, such as variance explained by each principal component in PCA. Non-orthogonal relationship between components captured by NMF is potentially a more relevant representation of biological data, but by combining replicative principal components, we overcome the orthogonality constraint imposed by PCA. We also ruled out independent component analysis (ICA) because it separates independent signals to reduce the effect of noise or artifacts37.We applied log2 transformation and row normalization across all samples, not at the dataset level, to maintain the differences in scale between the datasets. Variance-Stabilizing Transformation (VST) was excluded because it requires significantly more computing resources - over 200 times longer user CPU times, without any meaningful improvement on capturing biological signatures over log2 transformation because we removed low variable genes from our training datasetsWe searched for the minimum set of genes carrying the replicable biological signals, because more genes require more computing resources to process and some genes were measured only in certain training datasets. Also, low- or non-expressing genes can be indistinguishable from the background noise and including them could make interpretation harder. First, we examined the models built from two sets of common genes subset at the different entities—among training datasets versus between training datasets and annotation databases. The model using the common genes from both training datasets and annotation databases didn’t improve the accuracy of GSEA compared to the the other and made the model building process less scalable because RAVindexes need to be rebuilt for RAVmodels with different annotation databases even for the same training datasets. Next, instead of using a fixed cutoff for ‘low-expressing’ genes, we selected genes based on their expression variance within the dataset because we suspect that genes with a stable expression level within a dataset convey less information to capture. So for the RAVmodel building, we used the common genes among the top 90% varying genes from each training dataset.7. The PLIER::num.pc function applies z-score normalization, but because our method does normalization with all samples combined, we removed this internal normalization from PLIER::num.pc and provided the pre-normalized input data instead. The number of significant PCs from this modified PLIER::num.pc function ranged from 5 to 45, while the “elbow” of the scree plots was not always clear on manual inspection. We chose the median value, 20, as a pre-set cutoff for the different training datasets. Using a varying number of PCs would add complexity to the process that seemed unjustified given that the variance explained by each PC does not vary much by study size, ranging from 50 to 100 for our 536 training datasets. For example, after the 8th PC, less than 5% of the variance was explained by a single PC for all 536 training datasets—the maximum variance explained by PC7 and PC8 are 5.1% and 4.6%, respectively. Finally, one of the main works we benchmarked against was Ma et al., where the authors selected the top 20 PCs for their model building to extract colon cancer-specific signatures.We decided on the number of PCs to collect based on the following four reasons. First, a threshold of 20 PCs is adequate for stability of the RAV model, particularly for larger clusters of 3+ PCs. Most of the lower PCs (PC11-20) are in single-element (22%) or two-element (53%) clusters. We expect further relaxing the cutoff would contribute even less to clusters of 2+ size, which we validate using two RAVmodels consisting of (1) top 10 PCs (RAVmodel_10) or (2) top 20 PCs (RAVmodel_20) from each dataset. We chose the most similar pairs of RAVs between 2382 RAVs (RAVmodel_10) and 4764 RAVs (RAVmodel_20) using the Pearson coefficient. 79% of RAVs in RAVmodel_10 have a similar or identical RAVs in RAVmodel_20 with the Pearson coefficient > 0.7 and the average Pearson coefficient for the RAVs with more than 2 elements is 0.86, suggesting that the model is robust to cutoffs of > = 10 PCs, and that the added computational cost of a cutoff larger than 20 would provide little or no benefit. Second, we chose the top 20 PCs because they represent a majority of the gene expression variance in each study—the median percentage of total variability represented 63%. Third, we applied the elbow method to find the number of ‘significant’ PCs to collect, using the num.pc function implemented in the PLIER package with the following modificationsWe used the negative-control dataset to explore different clustering methods and the optimum number of clusters for hierarchical clustering. First, we constructed 50 synthetic datasets by randomly selecting 50 samples from 44,890 samples. We scrambled genes in each of 50 synthetic datasets and added random values between −0.1 and 0.1. The mean and standard deviation of 44,890 samples were used for row normalization of the synthetic datasets. We confirmed that these synthetics datasets can serve as a negative control based on the distance matrix: the minimum and maximum distance of PC1s from the synthetic datasets ranged approximately between 1st and 3rd quarters of the distance distribution of PCs from the actual training datasets, which we want to separate during the clustering process  using the Jaccard index. We also applied the different clustering methods on the top 5 PCs from 10 positive and 10 negative controls, which were synthetic datasets created through bootstrap and random sample selection, respectively. We evaluated each clustering method based on how often the top PCs from positive controls were clustered together and the top PCs from negative controls were assigned to different clusters.For the centroid-based clustering methods (k-means and k-medoids), we searched the optimum number of clusters using multiple measures including the elbow method, mean silhouette width, and within-cluster sum of squares. However, the number of clusters required to separate unrelated PCs was too high to keep the related PCs together, which could not be improved with different distance metrics. We suspect that PCs from biological data do not possess a spherical or ellipsoidal symmetry required for centroid-based clustering to work.We evaluated graph-based clustering and hierarchical clustering with the different combinations of distance metrics and agglomeration methods  on the same datasets used for the centroid-based clustering tests. From this evaluation, the clustering schema was narrowed down into two versions: graph-based edge-betweenness clustering using edge weight by Spearman correlation, and hierarchical clustering using Spearman distance and ward.D agglomeration. When we applied graph-based clustering approaches to larger datasets, however, there were scalability issues: it formed a very large cluster, containing more than 5% of all PCs, that failed to group even the positive control PCs due to the extreme distribution of the cluster sizes. So we decided to use hierarchical clustering based on Spearman distance with ward.D agglomeration.d), where d is 7, 6, 5, 4, 3, 2.75, 2.5, 2.25, and 2. All negative controls were separated when d = 2.25, regardless of the number of negative controls  and performed hierarchical clustering with the different numbers of clusters. Nine different cluster numbers were applied to each datasets: those nine cluster numbers were round adjusted p-value < 0.05 and among them, collected the pathways with the minimum q-values. The subset of GSEA results—NES, Description, pvalue, and qvalues - were included in the RAVmodel. RAVmodels used in this study are (1) RAVmodel_C2, which was annotated with Molecular Signatures Database (MSigDB) curated gene sets 43, excluding any MSigDB C2 gene set with fewer than 10 genes or more than 500 genes, and (2) RAVmodel_PLIERpriors annotated with the three prior gene sets  provided through the PLIER package7  is a common approach used to supply biological interpretation to lists or sets of genes44 were assigned to each study using the NCBI Medical Text Indexer (MTI) tool45. The relevance of MeSH terms in each RAV was assessed through the bag-of-words model: all the MeSH terms associated with the training datasets were considered as the ‘universe’ and each term in the cluster was reverse-weighted by the frequency of the given term in the universe. MeSH terms were also weighted by the variance explained by the principal component that they came from. The significance of MeSH terms associated with each cluster was evaluated based on their exclusivity. However, the simple sum of associated MeSH terms can be inappropriate in some cases. For example, noise can be a predominant signal in small clusters and common MeSH terms, such as ‘human’ or ‘RNA sequencing’ for the current version of RAVmodel, can be overrepresented and silence the other terms. To handle these extreme situations, we incorporated additional filtering and normalization terms. If the cluster contains less than 8 PCs, we considered any MeSH terms appearing half down of ‘cluster size × 0.5’ as noise and removed them. If the cluster has more or equal to 8 PCs in it, any MeSH terms appearing less than or equal to 4 times were eliminated. These cutoff values for ‘noise’ can be customized by users to fit their needs. We also provide the option to exclude potentially non-informative terms due to the lack of specificity (e.g. ‘Human’ and ‘RNA sequencing’ for the current model) as the ‘dropList’ in the GenomicSuperSignature package, which is customizable as well. The remaining MeSH terms were scored as the sum of the variance explained by PCs divided by the frequency of that term in the universe. This final score can be displayed as a table or a word cloud, using meshTable or drawWordcloud functions, respectively.MeSH termsThe GenomicSuperSignature can be applied to gene expression profiles generated from both microarray and RNA sequencing with the minimum pre-processing. The major requirement for inputs is that the gene expression profile should approximate a normal distribution.40. Any genes with count-per-million (CPM) less than 2 were excluded, and the count matrix was log2-transformed and centered but not scaled before PCA. Eighteen colon cancer microarray datasets from curatedCRCData were also used for validation46. Missing and infinite values were removed from these microarray data and the remaining expression values were centered at each gene level. To evaluate the transfer learning capacity, we used the pre-processed versions of NARES24 and SLE-WB datasets23.For validation, we used five TCGA RNA sequencing datasets  acquired from GSEABenchmarkeRFurther information on research design is available in the Supplementary InformationEditorial Assessment ReportDescription of Supplementary DataSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Dataset 7Supplementary Dataset 8Reporting Summary


The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities. Analyses based on single-cell data have also provided reliable databases for biomedical research and valuable references for medical discovery. As the number of single-cell omics datasets grows, there is increasing demand for fast and accurate computation. Consequently, deep learning has become a trending topic in single-cell data analysis. Much recent research has focused on developing reliable and fast deep learning tools to accommodate the scaling demand, such as cell-type annotation6, doublet identification7, data de-noising8, and batch correction9.Within the last decade, single-cell technologies have advanced our understanding in a broad range of biological systems. Single-cell RNA-seq and single-cell ATAC-seq, along with other single-cell assays, have revealed distinct cellular heterogeneity at a comprehensive level, from genomic variations to epigenomic modifications and transcriptomic regulation11. Multiple single-cell RNA-seq platforms were simultaneously developed, leading to an initial focus on methods to integrate datasets from different platforms. Batch effects are usually the most prominent variation when datasets from different sources are collected for integrative analysis, and this can obscure meaningful biological information. Therefore, removing batch effects is a critical step to reveal true biological variation and is necessary for building batch-invariant and applicable single cell atlases. So far, multiple methods have been proposed to address this problem16. Among these integration methods, deep generative models were extensively tested and demonstrated their efficacy for learning discriminative representations from the original high dimensional space. The most common generative models are Variational Autoencoder (VAE). Variants of VAE models, which differ in their sampling approaches, have been proposed to learn representations for single-cell gene expression data20. The core component of VAE is the use of reconstruction loss, which encodes a sample in a representation that is drawn from a certain distribution, for example, a Gaussian distribution. The use of reconstruction loss also has an advantage of mapping noisy data to high-quality data, which further extends the ability of generative model to de-noise data or impute gene expression. Instead of using VAE to learn representation for single-cell RNA-seq data, two research groups simultaneously modified VAE to address batch effects using an adversarial approach20. These two methods, named scGAN and AD-AE, respectively, used generative adversarial network (GAN) as the main framework for learning the latent space that is not entangled with batch effects. Starting from a VAE model, both scGAN19 and AD-AE20 introduced adversarial domain loss into the generative model and transferred the learning from reconstruction of data to diminishing of non-biological variation. This approach turned out to be effective in removing batch effects within single-cell gene expression data.Among all applications of deep learning in single-cell analysis, data integration remains one of the grand challenges in the community21. We term our method sciCAN , which removes modality differences while keeping true biological variation. We previously developed a deep learning method, SMILE, to perform integration of multimodal single-cell data22. SMILE requires cell anchors for integration. This limits the use of SMILE only to cases where corresponding cells are known across modalities, as is true for joint profiling experiments. In contrast to SMILE, sciCAN does not require cell anchors and thus can be applied to non-joint profiled single-cell data. We first benchmarked our method with 5 existing methods across 5 ATAC-seq/RNA-seq datasets, and we demonstrate that our method deals with data integration with a better ability to transfer cell type labels in both directions between modalities than the other 5 methods. To demonstrate the method’s utility in integrative analyses, we applied sciCAN to joint-profiled peripheral blood mononuclear cells (PBMC) data by 10X Multiome platform and showed that the derived integrated representation preserves the hematopoietic hierarchy at both chromatin accessibility and gene expression levels. Finally, we investigated CRSIPR-perturbed single-cell K562 ATAC-seq and RNA-seq data, and we identified that some cells in both modalities share common biological responses, even though the two modalities were profiled with different gene perturbations. Combining the results above, we expect our work will fill the gap to allow generative models to be used in integrative analysis of multimodal single-cell data.However, both scGAN and AD-AE solely focused on the use of adversarial learning in single-cell RNA-seq data. Considering their success in batch-effect correction, here, we aim to extend the use of adversarial learning to single-cell data integration across different modalities. In this study, we focus on modality differences and develop an improved adversarial domain adaptation approach to address multimodal data integration for chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) data. Our method differs from both scGAN and AD-AE in that it uses a cycle-consistent adversarial network to learn the joint representation for both chromatin accessibility and gene expression dataE serves as a feature extractor that projects both high dimensional chromatin accessibility and gene expression data into the joint low dimension space. For representation learning, we use noise contrastive estimation (NCE) as the single loss function to guide E to learn the discriminative representation that can preserve the intrinsic data structure for both modalities. For modality alignment, we use two separate discriminator networks for two distinct uses. The first discriminator network Drna is attached to E and is trained with adversarial domain adaptation loss. Drna aims to distinguish which source the latent space z extracted by E comes from, while E is pushed to learn the joint distribution so that Drna is less able to distinguish the modality source of latent space z. The second discriminator network Datac follows a generator network G that generates chromatin accessibility data based latent space z from gene expression data. Adversarial training here will push G to find a connection between chromatin accessibility and gene expression data. Since the generated chromatin accessibility data is based on the latent space z of real gene expression data, the new latent space z’ of generated data should align with its corresponding z of real gene expression data. Therefore, we add cycle-consistent loss as demonstrated in cycleGAN method to facilitate finding the connection between two modalities21. In practice, we build E with fully connected layers, which are followed by a batch normalization layer with Rectified Linear Unit (ReLU) activation. Drna takes the 128-dimension z as input and forwards it through a three-layer multi-layer perceptron (MLP) to produce 1-dimension sigmoid activated output that predicts if the input z comes from single-cell RNA-seq data. Differently, Datac takes output from G and forwards the input through a three-layer MLP to produce 1-dimension sigmoid activated output that predicts if input is generated by G. G is a decoder structure, which has two-layer MLP to restore dimension-reduced z to the original dimension of input data. Instead of calculating NCE directly on z, we further reduced z to 32-dimension output with linear transformation and 25-dimension SoftMax activated output, through two separated one-layer MLPs. This practice is the same as our previous study, in which we demonstrated an effective approach to learn discriminative representation for single-cell data22. Once model training is done, we use encoder E to project both modalities into the joint representation for downstream analyses  or with further modifications to Seurat (ArchR). Thus, we included ArchR in our benchmark test. As sciCAN shares the same architecture as SMILE to learn representations for single-cell data and both methods are proposed for data integration, we also included SMILE in our set of methods to test. However, SMILE requires cell anchors across modalities to learn the joint representation, while not all benchmark datasets include this information. Therefore, we used Seurat to identify cell anchors so that SMILE could rely on these anchors to integrate ATAC-seq and RNA-seq data. For the benchmark purpose, we collected 5 datasets that consist of distinct cellular systems. They are mixed cell lines28, human hematopoiesis29, human lung30, mouse skin31, and mouse kidney32, respectively. RNA-seq and ATAC-seq modalities may have different numbers of cells and even different numbers of cell types, except where both modalities were jointly profiled . Across 5 datasets, Harmony, Seurat, and sciCAN integrated chromatin accessibility and gene expression data well, giving a larger modality silhouette value. Among all methods, LIGER ranked the last in modality mixing, with the worst modality silhouette values in 3 datasets Fig. . Though Next, we focused on label transferring. Here, our goal is that the user could rely on the integrated space to predict cell-type labels for data from a single modality, given availability of cell-type labels from the other modality. We found Seurat has overall the best performance for label transferring from RNA-seq to ATAC-seq Fig. . This ma35. We wondered if this setup is critical to good integration by sciCAN. Thus, we switched the roles of RNA-seq and ATAC-seq data in the model training. Indeed, the ATAC-centered sciCAN model is consistently less accurate than RNA-centered sciCAN, suggesting discriminative representation learning benefits from taking advantage of the cell-type discriminative power of RNA-seq  when we calculate positive velocity as an increase in gene expression first, followed by an increase in gene activity (accessibility). This directionality suggests that in this system gene expression may be activated first, followed by a chromatin state encoding of this expression pattern as the new cell type is established. Given the joint representation, we predicted gene expression based on gene activity. Then, we used the true activity matrix and the predicted expression matrix to compute the activity-expression velocity with scVelo46. Taking advantage of the ground truth from the cell pairing information, we also performed the same analysis using the true activity matrix and true expression matrix. We found that velocity computed with the predicted expression data resembles and correlates well with the velocity computed with true expression data, in accordance with the correlation between predicted and actual expression , so the integration cannot simply group like targets, but instead will be challenged to find similar biological responses to different gene perturbations. First, sciCAN enabled us to co-embed and co-cluster RNA-seq and ATAC-seq data, and we identified 3 distinct clusters . In an N-sample batch, there will be 2 N samples through data augmentation, and each augmented image i has its corresponding counterpart j which is the same, despite the added image perturbation. Then, cos quantifies the cosine similarity of image i and j/k in the latent space z. Chen et al. demonstrated that this simple framework turns out to be a highly effective way to learn the discriminative representation without supervision. We adapted this approach in our previous study and showed the sample framework can produce discriminative representations for single-cell data22. (See this previous SMILE study for in-depth details of how perturbations are added to duplicated data). Because of the property of this metric learning, our method is fully unsupervised. Users do not need to provide cell-type labels to start model training.Deep metric learning has shown effective representation learning without supervision. Chen et al. used a simple framework to learn visual representations in a self-supervised manner51. Recently, both scGAN19 and AD-AE20 incorporated adversarial domain adaptation into a generative model for removing batch effects within single-cell expression data. For both studies, the goal is to find a batch-invariant representation for single-cell gene expression data from various sources. To achieve this, they stacked a discriminator to the encoder and trained the discriminator to distinguish which source the cell comes from using the latent space z projected by the encoder. Adversarial training, in this case, will push the encoder to approximate the joint distribution and become capable of projecting cells with data from different modalities to the same integrated representation. Here, we also used domain adaptation to train a discriminator to identify the modality source while the encoder is pushed to diminish modality difference.Generative models with adversarial domain adaptation were successfully shown to transfer targets to source style and have general applications in image translation21. The success of establishing a connection between two image domains relies on the concept called “cycle consistency”. Starting from the original image, a generator network translates the image to the other domain. Then, a second generator network translates the image back to its original domain. Through this cycle, the translated-back image should be the same as the original image. Based on this information, adversarial training of generators can establish a reversible connection between two image domains. Different from the goal of cycleGAN, we aim to learn joint representation instead of translating chromatin accessibility to gene expression or vice versa. However, the fundamental concept is the same: we establish a cycle from encoder to generator, and from generator back to encoder. Then, the cycle-consistency loss is applied at the level of latent space z.Besides the use of adversarial domain adaptation above, we further introduced a cycle-consistent adversarial part. This practice stems from a method called cycleGAN, which presented a high-performing outcome for the task of transferring image styles from one domain to another53. Here, we briefly explain the rationale behind this transformation. RNA-seq measures gene expression, so in a matrix of single-cell gene expression data, each row represents one cell, and each column contains expression values of one gene. The whole matrix represents gene expression levels of all genes across all cells. ATAC-seq, on the other hand, quantifies how accessible genomic loci are to regulatory proteins. Therefore, in a matrix of single-cell chromatin accessibility data, each row is one cell (the same as single-cell gene expression data) and each column contains accessibility values of one genomic locus. The sum of accessibility values of all genomic loci upstream of and within one gene body may relate to the potential of transcription of that gene. Therefore, to convert ATAC-seq data to a form that can be compared to RNA-seq data (a matrix of cells by genes), all accessibility peaks upstream of and within each gene body are summed to represent gene activity. In the converted gene activity matrix, each row is one cell, and each column is accessibility values of one gene. Therefore, after conversion, we can do a simple filtering and reordering to match features of chromatin accessibility and gene expression data. The Signac package provides this conversion process, and we ran the code available at https://satijalab.org/signac/articles/pbmc_vignette.html53. After we have both a gene activity matrix and a gene expression matrix, we normalize both modality data with (Log + 1)-transformation, which adds 1 as a pseudo count to the matrix before log-transformation. Then, we identify the top 3000 highly variable genes (HVG) for each modality and use all identified HVG as features for integration. To identify the top 3000 HVG, we use Scanpy by calling the highly_variable_genes function54.All methods benchmarked in our study require anchoring genes for integration. We used a common practice that transforms the sparse ATAC-seq peak matrix to a gene activity matrixNCE loss function, temperature τ is a crucial parameter that affects discriminative power of the final representation. We set as τ = 0.15 for the 32-dimension linear-transformed output and τ = 0.5 for the 25-dimension SoftMax activated output, which is consistent to the practice in our previous study22. Detailed training code is also provided on sciCAN GitHub (https://github.com/rpmccordlab/sciCAN).We trained sciCAN in all datasets for 100 epochs. The learning rate starts from 0.005 with 0.0005 weight decay. All weights in the sciCAN model are updated through stochastic gradient descending. In the 14. We used default parameters to perform integration of chromatin accessibility and gene expression data, and the final dimension of integrated representation by LIGER is 20 for all 5 benchmark datasets. Briefly, LIGER uses integrative nonnegative matrix factorization (iNMF) to identify metagenes that are shared between ATAC-seq and RNA-seq55. These metagenes are a weighted matrix of factor loadings of observed gene expression/activity. Then, cell loadings of these metagenes are used to perform joint clustering and other downstream analysis. Ideally, representations of cells from both modalities after iNMF should have been integrated in the same latent space and can be visualized via tSNE or UMAP57.Multimodal single-cell data integration by LIGER was demonstrated in its published tutorial15. Later, the use of Harmony in multimodal single-cell data integration was discussed in reviews59. Meanwhile, a batch-correction benchmark study showed that Harmony was ranked among the top 3 methods, with LIGER and Seurat, for integrating single-cell RNA-seq data23. Therefore, we included Harmony in our benchmarking of multimodal single-cell data integration. Harmony learns the joint representation through an iterative k-means clustering, and the outcome is a linear correction function that transforms the original principal components (PCs) to the batch-corrected PCs. Batch information is necessary to guide Harmony to distinguish what variation should be diminished during the k-means iterations. Principally, to integrate chromatin accessibility and gene expression data, modality information serves as the same role of batch information. Again, we used the default procedure of Harmony, in which we reduced the whole dataset into the first 30 PCs.Harmony is the second integration method benchmarked in our study. Originally, Harmony was designed to correct batch effects within single-cell RNA-seq datasetshttps://satijalab.org/seurat/archive/v3.0/atacseq_integration_vignette.html, and the final dimension of integrated representation by Seurat would be 50.Seurat uses canonical correlation analysis to learn the shared latent space between two modalities. This approach is different from LIGER, Harmony, and our method, in a way that Seurat will first identify confident cell pairs between the two modalities. Then, Seurat uses these paired cells as anchors to learn a mutual neighborhood graph. Finally, it computes a projection that brings all other cells to this shared latent space. Because of its “anchor” design, Seurat needs pairwise computation of anchor points when datasets come from more than two sources. Since we only deal with the modality difference between chromatin accessibility and gene expression in this study, we do not need to perform pairwise computation of anchor points with Seurat. For benchmarking, we ran Seurat v3 with the tutorial on ArchR uses Seurat as infrastructure to integrate RNA-seq with ATAC-seq data. Different from Seurat, ArchR constrains the mapping from ATAC-seq to RNA-seq in a “subspace”. An initial unconstrained mapping was done through Seurat. This step is aimed to estimate what clusters in ATAC-seq have good correspondence to a certain number of clusters in RNA-seq. Then, the “subspace” or constrained mapping will only project a number of clusters in ATAC-seq onto their corresponded clusters in RNA-seq.https://github.com/rpmccordlab/SMILE).We previously developed SMILE to integrate multimodal single-cell data when cell anchor information was obtained from co-assay profiling. Because sciCAN and SMILE share the same architecture to learn lower dimension hidden space for single-cell data, SMILE also generate 128-dimension hidden space. To use SMILE for integration in this situation, we had to rely on external tool, like Seurat, to identify cell anchors. Once cell anchors identified, SMILE was trained based on anchored data and projected the rest of unanchored data into the joint representation space. A tutorial can be found at SMILE GitHub  and data 4 (Mouse skin) have the same number of cells and cell-types. This is because SNARE-seq and SHARE-seq simultaneously profile chromatin accessibility and gene expression features from the same cells31. Experimentally, each cell in ATAC-seq has its corresponding cell in RNA-seq in SNARE-seq and SHARE-seq data. However, in our study, we blind ourselves to this paired information for all 6 methods. For the other 3 datasets, data from ATAC-seq and RNA-seq were collected separately on different groups of cells. Therefore, they do not necessarily consist of the same number of cells and do not necessarily share the same cellular components. Cell-type annotations were also annotated separately by the authors, and the author-reported annotations serve as ground truth for integration evaluation. We also collected a joint-profiled human PBMC data by 10X Multiome platform to demonstrate that integration by sciCAN preserves biological information. Finally, we collected two independent CRISPR-perturbed single-cell K562 datasets that profiled chromatin accessibility49 and gene expression47, respectively. The brief description and citations for these 7 datasets are shown in Supplementary Table We collected unpaired chromatin accessibility and gene expression from 5 studies for benchmarkingTo evaluate integration by each method, we proposed 4 metrics:60. Then, we calculated modality and cell-type silhouette scores on the 2D UMAP spaces. A typical silhouette score S ranges from −1 to 1. To better reflect the integration outcome, we define modality silhouette as 1-abs(S) and cell-type silhouette as (1 + S)/2. Of note, we used different labels to calculate modality and cell-type silhouette. For modality silhouette, the label used is modality information. A good integration should have chromatin accessibility and gene expression data largely overlapped. Therefore, the absolute value of S should be close to zero (regardless of sign), and we then subtract the absolute value of S from 1 so that the best score will have a value of 1. For cell-type silhouette, we used the author-reported annotation label to calculate S and then scale the output to the range from 0 to 1. Thus, cell-type silhouette of 1 indicates the best integration that preserves cell-type structure.As we mentioned before, sciCAN and SMILE reduce each dataset into 128-dimension spaces, while LIGER reduces the data to 20 dimensions, Harmony to 30, and both Seurat and ArchR to 50. Since the final dimensions of the integrated representations by the 6 methods are not the same, we further used Uniform Manifold Approximation and Projection (UMAP) to reduce them into 2-dimensions with the same UMAP running parametersA useful integration of modalities should have the ability to transfer cell type labels from one datatype to another, either from RNA-seq to ATAC-seq or from ATAC-seq to RNA-seq. Given cell-type label availability from a single modality, the user should be able to predict cell-types for the other modality, with a fair accuracy. To evaluate how friendly the joint representation is for label transferring, we trained a Support Vector Machine (SVM) classifier with one modality and tested it with the other modality. The choice of SVM is simply based on a constant superior performance of SVM classifier across datasets. Then, we used macro F1 and F1 score for each cell type to evaluate SVM classifiers trained with different joint representations by these 6 methods. Macro F1 score is the average of F1 scores for all cell-types, and it can help us reveal if integration is good for non-major cell-types. This is because cell-types are not balanced in most single-cell data and revealing non-major cell-types is critical for most single-cell analysis. A high macro F1 score can suggest that integration is also good for non-major cell-types. Meanwhile, individual F1 scores for all cell type also report which cell-type prediction is the hard case and what is the highest F1 score the classifier can reach to.Across all 5 datasets, we first used Supplementary Figures and Tables


Endometrial carcinoma (EC) is one of the three major gynecological malignancies, in which 15% - 20% patients will have recurrence and metastasis. Though there are many studies on the prognosis on this cancer, the performances of existing models evaluating the risk of its recurrence and metastasis are yet to be improved. In addition, a comprehensive multi-omics analyses on the prognostic signatures of EC are on demand. In this study, we aimed to construct a relatively stable and reliable model for predicting recurrence and metastasis of EC. This will help determine the risk level of patients and choose appropriate adjuvant therapy, thereby avoiding improper treatment, and improving the prognosis of patients.The mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), copy number variation (CNV) data and clinical information of patients with EC were downloaded from The Cancer Genome Atlas (TCGA). Differential expression analyses were performed between the recurrence or metastasis group and the non-recurrence/metastasis group. Then, we screened potential prognostic markers from the four kinds of omics data respectively and established prediction models using three classifiers.We achieved differential expressed mRNAs, lncRNAs, miRNAs and CNVs between the two groups. According to feature selection scores by the random forest algorithm, 275 CNV features, 50 lncRNA features, 150 miRNA features and 150 mRNA features were selected, respectively. And the prediction model constructed by the features of lncRNA data using random forest method showed the best performance, with an area under the curve of 0.763, and an accuracy of 0.819 under 10-fold cross-validation.We developed a computational model using omics information, which is able to predicting recurrence and metastasis risk of EC accurately. Endometrial carcinoma (EC) is a kind of epithelial malignant tumor occurring in the endometrium and is one of the three major gynecological malignancies –3. In NoNowadays, with the development of liquid biopsy technology and the popularization of artificial intelligence in the field of medical images, there are many new explorations and novel methods in predicting tumor recurrence and metastasis –22. For http://cancergenome.nih.gov/) were downloaded and organized to study the association between gene mutation/expression and recurrence or metastasis of EC. Specifically, we first compared the differential expressions of mRNA, long non-coding RNA (lncRNA) and microRNA (miRNA) between patients with recurrence or metastasis and patients without recurrence or metastasis using the DESeq2 package, and then analyzed differences of copy number variations (CNVs) between the two groups by rank-sum test. Furthermore, we analyzed the function of these differential genes, and discussed the molecular mechanism of recurrence and metastasis of EC. After that, characteristic variables were selected by a random forest (RF) algorithm with feature selection and were used to establish prognostic prediction models using three different classifiers. Finally, the RF model based on lncRNA showed the best performance among the twelve models.In this study, all sequencing data and clinical information of patients with EC from TCGA  and the expression levels were normalized by DESeq2  package p_value < 0.05 and q_value > 1 as the threshold.To explore the potential biological functions of these differential genes and the signal pathways they may participate in, we performed Gene Ontology (GO) , 28 and The patients in the recurrence and metastasis group and in the group without recurrence or metastasis were divided into the training set and the test set with the ratio of 7:3, respectively. After the division, the patients were fixed in the training set or the test set, that is, in different omics analyses, the same patient was always in the training set or test set.For each omics data of the training set, a feature selection algorithm based on RF was applied to screen important features –33. SpecBased on the selected characteristic parameters, RF, logistic regression and support vector machine classifiers were chosen for model construction to select the best model to predict the recurrence or metastasis of patients with endometrial cancer. Specifically, omics data in the training set were grid searched in each classifier to select the best super parameter, and then the final super parameter was determined through 10 fold cross-validation. Finally, we obtained 12 prediction models and compared the prediction performance mainly using the AUC of receiver operating characteristic (ROC) curves, precision and accuracy.The overall process of exploring the molecular mechanism of recurrence and metastasis, and establishing risk prediction models using a machine learning algorithm was described in P_value of 0.000656. Whereas, there were no significant differences between the recurrent or metastatic group and non-recurrent or metastatic group in ages (median), body mass index (BMI) and pathological type.After matching data according to the patient ID, 238 EC patients with both prognostic information and four kinds of omics data were obtained. Of these patients, 39 patients (16.39%) had cancer recurrence or metastasis whereas 199 patients (83.61%) had no recurrence or metastasis. Clinical and pathological information of these two groups of patients in this study was shown in Among the expression data of 17,958 mRNAs, 592 mRNA genes were expressed significantly different between the recurrent and metastatic group and non-recurrent or metastatic group, with 169 up-regulated genes and 423 down-regulated genes in the recurrent or metastatic group compared to the non-recurrent or metastatic group , it was Then GO and KEGG enrichment analyses were performed to explore the function and involved signal pathways for further investigating the prognostic value and molecular mechanisms. For significantly differentially expressed mRNAs, the top twelve molecular functions with the highest proportion of genes were displayed in Among the data of EC downloaded from TCGA, there are 17958 mRNA expression information, 7315 lncRNA expression information, 1881 miRNA expression information and 16383 copy number variation information. Variable selection for these biological data was performed using the RF to determine variable importance measures. The scoring of different number of features screened by RF is shown in For each kind of omics data, three classifiers  were used to construct the prediction model. The ROC curves of three models based on lncRNA data were displayed in The Oncotype Dx (21 genes) and MammaPrint (70 genes) are two products for predicting recurrence and metastasis of breast cancer which have been internationally recognized . HoweverHere, starting from the data of TCGA, we analyzed the differences of mRNA expression, miRNA expression, lncRNA expression as well as CNVs between patients with recurrence and metastasis and non-recurrence or metastasis, and further analyzed their molecular biological functions and involved signal pathways, trying to explore the molecular biological mechanism of these differences and recurrence and metastasis. Although these molecules are related to recurrence and metastasis, it does not mean that these differential molecules can accurately and reliably predict recurrence and metastasis. To build the prediction model, it is still necessary to select the most appropriate feature combination by statistical methods , 34. TheThere are still several limitations in this study. Firstly, the sample size was limited and the survival time of tracking was not long enough, which may lead to inaccurate results. However, we have downloaded all samples information from TCGA, a relatively large-scale cancer genome database. Looking for more samples from other open databases or hospitals may be a solution. Secondly, the research on molecular mechanism was not deep enough –39. MaybThe original contributions presented in the study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.YS and XS designed the project. LLi, WQ, LLin, and JL collected and analyzed the data of patients with endometrial cancer. LLi and XS searched literatures and wrote the manuscript. All authors have approved the final version of the manuscript.WQ, JL, and XS were employed by the company Geneis Beijing Co., Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.