Compared to current gold-standard methods that do not account for group heteroscedasticity, we show results from simulations and various experiments that demonstrate the superior performance of voomByGroup and voomQWB in terms of error control and power when group variances in pseudo-bulk single-cell RNA-seq data are unequal.Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the detection of differentially expressed genes. Since most bulk RNA-seq methods assume equal group variances, we introduce two new approaches that account for heteroscedastic groups, namely The online version contains supplementary material available at 10.1186/s13059-023-02949-2. Single-cell RNA sequencing (scRNA-seq) allows the quantification of transcript profiles across individual cells and has become widely adopted over the past few years. A major advantage of scRNA-seq is the high resolution it offers, enabling researchers to study molecular responses to different biological perturbations at the cellular-level  rather tMAST [BPSC [DEsingle [Early DE analysis of this data type aimed to fully leverage information from individual cells, whereby each cell in comparison is treated as an independent biological unit (or “replicate”). To achieve this, a number of studies used established methods developed for bulk RNA-seq data . HoweverMAST , BPSC [7DEsingle ). To guiDEsingle , 10, whiDEsingle . AnotherDEsingle . This stDEsingle , 14. ThrDEsingle  so that DEsingle , 16, it DEsingle , 14.limma-voom, limma-trend [edgeR [DESeq2 [Limma was developed for microarray data, assumes the log-transformed expression values are normally distributed, and employs linear modeling and empirical Bayes shrinkage to improve the stability and power of statistical tests. For RNA-seq data, voom and limma-trend were subsequently developed based on the assumption of normality of the log-transformed counts, using different strategies to address the dependence that is observed between the mean and the variance (referred to as heteroscedasticity) in this kind of data. Voom models the relationship between the mean and variance across all observations using a fitted LOWESS trend and calculates precision weights based on the estimated trend for use in linear modeling. On the other hand, edgeR employs empirical Bayes shrinkage and was developed assuming gene-level counts follow a negative binomial distribution.Most of the DE analysis methods applied on pseudo-bulk data in the literature are “gold-standard” bulk DE analysis methods, including ma-trend , 18, edgd [edgeR , and DES [DESeq2 . Limma wDue to limited sample numbers, most bulk DE analysis methods including the aforementioned gold-standard methods borrow information between genes to estimate the variance , 21–23 at-test when group variances are unequal [voom was unable to model the variability appropriately and they noted that the weighting strategy used in voomWithQualityWeights (voomQW) may be more helpful [Heteroscedasticity has been frequently observed in microarray gene expression data , 26, for helpful  on accou helpful . They mavoomByGroup and voomQW using a blocked design, have superior performance in this regard when group variances are unequal.In this article, we examine whether group-specific variances are homoscedastic  or heteroscedastic  in pseudo-bulk scRNA-seq data. We show that heteroscedastic groups are frequently observed in the data and that the application of current DE analysis methods has variable performance. Importantly, gold-standard methods that do not model group-level variability can both under- and over-estimate variances leading to poor error control or reduced power to detect DE genes. We demonstrate that methods that account for heteroscedastic groups, namely voom trends”). Larger distances between samples in a group on the MDS plots indicate more within-group variation. BCV is a measure of the biological variability in gene expression between biological replicates and is frequently used to estimate the variance of gene expression in RNA-seq data [edgeR. For the group-specific voom trends, we are interested in observing where the curves sit relative to other groups in the same study, as well as the shape of the curve. The shape and “height” of the curves reflect the total variation within groups—both technical and biological.To study whether group variances are equal or unequal in scRNA-seq pseudo-bulk data, we explored pseudo-bulk scRNA-seq datasets generated with cells from specific cell types obtained from various sample types ranging from experimental replicates of mice to human samples see the “” sectionseq data . Higher Xenopus tail [voom trends, with the curves sitting close together and mostly overlapping one another  , healthyvoom trends had distinct shapes and were well separated from one another along the vertical axis (with the exception of IPF and control groups which were quite similar). Moreover, the plateauing of voom trends at higher expression values that are commonly observed in many datasets was not observed here. This might be on account of the complexity of regions in the lung where samples are collected, the diverse causes of lung fibrosis, and limited patient numbers for some groups. High levels of biological variation are reflected in the large BCV values in this dataset.A separate study on human macrophages collected from lung tissues  showed evoom trends.While we have not commented specifically on MDS plots, these plots  provide a useful first glance of the data and are already part of many analysis pipelines Fig. . For exaIn conclusion, we observe unequal group variability across multiple scRNA-seq pseudo-bulk datasets. At this stage, it is unclear whether gold-standard bulk DE analysis methods are robust against heteroscedasticity, and how different group variances need to be before it affects their performance. We test this in later sections of this article, using three gold-standard methods that do not account for heteroscedasticity and two novel methods that do.voomQW method. The standard use of voomQW assigns a different quality weight to each sample, which then adjusts the sample’s variance estimate—a strategy used to tackle individual outliers in the dataset. Rather than adjusting the variance of individual samples, we adjust the variance of whole groups by specifying sample group information via the var.group argument in the voomWithQualityWeights function. This produces quality weights as “blocks” within groups  and adjusts each group’s variance estimate—we refer to this method as “voomQW using a block design,” or simply voomQWB.The first method that accounts for group-level variability makes novel use of the existing voomQWB for a study comparing healthy controls to COVID-19 patients that are moderately sick and those that are asymptomatic [voom , resulting in lower variance estimates and larger precision weights. There are a couple of things to note here: (1) the group-specific voom trends from voomQWB  of each gene in “Group 1 (Moderate symptoms)” and a curve, or trend, is fitted to these values from which precision weights are then calculated , and edgeR quasi-likelihood (edgeR QL). The methods that account for group heteroscedasticity (“group variance methods”) are voomQWB and voomByGroup. Using simulations of pseudo-bulk data, we can examine the effects of unequal group variation while controlling other factors. Specifically, group variation can be divided into biological variation between RNA samples and technical variation caused by sequencing technologies.Using simulated data, we test the performance of 3 gold-standard methods against the 2 new methods that account for heteroscedastic groups. The gold-standard methods are scenario 1), we looked into unequal group variation as a result of biological variation. To obtain pseudo-bulk data, we simulated single-cell gene-wise read counts that followed a correlated negative binomial distribution and aggregated the reads from each sample .We then performed differential gene expression analysis for all pairwise group comparisons, which gave a total of 6 comparisons. In these simulations, 50 genes were generated to be upregulated in each group, such that 100 genes are differentially expressed in each pairwise comparison see the “” sectionedgeR LRT, followed by edgeR QL, then voom. In comparison, the group variance methods, though not perfect in terms of type I error control, had a substantially tighter range of FDR values, and the comparisons that exceeded the FDR threshold only exceed it by a small margin.A closer look at these plots revealed that the gold-standard methods had FDR values that spanned a broad range, with some comparisons having FDR values that were well under the threshold, and others that exceeded the threshold by 2- or 3-fold. This means that it could be difficult to gauge whether the DE results are too conservative, too liberal, or perhaps “just right” for a given comparison in real datasets when applying these methods to heteroscedastic groups. The range of FDR values is broadest for p-value cut-off. Across the 6 comparisons, group variance methods tend to detect similar numbers of differentially expressed genes, the same goes for gold-standard methods  and group 1 and group 2 (“Low vs Low”) shows where the 2 classes of methods differ.To understand how heteroscedasticity influences DE analysis in more detail, we focused on results obtained using a 0.05 adjusted ods Fig. : Fig. S3High vs High comparison, gold-standard methods detect more DE genes than group variance methods. However, the DE genes contain a much higher proportion of false discoveries than it was controlled for. It is not as though gold-standard methods were prioritizing false-positive genes in terms of significance—it had similar numbers of true- and false-positive genes when looking at top-ranked genes  where all groups had equal underlying biological variation , we first vary the library sizes of samples. The provided number of cells are 250, 250, and 250 in group 1, 250, 200, and 200 in group 2, 250, 500, and 500 in group 3, and 250, 750, and 750 in group 4 Fig. ” sectionscenario 3), the mean-variance trends generated appear to mostly overlap each other on the right-hand side as expected on account of equal group dispersions, while on the left-hand side, slight differences appear  with both aspects of variation incorporated  exhibit slightly more variation and sit at the top, while the group with the largest number of cells (group4) is at the bottom . Both of the voom-variants, voomQWB and voomByGroup, detected all of the genes that were also detected by voom. With the exception of 1 gene, voomByGroup also detected all of the genes that were detected by voomQWB. From voom to voomQWB then voomByGroup, the methods increase in their level of group-specific variance modeling. The overlap between these methods and the extra DE genes reflects the hierarchy in variance modeling for these methods and demonstrates the potential gain in statistical power when capturing group variance more accurately.To understand our results further, we looked at the consistency at which genes were detected as DE between methods  enrichment analysis to study the biological processes that play a role in COVID-19. We looked for any enrichment in GO terms for significantly upregulated genes in asymptomatic patients for each of the methods under examination. .01 Fig. b. None oseverity . The oriPBMC2); (3) when variance trends are distinct, group variance methods may benefit from a gain in statistical power, as observed in the first dataset (PBMC1); and (4) by modeling group-specific variances closely, voomByGroup was the only method that obtained statistically significant results for the biological process of interest in both datasets. These two datasets are used here to highlight how results of biological interest may be “missed” if heteroscedasticity is not carefully considered.Taking the two COVID-19 datasets into consideration, we noticed a few things: (1) group variances can change between one dataset and another, even for studies on similar cell types and similar subjects—this perhaps has to do with how samples are processed  and/or the “grouping” criterion plus the individual subjects involved ; (2) when variance trends are not too distinct from one another, all methods perform similarly, as observed in the second dataset  would be more appropriate. In our study, we did not explore datasets with outlier samples and leave such investigations as future work.In situations where there are individual samples with higher variability (outliers), the edgeR-based methods returned relatively different results compared to voom-based methods  compared to the normal distribution, methods were first developed for modeling group heterogeneity in limma , which assumes normally distributed data. When modeling data using a NB-GLM, modeling group-wise variation is more challenging. An example of weighted regression in this context comes from Zhao et al. [In this study, we also observed that ods Fig. a. One mao et al. , who usevoomQWB method together with the duplicateCorrelation function in the limma package.In our article, we focus on DE analysis of scRNA-seq pseudo-bulk data because recent benchmark studies have shown that it gives better results relative to analyzing scRNA-seq data in its original non-aggregated form , 14, 38.voomQWB or voomByGroup can be extended to cell types or clusters to find marker genes.Whilst we apply group variance methods on pseudo-bulk samples in this article, the idea of modeling group variances more closely can in theory be extended to DE analysis of other data types such as bulk RNA-seq data, pseudo-bulk of spatial scRNA-seq data, and surface protein data from CITE-seq. Moreover, the “groups” that are used by voomQWB and voomByGroup methods have superior performance to approaches that do not account for distinct group-specific variation. These methods offer a better balance between false discovery control and the power to detect DE genes on pseudo-bulk scRNA-seq datasets. We recommend both group variance modeling methods, with voomByGroup having accurate variance estimation for simple designs, and voomQWB capable of modeling data with more complex study designs. To guide an analyst’s choice of the most appropriate variance modeling method to apply to a given dataset, we recommend checking the relevant diagnostic plot to assess whether the model assumptions are met.In the presence of group heteroscedasticity, the voomQWB and voomByGroup, are adaptations of voom method by Law et al. [voom method fits a linear model to each gene using a design matrix with full column rank, X, such thatg, and g. The fitted model allows us to calculate residual standard deviations voom-style mean-variance plots seen throughout this article  c. As a result, each group has its mean-variance curve and set of weights , the dynamic can remain FALSE, which means that to estimate the variance, the group-specific trends are still used.Additionally, voom, voomByGroup, and voomQWB methods are run in R using the following functions:The voomvoomByGroupvoomWithQualityWeightsvoomByGroup and voomWithQualityWeights. y represents pseudo-bulk count data with N samples and G genes. design is the design matrix with N rows matching the number of samples and P model parameters. group is a factor vector that is of length N.All functions are run similarly, with 2 common arguments and an additional argument for edgeR are automatically added to the plots.Group-specific mean-variance plots are produced in the voomByGroup function, by specifying plot=“separate” to get individual mean-variance plots for each group Fig. d-f or plvoom method, two further options for DE analysis using edgeR, namely edgeR LRT (likelihood-ratio test) [edgeR QL (quasi-likelihood) [edgeR LRT, glmFit was used with default settings, and only the count matrix and design matrix were provided, followed by glmLRT. To run edgeR QL, glmQLFit was used with default settings, and only the count matrix and design matrix were provided, followed by glmQLFTest. All genes with associated p-values from the DE test used were then extracted with the topTag function.Besides the standard io test)  and edgeelihood) , were alSingle-cell gene-wise read counts were generated to follow correlated negative binomial distributions  groups having different biological variation, (2) no biological or technical variation between samples or groups, (3) samples having different cell numbers , and (4) samples having different cell numbers and groups having different levels of biological variation. We generate 50 simulations for each scenario, with each simulation involving 12 samples  and 10,000 genes. To induce differentially expressed genes in the datasets, 50 genes are randomly selected to be upregulated in each group with a true logscenario 1), we keep the expected library size of each sample consistent by generating 250 cells for each sample. By varying the dispersion in our simulation, we obtain common BCV values that are variable between groups – 0.20, 0.22, 0.26, and 0.28.In the first scenario (scenario 2) generates homoscedastic groups, such that there should be no true differences  in group variability. We use the data here to confirm whether the methods behave in the way we would expect—that voomQWB and voomByGroup perform similarly to voom if there are no group differences. Here, each sample contains 250 cells and groups have BCV values of The second scenario (scenario 3), the biological variation is consistent between groups (common BCVIn the third scenario (scenario 4) combines elements from the first and third scenarios. Biological variation is adjusted such that group 1 and group 2 have less biological variation  under accession number CNP0001250.PBMCs from healthy controls and COVID-19 patients of varying severity  . Pseudo-Human lung tissue from non-fibrotic and pulmonary fibrosis lungs . Pseudo-Publicly available scRNA-seq datasets that were examined in this article in Fig. PBMC1”. The second dataset, which we refer to as “PBMC2” [https://covid19cellatlas.org/.We examined two separate scRNA-seq datasets that sequenced PBMCs from COVID-19 patients with varying severity  and healthy controls. We refer to the first dataset described above as “ “PBMC2” , is avaiPrior to creating pseudo-bulk samples, we performed filtering at the gene- and cell-level. We then selected one cell type per dataset to create pseudo-bulk samples. We filtered out pseudo-bulk samples with relatively fewer cells or smaller library sizes before performing DE analysis  method  using thlimma was used to carry out Gene Ontology (GO) analyses on DE results from the COVID-19 NK cells, using the org.Hs.eg.db annotation package (version 3.14.0) [The goana function in  3.14.0) .limma version 3.50.0, edgeR version 3.36.0, and ggplot version 3.3.5 [Results were generated using R version 4.1.3 , and sofon 3.3.5 .Additional file 1: Supplementary Figures.Additional file 2: Table S1. Summary of filtering steps applied to each dataset.Additional file 3: Review history.


Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer. Recent progress in WGS technology made sequencing individual cells cost-efficient , which, compared to traditional bulk RNA sequencing, enabled detection of gene expression profiles for individual cells in the tissue sample -based dataset of breast cancer-derived xenografts (BCX), and two previously published datasets, a UMI-based dataset of small intestinal neuroendocrine cancer (INC)  samples CTC1 and CTC2. Each tumor was derived from the same population and shares a common ancestor; each tumor thus represents a distinct regional sample. We expected that cells isolated from each individual and cells for each sample form clusters. As the cell lineage MDA-MB-231-LM2 is highly metastatic   and associated GTF annotation file. The Cellranger software performs mapping, demultiplexing, cell detection, and gene quantification for the 10 × Genomics scRNA-seq data.Reads for the non-UMI GC dataset were mapped with the STAR v2.7.9a  to 5 (high level of expression). The five-level scale was chosen to capture the data distribution of rescaled expression values and represent a compromise between introducing data noise with too many levels or artificial similarity with only a few categories.Interval ranges, according to which the values were categorized, were chosen according to the Genes that contain only a single categorized value for each cell were removed as phylogenetically irrelevant and the discretized values were then transformed into fasta format.2.5.3.The amount of coverage in a standard bulk RNA-seq expression analysis is usually sufficient to conclude that genes for which no molecule was detected are not expressed  v4.2.3.0  for the expression data, and a standard model selection was performed for the SNV data (-m TEST). Where the size of the dataset allowed, tree support was evaluated using the standard nonparametric bootstrap  Webb,  and meanses.mntd functions implemented in the package picante  and two CTC samples (CTC1 and CTC2). The number of cells isolated from the CTC3 sample was too small for scRNA sequencing and the sample was removed from the study. The number of detected cells in the tumor samples was generally smaller than in the CTC samples, but the reverse was true for the total number of detected UMIs—the number of unique mRNA transcripts . In the Compared to the fluorescent-activated cell sorting, Cellranger detected fewer cells for tumor samples, but more cells for the CTC samples. Cellranger classifies barcodes as cells based on the amount of UMI detected to distinguish real cells from a background noise , which bars us from testing the phylogenetic structure using the phylogenetic clustering tests. For this reason, we have selected a small number of cells with the least amount of missing data from each sample using the selection filtering method. The small number of cells is not sufficient to represent the full diversity of the tumor, but allows us to test the phylogenetic relationship between individual samples without introducing a bias due to an unequal size of the samples.A total of 58 cells were retained for both the expression and SNV datasets: 20 cells for T1 and T3 samples and six cells for T2, CTC1, and CTC2 samples. In these reduced datasets, genes that were not present in any of the cells or present only in a single cell are removed. The reduced expression data set contained ,Reconstructed trees and phylogenetic tests for the data filtered to the 3.1.4.p-value  is highly metastatic  and their cell type, which was determined by replicating the analysis from Rao et al . We haveHowever, not all cells found in the expression subsets were found in the SNV data. This is likely due to a different version of the Cellranger software used in this work compared to the Rao et al . In bothe clades ; Table 4In addition, in the derived subset containing all cell types, the cancer cells showed a significant clustering (tendency . Howevertendency . In both3.3.For both the expression and SNV data from the GC dataset published by Wang et al , only a 4.Phylogenetic methods using scDNA-seq data are becoming increasingly common in tumor evolution studies. scRNA-seq is currently used for studying expression profiles of cancer cells and their behavior. However, while clustering approaches to identify cells with similar expression profiles are common and frequently used, scRNA-seq data are yet to be used in phylogenetic analyses to reconstruct the population history of somatic cells.To test if the scRNA-seq contains a phylogenetic signal to reliably reconstruct the population history of cancer, we have performed an experiment to produce a known history by infecting immunosuppressed mice with human cancer cells derived from the same population. Then using two different forms of scRNA-seq data, expression values, and SNVs, we reconstructed phylogenies using maximum likelihood and Bayesian phylogenetic methods. By comparing the reconstructed trees to the known population history, we confirmed that scRNA-seq contains a phylogenetic signal to reconstruct the population history of cancer, with both the expression values and SNVs producing a similar phylogenetic pattern.However, this signal is burdened by uncertainty in both the source data as well as reconstructed phylogeny. Accurate phylogenies might thus need an explicit error model to account for this increased uncertainty (Hicks et al, While the expression phylogeny can be obtained for virtually no added cost, the choice of normalization method, batch-effect correction, discretization, or the choice between biological and technical zeros can greatly influence the phylogeny. The phylogenies reconstructed from SNV do not suffer from these decisions and SNV-calling pipelines will likely differ only in the number of false negatives and false positives.Without any specialized phylogenetic or error model for the scRNA-seq data, conventional methods and software tools developed for systematic biology are able to reconstruct population history from these data, potentially at low computational cost. This implies that more accurate inference will be possible when and if specialized models and software are developed, and serious computational resources are employed. For example, computationally more intensive standard nonparametric bootstrap or Bayesian methods on the unfiltered data sets are certainly within the reach of modern computing clusters. This is a future direction for research.In this work, we tested for phylogenetic signal on three data sets, a new data set consisting of five tumor samples seeded using a population sample, and two previously published data sets consisting of a primary tumor with a paired lymph node or a metastatic sample. The nature of the experiment and the amount of uncertainty in scRNA-seq data barred us from a more detailed exploration of the tree topology as only broad patterns, the phylogenetic clustering of cells according to sample and individual of origin, could be considered. Our clustering analyses show that the phylogenetic trees conform broadly to the expected shapes under different experimental conditions, and thus expression and SNV data can both be used to infer phylogenetic trees from scRNA-seq. Nevertheless, our results also demonstrate that all such trees contain significant uncertainty, so new datasets and methods will be required to extend this work.The degree to which low and uneven gene expression plays a role in scRNA-seq requires special attention, especially for non-UMI based data sets, as this not only causes a large proportion of missing data but also burdens the known values with a significant error rate. Research should aim at trying to quantify this expression-specific error rate and build specialized models to include the uncertainty about observed data in the phylogenetic reconstruction itself. This could potentially include removing a large proportion of low-coverage data in favor of robust analysis and proper uncertainty estimation of the inferred topology.The estimation of the topological uncertainty, be it the Bootstrap branch support or the Bayesian posterior clade probabilities, is a staple for phylogenetic analyses. Currently existing methods for the phylogenetic analysis of scDNA-seq, such as SCITE (Jahn et al, Of packages we are aware of, only CellPhy, through its integration in the phylogenetic software RAxML-NG (Kozlov et al, An aspect of scRNA-seq expression data that was not considered in this study is correlated gene expression (Bageritz et al, Multiomic approaches are increasingly popular as they integrate information from multiple biological layers (Bock et al,


Protein biomarkers of cancer progression and response to therapy are increasingly important for improving personalized medicine. Advanced quantitative pathology platforms enable measurement of protein expression in tissues at the single-cell level. However, this rich quantitative cell-by-cell biomarker information is most often not exploited. Instead, it is reduced to a single mean across the cells of interest or converted into a simple proportion of binary biomarker-positive or -negative cells.We investigated the utility of retaining all quantitative information at the single-cell level by considering the values of the quantile function (inverse of the cumulative distribution function) estimated from a sample of cell signal intensity levels in a tumor tissue. An algorithm was developed for selecting optimal cutoffs for dichotomizing cell signal intensity distribution quantiles as predictors of continuous, categorical or survival outcomes. The proposed algorithm was used to select optimal quantile biomarkers of breast cancer progression based on cancer cells’ cell signal intensity levels of nuclear protein Ki-67, Proliferating cell nuclear antigen, Programmed cell death 1 ligand 2, and Progesterone receptor. The performance of the resulting optimal quantile biomarkers was validated and compared to the standard cancer compartment mean signal intensity markers using an independent external validation cohort. For Ki-67, the optimal quantile biomarker was also compared to established biomarkers based on percentages of Ki67-positive cells. For proteins significantly associated with PFS in the external validation cohort, the optimal quantile biomarkers yielded either larger or similar effect size  as compared to cancer compartment mean signal intensity biomarkers.The optimal quantile protein biomarkers yield generally improved prognostic value as compared to the standard protein expression markers. The proposed methodology has a broad application to single-cell data from genomics, transcriptomics, proteomics, or metabolomics studies at the single cell level.The online version contains supplementary material available at 10.1186/s12859-023-05408-8. Protein markers in histological sections of malignant tumors hold great promise as predictors of response to therapy and prognosis . ImmunohFor the purpose of illustration, Fig. In this work, we develop a novel approach for deriving tumor biomarkers based on values of empirical quantile function (or simply quantiles) of CSI distributions of protein signals. The empirical quantile function is the inverse of the empirical distribution function . The quaThe new approach was applied to identify optimal CSI quantile biomarkers based on protein expression of Ki-67, PCNA, PD-L2, and PR in breast cancer tissue. The performance of the resulting optimal quantile biomarkers was validated and compared to the standard ccMSI markers using an independent external validation cohort. For Ki-67, it was also compared to International Ki67 in Breast Cancer Working Group (IKWG) scoring methods by defining high vs. low scoring at commonly used cutoffs of 5%, 15%, 20%, and 30% [The immunohistochemistry image data were available for a large cohort of breast cancer specimens in a standard core-based tissue microarrays (TMAs). The breast cancer specimens were unselected consecutive cases of primary invasive breast cancer from Thomas Jefferson University Hospital, Philadelphia, PA (screening cohort) and an independent validation cohort from the Clinical Breast Care Program (CBCP) at the Walter Reed National Military Medical Center, Bethesda, MD.We defined progression event only if it was a documented recurrence or death related to disease. Patients who died from other or unknown causes were censored at the time of death. The screening cohort included 845 non-metastatic hormone positive + or progesterone receptor(PR)+) breast cancer patients with 142 progressions and the clinical follow-up ranging from 2 months to 238 months with median follow-up time of 115 months. The external validation cohort included 340 non-metastatic HR+ breast cancer patients with 42 progression events and clinical follow-up ranging from 0.8 months to 297.5 months with a median follow-up time of 148 months. Table Both screening and validation cohorts included all patients for whom appropriate cell-level immunohistochemistry data were available for at least one, but not necessarily all considered proteins. The cell-level immunohistochemistry data were considered appropriate if the IF-IHC image passed quality control and at least 20 cancer cells were identified in the tissue core. Respectively, the actual sample size for analysis of each protein was lower than 845, as detailed in Additional file Immunostaining was performed on an autostainer (Dako Link). The following antibodies were used for Cy5-tyramide-based fluorescence immunohistochemistry: Ki-67 , PCNA , PR  and PD-L2 . Stained slides were scanned at 20x magnification on the Scanscope laser scanner (Leica/Aperio), and fluorescent images were captured in three channels (DAPI (cell nuclei), Alexa555 (pan-cytokeratin) and Cy5 (protein of interest). Protein signals were quantified from digitized immunohistochemistry image data using the Definiens Tissue Studio software platform , 21, 22.p, k is such that Q(p), which is the inverse of the distribution function F(x), that is Q(p) values for For a sample mentclass2pt{minimQ(p) predictor of a binary or survival outcome in a screening data set: Select the set of quantiles to be evaluated as predictors and the desired ratio for training/test sets.Split the data into groups with and without event (e.g. separate patients with and without recurrence).Split the group of subjects with event into a training set and a test set randomly with desired ratio. Similarly, split the group of subjects without event into a training set and a test set randomly with desired ratio. Combine training sets with and without event and test sets with and without event.For each training/test set pair and each considered quantile,Determine the optimal cutoff (e.g. using the R package rpart ) in the Apply the optimal cutoff to the combined test set and estimate the effect size (odds or hazard ratio).Repeat steps 3-4 for 100 training/test splits, compute the median log effect size (log odds ratio(OR) or log hazard ratio(HR)) for each quantileRank the absolute values of the median log effect sizes for all considered quantiles and select the optimal quantile with the highest effect size.Perform bootstrap-based optimism correction for the selected optimal quantile(s).In this work, for each random split, 80% of subjects were assigned to the training set and 20% of subjects were assigned to the test set. We considered every fifth quantile starting from the 5th quantile to the 95th quantile plus 99th quantile as candidate predictors of PFS . Also, we identified quantiles with the second and third highest effect sizes to compare them to the optimal ones.The following algorithm is proposed to identify the optimal The bootstrap optimism correction procedure  is perfoFor multivariable analysis of progression-free survival (PFS) in the screening cohort of breast cancer patients, multiple imputations were used due to the missing values for clinico-pathologic covariates for some patients  the apparent performance cutoff in the entire screening data set; (ii) the median of the cutoffs for 100 random training set samples drawn from the screening data set. The prognostic value of dichotomized optimal quantile biomarkers using both types of cutoffs was evaluated in the univariable and multivariable Cox proportional hazards model. Due to the limited number of progression events in the validation cohort, the initial parsimonious multivariable Cox model was developed (reduced to significant predictors at the level 0.05) without quantile biomarkers and then each quantile biomarker was added to the model separately. The parsimonious multivariable Cox proportional hazards models were used with clinico-pathological prognostic predictors listed in Table We have included for comparison the results of using dichotomized ccMSI of the considered proteins as a predictor of PFS. The apparent performance cutoff was computed in the entire screening data set and applied to dichotomize ccMSI in the external validation cohort. We have also evaluated performance of the standard Ki-67 biomarkers based on dichotomizing proportions of Ki-67 positive cells. The validation cohort included the proportions of Ki-67 positive cells, as evaluated by the pathologist. Previously established cutoffs 5%, 15%, 20%, and 30% for percentages of Ki-67 positive cells [The optimal quantiles for Ki-67 PCNA, PD-L2 and PR proteins with the corresponding median hazard ratio (HR*) in 100 split samples in the screening cohort are shown in Table For Ki-67, the 30th quantile had the highest median hazard ratio HR* (1.81) in split-sampling screening and the highest optimism corrected hazard ratio HR**  in split-sampling screening and the highest optimism corrected hazard ratio HR**  in split-sampling screening and the highest optimism corrected hazard ratio HR**  in split-sampling screening and the highest optimism corrected effect size  were identical for Ki-67, PCNA, and PD-L2 and only slightly different for PR quantile biomarkers. Very small differences in the two types of cutoffs were observed for the second and third best quantile predictors. The corresponding results from the univariable and multivariable Cox models were very similar based on apparent performance cutoff and the median of the cutoffs for 100 random training set samples. Therefore, we report the external validation results based only on the apparent performance cutoffs derived in the screening cohort.Table In univariable analyses, the dichotomized Ki-67, PR and PD-L2, but not PCNA optimal quantile biomarkers were significant predictors of PFS. Only dichotomized PR ccMSI and PD-L2 ccMSI, but not Ki-67 ccMSI or PCNA ccMSI were significant predictors of PFS. Neither ccMSI nor quantile PCNA biomarkers were significant predictors of PFS. The Kaplan-Meier estimators of PFS by the optimal quantile predictors dichotomized using the apparent performance cutoff are shown in Fig. For PCNA, the effect size for the optimal quantile biomarker has somewhat larger than the effect size for ccMSI (HR=1.32 for PCNA 5th percentile vs. 1.22 for PCNA ccMSI) while for PR the effect size for ccMSI biomarker (HR=0.40) is slightly larger than the effect size for 55th percentile biomarker (HR=0.43).Notable, the performance of the optimal quantile predictors is stable as the three best quantile predictors identified in the screening cohort yield similar results in the external validation cohort  package Qindex, available at https://CRAN.R-project.org/package=Qindex. In the data analyzed, we had survival outcomes, but our package Qindex can be used with continuous or categorical  outcomes with 3 or more categories.The proposed approach involves two levels of model selection. The algorithm selects both the optimal CSI distribution quantile (among pre-specified candidates) and the optimal cutpoint for dichotomizing the optimal CSI distribution quantile. The selection of the optimal CSI cutpoint for dichotomization can be internally validated using the bootstrap-based optimism correction applied to a given quantile. In contrast, the selection of the optimal CSI quantile should be validated in an external data set.For the proteins considered, our analysis resulted in externally validated optimal quantiles for cancer cell expression levels of Ki-67 (30th quantile), PD-L2 (45th quantile), and PR (55th quantile). The effect sizes in the external data set were comparable to the ones obtained in the screening cohort. The corresponding p-values were not significant at the standard level of 0.05 due to sample size limitation of the external cohort. For PCNA, none of the biomarkers derived in the screening cohort were significant predictors of PFS in the validation cohort. The discrepancy between the screening and validation cohorts may have contributed to differences in results for PCNA.An optimal cutpoint for dichotomizing a continuous marker for predicting survival outcome may be also selected using the stand alone software package X-tile . In contHowever, the main novelty and contribution of our work is to provide a tool for objective selection of the quantile(s)  with the highest predictive value for the outcome of interest. In the data used in this study, the outcome of interest is progression-free survival, but our method and R package can be used also for categorical and continuous outcomes. Our additional contribution is the R function that computes bootstrap-based correction of optimistic bias for the effect size of a dichotomized continuous marker  and p-values (p) for testing the null hypothesis that Rho=0. Only weak correlation was observed for Ki-67 log 30th quantile and PCNA log 5th quantile (a) and for PD-L2 log 45th quantile and PCNA log 5th quantile (d). All other pairs of optimal quantiles are not correlated .Additional file 2. This file contains the R source code.


Molecular subtypes predict prognosis in muscle-invasive bladder cancer (MIBC) and are explored as predictive markers. To provide a common base for molecular subtyping and facilitate clinical applications, a consensus classification has been developed. However, methods to determine consensus molecular subtypes require validation, particularly when FFPE specimens are used. Here, we aimed to evaluate two gene expression analysis methods on FFPE samples and to compare reduced gene sets to classify tumors into molecular subtypes.RNA was isolated from FFPE blocks of 15 MIBC patients. Massive analysis of 3’ cDNA ends (MACE) and the HTG transcriptome panel (HTP) were used to retrieve gene expression. We used normalized, log2-transformed data to call consensus and TCGA subtypes with the consensusMIBC package for R using all available genes, a 68-gene panel (ESSEN1), and a 48-gene panel (ESSEN2).Fifteen MACE-samples and 14 HTP-samples were available for molecular subtyping. The 14 samples were classified as Ba/Sq in 7 (50%), LumP in 2 (14.3%), LumU in 1 (7.1%), LumNS in 1 (7.1%), stroma-rich in 2 (14.3%) and NE-like in 1 (7.1%) case based on MACE- or HTP-derived transcriptome data. Consensus subtypes were concordant in 71% (10/14) of cases when comparing MACE with HTP data. Four cases with aberrant subtypes had a stroma-rich molecular subtype with either method. The overlap of the molecular consensus subtypes with the reduced ESSEN1 and ESSEN2 panels were 86% and 100%, respectively, with HTP data and 86% with MACE data.Determination of consensus molecular subtypes of MIBC from FFPE samples is feasible using various RNA sequencing methods. Inconsistent classification mainly involves the stroma-rich molecular subtype, which may be the consequence of sample heterogeneity with (stroma)-cell sampling bias and highlights the limitations of bulk RNA-based subclassification. Classification is still reliable when analysis is reduced to selected genes.The online version contains supplementary material available at 10.1186/s12885-023-11016-9. Sequencing techniques have advanced, leading to broad genomic analyses of bladder cancer cohorts and enabling molecular subtyping. Subtyping of muscle-invasive urothelial bladder cancer (MIBC) categorizes heterogenous cancers with similar molecular and biological characteristics, which has significantly contributed to our knowledge in the recent years . SeveralThe aim of classifying cancer in subgroups is to identify tumors that share similar prognosis and response to various therapies. In several reports molecular subtypes have been described as predictors of response to chemotherapy and immunotherapy , 9–12. HMany molecular profiling studies and molecular subtyping in The Cancer Genome Atlas (TCGA) are based on fresh frozen tissue, which allows high quality transcriptomic analyses based on RNA sequencing. However, fresh frozen samples are rarely available in clinical practice and for retrospective research projects. The use FFPE tissue is the gold-standard for pathological analyses and long-term storage in hospitals. The paraffin material is usually archived for 10 and more years, allowing correlation with long-term patient outcome. The disadvantage of FFPE tissue is that the RNA is highly degraded by fixation and storage, leading to sequencing artifacts and limiting detection of transcripts , 22. HowIn this study, we tested and compared the feasibility of two RNA sequencing methods with FFPE tissues from MIBCs to determine uniform molecular subtyping. In addition, we used two reduced predefined gene sets to determine molecular subtypes and compared results with the comprehensive transcriptome data.Tumor samples were provided by the University Cancer Center Frankfurt (UCT). Written informed consent was obtained from all patients. The study was approved by the institutional review boards of the UCT and the ethical committee at the University Hospital Frankfurt , and conducted according to local and national regulations and to the Declaration of Helsinki. Fifteen FFPE samples from MIBC patients were obtained from the Dr. Senckenberg Institute of Pathology.Samples were stained for CK5/6  and GATA3  as described before .For each RNA isolation, a 1-mm punch was taken from FFPE blocks of an annotated, representative tumor area with at least 50% tumor content. RNA was either extracted using the truXTRAC FFPE total NA Kit  or by GenXPro GmbH.The mRNA expression was determined using the HTP  as describes before , 26. Brihttps://github.com/marcelm/cutadapt/) and poly(A)-tails were clipped by an in-house Python-Script. The reads were mapped to the human genome (hg38) and transcripts were quantified by HTSeq [Massive Analysis of cDNA (MACE) is a 3’mRNA sequencing method based on the analysis of Illumina reads derived from fragments that originate from 3’ mRNA ends , 27. Samby HTSeq .https://github.com/cit-bioinfo/consensusMIBC), TCGA classes were assigned using the BLCAsubtyping package (https://github.com/cit-bioinfo/BLCAsubtyping) as described by Kamoun et al. [p-values associated to the Pearson’s correlation of the samples with the nearest centroid  and the separation level.Molecular consensus classes of MIBC were assigned using the consensusMIBC package for R for the nearest-centroid transcriptomic classifier  using log2-transformed normalized gene expression values. For hierarchical clustering we used Pearson’s correlation metric with complete linkage.The heatmap was constructed with the open-source Morpheus software , for either method  symbols. With the HTP, 19,399 mRNA transcripts were detected. 16,645 genes had the same HGNC symbol, and 6084 and 2754 genes were detected with either MACE or HTP only, respectively. One HTP sample failed quality control (after sequencing) because of minimal expression variability as determined by median log2 for negative control probes. The median time from sample collection (time of surgery) to analysis was 5 years (range 3–10 years); seven samples were < 5 years and eight samples were ≥ 5 years. There was no significant correlation between the age of the samples and the overall degree of separation, nor was the association of the Pearson correlation coefficients of the samples with the nearest centroid  was < 0.0001 for all samples with either method. However, we noticed significantly higher median correlation p-values for subtype calls resulting from HTP vs. MACE vs. TCGA data  = 0.0092). With the HTP data, NE-like and Ba/Sq subtype had the highest median separation levels of 0.67 and 0.79, like the TCGA data. Using the MACE data, the stroma-rich subtype had the highest median separation level of 0.71. Based on MACE data, the NE-like subtype had a very low separation level of 0.03. However, this was only based on a single sample. With the HTP and MACE data LumNS subtype and with the TCGA data LumNS and LumU had rather low separation levels, which might be due to other luminal groups.Figure Samples were classified as Ba/Sq in 7 (50%), LumP in 2 (14.3%), LumU in 1 (7.1%), LumNS in 1 (7.1%), NE-like in 1 (7.1%) and stroma-rich in 2 (14.3%) cases with HTP data. And classified as Ba/Sq in 8 (53.3%), LumP in 2 (13.3%), LumU in 1 (6.7%), LumNS in 1 (6.7%), NE-like in 1 (6.7%) and stroma-rich in 2 (13.3%) cases with MACE data  and 7 (50%), LumP in 3 (21.4%) and 2 (14.3%), LumU in 1 (7.1%) and 1 (7.1%), LumNS in 0 and 1 (7.1%), NE-like in 1 (7.1%) and 1 (7.1%), stroma-rich in 3 (21.4%) and 2 (14.3%) cases using ESSEN1 and ESSEN2 gene sets, respectively. Using MACE data, samples were classified as Ba/Sq in 5 (35.7%) and 8 (57.1%), LumP in 2 (14.3%) and 1 (7.1%), LumU in 1 (7.1%) and 2 (14.3%), LumNS in 1 (7.1%) and 2 (14.3%), NE-like in 1 (7.1%) and 0 and stroma-rich in 3 (21.4%) and 2 (14.3%) cases using ESSEN1 and ESSEN2 gene sets, respectively  could not be classified with the MACE data and was excluded for the further analyses because the correlation p-value was above the set threshold of 0.2. Of note, this sample showed a lymphoepithelial histological phenotype, and the heavy infiltration of immune cells might have hindered correct classification.Compared to the comprehensive transcriptomic data, the reduction in gene sets resulted in a significant increase of the correlation  Figure S. The medThe Pearson’s correlations coefficient for samples and each consensus class stratified for the called consensus classes are included in the For the ESSEN1 panel, consensus molecular subtypes were concordant using the HTP data in 86% (12/14). One sample (46) changed from LumNS to LumP and one sample 32) changed from Ba/Sq to stroma-rich. Using the MACE data, concordance was 86% (12/14). One sample (32) changed from Ba/Sq to LumNS and one sample (8) from Ba/Sq to stroma-rich  with the HTP data Fig. . Using tThe overlap between subtypes according to the TCGA classification was 86% with the ESSEN1 and 93% with the ESSEN2 panel using the HTP data  and ESSEN2 (n = 48) gene panels were developed to discriminate between 5–6 gene expression-based molecular subtypes according to different classification systems  by using the qRT-PCR method on fresh frozen samples and the NanoString technique on FFPE samples, respectively [Efforts are being made to methodologically simplify subtype classification by using reduced gene sets to enable its implementation in clinical practice. Stratifying patients will be important to select patients, that respond to chemo- or immunotherapy to reduce unnecessary toxicities and costs, as soon as prospective and validated evidence is provided. In a previous study, a 47-gene panel (BASE47) was proposed for the discrimination between luminal and basal subtypes using the NanoString platform . Recentlectively , 20. TheSo far as no use-case for the application of molecular subtypes exists, a comprehensive gene expression analysis provides additional information on further genes and enables the analysis of additional immune or stroma signatures, which could find application as predictive markers –32.Limitations include the small number of samples. With only one sample for some of the subtypes general conclusions on which method is superior cannot be drawn. Furthermore, we did not perform a direct comparison between FFPE and fresh tissue. Our study lacks the comparison of HTP and MACE to further sequencing techniques used in previous molecular subtyping studies, like Illumina RNA seq or Affymetrix. Most importantly, sequencing was not performed with the exact same RNA, but RNA from neighboring tumor areas. Thus, discordant results can be the result of slight differences in tumor and stroma cell content or differences in deeper tissue layers not represented on the slide. However, this issue might reflect the heterogeneity of bladder cancer itself. This becomes particularly evident with the stroma-rich molecular subtype, since it was present in all divergent classified samples and highlights problems affecting bulk-RNA sequencing of bladder cancer samples in general. Molecular subtyping is performed on RNA derived from tissue (cores) of biopsy specimens representing only a fraction of the tumor. The subtype determined from the isolated RNA might not be the only and/or predominant subtype of a tumor, which is known to be heterogenous, especially in bladder cancer , 33. FurThe consensus molecular subtypes represent a robust classification and can be determined based on comprehensive or selected FFPE transcriptome data. Using the data of matching pairs, the agreement of subtypes was high, but differences were observed when the stroma-rich molecular subtype was involved. Based on our results, it seems important to further unravel the heterogeneity of bladder cancer and the influence of stromal components on molecular subtypes to reduce sampling bias and allow more accurate assignment.Additional file 1:Supplementary material. Table S1. ESSEN1 gene set. Table S2. ESSEN2 gene set. Figure S1. Comparison of correlation p-value between for samples older and younger than five years for MACE and HTG. Figure S2. Pearson’s correlation between samples and each consensus class stratified for the different methods and for the called consensus class. Figure S3. Relative distribution of subtype calls with each method and gene panels. Figure S4. A: Comparison of p-values associated to the Pearson’s correlation of the samples with the nearest centroid for each method (HTG and MACE) for the ESSEN1 and ESSEN2 gene panels. Figure S5. Pearson’s correlation between samples and each consensus class stratified for the different gene sets.


Recent research revealed a rejuvenation event during early development of mice. Here, by examining epigenetic age dynamics of human embryogenesis, we tested whether a similar event exists in humans. For this purpose, we developed an epigenetic clock method, the intersection clock, that utilizes bisulfite sequencing in a way that maximizes the use of informative CpG sites with no missing clock CpG sites in test samples and applied it to human embryo development data. We observed no changes in the predicted epigenetic age between cleavage stage and blastocyst stage embryos; however, a significant decrease was observed between blastocysts and cells representing the epiblast. Additionally, by applying the intersection clock to datasets spanning pre and postimplantation, we found no significant change in the epigenetic age during preimplantation stages; however, the epigenetic age of postimplantation samples was lower compared to the preimplantation stages. We further investigated the epigenetic age of primed (representing early postimplantation) and naïve (representing preimplantation) pluripotent stem cells and observed that in all cases the epigenetic age of primed cells was significantly lower than that of naïve cells. Together, our data suggest that human embryos are rejuvenated during early embryogenesis. Hence, the rejuvenation event is conserved between the mouse and human, and it occurs around the gastrulation stage in both species. Beyond this advance, the intersection clock opens the way for other epigenetic age studies based on human bisulfite sequencing datasets as opposed to methylation arrays. We developed an epigenetic clock method, the intersection clock, that utilizes bisulfite sequencing in a way that maximizes the use of informative CpG sites with no missing clock CpG sites in test samples. Using the new method, we revealed a rejuvenation event during human embryogenesis around the gastrulation stage. The intersection clock opens the way for other epigenetic age studies based on human bisulfite sequencing datasets. CVcross‐validationDCMdilated cardiomyopathyDNAmDNA methylationESCembryonic stem cellGEOgene expression omnibusGWgestational weeksHNES cellshuman naïve embryonic stem cellsICMinner cell massiPSCsinduced pluripotent stem cellsMedAEmedian absolute errorMDDmajor depressive disorderMII oocytemetaphase II oocytePpassagePSCspluripotent stem cellsRRBSreduced representation bisulfite sequencingSAsuicide attemptersSRAsequence read archiveWGBSwhole genome bisulfite sequencing1The germline as a trans‐generational cell lineage is immortal but data show it sustains age‐related changes over adult life ; however, there was a significant decrease in epigenetic age between blastocysts and ES cells (p = 1.582e−12)  Figure ; therefo) Figure . Togethep = 0.0136) and dilated cardiomyopathy patients (2.679e−05)   Figure .Datasets 2–4 do not only contain embryo data, but also sperm data, and in Dataset 3, MII oocytes samples. Applying the intersection clock to the methylation profiles of these samples, we found that the epigenetic age of sperm cells was in some cases lower than that of preimplantation stages: the difference was significant in the case of Datasets 2 and 3 Figure , and notConventional human pluripotent stem cells (PSCs), whether derived from blastocysts or generated by reprogramming, differ from mouse embryonic stem cells and are considered to represent a developmentally advanced, or primed, stage of pluripotency , the oldest healthy patient was 40 years old, whereas older patients exhibited adverse health phenotypes  during reprogramming Horvath, . We confGlobal CpG methylation of the human embryo decreases during preimplantation development, from the zygote to blastocyst stages followed by an increase of global CpG methylation in the postimplantation embryo (represented by primed PSCs)  database (PRJNA531784) by using the prefetch program of the sratoolkit (v2.10.4). This process resulted in a paired‐end read file (SRR format) for each of the 182 peripheral blood samples. The samples were collected from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls Figure . Then, w4.3We downloaded processed methylation data from Gene Expression Omnibus (GEO) and selected human bulk samples for our analysis . The final model was used to predict the age of the restricted test sample. To achieve a more robust prediction, we performed a fivefold cross‐validation (CV) repeating the same procedure as above. The fivefold CV resulted in five clocks for each test sample. In this study, we used 182 human blood samples from Dataset 1 as a training set and Datasets 2–8 for testing. We considered only the test samples that had at least 10,000 intersected CpG sites.The idea behind the intersection clock is to maximize the use of informative CpG sites in the training and test sets. For each test sample, we performed the following workflow separately: first, we determined the intersection of CpG sites between training and test datasets. Then, we restricted the training set and the test sample to the intersected CpG sites. Subsequently, we used the restricted training set and test sample to train and test a clock as follows: we trained an ElasticNet regression model using random 80% of the samples of the restricted training set and validated on the remaining 20% of samples. We optimized the lambda parameter by the built‐in 10‐fold cross‐validation of the Python package Glmnet .We used the Python packages SciPy (v1.3.1) and Scikit‐learn (v0.23.2) for statistical analysis. Two‐sided t‐tests were calculated for comparing two groups: ns, s Figure , we usedCK and VNG conceived the study. CK acquired data, performed data analysis, and developed the intersection clock. VNG supervised the study. CK and VNG wrote the manuscript.The authors have no conflict of interest to declare.Data S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.


Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.The online version contains supplementary material available at 10.1186/s13059-023-03104-7. Cellular function varies with cell type and environment and differences in cell function can largely be characterized by gene expression profiles , 2. The Despite the great potential of differential expression analysis, the methods employed for this type of analysis yield results which are often not reproducible and may return thousands of significant DEGs , making Most often differential expression analysis relies on negative binomial generalized linear models, which can account for the over-dispersion associated with count data . Neural p value for each gene in a sample and thus identify a set of significant DEGs. We first focus on analyzing breast cancer (BC) and calculating the enrichment of known cancer driver genes and subtype marker genes among the set of significant genes. This analysis is compared to a standard case–control analysis using DESeq2, which generally yields many more significant genes and much lower enrichment scores. Finally, we show that cancer driver genes are enriched among the significant genes derived from the DGD across cancers in TCGA.In this work, we present a gene expression model of normal tissue that replaces control samples and enables differential expression analysis in cancer using only a single sample. The model is based on the Deep Generative Decoder (DGD) , 20, a gwithout controls. We believe this method can significantly impact the utility of gene expression analysis, target identification, and personalized treatment.In summary, our model of normal gene expression drastically improves differential expression analysis by yielding fewer false positives and extending the analysis to single cancer samples (N-of-one) representation for every sample. After hyperparameter optimization  of the latent space Fig. A shows tNext, we evaluated whether our model of healthy gene expression could find meaningful representations for cancer samples. To do so, we used our model (trained on GTEx) to find representations for tumor samples  using the whole GTEx and 179.05 when using 5 random samples with 20 repetitions as controls. The DGD, on the contrary, found almost no false positives with an average of 4.25 called genes (using the same filtering criteria as DEseq2).We assessed the model’s specificity in a normal vs. normal analysis using healthy breast tissue from GTEx. It is assumed that there should be no or very few DEGs when comparing normal samples. Therefore, the number of DEGs here functions as the number of false positives and is thus related to the specificity of the model. We randomly selected 20 samples from the breast test set (out of 42 GTEx samples) and compared each one against the 440 control samples from the training set using DEseq2. We used the same 20 random samples and compared each one to the DGD model. The number of significant genes found by the two methods are shown in Fig. To compare the sensitivities of DGD and DEseq2 in a “positive control” experiment, we analyzed their ability to correctly identify marker genes known to be important in BC. Two sets of BC-related genes were curated for this purpose: (I) driver genes from the DriverDBv3 database  and (II)We evaluated enrichment scores across four PAM50 breast cancer subtypes: basal-like, HER2, luminal A, and luminal B that we annotated in TCGA from a previous study . For thip value in at least 10 replicates of the PAM50 experiment performed above , here interpreted as false positives, our model reports only 0.03% of the genes to be differentially expressed under the same conditions. Secondly, we calculate the enrichment of relevant known cancer genes among the significant genes derived from differential expression analysis of breast cancer samples. In this positive control, we used a set of known breast cancer driver genes and a PAM50 set of genes discriminating between breast cancer subtypes. In the general breast cancer case, we see consistently higher enrichment when using the DGD compared to DESeq2. This is also true, for the most part, for the cancer subtypes. However, there are some discrepancies with respect to the expected driver genes. For instance, according to our model, TMEM45B should be upregulated in the HER2 subtype but is downregulated on average. Yet, we do not expect a perfect concordance between the PAM50 panel and tumor samples, as many patient-specific factors can affect gene expression. Altogether, our evaluation of breast cancer shows that the DGD returns much fewer false positives and a higher proportion of truly relevant genes compared to DESeq2. More importantly, we expect our model to improve the classification of different cancer subtypes. Subtype classification is performed based on which genes are differentially expressed, and the ability to better detect differential expression should improve classification.It is difficult to assess the performance of differential gene expression analysis without knowing the ground truth. We have therefore used an enrichment score for known cancer genes among the significant genes to compare our model to a standard analysis using breast cancer as a case. Firstly, we assessed the specificity of our model and DESeq2 by comparing normal vs. normal as a negative control. While DESeq2 yields large numbers of significant genes  of the distributions. The trainable dispersion parameters (r) of the negative binomials are gene-specific and initialized with 2. The GMM consists of 45 mixture components. The priors are a mollified uniform distribution  with a spread of 7 and a sharpness of 10 for the means, a Gaussian with a mean of 1 (corresponds to a standard deviation of 0.1), and a standard deviation of 1 for the negative logarithmic diagonal covariance and a Dirichlet distribution with all alphas equal to five. For more details, please refer to [The full model consists of a Gaussian mixture model as the parametrized distribution over latent space and a decoder as presented in . We perfrefer to .−4, 0.01, and 0.01, respectively.The model was trained for 200 epochs with a batch size of 256. The optimizer of choice was Adam  without Representations for single test samples are optimized as described in  with allp value for K is the library size and I is an indicator function taking a value of 1 when the condition is fulfilled and otherwise 0. The above expression yields an exact p value for the negative binomial distributions. However, it requires summing over all read counts across genes. For the sake of efficiency, we therefore obtain an asymptotic p value by summing over an evenly spaced grid of 104 in the domain of We used the DGD to find differentially expressed genes for tumor samples. An optimal representation is found for each tumor sample as described above. These representations are interpreted as the closest-normal for each tumor—an in silico normal. In our setup, we want to test if counts for a given gene are significantly different between the tumor sample and the normal tissue output of the neural network. Let https://rna.recount.bio/) using the built-in R packages. Additionally, the sample metadata files were also acquired from Recount3 [The raw gene count expression data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) were downloaded from the Recount3 platform   there were 31 different tissue types in GTEx and one group without tissue information, with a total of 19,214 individual samples  Program provided 33 different cancer types. We selected the set of genes present in our processed GTEx dataset and separated the TCGA samples into a normal adjacent set (747 samples) and a tumor set  following the metadata file.For an assessment of the model’s capability to assign independent samples to the correct tissue clusters according to the training data, we selected 14 cancers from TCGA that fulfilled the following two conditions: (1) the tissue corresponding to the cancer is present in GTEx and (2) the tissue must have at least ten adjacent normal samples from each cancer type . We alsoIn applying our differential expression analysis to cancer types other than breast cancer, we include TCGA cancer samples from the 31 cancer types overlapping with those found in DriverDBv3 .To obtain a homogenous breast cancer (BC) dataset for testing, we curated the TCGA BC samples to only include primary tumors from women between 40 and 70 years of age. We excluded samples with low tumor cell percentage (defined as < 50%) or a high level of necrosis (defined as > 5%), in addition to samples from patients with known metastasis, stage IV or stage X tumors, or prior cancer diagnosis. For a full list of selection criteria and columns from metadata used for curation, see Additional File genefu [We downloaded a list of cancer driver genes for each cancer type from DriverDBv3 , and we genefu . As our genefu , 28, 29 genefu . The PAMClustering performance according to tissue type was evaluated based on the GMM probability densities for each sample’s representation. For this purpose, samples are assigned the GMM component that achieves the highest probability density for their inferred representations. We calculated the percentage of each tissue per component as the number of samples of a given tissue clustered in a given component divided by the total number of samples assigned to this component.This is done independently for the train set Fig. B and tesWe use the TCGA data described above to evaluate the mapping of unseen and independent data onto the latent space. New representations for all 6111 TCGA tumor samples were found using the DGD trained on GTEx data. The resulting GMM probability densities for the TCGA representations are used to evaluate how well new samples match the correct tissues of the training representation. We define the “correct” tissue as the tissue that best represents a given GMM component. Our evaluation metric is the percentage of TCGA samples of a given tissue matched to the corresponding GMM component with respect to the total number of TCGA samples for this tissue.Bladder samples were evaluated differently due to the lack of a bladder-specific component in the normal model. Instead, we evaluated a correct match as TCGA and GTEx bladder samples were assigned to the same component(s).The total probability of a sample is the product of the probabilities across all 16,883 genes given by the model . We calculate the negative log-probability mass of each sample for three datasets: GTEx test, TCGA adjacent normal, and TCGA tumor. For this comparison, we use subsets containing ten tissues: adrenal, brain, breast, colon, kidney, liver, lung, prostate, stomach, and thyroid. We apply this analysis for a pan-tissue comparison based on the eight tissues only, because adrenal and brain are missing in the TCGA adjacent normal subset. For a fair comparison, we ensure equal samples for a given tissue across the three datasets. If all datasets have more than 20 samples for a given tissue, we randomly select 20 samples from each subset for that tissue.Differential expression analysis (DEA) performed by our model is compared to DESeq2 by the enrichment of cancer driver genes among DEGs.p values below 0.01 are accepted as differentially expressed. The enrichment score is then given as the normalized number of DEGs belonging to the group of breast cancer driver genes or PAM50 genes, respectively.For a general comparison, we perform 20 single-sample experiments using 1 random breast cancer sample (case) from the population of 40–50-year-old Caucasian females. This procedure leaves us with 166 samples. Genes that result in absolute log2-fold changes greater than one and adjusted We perform a comparable DEA with DESeq2 using all 44 GTEx samples (control) under the same conditions .We also perform single-sample analyses of the four available breast cancer subtypes: basal-like (84 samples), HER2 (37 samples), luminal A (176 samples), and luminal B (84 samples), both for our model and DEseq2. We randomly choose one sample from each of the four subtypes as a case sample and used all GTEx breast tissue samples  as controls in the DEseq2 method. The experiment is repeated 20 times for each subtype.p values ranging from 0.01 to 0.1 (absolute log2-fold change greater than 1). We performed this 20 times and reported the resulting DEGs as false positives. As controls for DESeq2, we used all breast samples from the GTEx training set (440 samples) as controls. We also perform the analysis for DEseq2 by choosing five controls, which are randomly selected from the breast samples mentioned above.In order to assess the false positive rates of the methods, we selected a random GTEx breast sample from the test set (42 samples) as a false case sample. We performed DEA with our model and DESeq2 to yield false positive DEGs for adjusted For the enrichment analysis of Cancer Driver Genes for multiple cancer types, 31 different cancer types are used: adrenocortical carcinoma, bladder urothelial carcinoma, brain lower grade glioma, breast carcinoma, colon adenocarcinoma, kidney cancer , liver hepatocellular carcinoma, lung adenocarcinoma, prostate adenocarcinoma, stomach adenocarcinoma, and thyroid carcinoma. We perform 20 single-sample experiments for each of the cancer types. For each cancer type, its respective cancer driver gene list was used in the enrichment score calculation.Additional file 1: Fig S1. Loss curves. Fig S2. Matrix of associations for the GTEx test set. Fig S3. Enrichment scores for TCGA Breast data.Additional file 2: Table S1. Overview of the samples from GTEx.Additional file 3: Table S2. Overview of TCGA tumor and TCGA adjacent samples.Additional file 4: Table S3. Clinical filters for breast cancer subtypes.Additional file 5: Table S4. Expression trend of the selected 34 PAM50 genes and the number of times it was found to have a significant differential expression.Additional file 6: Table S5. Number of cancer driver genes.Additional file 7. Review history.


Enhancers are vital cis-regulatory elements that regulate gene expression. Enhancer RNAs (eRNAs), a type of long noncoding RNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in the regulation of gene expression and cancer development. The methods that identify eRNAs based solely on genomic sequence data have high error rates because they do not account for tissue specificity. Specific histone modifications associated with eRNAs offer valuable information for their identification. However, identification of eRNAs using histone modification data requires the use of both RNA-seq and histone modification data. Unfortunately, many public datasets contain only one of these components, which impedes the accurate identification of eRNAs.We introduce DeepITEH, a deep learning framework that leverages RNA-seq data and histone modification data from multiple samples of the same tissue to enhance the accuracy of identifying eRNAs. Specifically, deepITEH initially categorizes eRNAs into two classes, namely, regularly expressed eRNAs and accidental eRNAs, using histone modification data from multiple samples of the same tissue. Thereafter, it integrates both sequence and histone modification features to identify eRNAs in specific tissues. To evaluate the performance of DeepITEH, we compared it with four existing state-of-the-art enhancer prediction methods, SeqPose, iEnhancer-RD, LSTMAtt, and FRL, on four normal tissues and four cancer tissues. Remarkably, seven of these tissues demonstrated a substantially improved specific eRNA prediction performance with DeepITEH, when compared with other methods. Our findings suggest that DeepITEH can effectively predict potential eRNAs on the human genome, providing insights for studying the eRNA function in cancer.https://github.com/lyli1013/DeepITEH.The source code and dataset of DeepITEH have been uploaded to  The regulation of gene expression is a complex process influenced by various factors. Among them, enhancers play a crucial role as cis-acting elements involved in the transcriptional regulation of genes, leading to the enhanced expression of target genes . The dirComputational approaches for enhancer identification commonly involve extracting sequence features from the genome and constructing classification models for enhancer identification through machine learning or deep learning techniques . TherefoIn this study, we present a novel deep learning framework, named DeepITEH, for accurately identifying tissue-specific eRNAs from the human genome. DeepITEH integrates RNA-seq data and histone modification data from multiple samples within the same tissue to improve the accuracy of eRNA identification. Initially, DeepITEH classifies eRNAs into regularly expressed eRNAs (RE) and accidental eRNAs (AE) based on histone modification data. Then, it combines histone modification features with sequence features to identify tissue-specific eRNAs. DeepITEH is evaluated on four datasets of normal tissues and four datasets of cancer tissues separately. The study finds that DeepITEH outperforms existing enhancer prediction methods on seven independent test sets, namely, stomach, lung, liver, pancreas, liver cancer, lung adenocarcinoma, and prostate cancer tissues. This demonstrates that DeepITEH can accurately identify eRNA in specific tissues.The eRNA data of four normal tissues, namely, stomach, lung, liver, and pancreas, are obtained from the Human eRNA Database (HeRA)   is outlined as follows:The first step in quantifying histone modification features on eRNA/NE sequences involves collecting data on the length of peaks for each type of histone modification in each tissue, as well as the average length of peaks Determine the size of the division window. The determination of the window size for extracting histone modification features involves considering the average length Windowing for eRNA. The Selection of the optimal window. The eRNAs of a given tissue are aligned and stacked, with the central base as the midpoint. Subsequently, all eRNAs are divided according to the window size Extraction of histone modification feature. Each tissue contains data on histone modifications for Our hypothesis posits that eRNAs expressed in a particular tissue at a particular time can be divided into two categories: RE and AE. We postulate that REs should exhibit consistent histone modification patterns across multiple histone modification experiments (ChIP-seq). To identify eRNAs with patterns similar to RE, we cluster and employ voting mechanisms to combine histone modification data from various samples.k-means machine learning algorithm to cluster the eRNAs and NEs into two separate classes, one for candidate REs and the other for candidate AEs or candidate NEs. As The process for identifying REs from NE histone modification features and eRNA histone modification features extracted from the same sample of the same tissue involves several steps. First, the eRNA and NE histone modification features are spliced together to create a 5D feature matrix. This matrix is then used in the To combine histone modification data from different samples, we add the RE, AE, and NE features as binary variables to the eRNA and NE histone modification features extracted from each sample. The RE feature value is set to 1, and the AE and NE feature values are set to 0, resulting in a 6D feature matrix.For a given eRNA or NE sample Moreover, the resulting feature matrix To determine the hyperparameters in DeepITEH, such as epoch, batch size, and number of hidden cells, a 5-fold cross-validation and grid search strategy is used. Grid search can find global optimal hyperparameter values by traversing all possible combinations, which improves model stability and generalization. Combining it with cross-validation can further enhance search accuracy. The training set of each tissue is first divided into five subsets that are consistent in number and independent of each other. Then, for a given set of hyperparameters, one subset is selected as the validation set for evaluating the hyperparameters, and the remaining four subsets are used as the training data set for model training. The process is repeated five times, and the five validation results are averaged to evaluate the set of hyperparameters and ultimately obtain the best hyperparameter settings .Once the optimal hyperparameters of the model are obtained through the 5-fold cross-validation and grid search strategy, the final model is trained using the original training set data  accordingly. The binary cross-entropy loss function is utilized as the objective function during the model training process, i.e.where The Adam  optimizeIn this study, four standard evaluation metrics are utilized to evaluate the performance of DeepITEH. These evaluation metrics include accuracy (ACC), sensitivity (SN), specificity (SP), and Mathews correlation coefficient (MCC). The definitions of these evaluation metrics are as follows:where TP, TN, FP, and FN represent true positive, true negative, false positive, and false negative, respectively. Furthermore, receiver–operating characteristic (ROC) curves and the area under the curve (AUC) are included as performance indicators to better evaluate the overall performance of DeepITEH. Subsequently, we assess the performance of DeepITEH using independent datasets from each tissue, based on the specified performance metrics.The window size used for dividing the eRNA is determined based on the average length of enhancers in the FANTOM database, which is 300 base pairs. The eRNA sequence is 6001 base pairs in length and is divided into 21 windows using this window size. The sequence features of each window are assigned scores using a scorer, and the window with the highest score is selected for extracting the sequence features of the eRNA. Determining the location of histone modification features of eRNAs requires a comprehensive consideration of the definition of enhancers in three databases of eRNA sources. First, the average lengths of the peaks in each type of histone modification data for different samples in eight tissues are obtained as The proportions of RE and AE are analyzed in four normal tissues and candidate REs and AEs in different samples in each tissue, as shown in In this study, we utilize histone modification data from different samples of the same tissue, ensuring that at least one sample of each tissue has such data. For tissues with multiple samples, we construct models to recognize eRNA based on each sample separately. We assess the performance of the models using five metrics to evaluate the accuracy of different samples in identifying eRNA, and then select the sample used in the optimal model. t-test with P-value ≤0.05 is used to demonstrate the significant difference between the means of the two samples. The differences are significant in all four indicators, ACC, Sp, MCC, and AUC, as shown in In this section, the aim is to evaluate the impact of postfusion features and showcase the enhancement of DeepITEH in accurately identifying tissue-specific eRNAs using DNA sequence data alone. To achieve this, a comparison between postfusion features and sequence-only features is conducted on an independent test set of eight different tissues using five metrics. Results indicate that the postfusion features outperform sequence-only features on all four metrics, including ACC, Sp, MCC, and AUC, as illustrated in In order to demonstrate the accuracy of eRNA identification in normal tissues using DeepITEH, we conducted a comparative analysis with four prediction methods that rely solely on sequence features, namely, the SeqPose, iEnhancer-RD, LSTMAtt, and FRL. This analysis was performed using five evaluation metrics on independent datasets of four normal tissues. Our results indicated that the ACC metrics of DeepITEH were consistently superior to those of the other methods across different independent test sets, as presented in To verify the effectiveness of DeepITEH in identifying eRNAs in cancer tissues, we conducted a comparative analysis between DeepITEH and the four aforementioned methods, using independent datasets of four cancer tissues. Our results revealed that, with the exception of PAAD, DeepITEH consistently demonstrated higher ACC scores than the other methods across the independent test sets of the remaining three cancer tissues, as presented in Our study introduces a novel deep learning framework, called DeepITEH, for accurate prediction of eRNAs. DeepITEH combines RNA-seq data with histone modification data from various samples of the same tissue within a deep learning framework. Extensive experiments conducted in different tissues demonstrate that DeepITEH is highly effective in improving the accuracy of eRNA prediction for specific tissues. Furthermore, our proposed method outperforms the current state-of-the-art methods, namely, SeqPose, iEnhancer-RD, LSTMAtt, and FRL, on independent test sets of both normal and cancer tissues in terms of ROAUC. Based on our findings, we conclude that DeepITEH is a precise, stable, and advanced prediction method capable of accurately identifying potential eRNAs from the human genome.Further exploration of DeepITEH may lead to promising improvements in enhancer identification. First, integrating additional data sources such as ChIP-seq data for transcription factors, ChIP-seq data for the transcriptional coactivator p300, and chromatin accessibility data may enhance the accuracy of enhancer identification, as evidenced by some previous studies. Second, DeepITEH could be extended to predict enhancers that are not associated with transcriptional activity. Third, recent research has shed light on enhancer action sites, and the eRNA feature extraction method used in this study could be applied to promoters to facilitate the construction of associations between enhancers and promoters.btad375_Supplementary_DataClick here for additional data file.