The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data. Genomic data are often produced in batches due to logistical or practical restrictions, but technical variation and differences across batches, often called batch effects, can cause significant heterogeneity across batches of data . Batch eBatch effects often cannot be fully addressed by normalization methods and procedures. The differences in the overall expression distribution of each sample across batch may be corrected by normalization methods, such as transforming the raw counts to (logarithms of) CPM, TPM or RPKM/FPKM, the trimmed mean of M values (TMM) , or relaMany methods have been proposed to address batch effects in RNA-seq studies. For example, ComBat  remains Despite the established progress, there are still gaps in batch adjustment methodology for RNA-seq data which need to be bridged. Often times, batch effect adjustment methods do not adequately model the complexity of batch-to-batch heterogeneity, do not directly provide adjusted data with batch effects removed, or do not preserve the integer nature of counts in the adjusted data, despite the requirement of software such as edgeR and DESeq2 that specifically require untransformed count matrices as inputs. This results in an inconsistency in the analysis pipeline of RNA-seq studies, as batch corrected data cannot be used as inputs for these differential expression software. For these practical issues, it is favorable to develop a method which generates adjusted data and is able to preserve the count nature of data.More importantly, many popular adjustment methods, including ComBat, assume Gaussian distributions for the underlying distribution of the data, which is not an appropriate distributional assumption for counts. These methods typically estimate parameters representing differences in the statistical moments across batches . Then they adjust all batches to the same overall level in these moments. Such adjustment does not preserve integers, and may results in negative values in adjusted count matrix, which is difficult to interpret biologically. In addition, it has been well-established that there exists a mean-variance dependence in RNA-seq count data . DistribFinally, existing methods may not be flexible enough to address all types of batch effects. In particular, including batch variables in software for differential expression may be sufficient to account for batch effects in the mean expression. However, since both software assume a single dispersion parameter for all samples, variance batch effects is restricted, and completely determined by mean batch effects, due to the properties of negative binomial modeling. Such assumption is strong and may not always hold for real data. Therefore, we propose a more flexible approach to address batch effects in the variance.In this paper, we present a batch effect adjustment method, ComBat-seq, that extends the original ComBat adjustment framework to address the challenges in batch correction in RNA-seq count data. It generates adjusted data in the form of counts, thus preserving the integer nature of data. We demonstrate that ComBat-seq adjustment has potential benefits in differential expression compared to the other adjustment methods, especially when there is a large variance batch effect in the data.We propose ComBat-seq, which uses a negative binomial regression model to estimate batch effects based on the count matrix in RNA-seq studies. Parameters of the regression model are estimated using established methods ,10–11. Wg of sample j from batch i be denoted by ygij. We assume that ygij follows a negative binomial distribution NB, where μgij and ϕgi are the mean and the dispersion parameters. We propose the gene-wise model:g denotes the logarithm of expected counts for ‘negative’ samples. Xjβg reflects changes to the log of expected counts due to biological conditions, which is preserved in the data after adjustment. In this term, Xj may be an indicator of the biological condition for sample j, or a continuous value for a clinical covariate. βg denotes the corresponding regression coefficient. Nj represents the library size, i.e. total counts across all genes in sample j. The mean and dispersion batch effect parameters are denoted by γgi and ϕgi, respectively, modeling the effect of batch i on gene g. We estimate the model parameters, especially batch effect parameters γgi and ϕgi, following the established methods in edgeR   = P(y ≤ ygij). Zero counts are mapped to zeros. We perform this mapping for every value in the count matrix, which completes the adjustment.Then, the adjusted data We evaluated the performance of ComBat-seq with simulation experiments consisting of three steps: (i) we simulated RNA-seq studies with biological conditions and batch effects, (ii) adjusted the batch differences with ComBat-seq as well as other available methods and (iii) evaluated the performance of batch effect adjustment by the impact on differential expression using the adjusted data.polyester R package ’ and ‘positive (1)’, which can represent ‘control’ and ‘tumor’ samples in a real dataset, for example. We simulated both upregulated and down-regulated true differentially expressed (DE) genes in both gene groups with increased expression in the positive (upregulated) or the negative (downregulated) biological condition. The remaining genes are only affected by batch, not by the condition. Differences in the average count of genes are simulated by specifying fold changes across biological and batch sample groups using polyester. We also made the dispersion of batch 2 a number of times larger than that of batch 1, allowing for different true dispersion parameters across batches. We used the  package  to simulWe repeated the simulation while varying the level of mean and dispersion batch differences. Specifically, we changed the parameters for simulation such that mean of batch 2 is 1.5, 2 or 3 times that of batch 1. The dispersion of batch 2 was set to be 2-, 3- or 4-fold of that of batch 1. Experiments with no mean batch effect or no dispersion differences were also included. Results were averaged over 300 repeated simulations under each parameter setting.Our selected parameters in simulations are consistent with the degree of batch effect in real data, as summarized in The batch effects in mean and dispersion (variance) were adjusted with ComBat-seq, the ‘one-step’ approach, i.e. to include the batch variable in differential expression linear models, as well as with SVA-seq and RUV-seq. We also included another commonly used method in practice, which is to transform the count matrix to logCPM, then use the batch correction methods designed for Gaussian distributed data, such as the original ComBat method. For SVA-seq, we computed a single surrogate variable, then included it as a covariate in downstream differential expression. For RUV-seq, we used the RUVg method, and randomly sampled 10 genes that we simulated to not respond to the biological condition, and used them as negative control genes. We selected only one surrogate/latent variable, to reflect the design of the simulated study, where we simulated only biological and batch differences.Aside from batch adjusted data, we included two additional experiments for comparison: differential expression performed on (i) data without simulated batch effects, and (ii) data with simulated batch differences, but no adjustment. We compared both the statistical power  and control of type-I errors  in detection using data without batch effects, data with batch effects before and after different adjustments.We applied the proposed ComBat-seq approach on an RNA-seq data from a perturbation experiment using primary breast tissue attempting to profile the activity levels of growth factor receptor network (GFRN) pathways in relation to breast cancer progression ,14. We tNote that this is a challenging study design for batch effect adjustment: the control samples are balanced across batches, while each of the 3 kinds of treated cells, with different levels of biological signals, is completely nested within a single batch. A favorable adjustment would pool control samples from the three batches, while keeping all treated cells separated from the controls and from each other.We combined the three batches and performed batch correction. Among the batch correction methods considered, only RUV-seq, the original ComBat used on logged and normalized data and ComBat-seq output adjusted data. We apply these methods to address the batch effects in the pathway signature dataset. We compared ComBat-seq with the other methods, both qualitatively through principal component analysis (PCA) and quantitatively with explained variations by condition and batch.The ‘one-step’ approach and SVA-seq are not considered in PCA because they do not generate adjusted data after batch correction. For RUV-seq, we do not know which genes are appropriate for negative control genes, unlike in the simulation studies. Therefore, we used the RUVs method, which is more robust to the choices of negative control genes than RUVg . We compIn the sections below, we justify the necessity of using negative binomial distribution instead of Gaussian distribution for count data. We develop and implement ComBat-seq, and apply the method to the simulated and real data examples. We then summarize our observations, showing the potential benefits of ComBat-seq adjustment.polyester (P = 0.0033). These observations demonstrate the potential issue of applying batch correction method using Gaussian distribution on count data. A more appropriate model for integer counts would avoid such limitations.An example demonstrating the weakness of Gaussian-based models is given in Figure olyester  with balWe then applied the ComBat-seq method, which assumes negative binomial distributions for the underlying data. As shown in Figure We evaluated ComBat-seq and compared its performance with other available approaches in our simulation studies as described in the Method section. Results comparing all batch adjustment methods under different settings of degree of batch effects are summarized in Figure Having batch effects in the data causes decrease in both true and false positive rates, compared to data without batch effects . For example, having only batch effects in the mean of at 1.5-fold but no dispersion differences reduces FPR to 0.028, and TPR to 0.94. Having a 2-fold dispersion batch effect but no mean batch effect results in a 0.046 FPR and a 0.88 TPR.ComBat-seq is able to control false positive rates under 0.05 when there are dispersion differences, but for the unrealistic case where there is no dispersion difference across batches both ComBat-seq and ComBat on logCPM have FPRs in the 0.059–0.067 range. Other methods are able to control the FPR under 0.05 on all cases. Therefore existing methods, such as including batch as a covariate in the differential expression methods, may be sufficient to address batch effects in the mean. In this case, ComBat-seq which assumes separate dispersion across batch may be redundant and lead to higher false positives. In all other scenarios, which are more common in real data, ComBat-seq is able to control false positive rates. When there are large dispersion differences across batch, which often occurs when combining heterogeneous batches, e.g. from different studies or profiling platforms, ComBat-seq is more conservative than the other methods, though almost all methods have an appropriate level of false positive control. For example, when applied on data with no mean batch effect and a 3-fold dispersion differences, ComBat-seq generates the the most conservative FPR of 0.039, compared to the other methods . The false positive rates using data adjusted by ComBat-seq further decrease as the level of dispersion difference increases .In a realistic range of a 1.5-fold mean batch effect, and a 2-fold dispersion batch effect, we observed a 0.89 TPR from ComBat-seq, which is higher than the other methods . While in a more extreme scenario, the benefit of ComBat-seq is more visible. With a 3-fold mean change and a 4-fold dispersion effect, ComBat-seq achieves a 0.73 TPR, at least 6% higher than the other methods .Proportions of simulations where each of the five batch correction method achieves the highest power or lowest false positive rates are summarized in K = 2, We applied our ComBat-seq approach to address batch effects in a real RNA-seq dataset designed to develop pathway signatures for breast cancer progression and treatment response  as descrThough ComBat-seq does not show clearly improved results compared to using ComBat on logCPM in this application example, we re-emphasize that as shown in the simulations, using ComBat-seq which preserves integer counts instead of log-transforming the data results in better statistical power in differential expression.We further evaluated DE genes, comparing treated samples to pooled GFP controls from the three batches. The top 100 DE genes for each condition, under each batch adjustment situation, are given in P-values inclded as well): ‘one-step’: ranked 94.3% (FDR = 0.043); ComBat: ranked 26.6% (FDR = 0.0003); ComBat-seq: ranked 42.3% (FDR = 0.0016). The expression values, in logCPM, of EGFR is included in These data were derived using samples from quiescent human mammary epithelial cells that were transfected with adenoviruses expressing either one of HER2, EGFR, KRAS or (in controls) GFP. This means that these specific genes should clearly be overexpressed in these samples. As expected, HER2 and KRAS are constantly ranked at the top do the list in the DE genes for all methods, including the unadjusted data. However, due to the batch effect between GFP controls, EGFR is not detected as DE in the unadjusted data using an FDR ≤ 0.05. In data adjusted by RUV-seq or SVA-seq, EGFR is not detected either, regardless of the number of latent factors/surrogate variables used. In data adjusted by the ‘one-step’ approach, ComBat and ComBat-seq, EGFR is found to be DE using an FDR ≤ 0.05. We also looked at the relative percentile ranking of EGFR in the DE gene list (FDR adjusted P = 0.002) and ComBat-seq found 24 (P = 0.004) of the 222 genes in RAS signaling pathway. SVA-seq found 21 genes (P = 0.029), followed by original ComBat with 20 genes (P = 0.043), and RUVSeq with 19 genes (P = 0.080). There were only 16 RAS genes found in unadjusted data results (P = 0.306). These results suggest that batch correction approaches, including ComBat-seq are able to increase the amount of meaningful biological knowledge than can be obtained from the data.In addition to the genes mentioned above, we also expect to see changes in their downstream targets. For example, for KRAS, we took the genes in RAS signaling pathway , and comWe developed ComBat-seq to adjust batch effects from known sources in count data from RNA-seq studies. ComBat-seq is able to preserve the integer nature of count data, making the analysis pipeline more compatible for RNA-seq studies. We showed in simulations that ComBat-seq generally out-performs other methods in terms of the impact on downstream differential expression. When variance batch effect is present in the data, ComBat-seq is able to achieve better statistical power, while controlling false positive rates, compared to the other available methods. We further demonstrated the utility of ComBat-seq in addressing batch effect in the GFRN signature dataset, showing its potential to recover biological signals from data affected by batch.ComBat-seq is an extension of the original ComBat approach. Both methods use similar linear models to describe gene expression and parameterize the expression with background level, changes caused by biological condition and mean and variance batch effect. These parameters are estimated, and used for adjusting batch effect.The differences in the underlying probabilistic assumption leads to the discrepancies in estimation and adjustment between ComBat-seq and original ComBat. The original ComBat assumes Gaussian distribution, which is more sensitive to outlying data points. Therefore, ComBat uses empirical Bayes shrinkage, pooling information across genes to generate more robust estimates for gene-wise mean and variance batch effect. In ComBat-seq, however, data are assumed to follow a Negative Binomial distribution, which is a more flexible family of models that can better handle outliers and skewed data. Estimates are directly obtained from the negative binomial regression model, using standard Fisher scoring approach. We have shown  that applying empirical Bayes shrinkage is not necessary for ComBat-seq because the approach is already sufficiently robust due to the distributional assumption.An additional difference between ComBat and ComBat-seq lies in the adjustment approach. ComBat removes batch effect from the data via standardization, i.e. subtracting the mean batch effect estimate, and scaling by variance estimates. This is mathematically equivalent to quantile-matching for Gaussian data. However, adjusting the quantiles of Negative Bionomial data is not as straightforward and cannot be accomplished by directly standardizing the data. Thus, the adjustment method in ComBat-seq resembles quantile normalization, i.e. mapping the empirical distribution of count data to a expected ‘batch-free’ distribution. This method ensures that the adjusted data remain integer counts, and thus are compatible as input for downstream differential expression software like edgeR and DESeq2.In simulations, we observed that when there is no true difference in dispersion across batch, applying ComBat-seq, which specifies different dispersion parameters for batches, results in increased false positive rates compared to the other methods without further increasing the detection power. ComBat-seq controls false positives and shows benefits in increased true positive rates only when a true dispersion batch effect is present in the data. This is consistent with the intuition of batch effect adjustment, that modifying the data in any way comes with a risk of jeopardizing biological signals in the data. Therefore, batch effects should only be adjusted when they are present and result in unfavorable impact on downstream analysis. Such observations emphasize the importance for careful diagnosis of batch effect before applying any transformation to the data, which our reiterates previous work in this area .In the simulation studies, we also compared ComBat-seq with the commonly applied approach to transform the count matrix to logCPM, and then applied batch correction methods based on Gaussian distributions. That method essentially assumes a log-normal distribution for the data. In simulations, we observed that ComBat-seq generally out-performs log transforming the data in terms of power and control of false positives. These results provide evidence that using appropriate probabilistic models for count data may be more beneficial than arbitrarily transforming the data.polyster package. We focused primarily on addressing the unwanted impact of batch effect on downstream differential expression. It is known that batch effects may also negatively impact other biological tasks, such as developing predictive models for genomic data. Performance of batch correction in these tasks requires further evaluation, but is beyond the scope of this paper. Our ComBat-seq method is based on a gene-wise negative binomial regression model, which, similar to other  linear models, may not work well on data with severely or even completely confounded study designs. However, batch correction in confounded designs is challenging for most if not all the state-of-the-art batch adjustment methods, and careful experimental design has been widely advised to mitigate the unfavorable impact of batch effects.Our study has several limitations. We used an idealistic data model in simulations, and characterized biological signals and batch effects in the form of fold changes in the average value across batch. Though there may be other methods to model count data with both condition and batch effects, our model is a valid and convenient assumption for the data, which is easy to implement with the https://github.com/zhangyuqing/ComBat-seq.The ComBat-seq software is available in the sva package in the Bioconductor project . Code tolqaa078_Supplemental_FilesClick here for additional data file.


Simulation analyses for t-test, RNA-seq, and microarray data demonstrated that wFisher and ordmeta, when only a small number of studies have an association, outperformed existing meta-analysis methods. We performed meta-analyses of nine microarray datasets (prostate cancer) and four association summary datasets (body mass index), where our methods exhibited high biological relevance and were able to detect genes that the-state-of-the-art methods missed. The metapro R package that implements the proposed methods is available from both CRAN and GitHub (http://github.com/unistbig/metapro).Meta-analyses increase statistical power by combining statistics from multiple studies. Meta-analysis methods have mostly been evaluated under the condition that all the data in each study have an association with the given phenotype. However, specific experimental conditions in each study or genetic heterogeneity can result in “unassociated statistics” that are derived from the null distribution. Here, we show that power of conventional meta-analysis methods rapidly decreases as an increasing number of unassociated statistics are included, whereas the classical Fisher’s method and its weighted variant (wFisher) exhibit relatively high power that is robust to addition of unassociated statistics. We also propose another robust method based on joint distribution of ordered  Meta-analysis usually increases statistical power because it combines the signals of moderate significance in each study, while false positives are kept controlled. Thus, meta-analysis has been widely applied to medical and genetic research, such as identifying differentially expressed genes in transcriptome analysis, finding causal variants in genome-wide association studies (GWAS), and assessing prognostic features for clinical trials6. Although meta-analysis methods that combine p-values or Z-scores have widely been used before, they were not able to address the effect size and heterogeneity in each study. For instance, even a small effect size in a dataset could lead to a significant p-value if a large sample size was used. Therefore, current meta-analysis makes use of methods that combine effect sizes in most cases. However, methods that combine p-values are still being used in genetic or systems biology study when analyzing low-frequency or rare variants when effect sizes are not available, or when results of different types of experiments need to be combined8.Meta-analysis is used to aggregate summary statistics  denotes the p-value obtained from the experiment with a positive (or negative) effect, and unassociated p-value , the uniformly distributed under the null hypothesis.Here, we propose two p-values in joint distribution of ordered p-values (denoted as ordmeta). The proposed methods use only p-values and their directionalities but outperformed the state-of-the-art meta-analysis methods in detecting incomplete associations for simulated t-tests, and simulated RNA-seq and microarray data. Further, our methods were applied to meta-analysis of prostate cancer microarray data and body mass index (BMI) association data, where they exhibited high biological relevance and were able to detect many genes missed by other meta-analysis methods.We propose a generalized Fisher’s method weighted by sample-sizes (denoted as wFisher). wFisher holds not only the high robustness of the original Fisher’s method, but also the high power of weighted methods. In addition, we propose another robust method that uses the minimum of marginal p-values have been developed thus far. The first one developed by Fisher used 9 (denoted as Fisher’s method). Lancaster generalized Fisher’s method by assigning different weights to degrees of freedom (DFs) of each study10 (denoted as Lancaster’s method). Stouffer further developed the method by using inverse normal distribution (denoted as Z-method)11, and its generalized version that weighted each experiment by sample sizes was also developed12 (denoted as weighted Z-method). The weighted Z-method exhibited increased power compared to other p-value or Z-score combining methods13. A similar high power was observed for Lancaster’s method when each experiment was weighted by sample sizes14. Because of this advantage, weighted methods have been strongly preferred despite the arguments against using weights in meta-analysis15. The high power of weighted methods has been demonstrated with each individual experiment involving identically distributed effect sizes. However, this condition is often not met in real situations because of the heterogeneity of the populations under consideration and the environmental and study-specific factors that lower the quality of data. For example, 25% of tagSNPs identified in European GWAS showed significantly different effect sizes in non-European ancestry population16. We found that the power of the weighted Z- and Lancaster’s methods rapidly decreases as an increasing number of unassociated p-values are combined, whereas the Fisher’s method holds a relatively high power. In particular, we found that the size of DFs assigned to individual experiments have a critical effect on the robustness of methods.More than a dozen methods to combine p-values obtained from n independent experiments where the model to generate p-values are specified correctly. Each p-value is assumed to have uniform distribution on the unit interval . Many existing methods to combine p-values hypothesize that none of the given p-values has an association. Thus, the hypotheses of interest are given as follows:0: none of the given p-values H1: one or more p-values are associated with the phenotype.HLet p-values) have an association to test the statistical power.However, most comparative studies for meta-analysis methods have been conducted using simulated data where all the statistics . Whereas the chi-square distribution uses only the integer DFs, its natural generalization (gamma distribution) accepts non-integer DFs. Our method, dubbed wFisher, exhibited both the high power of Lancaster’s method and the high robustness of Fisher’s method. Recall that Z-score method, the individual p-value p-value to a gamma variable with designated (weighted) DFs. Then, the sum of those gamma variables, which also has a gamma distribution, becomes our test statistic. One difference is that the weights are reflected in DFs in our method, whereas the weights in the Z-score method are applied to the transformed normal variables.We propose to use a gamma distribution to assign non-integer weights to each p-value, p-value (left-tail) is evaluated as follows:The p-value combination method using the joint distribution of ordered p-values. Let rth smallest among n independent p-values. These values follow joint distribution of ordered uniform random variables with beta marginal distribution as follows17:Ordmeta is a novel p-value for each marginal distribution becomes 18 used this marginal distribution of an order statistic to detect differentially expressed genes in meta-analyses of microarray data ). roP method used the marginal distribution of a fixed order (r) and the same distribution is applied to all genes. However, each study has a different group of differentially expressed genes, which means the optimal orders are different between genes. The main problem of roP is that the optimal order for each gene is not known. As will be seen in our simulation tests, the performance of roP largely varies depending on the selected value r. To address this problem, we propose a novel method, ordmeta that selects the smallest marginal p-value and evaluates its own p-value using joint distribution of order statistic. This approach adaptively selects the optimal order for each gene without any prior knowledge and evaluates the corresponding p-value.Let mentclass2pt{minimn marginal p-values.Let The probability p-value is given asThus, the combined p-value for the minimum marginal p-value. In other words, it assesses the positions of each marginal statistic p-values larger than the optimal p-value, which confers the robustness. This optimal value also suggests that the p-values smaller than the optimal one are p1-values.Ordmeta calculates p-values are obtained from two-tailed tests and effect directions are also given, those p-values are halved and synchronized according to the effect direction. In other words, each p-values. The integration is done once more for the complimentary p-values. The smaller of the two combined p-values is selected and multiplied by two for the two-tailed meta-analysis p-value.When individual t-tests similar to those of previous works14 to compare power and false positive control for six p-value combining methods: wFisher, ordmeta, Fisher’s method, Z-method, weighted Z-method, and a Lancaster’s method with p0-values to a given set of p1-values. First, 2, 4, or 8 p1-values were combined and the corresponding powers were compared. Then, p0-values were also combined with the p1-values. A maximum of 30 p0-values were included. Standard normal values were sampled with sample sizes of 30–2000, and one sample t-test was performed for mean values of 0.03–0.1 to generate p1-values. P0-values were sampled from uniform distribution for one of the three ranges: , , and . Each scenario was repeated 1000 times  were generated and combined 100,000 times, and the proportion of results with combined p-values less than 0.05 was measured (Table We simulated entclass1pt{minimahttp://gdac.broadinstitute.org). The mean and dispersion parameters of this dataset were estimated using edgeR package19. Using these parameter values, multiple RNA-seq datasets for 1000 genes were simulated using negative binomial distribution. 10%, 30%, or 60% genes were made differentially expressed (DE) with 1.3 or larger fold changes. See our paper20 for more detailed method to simulate read count data. The count data were then voom-transformed for moderated t-test 21. We simulated twenty RNA-seq datasets, where only two, five, or ten datasets included DE genes (incomplete association) and three different proportions of DE genes  were included in those datasets. Each simulation was repeated ten times and the results were represented as boxplots 23 and fixed effects model (FEM)] were implemented using the metaDE R package24. Because the optimal order r of roP is not known in advance, it was implemented for all the even orders from two to 20 and all those results were combined to compare with other methods. The performance of meta-analysis methods was compared by their area under the receiver operating curve (AUC), true positive rate (TPR), and true false discovery rate (true FDR). The true FDR is the proportion of non-DE genes among the significant genes  and indicates the extent of reliability of the predicted DE genes. We calculated true FDR only when five or more significant genes were detected in each method.The RNA-seq data for TCGA Kidney Renal Clear Cell Carcinoma/normal dataset were downloaded from GDAC  as assessed by MetaQC packageq-value is larger than 0.05 (less than 0.05) for at most one meta-analysis method were regarded as true upregulated DE genes . True and false downregulated genes were obtained similarly. These true and false DE genes served as gold standards to compare the performance of meta-analysis methods.The six datasets with a good quality (“good” datasets) were used to select true and false DE genes. After preprocessing the datasets using MetaDE package, 4436 genes commonly found in all the six datasets were used for meta-analysis. Among them, upregulated genes whose meta-analysis Then, we analyzed all the nine datasets. After preprocessing the data, only 676 out of the 4436 genes remained. These genes included 161 and 234 true and false upregulated genes and 195 and 206 true and false downregulated genes, respectively. We selected two out of the six “good” datasets . For the remaining four “good” datasets and the three “bad” datasets, we permuted the sample labels for each dataset, so that we expect no DE genes in these seven datasets. In other words, our test data represent an incomplete association where only two out of nine datasets include DE genes. We repeated the test for the 15 possible data combinations for both up and downregulation cases comprising 30 instances in total. The performance of the meta-analysis methods was analyzed by comparing the 30 AUCs, TPRs, and true FDRs between methods  was assessed by the enrichment (hypergeometric distribution) p-value and corresponding odds ratio (OR) of gold standard genes within DE genes as follows:We also analyzed the nine original datasets and compared the biological relevance of the meta-analysis results. Because all the REM DE genes were included in the FEM DE genes and Fisher and wFisher DE genes were similar to each other, we compared the results of only three meta-analysis methods  for simplicity. We downloaded the genes that belonged to the “Malignant neoplasm of prostate geneset” (C0376358) from DisGeNET databasep-value combining methods tested in this study. The package automatically imports ‘rSymPy’ package for the symbolic calculation of multiple integrals in ordmeta function. Metapro is available from both CRAN and GitHub (http://github.com/unistbig/metapro). For meta-analysis of RNA-seq and microarray data, we used the R packages metaDE24 and metaQC25. Individual expression dataset was analyzed using moderated t-test followed by meta-analysis pipelines provided by these R packages. MetaDE provides six meta-analysis methods, rankProduct, roP, Stouffer, Fisher, FEM, and REM for analyzing RNA-seq and microarray data. For the meta-analysis of BMI association data, weighted Z-method was implemented using METAL27 (https://genome.sph.umich.edu/wiki/METAL). Fixed and random effects models were implemented using GWAMA28 (https://www.geenivaramu.ee/en/tools/gwama). The random effects model developed by Han and Eskin29 (denoted as RE-HE) was implemented using METASOFT (http://genetics.cs.ucla.edu/meta_jemdoc/). MR-MEGA was implemented using MR-MEGA30 (https://www.geenivaramu.ee/en/tools/mr-mega).We developed an R package ‘metapro’ that implements wFisher and ordmeta methods as well as other p-value or Z-score combining methods . To test the false positive control, 2, 10, 30, and 100 p0-values that were generated from one-tailed t-test with effect size zero and were combined using each method  Fisher and Z-method are quite different methods, although the latter simply uses Z-scores transformed from p-values and (2) wFisher is superior to the original Fisher’s method, both with or without p0-values. Interestingly, another weighted Fisher’s method, Lancaster showed a rapid decline as opposed to Fisher or wFisher. Lancaster uses very large DFs (sample sizes), which make the individual chi-square distributions close to a normal distribution, while Fisher or wFisher use much smaller DFs.Next, the statistical power of the six methods was compared by combining 2, 4, or 8 p-values were included or some large p0-values  were combined. We also tested if ordmeta correctly predicts p1-values using the optimal p-value. For t-tests where half of the p-values were associated, the predicted p1-values exhibited high specificity with median 0.98–1.00 and decent sensitivity with median 0.5–0.6 for 10, 30, and 100 input p-values.In particular, ordmeta exhibited the best power when only a small number of associated p-value information.We compared the performance of two proposed methods (wFisher and ordmeta) and six existing meta-analysis methods for gene expression data by simulating RNA-seq data for the nine different scenarios of incomplete association: three different proportions of DE genes in each dataset  versus three different numbers of associated studies . The area under the receiver operating curves (AUCs) are compared for the nine scenarios in Fig. r = 2, 4, or 6. roP_2 and roP_4 showed a good performance but roP_6 performed much worse. This shows the order parameter r has a critical effect on the performance of roP; however, the appropriate value of r is not known in advance. In summary, wFisher, Fisher and ordmeta outperformed other meta-analysis methods in detecting incomplete associations in both model-based and real data-based simulation tests.The performance of the same eight meta-analysis methods were compared using the nine prostate cancer microarray datasets available from metaDE R package. The true and false DE genes were selected using the six “good” datasets and the incomplete association was simulated by permuting the sample labels of seven datasets as described in “p-values and corresponding ORs , wFisher and ordmeta exhibited even higher significance and ORs , demonstrating increased biological relevance of the DE analysis results. This result was rather expected because the nine datasets included the three “bad” datasets constituting the condition of incomplete association. Thus, we also compared the biological relevance using only the six “good” datasets. The difference between the methods were much reduced as expected, but wFisher and ordmeta still showed better results  compared to FEM . This implies even when only qualified data are used for meta-analysis, each gene can have different association patterns for the individual studies because of the heterogeneity of the populations and other study-specific factors.We then analyzed the original nine microarray datasets to compare biological relevance of the meta-analysis results. The counts of DE genes detected by each of three meta-analysis methods  are shown in Fig. Rs Table . Althoug31 demonstrated that SOD2 takes a role in suppressing prostate tumor by cell cycle interruption and apoptosis. U2AF1 is a splicing factor subunit that showed an association with various cancer types. Cao et al.32 suggested U2AF1 is associated with prostate cancer by regulating androgen receptor variant 7 (ARV7) splicing which is well known to promote proliferation and metastasis of prostate cancer. M6PR was related to apoptosis and proliferation, and differentially expressed in androgen-independent prostate cancer compared with androgen dependent prostate cancer33. IGF1R and ISNR were not included in the C0376358 gene set, but were reported of the oncogenic functions including tumor growth, cell migration and angiogenesis in prostate cancer34.Then, we removed all the meta-analysis DE genes that the analysis of individual study can also detect. This uncovers the true ability of each meta-analysis method to discover new genes Fig. B. As com35) were analyzed for four ethnic groups . The European data were excluded in this test because of its dominant sample size. For the four summary datasets, eight different meta-analysis methods such as ordmeta, wFisher, Fisher, Stouffer, RE-HE, MR-MEGA, fixed and random effects models were applied. The loci with p-value < 2.03E−7  were regarded significant. In total, 27 loci were detected significant by at least one method  was detected significant only by ordmeta, because the third individual p-value 1.85E−03 was significant as assessed by joint distribution of order statistic. This locus was detectable by neither individual study nor any of the existing meta-analysis methods because of the large last p-value of 0.95. The ordmeta p-values for the first two loci were 0 (< 1E−323), because the third individual p-values 1.70E−06 and 1.40E−06 had extremely small marginal p-values. wFisher also detected three loci with the most significant p-values, whereas Fisher’s method detected none. These results indicate that ordmeta and wFisher are complementary to the conventionally used meta-analysis methods , and demonstrate their potential to provide additional findings.The summary statistics of exome-based association study for BMI comprising 246,328 low-frequency and rare variants  p-values were present.We showed in this study that many existing meta-analysis methods, such as random-effects, fixed effects, p-value combining methods are only able to address the significance of association , but not the effect size. However, we demonstrated the usefulness of some p-value combining methods including ours. This does not mean that our methods are more powerful than the conventional methods. Indeed, conventional meta-analysis methods were more powerful than ours for the genes that are associated in most studies under consideration. Therefore, our methods and conventional methods are complimentary to each other, and can be used together to maximize our findings in meta-analysis.The two proposed methods, wFisher or ordmeta surmounted existing meta-analysis methods in detecting incomplete associations through several different simulation tests. In particular, they were able to detect many novel genes that existing methods missed, and also exhibited high biological relevance with or without unassociated data. This implies a substantial number of genes are associated in only a subgroup of studies in meta-analysis. We note that P-value combining methods are generally useful when combining heterogeneous types of data irrespective of the data models and statistical methods applied in each study. We expect the proposed methods will be able to provide many new findings in meta-analyses of gene expression, GWAS, and other genomic and medical data.Besides, it is also an important challenge to integrate the evidences from different types of medical and genomic data where many of them could be unrelated to the disease of interest. Supplementary Figures.Supplementary Table S2.Supplementary Table S1.


Mongolia has the highest incidence of hepatocellular carcinoma (HCC) in the world, but its causative factors and underlying tumor biology remain unknown. Here, we describe molecular characteristics of HCC from 76 Mongolian patients by whole-exome and transcriptome sequencing. We present a comprehensive analysis of mutational signatures, driver genes, and molecular subtypes of Mongolian HCC compared to 373 HCC patients of different races and ethnicities and diverse etiologies. Mongolian HCC consists of prognostic molecular subtypes similar to those found in patients from other areas of Asia, Europe, and North America, as well as other unique subtypes, suggesting the presence of distinct etiologies linked to Mongolian patients. In addition to common driver mutations  frequently found in pan-cancer analysis, Mongolian HCC exhibits unique drivers , the latter of which is associated with hepatitis D viral infection. These results suggest the existence of new molecular mechanisms at play in Mongolian hepatocarcinogenesis. Mongolia has the highest incidence of—and mortality from—hepatocellular carcinoma (HCC) in the world. Here, the authors examine the genomic and transcriptomic landscape of Mongolian HCC, uncover novel driver mutations, and suggest distinct disease etiologies. Hepatocellular carcinoma, the predominant form of liver cancer, has several known risk factors, including chronic hepatitis B virus (HBV) and/or hepatitis C virus (HCV) infection, autoimmune hepatitis, diabetes mellitus, alcohol abuse, obesity, and several metabolic diseases3. Mongolia has the highest reported incidence of—and mortality from—HCC in the world, which is between three and seven times higher than that observed in other high-incidence populations, such as South Korea, Thailand, and China5. In Mongolia, where cancer is the second most common cause of death accounting for nearly a fifth of all deaths, HCC is the most prevalent cancer type accounting for ~40% of all cancers. Besides chronic infection with HBV and/or HCV, present in more than 90% of Mongolian HCC cases7, the etiology of Mongolian HCC may also be related to the extraordinarily high prevalence of hepatitis delta virus (HDV)9, which depends on HBV for its life cycle. Among HBV-infected Mongolian subjects, ~60% were found HDV-coinfected, compared to the ~5% global estimate9. Despite the daunting magnitude of this longstanding health crisis, the molecular landscape of Mongolian HCC has not yet been studied. Our work fills this gap with the first comprehensive and integrative genomic characterization of Mongolian HCC, aiming to identify robust molecular subclasses with underlying unique tumor biology, as well as driver features informative of the etiology and progression of the disease.Liver cancer is the second most common cause of cancer mortality worldwide, with more than 840,000 annual new cases and 780,000 annual deaths recorded globally in recent years11 to uncover molecular subclasses) with a supervised approach (regularized Cox regression12 to find low- vs high-risk groups), followed by validation (mapping13 onto molecular subclasses from previous HCC studies). In order to uncover molecular subtypes of Mongolian HCC, we implemented an unsupervised clustering approach coupled with survival analysis  vs MO1 . This phenomenon was mirrored, albeit with marginal significance, by cirrhosis, which correlates positively with MO4  and negatively with MO1 . Tumor size, which correlates positively with MO3 , appeared as marginally significant . The risk scores are in good agreement with the molecular subclasses defined earlier . Transcriptomics-based gene signatures, either available from the Molecular Signatures Database21 or inferred from gene expression data (“Methods”), are provided in Supplementary Data p = 0.05 threshold level based on Fisher’s exact test. The association between the molecular subclasses from this study, MO1-4, and those from each one of the previous studies considered here, is statistically significant  , here PCA offers a straightforward low-dimensional representation to visualize the somatic mutational burden of many subjects across different cohorts. More specifically, we use PCA to focus on the 2 orthogonal directions of largest variance, PC1 and PC2, in order to uncover overall characteristics of Mongolian HCC compared with TCGA-LIHC’s Asian and Caucasian groups. Mutational frequency patterns are linked to mutation-causing mechanisms at the molecular level, as well as to disease etiologies at the organismal level. Therefore, the relative centroid location and spread of different cohorts provides a high-level view of their relative overall similarity and of their comparative mutational and etiological heterogeneity, respectively. In this representation, the Mongolian and Asian TCGA cohorts display larger spreads than the Caucasian TCGA cohort. The inset shows the centroids of each cohort, where it becomes apparent the Caucasian TCGA’s shift along both PC axes  have not been reported before (Supplementary Data p = 0.015). SPTA1 mutations have been linked to hereditary elliptocytosis and hereditary spherocytosis, a set of congenital hemolytic syndromes35. Although not reported as driver gene in previous HCC studies, SPTA1 was reported as a possible tumor suppressor in Glioblastoma Multiforme36. However, it is unclear how mechanistically α-spectrin contributes to tumorigenesis. GTF2IRD2B belongs to the TFII-I family of general transcription factors that play a role in chromatin structure modification and, consequently, in the regulation of gene expression37. It is plausible that mutations of GTF2IRD2B may lead to the disruption of gene expression regulation, thereby contributing to HCC carcinogenesis. In contrast, a number of genes found to be significantly mutated in other HCC cohorts  do not appear significantly mutated in Mongolian HCC . By comparing the observed HDV± differences to a null model distribution obtained from random permutations of sample labels, significant differences (p < 0.05) are observed in A[A > T]C (G[T > A]T) and G[A > G]T (A[T > C]C) substitutions, which appear in excess in HDV+ tumors, as well as G[G > C]G (C[C > G]C) and A[A > G]T (A[T > C]T) substitutions, which appear in excess in HDV- tumors , tobacco chewing, and exposures to 1,8-Dinitropyrene and furan, whereas HDV- appears differentially associated with aristolochic acid II. Tobacco smoking and HBV infection were found to positively interact in liver carcinogenesis;46 synergistic effects were also reported between tobacco smoking and chronic HCV leading to increased liver fibrosis47 and increased risk of cirrhosis and HCC46. Exposure to aristolochic acids, widely used in traditional Chinese medicine throughout Asia, has been linked to liver cancer48, and a prospective study of HCV-infected patients in Taiwan has very recently reported an association between the intake of herbal medicines containing aristolochic acid and the risk of primary liver cancer49. Therefore, although the etiology of this disease and the role of HDV remain poorly understood, our findings suggest links between exposure to certain carcinogenic agents and HDV status in Mongolian HCC that deserve further investigation.To explore the etiology of Mongolian HCC, we analyzed mutational signatures that consist of frequency patterns along 96 trinucleotides, formed by enumerating all single-nucleotide combinations before and after each one of 6 possible single-nucleotide substitutions50. Indeed, ~90% of solid tumors are aneuploid, ranging from 26% in some tumor types to 99% in others51. In addition to point mutations, our data allow us to characterize other types of genomic aberrations, such as gene fusions, CNVs, SVs, and germline analyses. The inner circle in Fig. 52. These results suggest that NELFE activation may contribute to hepatocarcinogenesis in Mongolian HCC. The inner ring in Fig. 53, we observe very significant gains in chromosome regions 1q and 8q, as well as significant losses in 1p, 4q, and 8p. Figure Chromosomal abnormality is a hallmark of solid malignancies54 and driver genes in the Mongolian cohort. As pointed out earlier, molecular subclasses are characterized by distinct up- and down-regulated pathways at the transcriptome level; correspondingly, each molecular subclass carries a distinct pattern of oncogenic signaling pathway alterations, which highlights the underlying molecular complexity of Mongolian HCC. For example, mutated TP53 has a different impact on tumor-vs-nontumor gene expression for molecular subclass MO3, in which tumor expression appears increased relative to nontumor expression, compared to MO1 and MO4, which exhibit the opposite trend. This ambivalent role of TP53 in tumorigenesis is well documented55 and consistent with our earlier study56 on the role of p53-mediated signaling in HCC. On the other hand, CTNNB1 mutations appear enriched in MO1 with good prognosis, which is consistent with TCGA data16. These results reaffirm several key signaling pathways commonly found during hepatocarcinogenesis, as shown in previous studies34. Remarkably, we also found several unique driver genes  and via clinical assessments based on CT scans and ultrasound diagnosis. Tumoral and adjacent nontumoral liver tissue samples were collected and frozen at −80 °C after surgical resection at the National Cancer Center in Ulaanbaatar, Mongolia. The study was approved by the Ethics Committee at the National Cancer Center in Ulaanbaatar, Mongolia, and written informed consent was obtained from all participants. Cohort details are provided in Supplementary Data Out of 76 subjects in the Mongolian HCC cohort, we obtained paired tumor/nontumor datasets from total RNA sequencing and whole exome sequencing for 65 subjects. For 5 subjects, only total RNA sequencing data were generated for downstream analysis. Similarly, for 6 subjects, only whole-exome sequencing data were generated for downstream analysis.57 (v. 2.0.3), Picard tools (v. 1.119), and RSeQC (v. 2.6.4). Reads were trimmed using Cutadapt58 (v. 1.14) to remove sequencing adapters prior to mapping to the human reference genome hg38 using STAR59 (v. 2.5.2b) in two-pass mode. Across the samples, the median percentage of mapped reads was 95.4%. Expression levels were quantified using RSEM60 (v. 1.3.0) with GENCODE61 annotation (v. 21). Genes with a mean count lower than one transcript were removed and the resulting data were normalized using the voom algorithm62 from the Limma R package63 (v. 3.40.6) for downstream analyses.RNA was extracted from 70 HCC tumors and paired nontumor liver tissues, for a total of 140 samples that were used for total RNA sequencing. Library preparation was performed using the Illumina TruSeq Stranded Total RNA Kit and sequenced across two flowcells on the Illumina NovaSeq platform, which yielded between 69 and 605 million reads per sample. The sequencing quality of the reads was assessed using FastQC (v. 0.11.5), Preseqn = 70) into biologically relevant molecular subclasses, we performed a grid-search-based clustering analysis coupled with survival analysis. Firstly, the most variable genes across tumor samples were selected using different median absolute deviation (MAD) thresholds . Using the R package ConsensusClusterPlus10 (v. 1.48.0), each of these solutions was generated from 1000 iterations, each iteration consisting of a randomized selection of 80% of samples and 80% of features (genes) to avoid overfitting. Each consensus clustering solution is summarized by the pairwise coclustering matrix, which captures the probability for two samples to be clustered together. By defining in = mean pairwise coclustering within a cluster and out = mean pairwise coclustering across clusters, the normalized ratio in/(in + out) was adopted as the objective function to find the optimal solution in a grid search across different MAD thresholds and different numbers of clusters .In order to determine the optimal partition of the RNA-Seq-based cohort   was analyzed by means of a cross-validated elastic net implementation of regularized Cox regression using eNetXplorer12 (v. 1.1.0). Significant pathways, selected based on feature frequency  using GenePattern’s NearestTemplatePrediction module68 (v. 4) and visualized with circlize69 (v. 0.4.8). For TCGA and TIGER-LC cohorts, molecular subclasses were originally obtained by iCluster70, an approach that merges molecular information from multiple sources. Based on the classification of individual subjects and their corresponding gene expression, we inferred transcriptomics-based signatures of up-regulated genes characteristic of each molecular subclass. For other HCC studies, transcriptomics-based signatures were readily available from the Molecular Signatures Database21 (v. 7.0). These gene signatures are provided in Supplementary Data 71 (v. 1.34.0), and glmnet72 (v. 3.0.1).Subjects in the Mongolian cohort were mapped into molecular subclass signatures reported in other HCC studies (TCGAz-score transformation) to both gene expression datasets. Only genes reported in both datasets were used. Following TCGA’s reported procedures16, the IDH-like gene signature was obtained by performing a t test comparison between IDH-mutant vs IDH-WT samples (p < 0.0001). To validate the approach, we used the gene signature to cluster TCGA samples hierarchically; IDH-mutant samples indeed formed a tight cluster, while IDH-like samples with score IDH_P > 0.8 were observed to form an adjacent cluster. By applying the hierarchical clustering procedure to the combined TCGA and Mongolian HCC datasets, we identified a group of 9 Mongolian samples that appeared to carry TCGA’s IDH-like signature  was used for detecting fusion events in the RNA-Seq data for each sample. Events reported with FFPM <0.5 or a split read count <30 were removed from all samples. Fusion gene pairs detected in any normal sample were removed from each tumor sample to produce a set of high confidence driver fusion events. More information about the annotations supplied for each fusion event in Supplementary Data https://github.com/FusionAnnotator/CTAT_HumanFusionLib/wikiSTAR-fusion74 (v. 0.7.17). Mapped reads were then de-duplicated using Picard tools (v. 1.119), followed by re-alignment, and base quality score recalibration was performed using the Genome Analysis Toolkit (GATK)75 (v. 3.8.0). One subject’s samples did not pass quality control and were removed from further analysis, therefore this work utilized whole exome sequencing data for 71 subjects.DNA was extracted from 72 HCC tumors and paired nontumor liver tissues, for a total of 144 samples that were used for library preparation using the Agilent SureSelect Human All Exon v7 exome capture kit and sequenced across three flowcells on the Illumina HiSeq 4000 Platform. Reads were trimmed for adapters and low-quality bases using Trimmomatic software before alignment to the human hg38 reference genome using BWA mapping software76 following the best practices guidelines for exome-seq analysis provided by the GATK authors77. Variants were hard-filtered for quality, annotated with functional and consequence prediction using Ensembl’s Variant Effect Predictor78 (VEP v. 92) and converted to Mutation Annotation Format (MAF) using the vcf2maf tool (v. 1.6.16). MAF files for individual samples were concatenated into a combined MAF file spanning the full cohort for downstream analysis. The combined MAF file was used as an input for MutSigCV22 (v. 1.41) for driver gene analysis. MutSigCV relies on a background model that takes into account mutation abundance, clustering, and site conservation to identify genes that were mutated more often than expected by chance. A benchmarking study79 shows that this model imposes conservative selection criteria and may, therefore, fail to recognize candidate driver genes; moreover, the sample size required for near-comprehensive detection of intermediate-effect driver genes (90% detection and 2% effect size/increase with respect to background) was shown to be >300, i.e., several-fold larger than this study’s cohort size. Therefore, we expanded the driver gene selection criteria to also include frequently mutated genes (>10% of the samples in the Mongolian HCC cohort), although frequently mutated genes found in publicly available exome cohorts, termed FLAGS80, were excluded. Variants annotated with a frequency larger than 0.001 in the ExAC, gnomAD, or 1000 Genomes databases  were also removed. In addition, variants with less than 20x depth in the tumor sample and an alternate allele frequency of less than 5% were removed. Somatic mutation data for the TCGA-LIHC dataset were retrieved using the TCGAmutations (v. 0.2.0) R package, which provides pre-built objects using MAF files from the MC3 working group81. Visualization and summarization were performed using custom scripts in R (v. 3.6.0), primarily utilizing the packages maftools82 (v. 1.8.10) for data summarization, ComplexHeatmap83 (v. 2.1.0) and circlize69 (v. 0.4.8) for generating oncoplots, ribbon plots, and other circular plots, and trackViewer84 (v. 1.44.4) for lollipop plots.Variant calling was performed using Mutect2 in tumor-normal mode85 (sequenza-utils v. 2.2 and sequenza R package v. 3.0), and these were used as inputs for CNV calling using the software package Control-Freec86 (v. 11.5). Regions with significant CNVs reported by Control-Freec were summarized for the cohort by first disjoining these regions into discrete non-overlapping segments. Next, segments were filtered for significance in each sample using the following criteria: Wilcoxon Rank Sum Test p value < 1e–3, Kolmogorov Smirnov p value < 1e–3, and Uncertainty between 0 and 20. Finally, the number of samples with CNVs in each segment were counted. Modified code from the R package svplucnv (v. 0.9.1) was used for summarization and visualization.For each tumor-non-tumor pair, ploidy and purity estimates were computed with Sequenza87 (v. 1.3.0) in paired tumor-non-tumor mode and annotated using AnnotSV88 (v. 1.1.1). These variants were filtered based on the sum of the split and spanning read counts for the mutant allele. SVs with more than 2 split or spanning reads in the nontumor sample, or less than 5 split or spanning reads in the tumor, were removed.Structural variants (SVs) were called using Manta89 in joint genotyping mode. Variants were then filtered for quality with the following criteria: QD < 2.0, FS > 60.0, MQ < 40.0, MQRankSum < −12.5, ReadPosRankSum <−8.0 for SNPs; QD < 2.0, FS > 200.0, ReadPosRankSum < −20.0 for INDELs. Sample relatedness and ancestry were computed using the tool Somalier90 (v. 0.2.9), which is an updated implementation of Peddy91 that analyzes ancestry based on 17,766 common variants across all human populations. In order to visualize genetic similarity, pairwise relatedness values computed by Somalier were transformed as value -> 10 – 10^value, and then used for hierarchical clustering. In addition, genetic distance based on all germline variants found in the Mongolian HCC cohort was analyzed using PLINK92 (v. 1.9.0). To prioritize cancer-related germline variants, we utilized the Cancer Predisposition Sequencing Reporter93 (v. 0.5.1) to analyze 218 manually-curated cancer predisposition genes for known or predicted pathogenic variants.Germline variants were called using GATK’s HaplotypeCaller82 (v. 1.8.10). Reference mutational signatures were obtained from the Catalogue Of Somatic Mutations In Cancer, versions v2 (March 2015)30 and v3 (May 2019)31, as well as from the Compendium of Mutational Signatures of Environmental Agents (May 2019)33. This information was fed into deconstructSigs43 (v. 1.8.0) to generate subject/signature weights from the non-negative least squares mapping of individual samples against the reference signatures. These weights are determined such that the reconstructed tumor sample matrix minimizes a given error threshold43. To reduce false positives, some corrections can be applied to the fitting approach; for example, deconstructSigs uses forward selection to estimate a minimal number of signatures and removes a signature’s contribution to a sample if it accounts for less than 6% of the sample’s mutations. Limitations of deconstructSigs and other mutational signature methods have been discussed and benchmarked elsewhere94. For each mutational signature compendium, the subject/signature weight matrix was obtained; then, signatures with distinct prevalence between HDV+ and HDV− groups were identified by the criterion of p < 0.05 in the Wilcoxon test performed between HDV+ and HDV− weight distributions. Only signatures that passed this selection criterion were selected  was used to export as plain-text a set of ten pan-cancer oncogenic signaling pathway graphical templates derived from TCGA54. Each pathway plain-text template was then modified to contain only genes mutated in Mongolian HCC; for each of these genes, we provided the fraction of mutated samples in each molecular subclass MO1-4 and the sign of the median gene expression log ratio among mutated genes in each subclass. Pathways with none or few mutated genes were removed. Those remaining were individually uploaded into PathwayMapper to generate graphical renditions of mutated gene percentages and activation, exported as graphics, and then merged.The online tool PathwayMapperFurther information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Reporting Summary


Phytophthora sojae infection, economic losses of 10–20 billion U.S. dollars have been annually reported. Studies have revealed that P. sojae works by releasing effect factors such as small RNA in the process of infecting soybeans, but research on the interaction mechanism between plants and fungi at the small RNA level remains vague and unclear. For this reason, studying the resistance mechanism of the hosts after P. sojae invades soybeans has critical theoretical and practical significance for increasing soybean yield. The present article is premised on the high-throughput data published by the National Center of Biotechnology Information (NCBI). We selected 732 sRNA sequences through big data analysis whose expression level increased sharply after soybean was infected by P. sojae and 36 sRNA sequences with massive expression levels newly generated after infection. This article analyzes the resistance mechanism of soybean to P. sojae from two aspects of plant’s own passive stress and active resistance. These 768 sRNA sequences are targeted to soybean mRNA and P. sojae mRNA, and 2,979 and 1,683 targets are obtained, respectively. The PageRank algorithm was used to screen the core functional clusters, and 50 core nodes targeted to soybeans were obtained, which were analyzed for functional enrichment, and 12 KEGG_Pathway and 18 Go(BP) were obtained. The node targeted to P. sojae was subjected to functional enrichment analysis to obtain 11 KEGG_Pathway. The results show that there are multiple Go(BP) and KEGG_Pathway related to soybean growth and defense and reverse resistance of P. sojae. In addition, by comparing the small RNA prediction model of soybean resistance with Phytophthora pathogenicity constructed by the three machine learning methods of random forest, support vector machine, and XGBoost, about the accuracy, precision, recall rate, and F-measure, the results show that the three models have satisfied classification effect. Among the three models, XGBoost had an accuracy rate of 86.98% in the verification set.Since the emergence of the  Soybean is an indispensable high-protein food, healthy vegetable oil, and raw material for essential health products. Widely planted across the globe and having a history spanning 5,000 years of cultivation, soybeans are integral in agricultural products. As a vital basic material related to the national economy and people’s livelihood, soybean is the most economically beneficial crop. Notably, the demand for soybean in China is about 110 million tons per year, while the output of soybeans is about 17 million tons. Thus, increasing soybean production is a significant issue linked to the livelihood of people.Phytophthora sojae is estimated to incur economic losses of more than 1 billion U.S. dollars in soybean production worldwide  public database, the aim of the present article was to obtain soybean sRNA data before and after being infected by P. sojae and blank samples (GSM1370294) for soybeans that were not infected by P. sojae. The data included P. sojae genome data (P. sojae V3.0), soybean genome data (Wm82.gnm1), P. sojae mRNA data (W05.gnm1), and soybean mRNA data (Wm82.gnm1.ann1).In the present article, data were obtained from NCBI for samples with the same culture conditions except for infection, namely, soybean sRNA infected by P. sojae and the blank group sequence not infected by P. sojae. The joint processing process is shown in Since the data downloaded on NCBI are in SRA format, the tools of sratoolkit.2.10.4P. sojae genes should be 18–25 nt, only sRNA sequences of 18–25 nt are retained. Upon removing joints and removing low quality and length control, sequence expression statistics were respectively performed on the blank group soybean sRNA data; and the experimental group sRNA data infected by P. sojae, the specific method, were the data de-duplicated to obtain the sequence type, and the number of each sequence in the statistical data file was the expression level of the sequence. The quantity statistics of the two groups of sRNA are reported in Upon analyzing the data after removing the joints, an observation could be made that the original data had not been operated on length, quality control, etc. The specific length and the sequence number distribution corresponding to each length are presented in P. sojae or pollutants, the present article mapped the sRNA sequence infected by P. sojae to the soybean genome and retained the matched sequence. Subsequently, the obtained sequence was mapped to the P. sojae genome, with unmatched sequences being kept. Here, the present article used bowtie2-2.3.4.1,P. sojae and the fasta file of the blank sample sequence, using the bowtie tool to perform strict matching based on the index package of the first step, setting the mismatch parameter to 0 and keeping all the comparison results output to the SAM file; (3) using SAMtools to convert SAM files to BAM files; (4) performing de-redundancy and keeping matching sequences; (5) converting BAM files to FASTQ files; and (6) repeating the same as above to build a library of P. sojae genes, performing the above operation on the FASTQ data retained in the soybean genome, and retaining the data that do not match the Phytophthora soybean genome.To guarantee that the sRNA data belonged to soybean and not P. sojae infection. Following statistics, after removal of the joints, quality information processing, and length control, but not yet mapping, the sequence types were 1,251,487. There were 1,112,350 kinds of small RNA sequences mapped to the soybean genome. There were 1,091,544 kinds of small RNA sequences that mapped to the soybean genome and could not be mapped to P. sojae. As revealed in The present article was written based on the type of analysis of the small RNA sequence after An observation can be made from P. sojae genome with the blank group. A finding was that 192,283 species existed in the sequence before and after infection, and only 899,261 species existed in the sequence after infection. In terms of the 192,283 species that appeared before and after infection, the present paper assumes that after normalizing the data, the expression level after infection is significantly higher than the expression level before infection; that is, after the multiple increase, the sequence with higher expression level after infection is a positive sample that is integral in the soybean resistance mechanism. The specific calculation method is denoted in The present paper compares the 1,091,544 sRNA sequences that match soybean and do not match the In the present paper, the positive samples were based on both the growth rate and the expression level, and the data after infection and mapping were screened. The first part is the sample shared by the infected group and the blank group that meets the growth rate higher than 10 and the expression level higher than 200. Two conditions resulted in 732 sequences. The difference in expression levels before and after infection is exhibited in P. sojae were respectively predicted. At this point, the Tapir-1.2P. sojae, respectively, using the default parameters of the Tapir tool, namely, mimic = 0, score ≤ 4, mfe_ratio ≥ 0.7. The target gene prediction results were counted after running on the server, with the results indicating that a total of 2,979 types of soybean mRNA and a total of 1,683 types of P. sojae mRNA can be targeted.Predicated on the 768 sequences selected above, in order to find the biological processes and regulatory pathways related to the soybean resistance mechanism, the target genes of soybean and In the present paper, String databaseAmong them, PR(a) refers to the PageRank value of the u-th node, B(a) signifies the set of incoming links of all a, and L(b) indicates the degree of all outgoing links of the v node pointing to the a node.The network – based on the core function clusters – was analyzed again to select the more significant functions further. The core functional cluster regulatory network diagram of the selected 50 nodes is revealed in P. sojae to obtain coexisting data. The sequences with a growth rate of 1 before and after infection were screened from the data, and then the same number of sRNA sequences as the positive samples was randomly selected as negative samples. The comparison chart of positive and negative sample expression is revealed in A total of 768 differentially expressed sRNA sequences in 2.2 were selected as the positive samples in the present article. Then calculations were performed on the intersection of the blank group of soybean data and the experimental group data infected by Under the verified features and the analysis of the current sequence, the present paper proposed a total of 114 features including sequence features  and structural features . Regarding the local base and 3'-end double base and 5'-end double base, the values were A, G, C, U, and N. If the length was less than 25 nt, N was used to substitute in addition to that. Since the features needed in machine learning were digital, one-hot encoding was performed for these sequence features. The specific encoding form is presented in Local base: used to indicate the type of base at positions 1 to 25 in the sequence. The length of the sequence is less than 25 nt, and the insufficient part is filled with N. Sequence length: indicates the length of sRNA; here, it refers to 18–25 nt. GC%: The ratio of the number of occurrences of “G (guanine)” and “C (cytosine)” in the sequence to the total length of the sequence. GC content has a significant effect on the stability and structure of sRNA. MFE: The MFE of sRNA is an important measure of its structural stability. In this paper, the MFE is obtained using the RNAfold tool. Under normal circumstances, this indicator can measure the probability of interaction between RNAs and can be used as a standard to distinguish all coding and non-coding RNAs. 5'-end double base, 3'-end double base: Since DNA transcription and RNA translation have specific sequence and directionality, the first two bases at the 3'-end and 5'-end are used to mark the beginning of various processes. Motif (1–3 nt): small sequence fragments with specific functions that repeatedly appear in the sequence. The number of their appearances can play a certain role in determining the overall feature function of the sequence. Among them, 1 nt has four features, 2 nt has 16 features, and 3 nt has 64 features.After the feature selection was completed, low-variance features were removed, and then subsequent model selection and training were performed. This was because there may have been redundant features in those mentioned above, which would affect the classifier’s prediction effect.Based on sRNA’s characteristics, constructing a predictive model of soybean differentially expressed sRNA can reduce the cost of experimental research. Because this article is a prediction model constructed for high-dimensional, small-sample binary classification problems and does not have too many requirements for time and space complexity, three machine learning models, which are support vector machines (SVMs), random forests, and XGBoost, are used to deal with classification problems.SVM is a binary classification technique based on supervised learning in machine learning. It is called the optimal edge classifier. It has good learning performance and has good performance in the classification of high-dimensional data. The effect is that it is not easy to overfit. It can effectively classify samples with small data volumes, avoid the problem of neural network structure selection and local minimum points, and have been widely used.Random forest is a supervised learning algorithm, it is the most representative of ensemble algorithms, and it shows amazing performance in classification and regression. The basic idea of the algorithm is to construct multiple weak classifier decision trees by randomly extracting samples and features. Only when more than half of the base classifiers make mistakes will they make wrong predictions. It can handle high-dimensional data without feature selection (because the feature subset is randomly selected). Due to random sampling, the trained model has small variance and strong generalization ability and is not easy to overfit.XGBoost is a scalable machine learning system for tree boosting. The system has been widely recognized in a large number of machine learning and data mining challenges due to its outstanding efficiency and high prediction accuracy. The XGBoost algorithm realizes the generation of a weak learner by optimizing the structured loss function , and the XGBoost algorithm does not use the search method but directly uses the loss function and the first-order and second-order derivatives of, and the presorting, weighted quantile and other techniques to improve the performance of the algorithm significantly.Negative Sample Selection section, there were 1,536 positive and negative samples. In the present article, the training set and the validation set were constructed according to the ratio of 3:1. Then, the training set data were employed for model training and parameter adjustment, and the validation set data were applied to evaluate the final trained model.As mentioned in To ensure that all the features had a similar effect on the model, the data set needed to be standardized after the feature extraction, and then the standardized data set was utilized for model training. Because the present article’s model needed to use distance to measure similarity, and individual feature data have outliers, the Z_Score standardization method was adopted to process 203 feature data sets. The specific calculation method is as shown in σ is the standard deviation of all sample data, where μ is the mean of all sample data, The aforementioned divided data set was employed to construct a binary classification model using three methods: random forest, SVM, and XGBoost. The models were adjusted to the optimal parameters through cross-validation, so that each model had the best effect. For a two classifier, the evaluation indicators mainly included accuracy, recall, precision, and F1 value. The accuracy rate was used to calculate the correctly classified samples’ ratio in the classifier to the total samples. The specific calculation method is as exhibited in Evaluating an algorithm model solely on accuracy is far from being scientific and comprehensive. Under normal circumstances, the precision rate, recall and F1 value can better predict skew problems. Accuracy refers to the proportion of correct predictions that are positive to all positive predictions, while recall denotes the proportion of correct predictions that are positive to all actuals that are positive. The F1 value is evaluated by calculating the harmonic mean of precision and recall. The calculation methods for precision, recall, and the F1 value are as presented in In the present article, TP refers to the number of positive samples classified correctly, TN signifies the number of negative samples classified correctly, FN indicates the number of positive samples classified as negative samples, and FP is the number of negative samples classified as positive samples.Feature Extraction section, accuracy was adopted as the main evaluation index, with recall, precision, and F1 being auxiliary evaluation indexes. The parameters of the three models were then accordingly selected. Because there are many parameters to be determined, the present paper utilized grid search combined with cross-validation to determine the optimal parameters. In the training process of SVM, the inner product kernel function could be used to replace the nonlinear mapping to the high-dimensional space. The final decision was only determined by a small number of support vectors. The overall complexity thereof depended on the number of support vectors and was not sensitive to abnormal data. Hence, the choice of the number of support vectors was integral in the overall effect of the model, and the gamma parameter was a parameter of the radial basis function (RBF) kernel function, and the specific value thereof was inversely proportional to the number of support vectors. Thus, in the present article, gamma and model error tolerance C are primarily determined.Since the deviations of the data set in the present article have already been discussed in Multiple base learners jointly determined the results of random forest and XGBoost, and the two parameters n_estimators and max_depth had a greater impact on the effects of these two models. n_estimators refers to the maximum number of base learners that need to be divided, and a compromise is needed to select the optimal number of base learners. This is because normally as the number of base learners increases, the model’s error rate will gradually converge, but the code complexity will gradually increase. max_depth denotes the maximum depth of the decision tree, where generally, the deeper the depth of the tree, the better the fit. Yet overfitting is common. The present article included a large number of features, and the selection of this value needed to be considered. In addition, the random forest also selects the max_features parameter, which indicates the maximum number of features considered when randomly selecting features and is equivalent to a decision tree when equal to the number of features. XGBoost also selects the gamma parameter and subsample parameter. The gamma parameter specifies the minimum loss function drop required for node splitting. The larger the value of this parameter, the more conservative the algorithm. The subsample parameter controls the random sampling ratio of each tree and does not put back sampling. Decrease the value of this parameter, and the algorithm will be more conservative and avoid overfitting. However, if this value is set too small, it may cause underfitting.The specific process for the grid search for optimal parameters was as follows: (1) selecting 1,152 test set sequences as the sample set for parameter optimization; (2) determining the value range of the main parameters to be optimized. For SVM, C was 0.001–100, and gamma was 0.001–100; for the random forest, n_estimators was 1–102, max_depth was 3–14, and max_features was 3–11. For XGBoost, n_estimators was 10–100, max_depth was 2–14, subsample was , and gamma was 0–100. (3) For each model, selecting different parameter values for combination, the specific process being using accuracy as a measurement index, using three-fold cross-validation for training and evaluation, and recording the parameter corresponding to the maximum score. (4) Repeating operations (2) and (3) above and selecting the optimal parameters corresponding to each model.The optimal parameters determined by the grid search were as follows: for the SVM, after selecting the RBF kernel function, the optimal parameters were 0.25 for C and 0.01 for gamma; for the random forest algorithm, the optimal parameters were 91 for the number of base learners, 6 for the maximum depth, and 5 for the maximum number of features; for the XGBoost algorithm, the optimal parameters were 90 for the number of base learners, 2 for the maximum depth, 0.7 for subsample, and 0.001 for gamma.Select the Core Node That Targets the Soybean Itself section were again imported into the String database, and the results reveal that the overall protein-protein interaction (PPI) enrichment p-value of the regulatory network was 1.96e−10. Among them, the p-value of 12 KEGG_Pathway was less than the credibility threshold of 0.05, as shown in In the present paper, these 50 core nodes in Phytophthora sojae can be predominantly divided into three categories: regulating the metabolic level thereof to resist Phytophthora infection, regulating the sRNA production process, and changing the membrane permeability. Among the aforementioned regulatory pathways, the higher-ranking pathways included plant-pathogen interaction , mRNA surveillance pathway , and protein processing in endoplasmic . This indicates that soybean sRNA can indeed directly resist the infection of P. sojae. Further, the phosphoinositide metabolism and phosphatidylinositol signaling system with a false discovery rate of 0.0126 can change cell membranes’ permeability, having crucial effects on the binding of P. sojae to the soybean cell membrane to release sRNA into the soybean cell.The role of soybean after being infected by p-values less than the credible threshold of 0.05, as presented in To further study the specific biological function of soybean for self-regulation, the GO biological function was analyzed in the present paper premised on mature annotation files. Since the target for target gene prediction was inconsistent with the mRNA version in SoyBase, the annotation file(Glyma_11_to_Glyma_20_Correspondence_Full) provided by SoyBase was first used to convert the mRNA version, and GO(BP) enrichment analysis was subsequently performed. The results highlight that there were 18 P. sojae and can regulate the pathway of Phytophthora infection by changing the membrane permeability.From P. sojae, the 1,683 P. sojae mRNA obtained in this paper was first converted into a format recognized by the String database according to the String annotation conversion file . Subsequently, the converted sequence file was imported into the String database, and the overall network PPI enrichment p-value obtained was 1.0e−16. Among them, the p-value of 11 KEGG_Pathway was less than the credible threshold value of 0.05, as shown in To further explore the regulatory effects of differentially expressed soybean sRNA on P. sojae, principally including P. sojae transmembrane transport, nucleotide excision, repair, and transportation processes. A finding was that differentially expressed sRNA can possess a regulatory function in transmembrane transport and the exercise of normal functions of P. sojae. In particular, the pathways directly linked to the entry of soybean sRNA into P. sojae cells through transmembrane transport were inositol phosphate metabolism , phosphatidylinositol signaling system , glycerolipid metabolism , and endocytosis . The pathways that inhibited the function of mRNA through shearing and degradation with soybean sRNA include RNA transport , nucleotide excision repair , RNA degradation , and phagosome . In accordance with the above analysis, an observation can be made from the data that the key soybean disease-resistant sRNA can completely regulate the permeability of the P. sojae membrane and provide an environment therefore to enter the P. sojae of active reverse infection. A further observation can be made that the key soybean disease-resistant sRNA can completely regulate the permeability of the P. sojae membrane and provide an environment to enter the active reverse infection of P. sojae. On the other hand, the key sRNA of soybean disease resistance can completely affect the pathogenicity of P. sojae by removing and degrading the mRNA of P. sojae.As can be observed from P. sojae endocytosis, mRNA shearing and translation, and transmembrane transport. These processes are the central processes of soybean sRNA transboundary regulation of P. sojae. This chapter verifies that the selected 768 differentially expressed sRNAs are the key sRNAs for soybean disease resistance.The results indicate that the differentially expressed soybean sRNA can regulate soybean metabolism, ligase, translation factor activity, transmembrane transport, and other biological processes related to disease resistance. Further, said sRNA can regulate the processes of In the vein of comparing the above three models’ classification effects, 5-fold cross-validation was performed on the training set for the models with the selected optimal parameters, and statistics were calculated on the accuracy, recall, precision, and F1 value thereof, as exhibited in The yellow, blue, and green lines in the above figure’s upper right label correspond to random forest, SVM, and XGBoost, respectively. The four subgraphs respectively represent the accuracy, recall, precision, and F1 value of the above algorithm for five cross-validations on the training set. As the above figure demonstrates, XGBoost and SVM performed well in all aspects.The reason is that based on support vectors, SVM can avoid the complexity of high-dimensional space and can effectively classify small data samples. Both the number of samples and the number of sample features are suitable for this data set. The disadvantage is that it is not effective for multi-classification problems and large sample data, but the data in this article are a small sample data and a two-classification problem. Random forest builds multiple classifier decision trees by randomly extracting samples and features. Only when more than half of the base classifiers have errors will they make incorrect predictions and handle high-dimensional data well. The disadvantage is that it will overfit in some noisy classification or regression problems, but the data in this article do not match it. It may not produce a good classification for small data or low-dimensional data (data with fewer features). Since this article is a small data sample, the effect will be relatively poor. XGBoost uses parallel optimization on feature granularity, adds a regularization term to the objective function to prevent overfitting, and draws on the advantages of random forest support column sampling, which not only reduces overfitting but also reduces the complexity of calculation. At the same time, XGBoost can perform the next iteration process after completing one iteration; that is, the subsequent iteration process contains the predicted value of the previous iteration process, so it has a better classification effect for the data set of this article. The disadvantage is that the space complexity of the presorting process is too high and the execution speed is slower than random forest (bagging), but this article does not have too many requirements for these.Receiver operating characteristic-area under the curve (ROC-AUC) was adopted to evaluate the model further. The ROC curve used the true-positive rate (TPR) as the ordinate and the false-positive rate (FPR) as the abscissa. The TPR is the recall rate mentioned above. The calculation method of the FPR is shown in In terms of the ROC curve, the  point in the upper left corner, that is, the point where the FPR was 0 and the TPR was 1, indicates that FN was 0 and FP was 1, implying that all samples were correctly classified. The  point in the lower left corner, specifically, the point where the FPR was 0 and the TPR was 0 implies that FP was 0 and TP was 0, indicating that the classifier predicted all samples as negative samples. In parallel, the  point in the upper right corner reveals that the classifier predicted all samples as positive samples. The  point in the lower right corner, that is, the point where the FPR was 1 and the TPR was 0, means that the classifier misclassified all samples, that is, predicted all positive samples as negative samples and predicted all negative samples as positive samples. On the diagonal, FP = TN and TP = FN, namely, random classification. That is, the closer the overall trend was to the  point, the more superior the model effect. In many cases, the ROC curve does not clearly indicate which classifier performs better. For this reason, the area under the ROC curve and the coordinate axis were used, in other words, AUC as the evaluation criterion, to compare the models more accurately. P. sojae sRNA mapped to the P. sojae genome after infection and has reference significance for the selection of differentially expressed sRNAs in plants infected by other types of fungi.From the above figure, a finding can be made that the areas under the three machine learning models’ curves were relatively large, the curves were relatively smooth overall, and no overfitting phenomenon occurred. In the present paper, XGBoost with the highest AUC value was selected as the final classifier. Meanwhile, the classifier also performed well in terms of accuracy and recall. The accuracy thereof on the verification set was 86.98%, and the AUC value was 0.9465. In the training set, the AUC value was 0.9805, implying that the overall effect of the model was better. Hence, the model can also predict the differential expression of In terms of differentially expressed sRNAs, because of the long running time, only TAPIR was utilized as a tool to predict the target genes thereof, upon which functional enrichment analysis was directly performed on these target genes. For plant-related target gene tools, psRNATarget and TAPIR are more commonly used. Concerning other target gene prediction software, the algorithms therein are different. Only TAPIR was adopted in the present article, meaning that the results obtained may not be complete. A better prediction effect may be produced if a variety of related software is employed.Phytophthora sojae. The present research has laid the foundation for the potential of other plants to resist fungal infection. Further, when various plants resist fungal infection, there are certain similarities between the biological processes and regulatory pathways thereof that are worthy of discussion and research. If the connection between these processes can be discovered, this will provide crucial theoretical basis and new control ideas for the control of plant diseases.The present study found that the differentially expressed sRNAs of soybean can regulate multiple disease-resistant and growth-related biological processes of soybean itself and the core processes of multiple P. sojae infection. This provides theoretical guidance for the conduct of biological experiments and reduces experimental costs. Be that as it may, whether this model is applicable to other plants and fungi-based resistance analysis based on sRNA remains undetermined and requires further study. With sufficient data, the key sRNAs for plant antifungal disease can be predicted premised on the data analysis and model construction methods proposed in the present article to test the model’s universality.XGBoost was selected as the final classifier in the present paper, which has a good predictive effect for predicting the correlation between unknown soybean sRNA sequence and resistance to Phytophthora pathogenicity. There are numerous models in machine learning that can also classify samples, using more models potentially finding better prediction results.The present article utilized only random forest, SVM, and XGBoost, which are the three methods to construct a sRNA prediction model for soybean resistance to The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.LD and YL conceived and directed the project. JC and SS obtained the raw data and interpreted the data. LD, JC, SS, HaZ, and HeZ conducted the data analysis and interpreted the results. JC and SS helped to design the study and reviewed the data. LD, JC, and HaZ wrote and/or edited the manuscript. All the authors have drafted and reviewed the manuscript and approved it for publication.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.