Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget. scRNASeq data is revolutionizing our understanding of biological systems, but is still expensive to generate. Here, the authors present a statistical framework that facilitates informed multi-sample experimental design to reduce unnecessary costs and maximize the utility of the generated data. Combining this with genetic information leads to the analysis of differential expression between genotypes and the identification of expression quantitative trait loci (eQTLs)9, supplying the molecular link between genome and phenotype10.Understanding the molecular basis of phenotypic variation, such as disease susceptibility, is a key goal of contemporary biomedical research. To this end, researchers use transcriptomic profiling to identify changes of gene expression levels  between sets of samples, e.g., patients and healthy controls15 allows for differential gene expression and eQTL analysis on the level of individual cell types. Typically, single cell differential gene expression analysis seeks to identify genes whose expression levels are markedly different between different cell types18. In contrast, multi-sample experiments aim at the identification of DEGs between sets of samples within the same cell type. These sets can be defined by different experimental conditions or genotypes and are each measured at the single cell level. Multi-sample experiments have been identified as one of the grand challenges for single cell data analysis19.Single cell RNA-sequencing (scRNA-seq)20 and the control of the false discovery rate24 for microarray studies. For bulk RNA-seq studies, power analysis methods based on the theory of negative binomial count regression26, other parametric models29, or simulations31 have been proposed and benchmarked32.Power analysis is an important step in the design of statistically powerful experiments given certain assumptions about the expected effect sizes and constraints on the available resources. Researchers need to decide on parameters such as the sample size, the number of cells per sample and the number of reads. The power is tightly linked with the statistical testing procedure. Several methods have been established based on the theory of linear regression models33.In principle methods for bulk RNA-seq power analysis could also be applied to compute power or minimally required sample sizes for given effect sizes for single cell experiments, however they fail to take into account specific characteristics of single cell data. In scRNA-seq experiments, individual cells are typically not sequenced to saturation, leading to sparse count matrices, where only highly expressed genes are detected with counts greater than zero. In addition, the overall number of transcripts as well as the number of transcripts of individual genes can be highly cell type specific36. Second, it has been established that the minimal number of sequenced cells required to observe a rare cell type with a certain frequency can be modelled with a negative binomial distribution38 or multinomial distribution39.Recently, individual aspects of single-cell specific experimental design were addressed 57. Comparing our method against established simulation-based approaches validates our power estimates. In contrast to simulation-based methods, our analytic method can efficiently test a multitude of design options, making it suitable for the optimization of experimental parameters. Our model provides the basis for rationally designing well powered experiments, increasing the number of true biological findings and reducing the number of false negatives.Here, we provide a resource that enables choosing the optimal experimental design for interindividual comparisons. It focuses on the power to detect DEGs and eQTLs while also addressing the power to detect rare cell types. Our model was specifically developed for the pseudobulk approach, including a quantification of the probability to detect cell type specific gene expression in scRNA-seq data. We ensure an accurate power estimation with our model by selecting appropriate priors for the cell type specific expression distributions and for the effect size distributions. We derive data driven priors on expression distributions from single cell atlases of three different tissueshttps://github.com/heiniglab/scPower. All code to reproduce the figures of the paper is provided in the package vignette. The repository includes a shiny app with a user-friendly graphical user interface, which is additionally available as a web server at http://scpower.helmholtz-muenchen.de/.Efficient calculation including a selection of different possible priors is easily accessible for the user, as we provide our model and parameters as an open source R package scPower on github 58 or using the metacell approach59. Individuals are annotated with different experimental covariates, such as disease status. We focused on two group comparisons, but more complex experimental designs, which can be analyzed with generalized linear models, can also be accommodated (see package vignette).Our power analysis framework targets multi-sample transcriptomic experiments analyzed with the pseudobulk approach. Each analysis starts with a count matrix of genes times cells. These counts can either be counts of unique molecular identifiers (UMI), in the case of droplet-based technologies, or read counts in the case of Smart-Seq. Cells are annotated to an individual and a discrete cell type or state. These can be derived by clustering and analysis of marker genes, potentially considering multiple levels of resolution49. This pseudobulk approach has been identified as one of the currently best performing approaches for multi sample DE analysis in recent benchmarking studies46. It is important to keep in mind that the pseudobulk approach on single cell data is distinct from traditional bulk RNA-seq. In pseudobulk the ability to detect the expression of a gene depends on the number of cells of the cell type and on the expression level of the specific gene. Therefore, we model the general detection power dependent on the number of cells per sample nc which is related to the number of cells per cell type. Two additional experimental parameters determine the power in our model and also the cost of a scRNA-seq experiment in general: the number of samples ns and the number of reads sequenced per cell r. In order to compute the power of the experiment, we either need to make explicit assumptions or use prior knowledge about unknown experimental parameters, such as the assumed effect sizes and gene expression levels of eQTLs and DEGs. This prior knowledge is combined with user-defined parameters and cost determining factors to model the overall detection power  counts over all cells of the cell typewer Fig. .Fig. 1DeIn order to choose the optimal parameter combination of sample size, cells per sample and read depth for an experimental design, there are two types of power to consider. First, the power to detect the cell type of interest and second, the power to detect DE/eQTL genes within this cell type .37, we model this problem using the negative binomial distribution (see “Methods”). Using prior knowledge of cell proportions in peripheral blood mononuclear cells (PBMCs) from the literature, we determine the number of cells required for each individual to detect a minimal number of cells of a specific type  and a certain fraction of individuals. Specifically, we compute the probability that the observed counts are greater than the user defined minimal count threshold in at least a given number of individuals. Summing up these gene expression probabilities allows for modelling the expected number of expressed genes (see “Methods” section for detailed formulas). On top of our default threshold criteria, our package offers the user alternative options for expression thresholds, e.g., that a gene is called expressed if it has a count > 0 in a certain percentage of cells.r2 > 0.9, Supplementary Table Subsampling of our data shows that the number of expressed genes per cell type depends on the number of cells of the cell type and the read depth Fig. . The obs47 that was not used during parameter estimation for the expression priors , which demonstrates that it can generalize well between data sets and different experimental parameters. Taken together, we now have a general model for the expected number of expressed genes, which is parameterized by the number of cells per cell type and the number of reads per cell. Of note, gene expression distributions are cell type specific and the model parameters have to be fitted from suitable (pilot) experiments, such as the human cell atlas project61.To validate our model, we applied it on a second PBMC data setors Fig. . This vaBuilding on our expression probability model, we can assess the DE/eQTL power of the expressed genes using existing analytical power analysis tools that have been established for bulk sequencing data. They estimate the power to detect an effect of a given effect size depending on the sample size, the gene mean expression level and the chosen significance threshold. Analytic power analysis compares the distributions of the test statistic under the null and the alternative model . Based on the significance threshold the critical value of the test statistic is determined from the null distribution. Then the power is given by the probability mass of the distribution under the alternative model that exceeds the critical value.62 or the false discovery rate (FDR)22. In the following analyses, we used the FDR adjustment for DE power and FWER adjustment for eQTL power, as proposed by the GTEx Consortium63 for a genome-wide cis eQTL analysis.An adjustment of the significance threshold is necessary due to the large number of parallel tests performed in a DEG analysis in order to avoid large numbers of false positive results. We provide two methods in our framework for that, either controlling the family-wise error rate (FWER) using the Bonferroni method64 and linear regressions20, respectively. This also leads to different effect size specifications; fold changes in the DE case and R-squared values in the eQTL case. Of note, the R-squared values combine allele frequency and beta value in the linear model.Specifically, the power analysis methods we apply for DE and eQTL studies are based on negative binomial regressions60 or edgeR44 for DEG analysis of both RNA-seq and scRNA-seq67. Benchmarking studies showed that these tools combined with the pseudobulk approach outperform other methods in multi-sample differential expression analysis46. We verified that all our training data sets could be modelled by negative binomial distributions after pseudobulk transformation and found no evidence of zero inflation (Supplementary Table 69), but aggregation to pseudobulk removed the excess of zero values. Hence, it is valid to apply analytical methods for the power analysis of negative binomial regression models64. To obtain a range of typical effect sizes and mean expression distributions in specific immune cell types, we analyzed several DEG studies based on FACS sorted bulk RNA-seq54  we find a maximum overall detection power of 74% , simple linear models are usually applied to transformed read count data70, as they can be computed very efficiently. For large mean values, the power is estimated analytically, for small mean values, this approximation can be imprecise and instead simulations are used that take the discrete nature of scRNA-seq into account. This introduces a dependency between the eQTL power and the expression mean and thus eQTL power is considered conditional on the mean. The mean threshold below which simulations are used, was defined by comparison of simulated and analytic power . In contrast, muscat is a recent method that already incorporates the pseudobulk approach for multi-sample comparison and can be used directly. Both simulation methods can be combined with different DE analysis methods for the downstream analysis of the simulated counts. We evaluated them in combination with different common DE methods, such as DESeq25, edgeR44 and limma45.For single cell DE experiments, we compared our model with powsimRThe simulation based power estimates from the adapted version of powsimR as well as from muscat matched the estimates from scPower very well Fig. . We comp71. This increases the power compared to non-batch corrected analyses74. Following this strategy, we could recover the same power as in experiments without batch effects, i.e., our power estimations stay accurate in experiments with batch effects, given that they are adjusted for in the analysis.Furthermore, we used the simulation-based methods to evaluate how well our power analysis method performs for different real-life conditions, such as batch effects or unbalanced cell type proportions between the groups. Simulating batch effects showed a clear drop in power, especially if the magnitude of the batch effect is larger than the effect size of DEGs . The library preparation cost is defined by the number of measured samples and the number of measured cells per sample, while the sequencing cost is defined by the number of sequenced reads, which also depends on the target read depth per cell.75 that the expression cutoff should correspond to the percentage of samples in the smaller group. For our DE example, this results in a percentage threshold of 50% due to the balanced DE design. For the eQTL example, we consider an eQTL with a minor allele frequency of 0.05, which is a common lower threshold for genetic variants tested for associations. We suggest that the gene should be at least expressed in the heterozygotes and thus pick a percentage threshold of 9.5% (see “Methods”).We evaluated exemplarily the three parameters maximizing detection power, given a fixed total budget Fig. . In thisWe use our method to calculate the overall detection power for different parameter combinations of cells per individual and read depth, while the sample size is defined uniquely given the other parameters and the fixed experimental budget. For the DE study with this specific prior combination, the optimal parameters are measuring 1200 cells in 4 samples with a read depth of 30,000. Measuring more cells per individual increases the expression probability and so the overall detection power Fig. , but duehttp://scpower.helmholtz-muenchen.de.For the eQTL study with this specific prior combination, the optimal parameters are measuring 1500 cells in 242 samples with a read depth of 10,000. Again a balance of the eQTL power, which depends mostly on the sample size, and the expression probability, which depends mostly on the cells per sample and the read depth, is visible Fig. . A user-We can expand our analyses with expression priors from our 10X PBMC data set and find the optimal parameter combinations depending on a given experimental budget Fig. . We systp values for DE power and FWER adjusted p values for eQTL power. Using FWER adjustment for DE power changes the observed overall power, but leads to very similar optimal parameter combinations and the same trends overall  and 48GB (powsimR) in our evaluation. Therefore, larger data sets with hundreds of samples, as required for eQTL studies, will be very difficult to simulate.Previous experimental design methods for multi-sample scRNA-seq50 has been done, but suffered from several limitations. First, it was based solely on the effective sample size, ignoring actual effect sizes and expression strength of eQTL genes. Second, it provided no generalizable tool. Third, it is limited to eQTL analysis and does not cover DE studies. In contrast, our approach provides gene level and overall power estimates based on prior data and we provide a generalizable tool for analytic power analysis of single cell DE and eQTL studies. This enables the user the evaluation of his target experiment in order to identify the use-case specific optimal parameter combination. The method is implemented in an R package with a user-friendly graphical user interface and is freely available on github. In addition, the graphical interface of our model is also available over this website http://scpower.helmholtz-muenchen.de/.A first analytic investigation of power optimization in single cell eQTL studies38 but also for the power to find DE/eQTL genes by increasing the sensitivity of gene expression detection. In line with Mandric et al.50, our analyses suggest that aggregating shallowly sequenced transcriptomes of a large number of cells of the same cell type is a more cost efficient way than increasing read depth to increase the sensitivity for individual level gene expression analysis. Most likely, multiple independent library preparations in individual cells lead to an improved sampling of the transcriptome as compared to fewer independent libraries sequenced more deeply, an effect that has previously been analyzed in the context of variant detection79.We identify the optimal experimental parameters based on expression priors from single cell atlases of three different tissues and cell type specific effect size priors from bulk DEGs and eQTLs. We show that the number of cells is not only crucial for the power to detect rare cell types81. However, in a systematic analysis of spike-in expression it has been shown that the accuracy of the measurements is not strongly dependent on the sequencing depth and consistently high for a read depth of 10,000 reads per cell35. Hence, we expect to accurately quantify the gene expression levels with the optimized experimental design.Specifically, we found the optimal read depths to be ~10,000 in most evaluations, which is relatively low compared to previous recommendations82. Shallow sequencing of more cells has been recommended for extracting the gene expression programs required for annotations, because it has achieved equal accuracy as deeper sequencing of fewer cells82. These recommendations match our optimal determined parameters. To ensure sufficient power for cell type annotation, our framework scPower can be combined with specific power analysis tools for cell type annotation82.On top of the DE/eQTL power, the number of cells and sequencing depth also determine accurate cell type annotationThe optimal sample size is mostly dependent on the effect size, with low effect sizes requiring large sample sizes and consequently optimal setting with high sample size typically lead to low sequencing depth and relatively low number of cells.In general, priors affect the optimal design and should therefore be selected carefully. In the optimal case, priors are known from well matched pilot experiments or knowledge from the literature. Of note, our data driven priors only allow for reliably assessing the overall power in sample sizes that are smaller or roughly equal to the sample size of the pilot data sets from which the effect sizes were estimated. Consequently, a larger sample size will identify new significant DEGs with lower effect sizes, which were not identified in the smaller pilot study and thus not included in the computation of the overall detection power.In the absence of well-matched pilot experiments, it is nevertheless important to make assumptions explicit by either selecting a prior based on a similar biological phenomenon or by choosing a prototypic case. In our study, we have compared the prototypic cases of strong effect sizes and relatively high expression versus intermediate effect sizes and expression levels across the whole range from highly expressed to lowly expressed genes. Both options, processing priors from a selected reference study and simulating proteotypic priors, are possible with scPower and described in the package vignette.43, it requires a discrete cell type definition and our approach is tightly linked to it. Therefore, continuous cell annotations such as pseudo time would need to be discretized before the power analysis.The pseudobulk approach presented here leverages well established power analysis methods based on  linear models. While the  regression model for pseudobulk is currently the most powerful method for assessing individual level differential expression78. Thus, actual experiments might result in higher doublet rates and lower number of usable cells. The choice of a threshold on the number of reads required for a gene to be called expressed also influences the choice of optimal parameters. In our examples we used a threshold of >10 and >3 reads, however, some eQTL analyses of bulk RNAseq data advocate using >0 reads70.Our model requires the user to choose between our defaults and custom settings for parameters such as doublet rate and expression threshold. The default for the doublet rate is based on reference values from 10X Genomics and is a lower bound compared to the doublet rates we estimate for our own data and to rates reported by other studies83, we additionally offer users to find the threshold optimizing the number of discoveries at a given FDR (see package vignette). The identified optimal thresholds are low and increase the number of detectable genes. However, the user needs to be aware that this strategy is likely increasing the number of false positives18. For this reason, best practice guidelines for differential gene expression with RNA-seq recommend cutoffs that remove between 19 and 33% of lowly expressed genes, depending on the analysis pipeline84. These percentages correspond to 1–10 reads per million sequenced, which translates to 1–5 UMI counts for a median of around 5000 UMI counts per cell in our data set. Our gene expression probability model is cell type specific and has to be fitted based on realistic pilot data. We have shown that our model can be applied to data generated with 10X Genomics, Drop-seq and Smart-seq2 and we are confident that it is applicable to similar technology platforms.Following the independent filtering strategy of DESeq238. Lastly, we did not address the power for the detection of variance QTLs (quantitative trait loci associated with gene expression variance across cells) from scRNAseq data48 due to the lack of data driven priors for the effect sizes.When using our approach, the user should keep in mind that our experimental design recommendations are optimized for differential expression between individuals. Other applications might result in very different optimal experimental designs. For instance, co-expression analysis requires a high number of quantified genes per cell, especially when one is interested in cell type specific co-expression and comparison of such co-expression relations between individuals. Furthermore, the power to annotate new rare cell types by clustering analysis of scRNA-seq data might have different optimal parameters85. This will create high quality priors that will further broaden the applicability of scPower. We are convinced that scPower will provide the foundation for building rational experimental design of interindividual gene expression comparisons with cell type resolution across a wide range of organ systems.The human cell atlas project has made it its goal to build a reference map of healthy human cells by iteratively sampling the cells with increasing resolution86. All individuals gave informed consent. Peripheral blood mononuclear cells (PBMCs) were isolated and cryopreserved in RPMI 1640 medium (Sigma-Aldrich) supplemented with 10% Dimethyl Sulfoxide at a concentration of roughly 1 M cells per ml.Blood was collected from healthy control individuals according to the clinical trial protocol of the Biological Classification of Mental Disorders study  at the Max Planck Institute of PsychiatryAll investigations have been carried out in accordance with the Declaration of Helsinki, including written informed consent of all participants. Study conduct complies with the recommendations by the ethics committee of the Ludwig-Maximilian University, Munich. Applicable national and EU law, in particular the General Data Protection Regulation (GDPR) (Regulation (EU) 2016/679) has been followed.Permission for using the data has been obtained from the Biobank of Max Planck Institute of Psychiatry. Consent for secondary use of the existing data has been obtained. In compliance with the consent for secondary use, the data generated in this project will be stored and can be used for future research. All data has been pseudonymized. Written informed consent of all participants allows for publication of data in online repositories.For single-cell experiments, 14 cell vials from different individuals  were snap-thawed in a 37 °C water bath and serially diluted in RPMI 1640 medium (Sigma-Aldrich) supplemented with 10% Foetal Bovine Serum (Sigma-Aldrich) medium. Cells were counted and equal cell numbers per individual were pooled in two pools of 7 individuals each. Cell pools were concentrated and resuspended in PBS supplemented with 0.04 % bovine serum albumin, and loaded separately or as a combined pool with cells of all 14 individuals on the Chromium microfluidic system (10X Genomics) aiming for 8000 or 25,000 cells per run. Single cell libraries were generated using the Chromium Single Cell 3′library and gel bead kit v2 (PN #120237) from 10X Genomics. The cells were sequenced with a targeted depth of ~50,000 reads per cell on the HiSeq4000 (Illumina) with 150 bp paired-end sequencing of read2 . The threshold for the female-specific error was set less strictly, as mismapping of a few reads to the chromosome Y occurs also in female cells. Instead, the female-specific error indicates which fraction of cells is assigned to a female donor among all cells having more reads mapped to chromosome Y than the Both error rates are calculated twice, once with all cells and once without doublets from Demuxlet and Scrublet.90. Genes which occurred in less than 3 cells were removed. Counts were normalized per cell and logarithmized. Afterwards the highly variable genes were identified, the effect of counts and mitochondrial percentage regressed out. We calculated a nearest neighbour graph between the cells, taking the first 40 PCs, and then clustered the cells with a Louvain clustering91. Cell types were assigned to the clusters using marker genes .The cell type frequencies were obtained by literature research, the frequencies in PBMC are approximately twice as high as in whole blood93. The number of reads was downsampled to 75%, 50% and 25% of the original number of reads. CellRanger was used to generate count matrices from the subsampled reads. Donor, doublet and cell type annotation were always taken from the full runs with all reads.We used subsampling to estimate the dependence of gene expression probabilities on read depths. The fastq files of all 6 runs were subsampled using fastq-sample from fastq-tools version 0.833. The UMI counts x per gene across the cells of a cell type are modeled by a negative binomial distribution. We used DESeq60 to perform the library size normalization as well as the estimation of the negative binomial parameters. The standard library size normalization of DEseq and the variant “poscounts” of DESeq25 were both used, depending on the quality of the fit for the specific data set. For the PBMC 10X data set  is linear and The number of transcriptome mapped reads is linearly related to the logarithm of the mean UMI counts per cell, with an increasing read depth leading to a saturation of UMIs. 10X Genomics describes this also with the sequence saturation parameter. The exact logarithmic saturation curve depends on multiple biological and technical factors, therefore, it needs to be fitted for each experiment individually. However, scPower provides example fits from the different scenarios observed in our analysis.The dispersion parameter is estimated dependent on the mean value using the dispersion function fitted by DESeq. The parameters of the mean-dispersion curve showed no correlation with the mean UMI counts, therefore the mean of the parameters of the dispersion function across all runs and subsampled runs were taken, resulting in one mean-dispersion function per cell type.The selection of expression cutoffs both on the individual level and the population level depends on the users and their research question, balancing the increase in power by more lenient cutoffs and the potential higher false positive rates associated with it. We applied different UMI count cutoffs for the individual level to prove the flexibility of our tool.75 that the expression cutoff should correspond to the percentage of samples in the smaller group. In the DE case, this results in a cutoff of 50% as we focus on studies with balanced design. In the eQTL case, the definition of groups depends on the genotype and is therefore not directly chosen by the user. We decided to select the cutoff based on the minor allele frequency For the population level, we followed in most of our analyses the recommendation of edgeRFurthermore, our R package provides alternative threshold criteria. On the population level, instead of a percentage threshold for the number of individuals, an absolute threshold can be chosen. On the individual level, instead of an absolute count threshold in the pseudobulk, a gene can be defined as expressed if it is expressed in a certain number of cells with count larger than 0. Both alternative criteria are based on the same model as explained above in the previous section.If the users want to choose a threshold that maximizes the power, our package provides an optimization function for that.64. An implementation of the method can be found in the R package MKmisc. Parameters are sample size, fold change, significance threshold, the mean of the control group, the dispersion parameter (assuming the same dispersion for both groups) and the sample size ratio between both groups. We focus in our analyses on balanced comparisons with the same number of samples in both groups, represented by a sample size ratio of 1. Zhu et al. implemented three different methods to estimate the dispersion parameter, we chose method 3 for the power calculation, which was shown to be more accurate in simulation studies in the paper. More complex experimental designs can be addressed using the method of95.The power to detect differential expression, also denoted as the probability Additionally to the DE analyses, the use of scRNA-seq for the detection of expression quantitative trait loci (eQTLs) was evaluated. We distinguish for the eQTL power between genes with high and with low expression levels, where the mean is used to parameterize a simulation. Therefore, the eQTL power is a function of the mean expression level.For genes with high expression level, the power to detect an eQTL is calculated analytically using an F-test and depends on the sample size 20. The degrees of freedom of the numerator are 1 and of the denominator are The implementation pwr.f2.test of the R package pwr is used for the F-test45. However, for genes with small mean values, i.e., only very few non-zero counts, this normalization might not be effective and the power is overestimated by the analytical power calculation based on the F-test. We performed a simulation study to assess the effect of the mean values on the eQTL power.This power calculation assumes that the residuals are i.i.d. normally distributed. For large count values, it has been shown that normalized log transformed counts have a constant variance independent of the mean value and can be analyzed with linear models45. As for the analytical power calculation, the effect size is given by the coefficient of determination Simulate genotypes. To also account for the discrete nature of the genotypes, we first draw allele frequency Simulate read counts. Using the allele frequency, the beta value The associated gene expression count vector 45 was not accurate enough, we used instead a numerical optimization. This numerical optimization is precalculated for a range of parameter combinations to speed up calculation for the user.To account for the discrete nature of the counts we adopted a simulation scheme similar to a negative binomial regression model and analyzed the log transformed counts using linear modelsp value Using the linear regression Finally, the simulation based power is estimated as The power of the simulation was compared with the analytic power calculated by scPower to assess at which value of the mean The overall detection power for DEGs/eQTLs is the product of the expression probability and power to detect DEGs/eQTLs, as both probabilities are conditionally independent given the expression mean of the gene. Expression probabilities were determined based on the gene expression rank in the observed (pilot) data. The number of considered genes To quantify the overall power of an experimental setup, we compute the expected fraction of detected DEG/eQTL genes with prior expression levels and effect sizes derived from the pilot data. We obtain gene expression ranks of DEGs/eQTLs and their corresponding fold changes to compute overall detection power for each gene. The overall power of the experimental setup is then the average detection power over all prior DEG/eQTL genes.63 based on FWER adjustment for the eQTL power. However, our framework allows for any combination of power analysis and multiple testing method. For all analyses shown, adjusted DE/eQTL power is computed using a significance threshold 63, which assumes that for each gene on average 10 independent (uncorrelated) SNPs are tested in a genome-wide cis eQTL analysis. Thus, the adjusted p value threshold is set at The family-wise error rate is defined as the probability of at least one false positive among all tests. Each expressed gene is tested once in the DE analysis, therefore, the adjustment for the family-wise error rate is done by correcting the threshold to In our tool, the number of independent SNPs can be flexibly chosen, for example if the user wants to perform a cis and trans eQTL analysis, he can define a higher number of independent SNPs.22. In contrast to the Bonferroni correction, which depends only on the number of tests, the FDR correction depends on the p value distribution of all genes. As our analytic method outputs the power without computing the p values, we can not apply the FDR correction directly and use the method of Jung.Alternatively, for DE analysis the significance threshold can be adjusted for the false discovery rate using the method of Jungp value The goal of the approach is to identify the raw p values for the p value significance threshold of The FDR is the fraction of false positives among all rejected null hypotheses (predicted positives), which includes both the false positives and the true positives. Based on the probability integral transform, the distribution of The expected number of true positives is directly derived from the power we reach for p value threshold Using numerical optimization of the complete formulaRealistic DE and eQTL priors, i.e., effect sizes and expression ranks, were taken from sorted bulk RNA-seq studies of matching tissues . For all studies, the significance cut-off of the DE and eQTL genes was set to FDR <0.05 and the expression levels of the genes were taken from FPKM normalized values. When published, we took directly the effect sizes, otherwise we recalculated the DE analysis with DEseq2.54. The data sets were used to rank the expression level of the DEGs among all other genes using the FPKM values. The cell types used in the studies were matched to our annotated cell types in PBMCs for the expression profiles. The expression profile of CD14+ Monocytes was used for the study of Macrophages, the profile of CD4+ T cells for the CLL study.Differential gene expression: To get realistic estimates for effect sizes (fold changes), data sets from FACS sorted bulk RNA-seq studies were taken55. As no effect sizes were reported, the analysis was redone with the given count matrix from GEO (accession number GSE85567) using DEseq2.Lung cell type specific priors were obtained from a DE study of freshly isolated airway epithelial cells of asthma patients and healthy controls56 was used to get pancreas cell type specific priors. We obtained expression ranks and gene length, which is needed for proper normalization of Smart-seq2 expression values.A DE study analyzing age-dependent gene regulation in human pancreas57. Neutrophils were excluded as they are not PBMCs. We took the most significant eQTL for each gene, using a significance cutoff of 10−6. We compared the FPKM normalized expression levels of the eQTL genes among all other genes to get the expression rank for each eQTL gene. Effect sizes were derived from the slope parameters of the linear regression against genotype dosage, its standard error and the sample size of the study.eQTLs: We used eQTL effect and sample sizes from the Blueprint study on bulk RNA-seq of FACS sorted Monocytes and T cells43. For both tools, a few changes needed to be implemented to compare the output exactly with our approach. powsimR is not designed for multi-sample comparison and for both methods the option to apply a vector of log-fold changes with matching expression ranks was not available. A detailed explanation of both methods and applied changes can be found below.To validate our model, we compared the DE power estimations of our framework with two simulation-based tools, called powsimR and muscatThe simulation-based methods perform random sampling of their count matrices and therefore the simulation was repeated 25 times for each parameter combination to generate stable results. Both tools allow the power estimation for different DE methods. We evaluated powsimR in combination with edgeR-LRT, DESeq and limma-voom, together with median-ratio normalization of DESeq (‘MR’), and muscat in combination with edgeR, DESeq2, limma-voom and limma-trend. No imputation or filtering was applied for any of the methods. In the comparisons with our model scPower, the expression probability parameters of scPower were set to minCounts >0 in at least one individual to match the detected genes of powsimR and muscat.Exemplarily, the CD4 T cells of our PBMC data set were used for fitting the simulation models of powsimR and muscat. We evaluated all DE methods for 4, 8 and 16 samples in combination with 200, 1000 and 3000 cells per person. Additionally, we performed a comparison for a large range of parameter combinations of powsimR with edgeR-LRT and muscat with edgeR, testing all combinations as evaluated in Fig. .In the following, it is important to distinguish the training data set, which is used for model fitting of powsimR/muscat and restricts the number of simulated genes, and the simulated data set which is sampled from the trained model.Expressed genes: The expected number of expressed genes for scPower is compared with the number of expressed genes in powsimR and muscat, which are all genes with at least one count in the simulated matrix. An important limitation of the simulation based frameworks is here that the number of expressed genes in the simulation tools can never be larger than the number of expressed genes in the training data set, while scPower can also approximate expression of unseen genes with smaller mean values and so estimate more expressed genes than seen in the pilot data.DE power: The reported power of powsimR includes only genes, which are expressed in the simulated data set (count > 0). The same value can also be calculated for muscat. To make the DE power of our framework comparable, the mean power for all expressed DE genes was calculated. An expressed DE gene for scPower is defined by its expression rank, which needs to be smaller than the expected number of expressed genes.Overall power: powsimR does not return directly an overall power, which we define as the power over all simulated DE genes (including genes simulated with count > 0 and count = 0). However, the overall detection power of powsimR can simply be calculated by dividing the number of true positives of powsimR by the number of all simulated DE genes. The same was done for muscat.The three main components of our statistical framework were evaluated in the comparison, the expression probability (by comparing the number of expressed genes), the power  and the overall detection power.powsimR: uses training data to fit the parameters of the expression distributions for each cell type and gene. Using these parameters, it is randomly generating count matrices for two groups of cells introducing differential gene expression between these two groups for a prespecified number of DE genes. These DE genes are randomly selected and the means of their distributions shifted by a given effect size. In the next step the simulated data is analyzed with different methods and results are compared to the simulated group truth to determine the power.96. After simulation of the new count matrix Adaptations of powsimR are required to simulate a multisample setting and thus make it comparable to scPower: We added an additional step that generates a pseudobulk count matrix for multi-sample comparison. For this, we included an additional parameter for the sample size muscat: was specifically implemented for multi-sample comparisons, in contrast to powsimR. It fits one negative binomial distribution separately for each sample and subpopulation in the training data set. The subpopulation definition is hereby equivalent to our cell type definition. We noticed that fitting each sample separately decreases the number of expressed genes quite drastically, if not a sufficient number of cells are available for each sample. Because again only as many genes can be sampled as are detected in the training data set. To get a robust fit of the negative binomial distribution with our training data set, we therefore decided to fit the negative binomial distribution for all samples together, for a very large training data set this is probably not necessary.Another difference to powsimR is that muscat provides different scenarios for simulating differential expression besides the shift of the mean expression . Additionally, they simulate genes with different proportions of low and high expression-state (DP), differential modality (DM) or changes in both proportions and modality (DB). For comparison with powsimR and scPower, we focus on the DE scenario.Similar to powsimR, we also incorporated here the option to assign genes of a specific expression rank a specific log fold change to simulate the same DE genes as in scPower.We applied the simulation-based methods to further validate the scPower estimations in regards to two different real life scenarios that can affect the power: batch effects and differences of cell proportions between the groups. The introduction of batch effects was already implemented in powsimR. Of note, we slightly adapted the code of powsimR again to the multi-sample setting by assigning all cells from one individual to the same batch. We separated the individuals into two different batches with 50% cases and 50% controls in each batch, which represent a setup based on a non-confounded experimental design. 20% of the genes were randomly sampled to show batch log fold changes with values between 2 and 6. We ran the downstream powsimR power analysis with edgeR once without accounting for batch effects and once with adjusting for the batch effects using a model that includes a batch covariate. This was repeated for different batch effects and experimental parameter combinations and each time the results were compared with the scPower estimation.The second real life scenario simulating differences of cell proportions between the groups is readily implemented in muscat. The cell proportion parameter represents the fraction of all measured cells that belong to group 1, i.e., a fraction of 0.3 means that 30% of measured cells belong to group 1 and 70% to group 2. We evaluated cell proportions between 0.1 and 0.5 in combination with different experimental parameter combinations and compared the results to scPower. For the scPower estimation, we calculated two versions, once the default approach assuming balanced distribution of cells between the groups and once a conservative approach, also assuming the balanced distribution, but reducing the cell frequency As no simulation-based power analysis for eQTLs exists , we benchmarked the eQTL power with our own simulation tool . Our simulation method uses our expression probability model to estimate the mean parameter, therefore only the power itself is compared . We tested again 25 rounds of simulation for all parameter combinations depicted in Fig. The overall experimental cost We optimized the three cost parameters for a fixed budget to maximize the detection power. A grid of values for number of cells per individual and for the read depth was tested, while the sample size is uniquely determined given the other two parameters and the fixed total costs. As an approximation of the sample size, the ceiling functions from the cost formula were removed.The same approach can also be used with a grid of sample size and cells per sample or read depth. In general, two parameters need to be chosen and the third parameter is uniquely determined given the other two and the fixed experimental cost.37, we model the doublet rate 77. A factor of Given the three cost parameters, the detection power for a specific cell type and a specific DE or eQTL study can be estimated. However, we also have to account for the appearance of doublets during the experiment. The fraction of doublets depends on the number of cells loaded on the lane. Following the approach ofThe number of usable cells per individual used for the calculation of detection power is then 37, we assume that doublets contain 80% more reads than singlets. In the following, the ratio of reads in doublets compared to reads in singlets is called doublet factor A second effect of doublets is that the read distribution is shifted, as doublets contain more reads than singlets. Again following the approach ofIn addition, the mapping efficiency is taken into account. Assuming a mapping efficiency of 80%, Instead of defining the number of samples per lane directly, usually the number of cells loaded per lane Model priors, i.e., effect sizes and gene rank distributions, were derived from FACS sorted bulk RNA-seq to get realistic assumptions. In addition, we simulated different extreme prior distributions to evaluate their influence on the optimal experimental parameters. The log fold changes for the DE studies were modeled as normally distributed. High effect size distributions were simulated with a mean of 2 and a standard deviation of 1, low effect sizes distributions with a mean of 0.5 and standard deviation of 1.Effect sizes  and high or uniformly distributed expression ranks, called in the studies highES_highRank, lowES_highRank, highES_unifRank and lowES_unifRank.52 and a Smart-seq2 data set measured in pancreas tissue51.We validated our expression probability model for other tissues and single cell RNA-seq technologies. Two data sets of the human cell atlas were used for that, a Drop-seq data set measured in lung tissueThe Drop-seq technology is also a droplet-based technique, similar to 10X Genomics. The same model can be used, only adapting the doublet and cost parameter. However, as there was no data available to model the linear increase of the doublet rate during overloading correctly, the doublet rate was modeled instead as a constant factor and the library preparation costs were estimated per cell. scPower provides models for both cases and with the necessary prior data, users can also model the overloading for Drop-seq.Smart-seq2 is a plate-based technique, which produces full length transcripts and read counts instead of UMI counts. To compensate the gene length bias in the counts, the definition of an expressed gene was adapted to at least For both data sets, the cell type frequencies varied greatly among individuals, therefore an estimation of expressed genes in a certain fraction of individuals could not be validated, as this requires similar cell type frequencies for each donor. Instead, the expressed genes were estimated to be above a certain count threshold in all cells of a cell type, independent of the individual.93 and the subsampled UMI count matrix was generated following the pipeline previously described in97. The Smart-seq2 read matrix was subsampled directly using the function downsampleMatrix of the package DropletUtils98.Both data sets were subsampled to investigate the effect of the read depth. The Drop-seq reads are subsampled using fastq-tools version 0.8We compared the budget restricted power to our PBMC 10X Genomics results, using the same simulated effect sizes and distribution ranks as well as matched observed priors from FACS sorted bulk studies.Further information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationPeer Review FileReporting Summary


The number of endometrial cancer (EC) cases is constantly growing. However, the current diagnostic approach is still rather imprecise, leaving 1/3 of patients temporarily undiagnosed. Moreover, final diagnosis is made after the surgery. That mean the histology of tumor, which influences scope of resection, is uncertain during procedure. This results in over- and undertreatment of EC patients. Those diagnostic problems might be solved by liquid biopsy—a new, minimally invasive method to obtain tumor biomarkers. Therefore, this study aimed to evaluate the usefulness of information obtained from liquid biopsy components (tumor educated platelets and circulating tumor DNA) coupled with random forest algorithm and deep neural networks to diagnose EC patients and evaluate tumor histology preoperatively.Background: Liquid biopsy is a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, the potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. Methods: TEPs from 295 subjects  were RNA-sequenced. DNA sequencing data were obtained for 519 primary tumor tissues and 16 plasma samples. Artificial intelligence was applied to sample classification. Results: Platelet-dedicated classifier yielded AUC of 97.5% in the test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was more challenging, with AUC of 84.1%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 96% and ctDNA blood samples with AUC of 69.8%. Conclusions: Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work involving more cases is warranted. Liquid biopsy, being a minimally invasive alternative to surgical tissue biopsies, has recently revolutionized cancer diagnostics. This type of analysis is typically achieved using a blood sample. The procedure enables interrogation of tumor-derived material such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating free RNA (cfRNA), tumor-derived extracellular vesicles (EVs), and more recently, tumor educated platelets (TEPs), all present in the body fluids of cancer patients ,2. So faLiquid biopsies are poorly explored in endometrial cancer (EC). There is an apparent need for improving preoperative diagnosis of EC. Limitations of the currently used diagnostic procedures  leave approximately 30% of EC patients temporarily undiagnosed . This prAn unexplored method of liquid biopsy in EC is TEPs technology. TEPs are platelets that interact with cancer cells and are heavily involved in the progression of solid tumors, and their transcriptomic profile changes dramatically under the influence of the disease. Best et al. have demonstrated that the platelet transcriptome allows for distinguishing between healthy, asymptomatic subjects and cancer patients in various malignancies including glioblastoma, sarcoma, non-small cell lung cancer, pancreatic adenocarcinoma, colorectal cancer, and breast cancer ,13. To tBoth TEPs and ctDNA can be obtained from one vial of blood. Thus, we decided to perform both assays in a parallel manner. We hypothesized that the analysis of TEPs could aid initial EC diagnosis by discrimination between healthy women and EC patients, whereas ctDNA interrogation would facilitate EC histology evaluation. As sequencing of either RNA or DNA generates massive, difficult to interpret amounts of data, we used the artificial intelligence for decision making: deep neural network and random forest, as presented in Sample collection: Liquid biopsies were collected from gynecologic patients treated at the Department of Gynecology, Oncologic Gynecology and Gynecological Endocrinology, Medical University of Gdańsk  between 2017 and 2019. Additionally, the sequencing data were obtained from: (a) platelets collected from 204 healthy women referred as healthy donors (Dutch cohort) ; (b) priThe inclusion criteria for this study included EC before the initiation of any treatment or benign gynecologic condition (BGC), and age above 18 years. Each patient signed an informed consent form. The study was accepted by the Independent Ethics Committee of Medical University of Gdańsk (NKBBN/434/2017). Procedures involving human subjects were in accordance with the Helsinki Declaration of 1975, as revised in 1983. Stage evaluation was based on classification of Federation of Gynecology and Obstetrics (FIGO) from 2014 [Blood processing: Blood from gynecologic patients was collected into 6 mL BD vacutainer tubes with EDTA as an anti-coagulant. Samples were processed up to 48 h after collection according to the protocol presented in Platelet RNA sequencing and sample classification: Expression data for each sample were normalized using DESeq2 package  with var® DNA Blood Mini Kit according to the manufacturer’s guidelines. Circulating tumor DNA was isolated from 0.5–3 mL of plasma, using the QIAamp® MinElute ccfDNA Mini Kit according to the manufacturer’s guidelines, with an additional step of double plasma spinning after thawing  in order to reduce plasma contamination with germline DNA. Quantification of the extracted ctDNA was performed on TapeStation 4200 platform using the Agilent High Sensitivity D1000 ScreenTape and Agilent High Sensitivity Assay Kit. Quantification of the extracted germline DNA was performed with the use of Qubit Flourometer and Qubit DNA High Sensitivity Assay Kit. Extracted material was stored at −80 °C for later library construction.Germline DNA and ctDNA extraction: Germline DNA was isolated from 100 µL buffy coat using QIAampLibrary construction and sequencing: For ctDNA and germline DNA sequencing, library construction was performed using QIAseq Targeted Human Colorectal Cancer Panel covering 71 genes. Complete gene list is available in Tumor DNA sequencing and sample classification: For DNA sequencing, the alignment was performed using bwa-mem  0.7.17-rThe study group included liquid biopsy samples collected from 53 EC patients , 38 patients with benign gynecologic conditions (TEPs) and 204 healthy donors (TEPs). Benign gynecologic conditions included myomas, endometriosis, cysts, polyps, and Brenner tumors. Primary tumor samples were only used for the training of the classifier. Patient characteristics are summarized in N = 168), validation (N = 111) and test set , respectively , the classifier reached an AUC of 96%. Classification of plasma ctDNA, using the optimal decision threshold, reached a specificity of 0.58, sensitivity of 0.778, and accuracy of 68.7%, with 11/16 samples classified correctly .Accurate diagnosis is crucial for effective EC management, and there is a need for new preoperative molecular diagnostic tools ,31. We dTEPs analysis so far has been successfully applied in NSCLC, ovarian cancer, and glioblastoma ,23. Our In a traditional model, EC is divided into type 1 and type 2 cancer. Both types have mutations in certain genes: type 1 in PTEN, ARID1A, PIK3CA and KRAS, and type 2 in TP53. Currently, the histology evaluation relies on the uterine biopsy or uterine curettage . This stN = 204) and primary tumors (N = 519) we had had at our disposal at the moment of the analysis. Although we admit that enrolling more samples would likely result in the further refinement, we believe our results are meaningful and worth presenting despite the small liquid biopsy sample size. Additionally, we admit that imPlatelet classifier needs refinement to better distinguish BGC from EC. Further, under ideal conditions, BGC cohort should be limited to myoma, cyst, polyp and endometriosis cases, which are not considered pre-cancer conditions. The proposed approach is also expensive in a clinical setting, but the sequencing costs are expected to continue decreasing [Several limitations of the study need be acknowledged. The first is a small number of liquid biopsies and the lack of matching between TEPs and ctDNA samples. Hence, the presented classifier accuracies should be intepreted with caution. However, for the training process, we have used all the asymptomatic controls (creasing ,35.Random forest algorithm also needs refinement. The results of algorithm stability analysis  suggest One of the advantages of applying machine learning is its flexibility. Recent publications provide evidence that type 1 and 2 EC classification should be replaced with ultramutated and non-mutated POLE gene discrimination ,40,41,42In conclusion, liquid biopsies show a great promise in EC. Our preliminary results show that both TEPs and ctDNA profiles constitute informative material for EC diagnosis and management. Further work, with a larger sample size and refined classifiers is warranted. In the future, the presented test might be strategically positioned as a screening tool. The interrogation of TEPs profile would aid initial EC diagnosis, particularly discrimination between healthy women and EC patients. Meanwhile, ctDNA analysis could serve as means of preoperative EC histology evaluation. This feasibility study shows that both TEPs and ctDNA profiles constitute informative material for EC diagnosis and management. Further work with a larger sample size and refined classifiers is warranted. In the future, both TEPs and ctDNA profiling can be considered in monitoring treatment response in EC patients.


Introduction: Proneural and mesenchymal subtypes are the most distinct demarcated categories in classification scheme, and there is often a shift from proneural type to mesenchymal subtype in the progression of glioblastoma (GBM). The molecular characters are determined by specific genomic methods, however, the application of radiography in clinical practice remains to be further studied. Here, we studied the topography features of GBM in proneural subtype, and further demonstrated the survival characteristics and proneural-mesenchymal transition (PMT) progression of samples by combining with the imaging variables.Methods: Data were acquired from The Cancer Imaging Archive . The radiography image, clinical variables and transcriptome subtype from 223 samples were used in this study. Proneural and mesenchymal subtype on GBM topography based on overlay and Voxel-based lesion-symptom mapping (VLSM) analysis were revealed. Besides, we carried out the comparison of survival analysis and PMT progression in and outside the VLSM-determined area.Results: The overlay of total GBM and separated image of proneural and mesenchymal subtype revealed a correlation of the two subtypes. By VLSM analysis, proneural subtype was confirmed to be related to left inferior temporal medulla, and no significant voxel was found for mesenchymal subtype. The subsequent comparison between samples in and outside the VLSM-determined area showed difference in overall survival (OS) time, tumor purity, epithelial-mesenchymal transition (EMT) score and clinical variables.Conclusions: PMT progression was determined by radiography approach. GBM samples in the VLSM-determined area tended to harbor the signature of proneural subtype. This study provides a valuable VLSM-determined area related to the predilection site, prognosis and PMT progression by the association between GBM topography and molecular characters. Glioblastoma (GBM), mainly diagnosed by magnetic resonance imaging (MRI) and accurate pathological examination, is the most aggressive brain tumor and indicates a poor prognosis . The curhttps://wiki.nci.nih.gov/display/CIP/VASARI) incorporates various visible subjective imaging features, which is designed to normalize grading of the distinct features of gliomas on MRI, containing different grades criteria corresponding to diverse score to depict severity  has described a robust gene expression-based molecular classification of GBM including proneural, mesenchymal, neural, and classical subtypes  database and elucidated the radiogenomic signatures in GBM via common MRI alignment. As for GBM topography for subtypes is not clear to date, we used VLSM method to evaluate the predilection sites of proneural and mesenchymal subtypes. The combination of VLSM method and VASARI features was applied to analyze overall survival (OS) and other significant information based on the VLSM-determined area, contributing to understand the genomics pathogenesis potentially.In our study, we screened 223 samples in TCIA  of the whole patient cohort were overlapped on the standard template . GBM topFirstly, separated tumor overlays were performed to detect GBM topography of proneural and mesenchymal subtypes, and we finally found that compared with integral overlay displayed in To further explore PMT progression, we selected 125 samples comprised of 52 proneural and 73 mesenchymal subtypes to conduct VLSM analysis. The normalized lesion maps in proneural and mesenchymal subtypes were calculated independently. VLSM specifically associated proneural subtype with lesions to a cluster of the left inferior temporal medulla, while no significant voxel was found for mesenchymal subtype . The resP = 1.20E-2, P = 3.00E-03, P = 1.28E-01, Survival analysis was carried out to find whether the VLSM-determined area could serve as a prognostic role. Initially, to exclude some recognized factors that influence survival outcome obviously, we included samples with standard tumor resection, radiotherapy and pharmaceutical treatment to make the survival result more persuasive. Since contrast enhancement of MRI was associated with survival , we seleP = 1.00E-02; T cells follicular helper, P = 1.00E-02; Student's t-test, Apart from clinical variables, the expression profiling data in TCGA was used to calculate the corresponding signal pathway score by single-sample Gene Set Enrichment Analysis (ssGSEA) to evaluate EMT process and tumor purity of samples . For cliin vitro and is markedly resistant to radiotherapy compared with proneural subtype  based on molecular and phenotypic differences. For instance, mesenchymal subtype has higher rates of proliferation  subtype . PMT pro subtype . In this subtype , 29.The non-invasive method of MRI materials provides effective mean to explore different molecular genetic signatures . Tumor lVLSM is a different method from overlay, which can provide statistical significance for the observed difference based on voxel level and bring forward to evaluate the relationship between lesions and clinical symptoms . In presIn the analysis of MRI radiography, tumor contrast enhancement is applied to predict GBM prognosis and malignancy, which can be easily distinguished, resulting to the narrowing of subjective error of visual inspection and facilitating feasibility or availability , 40. In Different metastasis and immune related genes manifest differential expression patterns among GBM subtypes , 42. MesIn present study, the difference of gene expression patterns between GBM in and outside the VLSM-determined area provided reference to molecular targeting treatment according to different topography . Among 1We realized the limitations of our techniques. Firstly, the VLSM method regarded each voxel as being independent and separated from other adjacent voxels, which may influence the calculation of involved regions. In addition, although the general MR imaging sequences ensure the reliability or reproducibility of the study, senior scans  are beneficial for further study of intratumoural transcriptional heterogeneity with novel algorithm.Our study demonstrated a valuable VLSM-determined area related to the predilection site, prognosis and PMT progression by radiography approach. GBM involved in VLSM-determined area exhibited the characters of proneural subtype. The results also revealed the differences of EMT process and tumor purity among GBM in and outside the VLSM-determined area.Original data used in this research were provided by TCGA, an open resource containing comprehensive genomics information on various cancers. TCGA data collection founded by the cooperation between National Cancer Institute (NCI) and several institutions, publicly available in TCIA database, was selected to explore the connection between GBM phenotypes and radiographs, for data were matched to store in TCGA and TCIA . All infMRI data of total 262 GBM samples including multiple sequences were acquired initially. The exclusion criteria referred to sample lacks post-contrast T1-weighted, subtype classification, and clinical features. After summarizing the entire data together, we identified 223 samples who had relevant variables available finally. A flowchart of the number of samples included or excluded for each analysis was shown in Prior to imaging processing, the original imaging dataset was evaluated by VASARI feature set, which served as a semi-quantitative imaging analysis for describing visual features on MRI . In thishttp://www.sph.sc.edu/comd/rorden/mricron) to delineate the boundary of the tumor. If there was an evident discrepancy (the ratio of VOI between two observers > 0.05), the VOIs of the samples would be rechecked by another neurosurgeon to make the final decision. Then VOIs of all samples were collected. The sequences were normalized into the stereotactic Montreal Neurological Institute (MNI) standard space. With Statistical Parametric Mapping 8  implemented in MATrix LABoratory , images and generated VOIs were registered to brain atlas and normalized at the MNI space using the standard normalization algorithm followed by examination of visual inspection   correction for multiple comparisons was applied. VLSM maps were displayed on ch2bet template with a statistical significance (P < 0.05).VLSM method was applied to relate lesion map to manifestation of proneural and mesenchymal subtype in MRIcron software by Non-parametric Mapping Statistics  . SubsequExpression profiling data from TCGA was used to evaluate EMT enrichment and tumor purity . SsGSEA 2-test was used to detect the distribution of several attributes on categorical variables, while Student's t-test checked the differences between two groups on continuous variables. R packages, such as pheatmap, limma and affy, were used to produce figures and calculate differential expressed genes. Regarding to survival analysis, Kaplan-Meier curve and log-rank test were applied to describe OS time. P < 0.05 was considered statistically significant.Statistical analysis  was carried out by IBM SPSS statistics and GraphPad Prism. All results were shown as mean ± standard deviation or number of observations and percentages. Kappa consistency test was applied to evaluate consistency in the diagnosis of VASARI scores between different observers , and thehttp://cancerimagingarchive.net, TCIA.Publicly available datasets were analyzed in this study. This data can be found at: 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.CJ and JC designed the experiments. TQ and XM performed the experiments. TQ, XM, and JC analyzed the data and wrote the manuscript. ZW, XW, NS, JM, and LR contributed to materials and analysis tools. All authors revised the manuscript.The 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.


Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especially deep learning-based methods have been receiving increasing attention, mostly owing to their good prediction accuracy in many applications. Machine learning methods can be applied to high throughput DNA mutation or RNA expression data to predict cancer stage. However, because the number of genes or markers generally exceeds 10,000, a considerable number of data samples is required to guarantee high prediction accuracy. To solve this problem of a small number of clinical samples, we used a Generative Adversarial Networks (GANs) to augment the samples. Because GANs are not effective with whole genes, we first selected significant genes using DNA mutation data and random forest feature ranking. Next, RNA expression data for selected genes were expanded using GANs. We compared the classification accuracies using original dataset and expanded datasets generated by proposed and existing methods, using random forest, Deep Neural Networks (DNNs), and 1-Dimensional Convolutional Neural Networks (1DCNN). When using the 1DCNN, the F1 score of GAN5 (a 5-fold increase in data) was improved by 39% in relation to the original data. Moreover, the results using only 30% of the data were better than those using all of the data. Our attempt is the first to use GAN for augmentation using numeric data for both DNA and RNA. The augmented datasets obtained using the proposed method demonstrated significantly increased classification accuracy for most cases. By using GAN and 1DCNN in the prediction of cancer stage, we confirmed that good results can be obtained even with small amounts of samples, and it is expected that a great deal of the cost and time required to obtain clinical samples will be reduced. The proposed sample augmentation method could also be applied for other purposes, such as prognostic prediction or cancer classification. Correct prediction of cancer stage is beneficial because it can help medical doctors determine more appropriate treatment for patients with cancer. For example, doctors can use staging information to determine type of surgery to perform, or whether chemotherapy or radiation therapy is required.th edition of the AJCC Cancer Staging Manual to the most recent 8th edition, few changes may be observed with respect to some cancers [Numerous measures or methods have been proposed for accurate prediction of cancer stage, and one of the most widely used for cancer stage prediction is the Tumor, Node, and Metastasis (TNM) staging system developed by the American Joint Committee on Cancer (AJCC). TNM is a clinically useful staging system for cancers of almost every anatomic site and histology. From the 7 cancers , 2, but  cancers –6 numeroRecently, alternative methods to predict cancer stage with additional clinical information or genomic information have been proposed. These methods, for the most part, adopt machine learning techniques to increase prediction accuracy. The machine learning methods used include Random Forest (RF) , 8, SuppHowever, most of this studies used machine learning methods on a relatively small number of samples. machine learning methods generally require a substantial number of samples to ensure high predictive power. To overcome this limitation of a small sample size, many sample augmentation methods have been developed. The Synthetic Minority Oversampling Technique (SMOTE) , 16 was In this study, we also used GANs to oversample small number of mRNA expression samples. GANs are difficult to use for data with a small sample size, especially when the number of features (genes) exceeds 10,000. To solve this problem, we first selected 300–800 genes depending on cancer types using DNA mutation data and RF. We synthesized the expression profiles of selected genes by applying GANs to gene expression of twelve cancer types including STAD (Stomach adenocarcinoma), BRCA (Breast invasive carcinoma), HNSC (Head and Neck squamous cell carcinoma), KIRC , KIRP , LUAD (Lung Adenocarcinoma), THCA (Thyroid carcinoma), READ , ESCA , KICH (Kidney Chromophobe), LIHC (Liver hepatocellular carcinoma), and LUSC (Lung squamous cell carcinoma) from the TCGA database . We thenThis paper is organized as follows. In the Materials and Methods Section, we first describe data used for the experiment, selected features, and normalization algorithm. Then, the sample augmentation method using GAN and three classification algorithms are described. In the Results Section, we describe the characteristics of the augmented sample, and compare the effects of the five known algorithms and four GAN series that we implemented. We also verify whether our method is effective for small samples, and evaluate the importance of the selected genes. In the Discussion Section, we compare the selection criteria of our experiment with the results of other groups, and mention various fields in which our method could be applied.●We use feature selection based on DNA mutation data and GAN for augmentation of mRNA expression data to increase the accuracy of our cancer-stage classification.●The augmented datasets obtained using the proposed method demonstrate significant increase in the classification accuracy.●By using GAN and 1DCNN in the prediction of cancer stage, good results are obtained even with a small amount of sample.We downloaded mRNA and DNA mutation data from the TCGA database  of twelvAs the feature space is too big compared to the number of samples for training the proposed model, we selected the most important features (= genes) for each dataset. RF classifier , 21, whiFinally, matched mRNA data with selected genes were normalized using ComBat  to correThe Generative Adversarial Networks (GANs) are composed of the generator and discriminator, which are trained in parallel. Typically, the generative network learns to map from a latent space to a data distribution of interest, while the discriminative network distinguishes candidates produced by the generator from the true data distribution.In this study, we used a GANs to augment mRNA samples. When images are generated using GANs, random values are input to the generator. In our case, random values from a normal distribution with mean and standard deviation of training mRNA data are fed into the generator. The training data are 70% of the entire data, selected at random. We used one hidden layer with 256 neurons for both a generator and a discriminator with reference to the previous study  and the n (= number of training samples) samples (GAN1), n * 20 samples (GAN20), and n * 100 samples (GAN100), using the trained generator, with the latent space generated by mean and standard deviation values that were used to train the generator. The mean and standard deviation created to make latent space in the Training Step are stored at a global variable and selected randomly as argument of the Generating Step. The ratio of stages is kept for augmented samples. Augmented samples are used as training data for classification of cancer stage.After the generator is trained, we generate We used three types of classifiers, 1DCNN , DNNs, aFinally, these classifiers were evaluated using the remaining 30% of the entire sample. The steps described above form one cycle, and are illustrated in .As mentioned in detail in the methods, we augmented samples by constructing GANs composed of components of a Generating Step and Training Step  for each of the nine datasets. The nine datasets are 1) original dataset (Ori), 2) original dataset with selected features only (FS), 3) synthesized data with mean and standard deviation (MS), 4) synthesized data using SMOTE  (SMOTE),Features of FS data are selected from DNA mutation data using RF classifier, and are the same as those used to create GAN1, GAN5, GAN20, and GAN100. MS is randomly generated samples using mean and standard deviation/2 of training samples of each stage. SMOTE data is generated using a basic algorithm in SMOTE .SMOTE is proposed to handle imbalanced data. For example, if SMOTE is run using 657 (110/383/152/12) training samples of BRCA, it generates 1,532 (383/383/383/383) samples. DA data is generated using a Denoising Autoencoder . DA usesWe can also see that the accuracies of FS increased up to 9% compared to Ori, and the error bars are narrowed except in the case of KIRP. In particular, the accuracy was 0.48 for the 19,738 gene features in BRCA, but increased to 0.57 using a selected 359 features. These results show the effect of gene selection using DNA mutation data.Next, we compared three classifiers, 1DCNN, DNN, and RF. Tables Next, we examined whether the proposed sample augmentation method is effective for datasets with small samples. We used whole samples and randomly selected 50%, 30%, and 10% of samples from BRCA, LUAD, and KIRC datasets, and applied 1DCNN. The results are shown as 100O, 50O, 30O, and 10O in . We nextWe additionally verified that genes were selected properly, in this section. Eight published studies –32 of TCSelected genes in the current study  frequentKIRC matches six out of nine genes. VHL and PBRM1 are major genes that cause mutations in more than 40% of clear cell renal cell carcinoma, and SETD2 and PTEN, which are quite frequent, are genes that cause both copy number loss and mutation. The BRAF gene of THCA is the most important gene with 60% missense mutation and more than 2% fusion, and includes a list of most oncogenes such as NRAS, TP53, PTEN, and RB1.Selected genes are also overlapped with genes in the Online Mendelian Inheritance in Man (OMIM) database , the ClA Warburg effect-like state achieved through downregulation of AMP-activated kinase (AMPK) and upregulation of acetyl-CoA carboxylase (ACC) has also been shown to be important in cancers. Among the genes in We noted that both GAN5 and GAN20 show good results in that the error bars are generally narrower in most of the carcinomas than those of GAN1, in . This obThe optimal number of samples differs for different cancer types, as observed in . Our modIn addition to the DNA mutation data used for feature selection in this study, various combinations of more omics data such as mRNA, DNA methylation, and miRNA data can be used to further increase the classification accuracy. Application of those data combinations will be the focus of our follow-up work. Moreover, various recently developed deep generative models such as DCGAN, cycleGAN, and Variational Autoencoder, could be explored for more accurate classification, which could be our future study.In this paper, we proposed the sample augmented method using GANs, and showed that augmented samples significantly increased the classification accuracy of cancer stages. In particular, we were able to confirm that the proposed method is efficient for a dataset with small number of samples. Therefore, the proposed sample augmentation method can be applied for other purposes, such as prognostic prediction or cancer classification.●The proposed method can generate additional data samples more accurately, which can increase the accuracy of cancer-stage prediction.The proposed method is generally applied to other types of mRNA expression data of which the aim is different from cancer-stage prediction.Advantages●If the number of features is large, the learning time is significantly slower than with other machine learning approaches such as random forest or gradient boosting.DisadvantagesS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.