One of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it does not detect changes in molecular regulation. In contrast to the standard differential expression analysis, differential co-expression one aims to detect pairs or clusters whose mutual expression changes between two conditions.We developed Differential Co-expression Network Analysis (DCoNA)—an open-source statistical tool that allows one to identify pair interactions, which correlation significantly changes between two conditions. Comparing DCoNA with the state-of-the-art analog, we showed that DCoNA is a faster, more accurate and less memory-consuming tool. We applied DCoNA to prostate mRNA/miRNA-seq data collected from The Cancer Genome Atlas (TCGA) and compared predicted regulatory interactions of miRNA isoforms (isomiRs) and their target mRNAs between normal and cancer samples. As a result, almost all highly expressed isomiRs lost negative correlation with their targets in prostate cancer samples compared to ones without the pathology. One exception to this trend was the canonical isomiR of hsa-miR-93-5p acquiring cancer-specific targets. Further analysis showed that cancer aggressiveness simultaneously increased with the expression level of this isomiR in both TCGA primary tumor samples and 153 blood plasma samples of P. Hertsen Moscow Oncology Research Institute patients’ cohort analyzed by miRNA microarrays.https://github.com/zhiyanov/DCoNA.Source code and documentation of DCoNA are available at Bioinformatics online.Supplementary data are available at  MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression by binding to mRNA targets and initiating their degradation or inhibiting their translation    are small. DCoNA uses the permutation method to tackle this problem: it shuffles groups’ labels, recomputes the statistics and compares them with initial ones.Aggregation scores. Note that pairwise correlation analysis may lack interpretability: some small correlation alterations related to a particular molecule may be discarded due to the multiple hypothesis adjustment. However, these changes may highlight the molecule as one with many reduced negative correlations. So, DCoNA computes scores aggregating z-statistics of targets and checks their significance using the permutation method.Efficiency. DCoNA was implemented using C++ language, while its analogs are based on R, Python and other interpreted programming languages. So, DCoNA works at least 100 times faster than the previous state-of-the-art analog: DGCA tool (Application. We executed DCoNA on TCGA-PRAD (Prostate Adenocarcinoma) dataset  using predicted 5Correlation equivalence test. Assume that we have two independent paired samples taken under two conditions: the first sample can be considered as expression levels of two genes ‘X’ and ‘Y’ taken from ‘Normal’ (N) group, and the second one consists of the expressions of the same genes taken from ‘Tumor’ (T) group. The ‘Tumor’ and ‘Normal’ notation is used only for the simplicity and the method could be applied to any two conditions. Denote by c can be replaced by p for standard correlation or by s for ranked one, and S can be replaced by N for ‘Normal’ sample or by T for ‘Tumor’ one. Moreover, for non-BVN pairs, the asymptotic normality still holds, but the asymptotic variance may depend on M denotes size of the sample S.Assuming that ‘Normal’ and ‘Tumor’ pairs are bivariate normally distributed (BVN) with standard correlation of ρc(S) , where con ρc(S) . Based oc is Pearson (p) or Spearman (s) correlation coefficient, and pρ that allows us to compute the test P-value ξ is a standard normal random variable. On the contrary, as follows from sρ. Thus, we use the valueP-value K and L are sizes of ‘Normal’ and ‘Tumor’ samples, respectively.In the co-expression difference analysis we test a hypothesis that correlations between genes ‘X’ and ‘Y’ are the same in ‘Normal’ and ‘Tumor’ groups, i.e. Permutation P-value. However, the discussed analytical approach has several limitations. In general, we do not know in advance whether the distribution of the samples is BVN. Moreover, the asymptotic normality does not define the sample size starting from which the asymptotic approximation can be used. Thus, we use the permutation test to compute P-value: we shuffle groups’ labels , preserving the size of each group; recompute the value of the statistic P-values using Benjamini–Hocheberg correction method . Thus, we adjust the obtained n method . We noteP < 0.05) changed correlations Gs associated with a particular source molecule G is uniformly distributed among all significant changes. More precisely, DCoNA assumes that Gs has hypergeometric distribution Gn is the number of interactions with the molecule G in the predefined interaction network, We note that the pairwise correlation analysis may lack interpretability: if the number of tested pairs is large, it may be difficult to extract source molecules (the first elements of the pairs) with a large percent of changed correlations. So, it may be helpful to group pairs with changed correlations by the source molecules and find overrepresented groups using the hypergeometric test, i.e. check that the number of significantly .zscore’]. All necessary parameters can be defined in a configuration file.Aside from the hypothesis testing, DCoNA can be used to test that significantly altered correlations of a particular source molecule are overrepresented among all significantly changed correlations [‘network(exhaustive).hypergeom’ mode]. Also, DCoNA can compute mean, median and other quantiles of https://github.com/zhiyanov/DCoNA.A more detailed description of DCoNA usage modes, the configuration file structure, and data format can be found at n × m, where n is the number of genes, and m is the sample size. Suppose we test all gene-gene pairs for equality of correlations in both states. In that case, the algorithmic complexity of such a procedure is P-values of the tests using the permutation method, then the complexity increases to https://github.com/pybind/pybind11) modules. Finally, all analysis steps are efficiently parallelized using ‘Concurrency support library’ of C++, reducing computational time.Pairwise differential co-expression analysis is computationally challenging. Indeed, let us assume that we have expression data of shape TCGA-PRAD (Prostate Adenocarcinoma). RNA-seq and miRNA-seq read count tables were downloaded from GDC portal at https://portal.gdc.cancer.gov/projects/TCGA-PRAD.Following the corresponding clinical description, 50 samples of the dataset were referred to ‘Normal’  group and 437 samples to ‘Tumor’ group (‘Primary Tumor’). All ‘Tumor’ tissues paired to ‘Normal’ samples were removed to ensure the independence of samples.M-values) algorithm implementation  tables. For a particular sample, these values reflect the proportions of reads mapped to different 5i denotes the sample. We assumed that mRNA expression levels of AGO2 are proportional to the protein concentration. In the same time, miRNA-specific Argonaute loading constants did not influence the validity of our analysis since correlations between miRNAs and their targets were calculated independently for each miRNA. Previously, a similar technique significantly enhanced the predictive power of regression-based miRNA target prediction  proteins , 40 patients of ‘Low’ risk group, 16 ‘Intermediate’ risk and 91 ‘High’ risk group rated according to the European Association of Urology—European Society for Radiotherapy & Oncology—International Society of Geriatric Oncology Guidelines on screening, diagnosis and local treatment with curative intent of clinically localized prostate cancer  according to the instruction.RNA was labeled with an Affymetrix FlashTag Biotin HSR RNA Labeling Kit  according to standard procedures. Labeled RNA was hybridized to Affymetrix GeneChip microRNA 4.0 arrays and run using a Fluidics Station 450 protocol . Resultant array data were deposited in NCBI’s Gene Expression Omnibus (GSE206793) per GEO guidelines. CEL files were processed using the oligo R package  in the dz-transformed miRNA expression clustering using Ward’s method  from a multivariate normal distribution with zero means and given covariance matricesm genes collected from two groups of size n. Using matrix NC we simulate a gene interaction network in ‘Normal’ state, where expression of the first gene positively correlates with expressions of the following TC (‘Tumor’ state).This synthetic dataset models expressions of ρ, fraction of changed correlations k and change sample size n computing the mean aggregation score of the first gene. We repeat each experiment for 10 times for a fixed sample size n.During the experiment, we fix a level of correlation Synthetic dataset to test permutation P-value method. Comparing DCoNA with DGCA  and iterate the size of ‘Tumor’ group from 10 to 100. For each covariance matrix NC, we generate the synthetic normal dataset and compute the P-value of the last pair of genes with covariance matrix of the form During the experiment comparing two permutation We developed an effective command-line tool  that perAs an input DCoNA tool expects the following data : a tableAside from the pairwise correlation comparison, DCoNA can compute the aggregation scores of source molecules. These scores are designed to distinguish the source molecules with a large proportion of significantly changed correlations.https://github.com/zhiyanov/DCoNA) whose core modules are implemented in C++ language, and thus DCoNA is extremely fast. Moreover, DCoNA can be used in parallel, further speeding up the analysis. Finally, DCoNA has a user-friendly interface since it was developed as a command-line tool and Python library.Among implementation features of DCoNA we emphasize that it is an open-source project . This dataset simulates expressions of m = 1000 genes: k of them significantly alter correlation with the first gene, while other m − k ones do not. Increasing k, we enlarge the proportion of changed correlations, while the value of the correlation parameter ρ regulates the level of the alterations.Other scores are based on mean, median and other statistics of n of ‘Normal’ and ‘Tumor’ groups would increase values of We studied how the sample size affects the mean aggregation score using this dataset. We expected that a simultaneous increase in sample size P < 0.05) not only for large sample sizes but also for small ones depending on the level of correlation ρ. For example, if a significant part (10%) of interactions changes, the mean aggregation score will detect these alterations even for n = 20 elements in both groups.P-valuePermutation . It is known that the test statistic .tatistic  is assymP-value computation approach to solve this problem. The common part of both methods is that they shuffle labels of the groups and recompute DCoNA and DGCA tools use P-value method’ paragraph of Section 2). This dataset simulates expression of 20 genes using multivariate normal distribution. Genes of this dataset break down into m = 10 independent pairs.To assess both procedures, we used a synthetic dataset similar to the previous one (see ‘Synthetic dataset to test permutation k gene pairs change more (from 0 to 1) than the correlation of remaining n − k pairs (from 0 to 0.5). For a small sample size of ‘Tumor’ group , even after label shuffling, we expected P-value computation on a pair with a relatively small change in correlation.During the experiment, correlations of P-value computation approach is more accurate than one proposed in DGCA tool, especially when a large proportion  with the number of permutations, so it can be executed even on personal computers.In the second experiment , we fixeUsing DCoNA, we aimed to study how co-expression of pairs of types (isomiR and mRNA) changed in prostate cancer. In the first step of the analysis, we extracted 38 highly expressed isomiRs, accounting for 95% of the total isomiR expression . As seenNext, we built a bioinformatically predicted regulatory network of the highly expressed isomiRs and their target mRNAs using miRDB tool . FurtherAmong other things, P-value P-value Further, we applied DESeq2  .Given the predicted oncomiR role of hsa-miR-93-5pU-test P < 0.05). At the same time expression of other miRNAs of our analysis (P > 0.05). This fact additionally emphasizes hsa-miR-93 among other miRNAs as a potential marker of aggressive prostate cancer.The aforementioned analysis was conducted with TCGA primary tumor samples. To extend our findings beyond primary tumors, we analyzed miRNA expression profiles in blood plasma samples derived from 147 patients with prostate cancer and 5 patients without any prostate pathologies , DCoNA checks all predefined interactions to have different correlations in these states. Note that the primary analysis comparing correlations may lack rigor: random fluctuations in expression can change correlation values. Hence, DCoNA uses a particular statistical test based on Fisher P-value computation implemented in DGCA is incorrect in some cases, which has been corrected in DCoNA.To the best of our knowledge, the current state-of-the-art algorithm providing similar analysis is DGCA . Also, iWe applied DCoNA to the regulatory network of miRNA isoforms (isomiRs) and their bioinformatically predicted mRNA targets using TCGA-PRAD dataset. It turned out that the most highly expressed isomiRs lost their negative correlations with putative targets in ‘Tumor’ samples compared to ‘Normal’ ones. Surprisingly, the decrease in the number of anti-correlated isomiR-target pairs could not be explained by the changes in isomiR expression levels.We recently made a similar observation in the context of breast cancer .btad051_Supplementary_DataClick here for additional data file.


Lactobacillus species. We apply the same techniques to select the best 50 genes out of 2426 sparse gene expression data. The classification based on our selected genes achieves 100% prediction accuracy. Furthermore, the first four principal components based on the selected genes can explain as high as Sparse data with a high portion of zeros arise in various disciplines. Modeling sparse high-dimensional data is a challenging and growing research area. In this paper, we provide statistical methods and tools for analyzing sparse data in a fairly general and complex context. We utilize two real scientific applications as illustrations, including a longitudinal vaginal microbiome data and a high dimensional gene expression data. We recommend zero-inflated model selections and significance tests to identify the time intervals when the pregnant and non-pregnant groups of women are significantly different in terms of  Sparse or zero-inflated data has a lot of applications in various disciplines such as microbiome , gene exTo model sparse data, zero-inflated and hurdle models have been widely used. Both of them consist of two data-generating processes. The first process generates purely zeros, while the second one is governed by some distribution, for example, Poisson distribution, which may or may not generate zeros. The zero-inflated Poisson (ZIP) model was proposed by Lambert (1992) with an application to defects in manufacturing . In 1994In this paper, we consider analyzing zero-inflated data in a more general and complex context. One motivating example is the vaginal microbiome data, which is longitudinal, and the goal is to identify the time intervals when the two groups of individuals are significantly different .Lactobacillus. Lactobacilli are the most abundant vaginal bacteria in women  1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000  [9] 1.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 [17] 0.00000 0.00000 0.00000 0.00000 1.00000 1.00000 1.00000 1.00000 [25] 1.00000 1.00000 1.00000 1.00000 1.00000 0.69333 0.00000 0.00000 [33] 0.00000 0.00000 0.00000 0.00000 0.00000Our analysis result matches the MetaLonDA result in some time intervals and is different in a small number of time intervals. The differences might be due to two reasons. First, the data mainly consists of real numbers while the negative binomial distribution assumed by MetaLonDA is for integers (counts). Secondly, the data contains a good proportion of zeros, which indicates zero-inflated or hurdle models may fit the data better.For example, according to MetaLonDA, there is no significant difference between the two groups from week 22 to week 31, which is different from our result. From The RNA-seq gene expression data is a high-dimensional dataset consisting of 801 samples and 20,531 genes (covariates). At our data preparation and screening stage, we first delete 267 genes (columns) which contain only zeros. Secondly, we remove the columns  that have no zeros at all so that we can focus on zero-inflated covariates . For the same reason, we choose only the genes (columns) that carry a good proportion  model is chosen for gene see , we are A critical question is how many genes should be chosen for predicting the class labels. To choose a good threshold, we utilize the 1-nearest neighbor classifier  with a ested by . Note thIt is known that the training error rate may underestimate the true prediction error rate due to over-fitting . To obtaFor comparison purpose, we further apply principal components analysis (PCA) to the top 50 selected genes. k-means clustering in conjunction with the gap statistic, and Table Invitation Prior (TIP) [Many clustering methods have been used for gene expression data, including Ewens-Pitman Attraction (EPA), MCLUST, hierarchical clustering in conjunction with the gap statistic, or (TIP) . Compareor (TIP) . For illLactobacillus. The vaginal microbiome dataset is longitudinal and we aim to compare the Lactobacillus abundance between pregnant and non-pregnant groups over 38 time intervals (weeks). Based on our proposed variable selection method, the two groups are statistically different after week 22. In addition, we compare the estimated parameters of the two groups at each time point and show the differences in terms of model parameters. Our selected time intervals overlap with the MetaLonDA method to some extent. This work can be extended to other vaginal microbiome species as well. The second example is the gene expression data, which is high dimensional, and the subjects are categorized into 5 different cancer groups. The goal is to select the most important genes among a list of 2426 genes. Based on our variable selection procedure and PCA, we select the top 50 genes with In this work, we use two different real-world examples to illustrate how the sparse data model selection could be used for choosing the most significant covariates. The first example is the vaginal microbiome which is dominated by one or two species of


Spatial transcriptomics is a novel technique that provides RNA-expression data with tissue-contextual annotations. Quality assessments of such techniques using end-user generated data are often lacking. Here, we evaluated data from the NanoString GeoMx Digital Spatial Profiling (DSP) platform and standard processing pipelines. We queried 72 ROIs from 12 glioma samples, performed replicate experiments of eight samples for validation, and evaluated five external datasets. The data consistently showed vastly different signal intensities between samples and experimental conditions that resulted in biased analysis. We evaluated the performance of alternative normalization strategies and show that quantile normalization can adequately address the technical issues related to the differences in data distributions. Compared to bulk RNA sequencing, NanoString DSP data show a limited dynamic range which underestimates differences between conditions. Weighted gene co-expression network analysis allowed extraction of gene signatures associated with tissue phenotypes from ROI annotations. Nanostring GeoMx DSP data therefore require alternative normalization methods and analysis pipelines.  •NanoString GeoMx DSP data are hindered by a systematic technical bias•Data distributions are incompatible with standard analysis due to background noise•Quantile normalization corrects technical bias and restores biological signal•Substitute analysis identifies biologically meaningful spatial gene signatures Biocomputational method; Data processing in systems biology; Transcriptomics This platform is an attractive and user-friendly option as NanoString provides a standard analysis platform that integrates all the required bioinformatical (preprocessing) tools.To better understand the heterogeneity of malignancies like glioma, spatially resolved transcriptomics techniques have been developed.The emergence of spatially resolved transcriptomics provides opportunities as well as challenges.In this study, we have assessed the quality of data from the NanoString GeoMx DSP platform for 12 resections obtained from six patients with IDH1-R132H mutant glioma . These analyses indicate that the large difference in data distributions (i.e. the range and frequency of counts that occur in a sample) between groups is not caused by a biological signal, and that a technical artifact might cause this problem. Consequently, comparison between primary and recurrent tumor samples will likely become biased and produce erroneous results, as exemplified by the highly asymmetric volcano plot  normalization), the differences in signal distribution remained and were most pronounced between primary and recurrent tumors A and S2Aano plot B.Figure The NanoString GeoMx DSP platform uses an indirect readout for RNA expression which is burdened with a background signal. Therefore, a set of control probes (probes that have no target) are used to discern signal from background noise. Several (predominantly primary) samples showed a higher average raw count for control probes and a lower average raw count for target probes compared to other (predominantly recurrent) samples, which resulted in significantly different average signal-to-noise ratio when comparing primary and recurrent samples D and 1C.,,,,,The dissimilarities in signal distributions visible in our own dataset were also present in all external datasets we have thus far examined.As bulk RNA-sequencing data demonstrated that our glioma dataset does contain biologically relevant information E, we con,In addition to addressing technical criteria in our benchmarking framework, we addressed biological criteria as well. Biological signal was evaluated by testing the correlation of an annotated set of marker genes for the major cell types from the CNS.To test whether we can extract information on T cell biology, we used weighted correlation network analysis (WGCNA) on the quantile-normalized data.The NanoString GeoMx DSP platform provides spatially resolved expression data coupled with easy-to-use software with build-in preprocessing tools. This allows users to focus directly on the biological interpretation of data. While convenient, it remains imperative to assess whether underlying data properties are appropriate for downstream analysis to substantiate the validity of the conclusions drawn from it.Comparative transcriptomics analysis generally assumes that most genes are not differentially expressed between conditions and thus follow a similar data distribution. In this study, we show that after Q3 normalization this assumption does not hold for any of the datasets that were evaluated, and that the data are heavily skewed. To address this issue, we evaluated the performance of four recognized normalization methods. We show that these methods uphold basic assumptions better compared to standard Q3 normalization. Of the normalization methods that were tested, quantile normalization was superior across our benchmarking parameters and yielded the required data distributions, providing the most comparable biological results with bulk RNA seq data.Q3 and quantile normalization are radically different in their approach. Q3 normalization relies a normalization factor that aligns the third quartile gene count value for all samples. This method does not address potential global variations in data distributions or expression ranges. In contrast, quantile normalization, which is widely used to normalize gene expression data derived from microarrays, aligns count values according to the rank of the genes and forces the data of all samples into the same distribution. It is therefore inherent to this method that the difference in count value range or signal-to-noise ratio is no longer dependent on raw data distributions.The underlying cause for the difference in data distribution seems to be rooted in how well the biological signal exceeds the noise signal. We were not able to find any consistencies in features like tissue age or processing methods that could explain the different data distributions. As primary and recurrent samples for each patient were processed together on one microscope slide, we judged the chance of batch effects to be limited. Possibly, the differences in signal-to-noise ratio are linked to tissue-type-specific qualities that make it more or less suitable for a readout using the NanoString GeoMx DSP platform.In summary, we demonstrate the critical requirement to assess the correctness of data distribution, particularly regarding the use of the NanoString GeoMx DSP platform. The high level of inter-sample variation can, at least partially, be overcome with alternative normalization strategies. The alternative preprocessing methods and downstream analysis using WGCNA that we present here can provide a starting point for a renewed workflow to correctly interpret the NanoString GeoMx DSP data.Some limitations of this study should be noted. First, we used bulk RNA-seq to compare with our spatial gene expression data. Even though averaged measurements from the ROIs distributed over a tumor section likely give a balanced pseudo bulk gene expression level, the NanoString GeoMx DSP and bulk RNA-seq methodologies for acquiring count data are very different and should be considered when interpreting the data. Second, it should be noted that we did not exhaustively examine existing normalization methods. The aim of our current study was to illustrate some of the difficulties that novel techniques like the NanoString GeoMx platform face and provide an alternative processing method that helps to overcome them. Third, we do note that quantile normalization, due to its course method of normalization, may limit the detection of more subtle differences in gene expression. Last, the CTA panel measures the expression of a relatively small number of genes (around 1800). We cannot exclude the possibility that the limited panel size has some effect on our test results.l.vanhijfte@erasmusmc.nl).Requests for resources and additional questions should be directed to Levi van Hijfte  astrocytoma, second resection of glioblastoma (WHO 2016 grade IV)). Immediately after resection, tissue was fixed in formalin and embedded in paraffin according to standard pathology procedures. FFPE tissue was cut into five micrometer sections and placed on Apex BOND slides . Paired tumor samples were placed on the same slide. For all samples, six consecutive slides were prepared for replicate experiments and immune stainings (see below). Slides were shipped to NanoString Seattle for ROI selection, data collection and preprocessing. ROI selection was performed based on an immune staining using four markers . A total of 12 ROIs per slide  were selected. For four of six tumor pairs, replicate experiments were performed. Samples from the primary and replicate experiment were processed together in one batch.Standardized IF and IHC stainings were performed for CD3 , CD4 , CD8  and IDH1-R132H . For CD3, CD4 and CD8 a multiplex was performed using the automated Ventana Benchmark Discovery . First, antigen retrieval was performed using CC1  for 64 min at 97°C. Subsequently, samples were incubated with CD3 antibody at 37°C for 64 min, after which antibody detection was performed for 12 min with Ultramap anti-rabbit HRP , and visualization was performed for 8 min with R6G . CC2  was used for denaturation at 100°C for 20 min. Second, samples were incubated with the CD4 antibody at 37°C for 32 min followed by antibody detection (as described above) and visualization for 4 min with Red 610 . Denaturation was again performed with CC2 (as described above). Third, slides were incubated with CD8 antibody for 32 min at 37°C followed by antibody detection (as described above) and visualization for 12 min with FAM . Last, slides were incubated in PBS with DAPI for 15 min and mounted with anti-fading medium .For IDH1-R132H, a single staining was performed on a Ventana Benchmark ULTRA  using the Ultraview universal alkaline phosphatase red detection kit . After deparaffinization and heat-induced epitope retrieval, Incubation of the primary antibody was performed for 32 min at 37°C. Counterstain was performed with hematoxylin II for 12 min and with a bluing reagent for 8 min.Quantification of the number of CD3 positive cells in the selected ROIs was done using the TME-Analyzer software (version 2.3).fastp -y -x -p --thread 4 -i < …>_R1.fastq.gz -I <...>_R2.fastq.gz -o <...>_trimmed_R1.fastq.gz -O <...>_trimmed_R2.fastq.gz -j <...>_fastp_log.json -h <...>_fastp_log.html --trim_poly_g -l 35.For bulk RNA sequencing of the samples identical to those used for NanoString GeoMx DSP were evaluated for high tumor content by a neuropathologist on an HE stained section. 10 μm sections were made, deparaffinized and macro-dissection was performed according to annotated high-tumor regions for RNA extraction for 5-20 sections. RNA was isolated using the RNeasy FFPE kit . Whole transcriptome sequencing was performed by GenomeScan  on rRNA depleted cDNA at depth of 50 million paired-end reads per sample. Raw FASTQ files were provided by manufacturer. Reads were trimmed for low quality bases, adapters were trimmed and low quality reads were discarded using fastp:STAR --outFileNamePrefix "<...>/" --runThreadN 6 --genomeLoad LoadAndKeep --limitBAMsortRAM 5000000000 --outTmpDir /dev/shm/<...> --genomeDir /mnt/data/ccbc_environment/general/genomes/hsapiens/GRCh38/STAR/34/ --readFilesIn 'input/fastq-clean/<...>_L004_trimmed_R1.fastq.gz,input/fastq-clean/<...>_L003_trimmed_R1.fastq.gz' 'input/fastq-clean/<...>_L004_trimmed_R2.fastq.gz,input/fastq-clean/<...>_L003_trimmed_R2.fastq.gz' --readFilesCommand zcat --outSAMtype BAM SortedByCoordinateThese processed FASTQ files were subsequently aligned with STAR 2.7.3asambamba-0.8.0-linux-amd64-static markdup -t 6 --tmpdir /dev/shm <…>/Aligned.sortedByCoord.out.bam <…>/Aligned.sortedByCoord.out.markduplicate.bam.to GRCh38 and Gencode 34 into BAM files. Duplicate reads in the bam files were marked using sambamba 0.8.0:These BAM files were used to count reads in a stranded manner with featureCounts (subread 2.0.1) and Gencode 34:subread-2.0.1-Linux-x86_64/bin/featureCounts -a <…>/hsapiens/GRCh38/gencode/v34/gencode.v34.primary_assembly.annotation.gtf -o GLASS.LGG.EMC.RNA.readcounts.deduplicated_s_2.txt --tmpDir /dev/shm -s 2 -T 24 --primary –ignoreDup –C –p <…1>.bam <…2>.bamOne sample was excluded from the analysis due to low sequencing depth (<10.000 raw counts). Bulk RNA sequencing data was normalized using the DESeq2 package.To compare average RNA expression between bulk and NanoString GeoMx DSP data, we calculated the median normalized counts for all ROIs per resection. To test correlations between marker genes for all normalization methods, we selected the top 50 genes that were enriched in astrocytes, oligodendrocytes, microglia, neurons and endothelial cells from an annotated dataset.,,,Five independent datasets were used for validation. Example data for the NanoString GeoMx DSP was kindly provided by NanoString, and published data was recovered from online archives.Weighted Gene Co-expression Network Analysis (WGCNA) was performed using the WGCNA R package.Statistical analyses were performed using R. For differential gene expression analysis, a Wilcoxon rank-sum rank sum test was performed on all targets and p-values were adjusted using Benjamini-Hochberg correction. For DESeq2 normalized data, log2foldchange and adjusted p-values of the Wald test were extracted using the results function from the DESeq2 R package. Similarity between data distributions was tested using the two-sample Kolmogorov-Smirnov test. The prcomp function was used for principle component analysis. Statistical significance for spearman correlation was calculated using the stat_cor function from the ggpubr package in R. The Pearson correlation between the T cell number and the WGCNA eigengenes of modules was calculated using the cor function in R.For WGCNA analysis, significance of the correlations was determined using the corPvalueStudent function from the WGCNA package in R. Significance of the relationship between gene count and T cell count was defined as the significance of the Pearson correlation between the T cell number per ROI and gene expression profiles as calculated by the corPvalueStudent function from the WGCNA package. For canonical pathway analysis, we used Qiagen Ingenuity Pathway Analysis (IPA) software (version 56367011) with the filtered NanoString CTA gene target list as a reference. Significance was defined as a p value of <0.05. Specifics on tests and visualizations can be found in the Figure legends.


Background: We aimed to construct and validate the esophageal squamous cell carcinoma (ESCC)-related m6A regulators by means of machine leaning.Methods: We used ESCC RNA-seq data of 66 pairs of ESCC from West China Hospital of Sichuan University and the transcriptome data extracted from The Cancer Genome Atlas (TCGA)-ESCA database to find out the ESCC-related m6A regulators, during which, two machine learning approaches: RF (Random Forest) and SVM (Support Vector Machine) were employed to construct the model of ESCC-related m6A regulators. Calibration curves, clinical decision curves, and clinical impact curves (CIC) were used to evaluate the predictive ability and best-effort ability of the model. Finally, western blot and immunohistochemistry staining were used to assess the expression of prognostic ESCC-related m6A regulators.Results: 2 m6A regulators (YTHDF1 and HNRNPC) were found to be significantly increased in ESCC tissues after screening out through RF machine learning methods from our RNA-seq data and TCGA-ESCA database, respectively, and overlapping the results of the two clusters. A prognostic signature, consisting of YTHDF1 and HNRNPC, was constructed based on our RNA-seq data and validated on TCGA-ESCA database, which can serve as an independent prognostic predictor. Experimental validation including the western and immunohistochemistry staining were further successfully confirmed the results of bioinformatics analysis.Conclusion: We constructed prognostic ESCC-related m6A regulators and validated the model in clinical ESCC cohort as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC. Identified ESCC-related m6A regualtors (YTHDF1 and HNRNPC) using machine learning methods2. Build a risk prediction model with YTHDF1 and HNRNPC3. High expression of YTHDF1 and HNRNPC is associated with poor prognosis in ESCC patientsEsophageal cancer (EC) ranks as the 7th malignancy worldwide in terms of its mortality rate with a 5-year survival ratio of 15%–20% . AccordiNumerous studies in recent years have shown RNA methylation is a common seen in human and it is also a key regulator of transcriptional expression. N6-methyladenosine (m6A), occurs in approximately 25% of genome-wide transcripts and occurs around stop codons, 5′- and 3′-untranslated regions, and long intra-exon enrichment, is regarded as one of the most frequent RNA methylation . RNA m6ANowadays, precision medicine has become more and more important in the diagnosis and treatment of diseases. However, with the massive expansion of available genomic data, and novel disease biomarkers, new analytical challenges arise to decipher complex relationships between vast amounts of information and multiple data types. Machine learning can detect indistinguishable patterns from large, noisy or complex datasets. This capability is particularly suitable for complex genomic data, especially in cancer research . At presIn this study, we identified differentially expressed m6A RNA methylation regulators between normal and tumor samples using whole-genome transcriptome sequencing data from 66 patients diagnosed with esophageal squamous cell carcinoma at our hospital. Modeling was performed using machine learning methods, the prediction effect of the model was evaluated, and the most important m6A genes that could distinguish ESCC from normal groups were screened. RNA-sequencing data from The Cancer Genome Atlas (TCGA)-ESCA dataset was then screened for differentially expressed m6A RNA methylation regulators between normal and tumor samples using the same machine learning and modeling approach. After taking the intersection between the two data sets, a risk prediction model is established. Finally, our results were validated in tissue samples and an independent clinical ESCC cohort.https://portal.gdc.cancer.gov/) as the validation cohort. Normalization of transcriptome counts was performed by the edgeR package (version 3.26.8).We used the RNA-seq data of 66 pairs of ESCC samples collected from the Department of Thoracic Surgery, West China Hospital of Sichuan University between 2020 and 2021 as the training cohort, which contained complete clinical characteristic data, including gender, age, tumor differentiation, TNM stage, and surgical method. In addition, we downloaded expression profiles of 159 ESCC samples from the TCGA-ESCA database  were collected from patients who underwent radical esophagectomy at our hospital from June 2022 to July 2022 were. All patients were informed of the risks of the operation. Permission to use resected specimens and written consent were obtained from the study participants preoperatively.We employed two machine learning approaches: RF (Random Forest) and SVM (Support Vector Machine) to screen ESCC-related m6A regulators. The RF modeling adopts the “Repeatedcv” method in the R language train function, and the SVM modeling adopts the “svmRadial” method. The outcome variables were normal group and ESCC group, and differential genes were put into the model as independent variables. Plot the boxplots and cumulative residual distributions of the two models to determine which model performs better. If the gene importance score is greater than 2, it is regarded as a characteristic gene .p < .05 was considered statistically significant.A nomogram was constructed using the eigengenes screened by machine learning to visualize the predictive power of the eigengenes for the control group and the ESCC group. The nomogram formulates the scoring standard according to the size of the regression coefficients of all independent variables, and assigns a score to each independent variable; for each patient, a total score can be calculated, and then through the conversion between the score and the probability of the outcome, probability of each patient’s outcome happening can be calculated. Calibration curves, clinical decision curves, and clinical impact curves (CIC) were used to evaluate the predictive ability and best-effort ability of the model. Statistics and graphics were performed using R-Studio (version 4.2.1). Two-sided β-actin was purchased from Sigma-Aldrich Co. .After washing with cold phosphate-buffered saline (PBS) and pelleting, the protein concentration was determined using a bicinchoninic acid assay. After electrophoresis on SDS-PAGE, proteins were transferred onto PVDF membranes. The membranes were blocked with 5% nonfat milk and incubated with primary antibodies at 4°C overnight. The corresponding horseradish peroxidase (HRP)-conjugated secondary antibody was added and incubated at room temperature for 2 h. Signals were visualized using an enhanced chemiluminescence reaction with an HRP substrate. The primary antibodies against YTHDF1  and HNRNPC  were purchased from Abclonal, China. The antibody against The ESCC tissue sections included 120 cases of ESCC from February 2016 to June 2017 with complete clinical information were collected and produced by our team. Immunohistochemical staining was performed subsequently. The tissue sections were first kept at 60°C for 24 h. Xylene deparaffinization and hydration were then carried out with an ethanol gradient (100%–60%). Antigen retrieval was performed by heating sections in 10 mM citrate (pH 6.0) boiling buffer for 15 min. ESCC tissue sections were incubated overnight at 4°C with rabbit monoclonal anti-YTHDF1 antibody  and anti-HNRNPC . Incubation with the corresponding secondary antibody  was performed the next day at room temperature (25°C) for 30 min, followed by staining with 3, 3′-diaminobenzidine (DAB) and hematoxylin. The results of ESCC tissue sections were viewed and photographed with the Olympus BX53 fluorescence microscope . A composite score was determined using a previously described method .U test. Overall survival (OS) in patients was analyzed using Kaplan-Meier curves, and log-rank tests were used to determine the statistical significance. Multivariate survival analysis was carried out with the Cox proportional hazard regression model, in which the covariates that met the proportional hazards assumption of the covariate interaction test were considered to pass. All statistical tests were two-sided. A p-value of less than .05 was considered statistically significant.The clinicopathological characteristics of the patients were analyzed by Pearson’s chi-square test or Fisher’s exact test to compare the dichotomous variables. Student’s t-test was applied for the mean values of continuous variables that conformed to a normal distribution; the others were analyzed using the Mann-Whitney We used the transcriptome data of 66 ESCC samples collected from the Department of Thoracic Surgery, West China Hospital of Sichuan University to analyze the mRNA expression levels of m6A RNA methylation regulators. A heatmap was generated to visualize the expression of 21 m6A regulators that were significantly different between ESCC and normal tissues . The mRNWe first used the RF (random forest) and SVM (support vector machine) machine learning methods on the transcriptome data of our hospital to take the expression of the above 21 m6A genes as independent variables, and the normal group and ESCC group as the outcome variables into RF and SVM models. We analyzed the boxplots and cumulative residual distributions of both models to determine which model had better performance. Through Venn diagrams, the m6A regulators significantly related to ESCC screened in our RNA-seq data and TCGA-ESCA database based on machine learning were overlapped, and then the final ESCC related m6A regulators in this study were screened: YTHDF1 and HNRNPC . We consp < .05) and M stage (p < .05)  . Meanwhip < .05) . We thenp < .05) .p = .003, p = .017, via our RNA-seq data in order to find out the potential correlated genes with YTHDF1 or HNRNPC  belongs to the YTH domain family and is a “reader” for m6A-modified mRNA. Recent studies have shown that YTHDF1 is involved in the occurrence and development of various cancers. YTHDF1 promotes lung cancer progression through its involvement in the m6A demethylase ALKBH5 pathway . In bladHNRNPC, an RNA-binding protein, is a member of the heterogeneous ribonucleoproteins (hnRNPs). HNRNPC contains only one RNA-recognition motif (RRM), which must interact with its RNA target after oligomerization into tetramers . It can The advantages of our research lie in the following aspects: 1) We used large sample RNA-seq data to find ESCC-related m6A regulators, and used the TCGA database for verification. We then performed further analysis on the overlapped genes to ensure the reliability and authenticity of our results. 2) For the above searched ESCC-related m6A regulators, we performed WB experiments in clinical tissues, and validated the results of bioinformatics analysis with ESCC independent cohorts using IHC methods. The experimental results are consistent with the results predicted by bioinformatics. 3) This study is different from previous studies because we used machine learning methods to find m6A regulators related to ESCC occurrence. Machine learning has been widely used in the diagnosis, classification and prognosis of ESCC, and these studies have shown that machine learning is more accurate than logistic regression . In clinIn this study, we used transcriptome data of our hospital and TCGA-ESCC database to screen out two ESCC-related m6A RNA methylation regulators through machine learning: YTHDF1 and HNRNPC. The association of the expression of YTHDF1 and HNRNPC with the prognosis and clinicopathological characteristics of ESCC patients was verified by WB and IHC. Based on our constructed risk prediction model of m6A RNA methylation regulators, this may provide important information for developing diagnostic and therapeutic strategies in the future.


Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. We developed the temporal population structure (TPS), a DNA-based dating method for genomes ranging from the Late Mesolithic to today, and applied it to 3,591 ancient and 1,307 modern Eurasians. TPS predictions aligned with the known dates and correctly accounted for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We also demonstrated how TPS improved the ability to study phenotypic traits over time. TPS can be used when radiocarbon dating is unfeasible or uncertain or to develop alternative hypotheses for samples younger than 10,000 years ago, a limitation that may be resolved over time as ancient data accumulate.  •∼50% of ancient genomes lack reliable dates, which leads to poor understanding•Genomic data contain markers indicative of the age of the sample•TPS is an AI-based genomic dating tool that converts DNA data to dates •TPS can be used to date ancient genomes and resolve dating disputes Accurate dating is essential to the interpretation of paleogenomic data. The gold standard method in archeology is radiocarbon dating. However, a major limitation of radiocarbon dating is the high amount of collagen extraction involved in the process. Consequently, almost half of all published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. Here, we present the temporal population structure (TPS), a machine learning-based genomic dating method for genomes ranging from the fringes of the Late Mesolithic to modern times. Accurate dating is essential to the interpretation of paleogenomic data, yet nearly half of all ancient genomes lack reliable dates, which results in obscure and contradictory reports. To overcome these problems, Behnamian et al. develop TPS, a genomic dating tool for genomes , and demonstrate its accuracy on ∼5,000 genomes. Ancient DNA (aDNA) has transformed the study of human demographic history, allowing us to directly analyze patterns of past genetic variation rather than infer them post-factum. In the last few years, we have witnessed a conspicuous increase in the volumes of ancient skeletal DNA and studies attempting to trace their origins . Dating 14C with their biosphere while alive and cease to do so when dead. At that point, their 14C atoms decay into 14N with a half-life of ∼5,700 years, whereas their 12C concentration remains constant  dates include various warnings. The remaining skeletons have either been dated according to the archeological materials found alongside the sample or remain undated. The subjective interpretation of skeletal data has already led to misunderstandings on numerous occasions. For instance, a bone from the Darra-i-Kur cave in Afghanistan, initially assumed to be from the Paleolithic  . For instance, sample MA2195, initially dated to 3800 BP (V44.3) was redated to 217 BP (V50) based on the context in both cases and without an explanation. Remarkably, in all cases of date changes, the original publication was cited. Notably, family members, dated initially to different times, are post-processed to appear closer. As misattributions can lead to erroneous conclusions, the uncertainties in the age of nearly half of the aDNA samples and the actual age of the remaining half pose a considerable risk of misinterpretation to the field, which calls into question the cost-effectiveness and overall usefulness of paleogenomic studies.Despite this progress, the dating of ancient remains is fraught with challenges. A major limitation of radiocarbon dating is its requirement for a large amount of collagen. The routine AMS requires at least 60–200 mg of bone , dependi000 YBP)  and ofte000 YBP) . Similar000 YBP) , but a l000 YBP) . Reevalunot rare . Not onlnot rare . Contaminot rare , redatednot rare    and can D statistics , which have attracted several criticisms. First, D is a relative test and always compares the proportion of archaic ancestry in one population relative to another. With recent reports that even Africans carry non-human ancestry  approach, a learning model that uses genomic data  to date samples using random forests . The SMLWe evaluated the accuracy of TPS by training the model on two portions of the samples  using 10-fold cross-validation. We then applied the model to the third portion of the samples (unseen set) and compared the difference between their TPS predicted and reported dates B.To identify the genomic markers that underlie the temporal components used by TPS, the temporal components were sorted from oldest to youngest , creatinr = 0.93, p value = 0). Similar results were obtained when we repeated the analysis for radiocarbon-dated  and archeological-dated samples . To gauge the reliability of TPS predictions, we defined the accuracy per sample as the absolute difference between its TPS result and its mean radiocarbon or archeological dates. TPS median accuracy for all ancient and modern samples was 259 years, 0 years for the modern samples, and 428 years for ancient samples, with 75% of the samples being assigned a TPS date within 445 years from their radiocarbon date. Only 816 (16%) of the samples were TPS dated over 1,000 years from their mean date  with close alignments to the ideal bisecting line    B, and 27, p = 0) C. Becaus, p = 0) A and 5B −240), indicating that TPS is robust to the high missingness common to aDNA data (within the limitation of the 600 SNPs used).TPS predictions of same-country samples did not cluster around a single period. Instead, they were spread over the timeline following their radiocarbon dates, confirming that the temporal components represent temporal rather than geographical variation K. TPS acWe contrasted the accuracy of TPS against two controls. First, we generated a random matrix of 4,898 × 10 with random values , associated them with the real dates, applied the TPS model training as in −64). TPS median accuracy for 318 relatives from 145 modern families (dated to 10 BP) was 0 years  them to reduce disparities between family members D. Here,  0 years D.Second, we TPS dated modern samples from 13 Eurasian populations A. The reFinally, to test whether TPS can resolve discrepancies in the literature, we TPS dated the Brandýsek individuals from Czechia. Two individuals (RISE569 and RISE568) from the Brandýsek site were originally attributed by archeological context to the Bell Beaker period  . After tTwo observations are noteworthy for these individuals. First, 11 of the remaining 12 individuals were TPS dated to fit within the Bell Beaker and Corded Ware period . Second, the last individual, I7272, was much older and predated the Corded Ware culture, as is evidenced by two additional features: First, I7272 lacked the ancient temporal component present in all of the other individuals at this site, which is ubiquitous among Bell Beaker samples and associated with the period following the Yamnaya invasion  A. SecondWe carried out five additional analyses to evaluate the robustness and stringency of TPS to erroneous or noisy input data and mismatching training data. In the first analysis, we tested to what extent samples with identical temporal components but incorrect dates affect the accuracy of the age prediction. For that, we divided the ancient samples  into 10 roughly equal-sized bins (332–382 samples per bin) by their ages and countries. We randomly sampled 740 samples stratified by age and geography and considered unseen samples. Training TPS on the remaining dataset yielded a median base accuracy of 164 years for these 740 samples. We copied these samples to the training set and added 1,000 years to their ages. Their temporal components remained unchanged. Retraining the model on the original training set and the biased samples yielded a median accuracy of 298 years for these samples. Repeating the process after increasing the bias by 2,000 years yielded a similar median accuracy (314 years) A. We theIn our second analysis, we tested the effect of introducing noise to the temporal components of the samples, such as can be generated due to genotyping errors. For that, we used the 740 samples for the unseen test (base median prediction accuracy of 164 years) E. We four = 0.52, two-sided t test, t statistic = 2.59, p = 0.018), suggesting that using the mean age of samples with a wide SD of the age is more challenging for TPS; however, when embedded with other training samples and after filtering samples with a high SD of the mean date  have a strict direct radiocarbon date . The remaining samples are imprecisely dated, mostly using archeological context and various estimates. The absence of a reliable alternative to radiocarbon dating is a challenging problem in paleogenomics, which relies on dates to study genomic data. Moreover, although radiocarbon dating is widely accepted as the benchmark standard for dating ancient remains , its relBy contrast, genomic dating relies solely on the DNA sequence, making it possible to date remains whose radiocarbon dating cannot be established directly, are in doubt, or are absent. Motivated by our observations that allele frequencies show temporal variability over time, we introduced the concept of TIMs and demonstrated their usefulness to genomic dating. For example, we showed that TIMs associated with traits have increased or decreased their frequency over time, as reported elsewhere  and can TPS is a powerful instrument in the growing paleogeneticist toolkit that can be used when dating is unavailable or in doubt and addresses contradictory findings in the paleogenomic literature. The advantage of adopting an admixture scheme to derive the temporal components over alternative techniques such as linear regression is its resilience to missing data, common to aDNA data. One of the strengths of random forest over dating based on sequence similarity is finding a consensus of correct dates and reducing the effect of outliers and incorrect radiocarbon dates. As an SML algorithm independent of physical measure, TPS can also identify incorrect radiocarbon dates. This is a major advantage for TPS since radiocarbon dating is a physical measure independent of previously dated samples, which is not informed by experience, whereas SML algorithms become more accurate with the increase in sample size .Overall, TPS can be used to date samples, evaluate dated samples, detect outliers or misdated samples, and develop alternative hypotheses to other dating techniques. We envision that genomic dating will become even more accurate with the increase in the number of sequenced populations over time. Therefore, our results should be considered a lower bound to the full potential genomic dating. We note that TPS is neither comparable to genetic dating methods based on measuring the level of Neanderthal inbreeding nor suffers from their biases .As with all machine learning methods, TPS requires a large training dataset to yield accurate predictions. This limits its applicability to humans and a few farm animals with the large availability of ancient genomes. In humans, TPS is further limited to samples dated from the past 10,000 YBP, where sufficient genomes are available. We note that all of the reported dates off by more than 1,000 YBP were for older samples, for which the training data are sparse. For the same reason, TPS may be limited to Eurasians due to the high availability of their ancient genomes. We also showed that the accuracy of TPS decreases for unsampled periods. This limitation can be resolved when more data are available. Compared to physical measures that are relatively independent of past analyses, the ability of TPS to learn from experience is its advantage—and its weakness, since it may incorporate incorrect data into its model. Solving dating conflicts in the literature  to improve the results without clear traces, as done in the AADR, is a form of genomic photoshopping that poses a challenge for TPS that trains on these data and could resolve such conflicts itself. Finally, we showed that genomes with a wide age range pose difficulties in TPS dating. This limitation may also be resolved when more data are available.eran.elhaik@biol.lu.se).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Eran Elhaik , a uniformly curated dataset with genotypes and metadata. We constructed a dataset of ancient and modern samples  (V50) . We next applied unsupervised ADMIXTURE (v1.3.0) , i.e., components that were evenly distributed in all samples within a certain period. Initially, ten and three putative modern and ancient components were identified, respectively, with a clear split between the ancient and modern components. Using ADMIXTURE’s allele frequencies output (p-file), 15 synthetic samples associated with each temporal component candidate were generated for each component (To identify the (v1.3.0)  for nined by age A. For eaomponent .supervised ADMIXTURE, as in temporal components , non-random temporal trends were observed over time (supervised ADMIXTURE’s p-file)  B and S4.omponent  to detecomponent . Overlapred TIMs . For a ntemporal components with age as the target variable to be predicted – we developed the Temporal Population Structure (TPS), a supervised machine learning (SML) algorithm that employs a random forest regression ) A. Prepro) , P. 10. ) , P. 93 aribution B. Overaltemporal components used as input for the SML model. To increase the number of features, we adopted simple mathematical operations  the absolute difference between the mean of the first ancient component  and the first ancient component, and 3) the sum of the first ancient component and three times the column added in 2), which substitute the first ancient component.Features are independent numeric variables that describe the To train the SML on these ten features, we split the ancient samples in a stratified fashion into ten roughly equal-sized subsets based on the mean date and country. The modern samples were stratified into ten roughly equal-sized subsets based only on the country as age was set to 10 BP. A small fraction of the ancient samples (63 samples (<1%)) that could not be matched by date and country appeared only in the training set. Next, the ten ancient subsets were collapsed into two groups stratified as follows: 85%  and 15% (unseen or test). For modern samples, the same process was executed. The training set of the ancient samples was combined with the training set of modern samples. Similarly, the testing set of ancient samples was combined with that of the modern samples. Overall, both ancient and modern samples from different ages and locations were included in both the training and testing sets. The combined training set was reshuffled. We then split the training set into 90% (training) and 10% . The training was performed using 10-fold cross-validation. We employed random forest regression that uses ensemble learning for regression with a maximum number of 20 trees . In thistemporal components of the test samples, which are calculated by applying supervised ADMIXTURE to its genomic data against the synthetic temporal components available from datadryad.To date a sample B, the TPWe applied the process above to several cohorts. First, we TPS-dated 740 random samples (15% of the data) stratified over time and space I and S5JT-test. To evaluate TPS performances to noisy data, we introduced noise with varying levels of 1%, 10%, and 100% to 1%, 10%, and 100% of the unseen samples. The noise affected all the temporal components of the unseen dataset as follows: Temporal components within 0.9 quantiles were randomly either increased or decreased by a random value selected from a uniform distribution . The noise level represented the proportion of temporal component values modified by this procedure. We also evaluated the accuracy of the TPS model by applying the model to an equally-sized dataset of principal components, calculated using Matlab’s pca.m function, by projecting the ancient DNA samples onto the top ten principal components defined by modern-day populations and to an equal-sized dataset with random numbers. When testing family relatives, we evaluated the accuracy against the radiocarbon date and, when unavailable, archeologically-derived date. Additionally, we calculated the predicted age difference among family members, which should be small, regardless of the dating method. Significance was assessed with the two-sided Wilcoxon rank-sum test.The accuracy of dating prediction was evaluated by comparing the sample’s predicted date  with its mean published date. Typically, this was assessed using linear regression with significance calculated using K-means clustering so that the seven analyses had: 15 clusters , 40 clusters , 20 clusters , 30 clusters , 30 clusters , 15 clusters , and 10 clusters . Cluster size varied in the first two methods and was similar in the latter five. The following procedure was applied for all the methods: Following the removal of 22 samples with invalid coordinates, the samples of each cluster were considered unseen, and TPS was trained using the remaining clusters using 5 to 10-fold cross-validation to predict the unseen samples. This process was repeated until all the clusters were predicted. The samples were then divided into a fixed number of 1018 groups based on their latitude and longitude, and their mean accuracies were calculated. The medians of the mean accuracy of each method were then calculated. For example, in the fourth analysis, the dataset was divided into 30 geographical clusters with 100-200 samples per cluster (We evaluated the effects of geography on TPS using seven geographical analyses. In each analysis, samples were split between clusters identified based on the similarity of their geographical coordinates using  cluster . Then, e


It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer have been discovered and documented in public databases such as COSMIC, but the vast majority of them have no functional or clinical annotations. Some databases, such as CiVIC are available with manual annotation of functional mutations, but the size of the database is small due to the use of human annotation. Since the unlabeled data (millions of variants) typically outnumber labeled data (thousands of variants), computational tools that take advantage of unlabeled data may improve prediction accuracy.To leverage unlabeled data to predict functional importance of genetic variants, we introduced a method using semi-supervised generative adversarial networks (SGAN), incorporating features from both labeled and unlabeled data. Our SGAN model incorporated features from clinical guidelines and predictive scores from other computational tools. We also performed comparative analysis to study factors that influence prediction accuracy, such as using different algorithms, types of features, and training sample size, to provide more insights into variant prioritization. We found that SGAN can achieve competitive performances with small labeled training samples by incorporating unlabeled samples, which is a unique advantage compared to traditional machine learning methods. We also found that manually curated samples can achieve a more stable predictive performance than publicly available datasets.By incorporating much larger samples of unlabeled data, the SGAN method can improve the ability to detect novel oncogenic variants, compared to other machine-learning algorithms that use only labeled datasets. SGAN can be potentially used to predict the pathogenicity of more complex variants such as structural variants or non-coding variants, with the availability of more training samples and informative features.The online version contains supplementary material available at 10.1186/s12859-023-05141-2. Therefore, there is a stark contrast of “unlabeled” variants in public databases versus “labeled” variants for which clinical significance is already known and widely recognized. Given the rapid pace of discovering additional somatic variants from various types of cancer and the rapid expansion of COSMIC database, it is clear that the development of predictive tools is urgently needed to assess the clinical impacts of somatic variants in cancer.A large number of somatic variants have been identified by next-generation sequencing (NGS) in cancer research studies and clinical genetic testing laboratories. Except for recurrent “hotspot” mutations, clinical interpretation remains a significant challenge for many newly discovered variants. As of March 31, 2021, there are already 10 million variants curated in the COSMIC  database [Numerous tools for predicting the oncogenicity of cancer variants have been developed –13, usinHowever, there are several limitations of the existing tools for the prediction of oncogenic mutations. First, these tools typically do not incorporate clinical evidence  as the predictive features, but only quantitative scores purely from computational predictions on effects of protein sequence, structure, conservation. Therefore, the prediction is more focused on prioritizing functionally important mutations instead of clinically important oncogenic mutations. Although the current tools achieved a better performance based on training and testing on several publicly available data sets, one major concern is that the data quality of public data resources is heterogeneous, and that they do not include clinical features. Second, the sample size of ‘labeled’ data from each data resource used for training is relatively small, in contrast to the millions of “unlabeled” somatic variants. The models trained on small labeled data using supervised ML learning methods may not achieve high predictive accuracy when applying to a large number of unlabeled samples. Several studies have shown that semi-supervised methods may improve predictive performance by incorporating “unlabeled” data in statistical models ; howeverTo address these limitations, in the current study, we developed a new semi-supervised generative adversarial neural network (SGAN) method, which incorporated 12 clinical features of somatic variants and unlabeled variants. Assuming all mutations, including oncogenic (driver) or passenger mutations, follow an underlying distribution, the SGAN method learns this underlying distribution by scanning all the possible variants from COSMIC and several other public knowledgebases. The 12 clinical evidence scores were derived from AMP/ASCO/CAP 2017 guideline, which is typically not used in previously published computational tools, to the best of our knowledge. Additionally, we also have access to ~ 6000 labeled variants that were manually labeled for their clinical significance by experts from a clinical diagnostic lab in our institute, which have a higher quality than public data sets. We comprehensively assessed the predictive performance of different algorithms, types of features, and training sample size in predicting cancer driver mutations to provide more insights in variant prioritization.An overview of the SGAN model for the prediction of oncogenic variants is shown in Fig. To gain insights into how SGAN works, we employed a two-dimensional t-distributed stochastic neighbor embedding (t-SNE)  for visuTo evaluate how the size of labeled samples in the training set influences the prediction accuracy of unlabeled data, we trained model with a different number of labeled variants, ranging from 250 to 4000 as mentioned in Methods. For each variant in the testing dataset, we calculated the interpretation scores using the Softmax function. We used interpretation scores for ROC AUC and PR AUC comparison. For the rest of the comparison metrics, such as accuracy, F1, and MCC, we used interpretation score > 0.5 as the cut-off to determine whether a variant is oncogenic or neutral for equal comparison. All eight-evaluation metrics were summarized into a barplot Fig.  and a taTo investigate how predictive features influence the prediction accuracy, we trained three models with different types of features as the input, including: 1) pathogenicity prediction scores, (2) evidence-based scores, and (3) ensemble scores. The performance metrics have been summarized in Table  pathogenCompared with supervised learning methods and existing pathogenicity prediction methods, the SGAN models achieved the best performance of discriminating oncogenic and benign variants according to ROC-AUC and PR-AUC values Fig.  Table S3https://www.cancerhotspots.org). According to Bozic et al.’s. theoretical estimation, the number of GoF in one oncogene is supposed to be fewer than LoF in one tumor suppressor gene[We compared the prediction of driver LoF and GoF mutations with statistically significantly recurrent mutations identified in large scale cancer genomics data reported in CancerHotspot . The presence of many VUS greatly impeded the effectiveness of clinical management of patients with cancer. For example, there are sporadic reports that doctors recommend bilateral mastectomies to patients with suspected P/LP in e benign . This crThe main rationale for using the semi-supervised GAN algorithm here is that the number of labeled variants is typically small, resulting in a computational challenge in learning from small samples in the real world; yet, by incorporating unlabeled data into a semi-supervised GAN model, the training procedure can be greatly improved. One unique advantage of semi-supervised GAN is that it has less overfitting issues when learning from small samples, due to the property of underlying distribution assumption of the data modeling in GAN. Traditional GAN model consists of 2 parts: generator to generate synthetic (fake) samples, and discriminator to classify samples as either real or fake. But here, we improved traditional GAN by including labeled samples, unlabeled samples, and fake samples that were generated by the generator. The generator’s goal is to generate samples similar to the real samples as much as possible in the learning process. This network has the unique advantage in dynamically learning the underlying distribution (clusters) of data samples by discriminating the synthetic samples and unlabeled real samples at each epoch. In principle, due to the advantage of the unique architecture, SGAN can be potentially used to predict more complex variants such as structural variants or non-coding variants with some modifications, considering they are typically suffered from the limited number of ‘labeled’ samples as the training set.SGAN uses a small amount of labeled data and a large amount of unlabeled data, which provides the benefits of both unsupervised and supervised learning while avoiding the challenges of finding a large amount of labeled data. Our results showed that, even with 1000 labeled samples, the prediction performance of SGAN is as good as using 4000 samples in the training set. Another major challenge to differentiate oncogenic variants from VUS accurately is that even within the small number of labeled samples, positive (oncogenic) and negative  classes are unbalanced. We overcome this issue through resampling technique during the training process. Additionally, many previously published methods use only deleteriousness prediction scores in machine-learning models, without considering evidence-based scores typically defined in clinical guidelines. In this project, our SGAN method overcomes these challenges, using both evidence-based and model-based score features, many of which are not used in existing tools, to achieve the best predictive performance.Although SGAN can accelerate the clinical interpretation process on cancer variants, our model-based approach cannot replace human reviewers. We need to explore more mutations with reliable labels manually to improve cancer diagnosis. We stress that our method has the following limitations: First, the number of testing datasets is limited, and we only used somatic mutations, which are not comprehensive enough to evaluate the performance of our model, and the best cutoff for determining oncogenicity probably needs to be adjusted on a case by case basis in real studies, depending on the data distribution. Second, the current SGAN model cannot interpret complicated genomic variants, such as inversions and gene fusions, and cannot interpret gene expression alterations, even though these genomic alterations may also play important roles in tumorigenesis and cancer progression. In principle, all these mutations can be used to build a new SGAN model with a different set of features, and there are already many unlabeled data in COSMIC. However, the major challenge here is that we may not have a sufficient amount of labeled data and a sufficient number of predictive features to train a reliable model. Third, we did not consider coding indels , because their clinical interpretations are generally straightforward in existing clinical guidelines. Nevertheless, we do acknowledge that many computational tools are developed for predicting functional significance of coding indels, in addition to SNPs, so we may explore the possibility of building a SGAN for these indels later. Additionally, we also acknowledge that a small fraction of non-coding variants may be highly penetrant to be oncogenic; however, such validation data is extremely sparse. Finally, we acknowledge that non-canonical splice variants may be oncogenic and can be interpreted by current clinical guidelines, but they do not have feature scores similar to missense variants. We will explore the use of several splice variants prediction algorithms  variants and 4829 benign variants as negative (N) variants. The labeled data for testing consists of 1335 oncogenic variants and 4829 neutral/benign variants that were manually collected from several experimentally annotated studies in PubMed –21. For Two types of predictive features were used to generate score metrics for the model: clinical scores and functional deleteriousness scores. First, 12 clinical evidence-based prediction scores according to the AMP/ASCO/CAP 2017 guideline  were obtOur model is an improved version of semi-supervised GAN. In detail, the SGAN contains 2 parts: 1) generator (G), which generates synthetic observations with a vector of Gaussian noise as input; (2) discriminator (D), which determines whether the observation is synthetic or real in unsupervised training process, and to classify whether the observation is oncogenic or benign in supervised training process. The generator consists of 4 linear layers, with LeakyReLU as the activation layer and Tanh as the last activation layer before output. We also used batch normalization after each linear layer and a dropout rate of 0.6 in hidden layers. For the discriminator, we used a 3-layer convolutional neural network, which is shown in Fig.  generatoIn detail, assuming z is a vector of Gaussian noise, a perfectly trained generator network In our SGAN model training process, we first trained the discriminator and then the generator in each minibatch. We trained the discriminator by minimizing the sum of supervised learning loss and unsupervised learning loss. For the supervised learning, the discriminator, working as a standard classifier, takes the labeled data point x as input and outputs a 2-dimenisonal vector Therefore, binary cross entropy between the true labels and the model predictive distribution For the unsupervised learning, we labeled the data point G(z) with “synthetic” and used mentclass2pt{minimAnd the generator (G) was trained by minimizing feature matching loss, which is referred to Tim et al. \documentWe used Adam optimizer  with an For comparison purpose, we employed seven supervised machine-learning methods, which were provided in a recently published paper AI-Driver , includiTo perform a comprehensive assessment on semi-supervised learning for variant interpretation, we trained five models with different number of labeled variants, ranging from 250 to 4000  from the training dataset for machine-learning methods. The rest variants (669 P and 565 N variants) in the training dataset were used for validation. Function prediction scores of these variants were obtained from dbnsfp30a using ANNOVAR (version 2020-06-07).We used 8 performance measurements for performance comparison, including accuracy, precision, sensitivity, specificity, F1 score, Matthew’s correlation coefficient (MCC), area under the receiver operating characteristic curve (ROC-AUC), and area under the Precision-Recall Curve (PR-AUC). The number of true positive predictions is denoted as TP . Thus, we computed the accuracy, precision, recall, specificity, F1 score, and MCC asMCC is a measurement of the quality of binary classifier, ranging from -1 to 1. Compared to F1 scores, it overcomes the issue of class imbalance in evaluation studies. A coefficient of -1 indicates that prediction is inconsistent with observation, 0 means like random prediction, and 1 for a perfect classifier. Considering that MCC value depends on the cutoff of binary classifier, we also calculated ROC-AUC and PR-AUC values for model comparisons.Additional file 1: Table S1. The number of variants used in training processTable S2. Performance of SGAN with different determination thresholds and different thresholdTable S3. Performance comparison among different methodsTable S4. Predictive score distribution for loss of function mutations and gain of function mutations.