Quantitative comparison of epigenomic data across multiple cell types or experimental conditions is a promising way to understand the biological functions of epigenetic modifications. However, differences in sequencing depth and signal-to-noise ratios in the data from different experiments can hinder our ability to identify real biological variation from raw epigenomic data. Proper normalization is required prior to data analysis to gain meaningful insights. Most existing methods for data normalization standardize signals by rescaling either background regions or peak regions, assuming that the same scale factor is applicable to both background and peak regions. While such methods adjust for differences in sequencing depths, they do not address differences in the signal-to-noise ratios across different experiments. We developed a new data normalization method, called S3norm, that normalizes the sequencing depths and signal-to-noise ratios across different data sets simultaneously by a monotonic nonlinear transformation. We show empirically that the epigenomic data normalized by our method, compared to existing methods, can better capture real biological variation, such as impact on gene expression regulation. Epigenetic features of chromatin, such as histone modifications, transcription factor binding and nuclease accessibility, play important roles in the regulation of gene expression. Advances in biochemical enrichment strategies and high-throughput sequencing technologies have made it possible to determine the landscape of epigenetic features at a genome-wide scale. In recent years, a large collection of genome-wide epigenetic profiles have been acquired in many cell types under different biological contexts . QuantitA commonly used strategy for data normalization is to calculate a scale factor between two data sets ,9, for eSome normalization methods focus on adjusting SNRs across data sets . MAnorm To illustrate the aforementioned issues encountered by the existing methods Figure , we applSimultaneously normalize the Signal in peak regions and the Signal in background regions of epigenomic data sets. Unlike TSnorm or MAnorm, in which either background regions or common peak regions contribute to normalization, our method matches both the mean signals in the common peak regions and the non-zero mean signals in the common background regions between two data , which includes eight epigenetic marks  in twenty hematopoietic cell types of mice  signal of all data sets for each genome position.For the reference data set, we choose the data set with the best SNR as the reference. In the S3norm package, users also have options to choose another data set The common peak regions and the common background regions for S3norm to learn the non-linear transformation model were defined by using FDR = 0.1 as the threshold. As shown in Using these common peak regions, we also tested our assumption that epigenetic events shared by multiple cell types tend to regulate processes occurring in all those cell types, and thus the epigenetic datasets should have similar mean signals. We found the genes associated (by proximity) with the ATAC-seq peaks shared by all 18 cell types in this analysis were significantly enriched in GO terms about nuclear processes common to all cells, such as chromatin assembly, DNA replication, and RNA metabolism . Thus, uTo facilitate use in downstream analysis, such as peak calling and genome segmentation, we generated signal tracks (bedgraph format) of the S3norm normalized signals. A script for generating these signal tracks is provided at GitHub. We followed a similar method as the one adopted in MACS  except tFor ChIP-seq, there are usually two data sets for each experiment, one is referred as an immunoprecipitation (IP) sample, which is a data set generated by sequencing the DNA after immunoprecipitation by target-specific antibody, and the other one is the corresponding control sample, which is another data set generated by sequencing either the input DNA without immunoprecipitation or the DNA after immunoprecipitation by non-specific antibody.P-value. The The NB background model was defined as follows. Let Just like we avoided bins with values of zero in the S3norm normalization (previous section), we also only used the non-zero data to estimate the p and the 10P-value of read count per bin, as derived from the NB background model, is used as the processed signal in the S3norm signal track.Let NM1 and NM2 denote the non-zero mean and non-zero mean of square of the read counts of all bins, respectively. The p and the lows see  details:et al. . The predictability of gene expression was measured by mean square error (MSE) between the observed gene expression and the predicted gene expression in the testing genes in the testing cell type. To prevent a bias from a specific regression model, we performed this analysis by using four different commonly used regression models, specifically a local regression (loess) model, the two-step linear regression model  of normalized reads count based on the previously described NB background model. We then used the bdgpeakcall in the MACS2 package in the default setting to call peaks from the signal tracks. The threshold was FDR = 1e–2. For each normalization method, the CTCF peaks were first called in 11 cell types that have CTCF occupancy data in VISION project. We used the UpSet method  in these CTCF peak calling results, we compared both the proportion of peaks with a CTCF binding site motif (Jaspar id: MA0139.1)  and the The signal consistency between the biological replicates was measured by the mean square error (MSE) between the two biological replicates.10P-value based on the dynamic NB background model for each 200 bp bin and used them as the input signal for IDEAS. The IDEAS algorithm learned the most commonly occurring states of ATAC-seq signal strength and assigned each 200 bp bin along the genome into one of those signal strength states. For this analysis, we kept the biological replicates separate so that the consistency of the state assignments between the two biological replicates could be used to evaluate the performance of the results. We used the Adjusted Rand Index (ARIs) to measure the consistency of the state assignment between each pair of two biological replicates.To compare the impact of normalization by QTnorm and S3norm  in a conboth the mean signal in the common peak regions and the non-zero mean signal in the common background regions between a target data set and a reference data set, which is the data set with the best SNR  cells (∼92 million reads for replicate 1 and ∼74 million reads for replicate 2) and LSK cells (∼53 million reads for replicate 1 and ∼59 million reads for replicate 2) because they have quite different SNRs  as the reference. The signals in the common peak regions showed substantial differences both between replicates and across all data sets after TSnorm Figure , illustrWhen properly normalized, histone modification data should enable a more accurate prediction of gene expression than achieved with inadequately normalized data. Thus, we evaluated the effectiveness of different normalizations by analyzing the ability of histone modification data normalized by each method to accurately predict gene expression. Modeling approaches have been used to predict gene expression from histone modifications, and the quantitative relationships learned from one cell type can be applied to predict gene expression in other cell types ,44. The Training Genes) to train four commonly used regression models . We first trained the regression models using the Training Genes in one cell type (Training Cell type) and then evaluated the models using the Testing Genes in both Training Cell type and a different cell type (Testing Cell type).Specifically, we randomly selected 90% of genes  than the models trained on the TSnorm signals and MAnorm signals. This result shows that the quantitative relationships learned from the histone modification signals normalized by QTnorm and S3norm transferred well to other cell types, thus indicating a more biologically meaningful normalization result.Then we evaluated whether the learned quantitative relationships can be applied to different cell types by comparing the performances of the trained models on the The previous evaluations showed the superior performance of S3norm and QTnorm than some other normalization methods. Both methods match the signal in peak regions and background regions simultaneously. However, QTnorm differs from S3norm in its assumption that normalized signals have the same distribution across data sets. This assumption is particularly questionable for epigenomic data, because the number of epigenetic peaks usually differs substantially across cell types. If two data sets have different numbers of peaks, QTnorm would force some background signals in the data set with fewer peaks to match the peak signals with the same rank in the data set with more peaks, potentially creating false positive peaks. Therefore, we evaluated the impact of each normalization method on identifying more meaningful peaks in ChIP-seq and ATAC-seq datasets.In this evaluation, we first called peaks on CTCF ChIP-seq data from the VISION project using the signal normalized by different methods. We first compared the number of peaks overlapping between sets by using the UpSet method Figure . As expeWe then used the presence of matches to the CTCF binding site motif to evaluate whether the CTCF peaks found only after normalization by QTnorm were false positives or a reflection of greater sensitivity enabled by that normalization. Given that about 80% of previously determined CTCF binding sites contain the CTCF motif ,46, we ecis-Regulatory Elements (cCREs) predicted in an independent ENCODE SCREEN project  to deteriorate over time. In such cases, one should not use any of the data sets in the time course as a reference for S3norm normalization. Instead, a spike-in control or a small number of unchanged peak regions identified by other techniques should be paired with the background regions at each time point in order for S3norm to work properly.In summary, S3norm is a simple and robust method to normalize multiple data sets. The results of applying S3norm to epigenomic data sets show that it is more effective in bringing out real biological differences than existing methods. As more epigenomic data continue to be generated, S3norm will be useful to normalize signals across these diverse and heterogeneous epigenomic data sets to allow downstream analyses to capture true epigenetic changes rather than technical bias. Improved normalization will aide studies that analyze data sets across multiple experiments, such as differential gene regulation, genome segmentation ,19, joinP-value converted signals, and signals from S3norm are available both for download and for viewing from the VISION website (http://usevision.org). The S3norm normalization package is available at GitHub (https://github.com/guanjue/S3norm).Files for raw signals, gkaa105_Supplemental_FileClick here for additional data file.


Our findings suggest that integrative analysis can lead to substantial relative bias in the estimate of association between predicted expression and the trait. The average estimate of association ranged from 51.3% less than to 96.7% greater than the true value for the biased sampling scenarios considered, while the average error was − 2.7% for the unbiased scenario. The corresponding 95% confidence interval coverage rate ranged from 46.4% to 69.5% under biased sampling, and was equal to 75% for the unbiased scenario. Inverse probability weighting with observed and estimated weights is applied as one corrective measure and appears to reduce the bias and improve coverage. These results highlight a critical need to address selection bias in integrative analysis and to use caution in interpreting findings in the presence of different sampling mechanisms between groups.Integration of independent data resources across -omics platforms offers transformative opportunity for novel clinical and biological discoveries. However, application of emerging analytic methods in the context of selection bias represents a noteworthy and pervasive challenge. We hypothesize that combining differentially selected samples for integrated transcriptome analysis will lead to bias in the estimated association between predicted expression and the trait. Our results are based on  Many of these approaches involve leveraging and combining transcriptome-wide association studies (TWAS) and independent raw or summary-level genome-wide association studies (GWAS) data in a unified analysis framework. While the theoretical underpinnings of these approaches may be sound, their application to existing data resources requires careful consideration of the defining clinical and demographic characteristics of the cohorts being integrated.The rapid emergence of large and publicly-available data resources across -omics platforms has fueled exponential interest in integrative analysis methods. These approaches involve combining data collected across independent groups of individuals to identify novel biological and clinical relationships. For example, there is an emerging literature on methods for unraveling the causal mechanisms of genetic associations with complex traits, and more specifically, for characterizing the mediating role of cell and tissue-specific gene expression in genetic associations, e.g.in silico case study in which data are derived from four established cohorts, namely: (1) Genome-Tissue Expression (GTEx) project cohort9 and independently generated data from (2) the National Health and Nutrition Examination Survey (NHANES)10, a population-based cohort; (3) the Chronic Renal Insufficiency Cohort (CRIC)11, an example “sick” cohort; and (4) the Genetics of Niacin and Endotoxemia (GENE) study cohort12, a representative “healthy” cohort.We hypothesize that integrating data arising from two dissimilar populations can lead to substantial bias in estimates of association. In the application of two-stage least squares to transcriptomics data, an increasingly popular approach for this setting, this bias manifests in the estimates of association between genotype and expression and, in turn, the estimates of association between predicted expression and the trait. We evaluate the magnitude and direction of bias through an The GTEx project data are derived from a convenience sample of cadavers, and as such, the anthropometric traits and related adipose gene expression may not be representative of a general US population, nor a chronic disease or very healthy population. The NHANES data, on the other hand, are a national representative sample of the general US population and therefore a referent population sample that is expected to be specifically generalizable. The CRIC data are additionally considered in our evaluation as a population-level disease sample enriched for multiple chronic diseases  that are common in the US and therefore of specific interest in complex disease gene discovery. Finally, the GENE data are derived from highly selected healthy and young volunteers and are of relevance in considering physiology and expression of optimal healthy states. These cohorts are chosen to reveal the range of potential bias in the estimates of association that can result from integrative analysis that disregards the heterogeneity between samples of individuals drawn from different populations.15 and also a known marker for multiple complex diseases and all cause mortality, e.g.20. Herein, BMI is used both as a surrogate measure of dissimilarly between cohorts and the primary phenotype under investigation. That is, we consider the setting in which the goal of analysis is to evaluate the mediating role of gene expression on the association between genotype and BMI through combining two independent samples. Our study investigates how the results of this analysis vary depending on the distributions of BMI in the two populations from which the independent samples are drawn. Simulations are performed to reflect known genotype-transcriptome and transcriptome-BMI associations as well as observed BMI distributions across established cohorts.A focus of this case study is the distribution of body mass index (BMI), a well-characterized quantitative trait with established heritability21. Also referred to as biased sampling and ascertainment bias in some contexts, selection bias can result from the sampling mechanism systematically favoring features related to both the exposure and the outcome22. The impact of biases on -omics investigations is beginning to receive attention23, and an increasing body of literature exists on the resulting lack of transportability of GWAS findings25. Given the extensive and broad-based integration of GTEx cohort data in analysis pipelines – at present over 1100 PubMed Central citations – closer investigation into the possible implications of biased sampling in the generation of this cohort is warranted. Herein we consider the impact of selection bias on analysis involving integration of two independent data resources – specifically, the use of reference transciptome data to elucidate the biological mechanisms underpinning genetic associations with a quantitative trait, as described for example in26.Selection bias refers to the situation in which the sampling mechanism results in an altered relationship between exposure and outcome37. These methods have been applied extensively and to a broad range of settings, including to address bias in the analysis of autopsy data38; however, to our knowledge, IPW has not been applied to integrated transcriptome analysis. While IPW as applied in our example appears to partially mitigate the bias observed in this setting, further work is needed to identify an optimal strategy. Our research aims to highlight this need by raising question about the validity of reported findings from application of integrative strategies without careful consideration of the representative-ness of cohorts across data resources.Inverse probability weighting (IPW) is applied (with known and unknown weights) as one potential corrective measure for this setting. IPW and covariate adjustments are well-described as the preferred approaches for addressing selection bias, e.g.41 and limited to individuals age 21 to 70 for consistency across the GTEx, CRIC and NHANES cohorts. The estimated distributions of BMI by cohort and sex for White/non-Hispanic individuals are given in Fig. kg/m2 equal to 51.0% and 49.7%, respectively. The GTEx cohort appears to be somewhat “healthier” than the NHANES cohort with a tighter BMI distribution and a slightly lower median value in both men and women. The distribution of BMI in the GENE cohort, with 72.5% and 67.6% of women and men, respectively, in the 18.5–24.9 kg/m2 range, is reflective of the relatively healthy group of young individuals selected for this study. Although sample sizes are limited, the results within Black/non-Hispanic women and men are consistent with these findings (Supplement Table SThe distributions of BMI across cohorts are described and compared as one marker to indicate whether the cohorts were derived from similar populations. These results are stratified by sex and race/ethnicity because of the established modifying role of sex and race/ethnicity in genetic associations with BMIKolmogorov-Smirnov (KS) and Wilcoxon rank sum (RS) tests are used to compare the distributions of BMI for each cohort to the NHANES cohort. The NHANES cohort is chosen as the referent group for these tests as it is a nationally representative sample, and therefore, a statistically significant test between a given cohort and NHANES suggests that the cohort is drawn from a population that differs from the general US population. P-values corresponding to each test stratified by sex are given in Table The impact of biased sampling on integrated transcriptome analysis is evaluated through a simulation study that draws directly from the observed distributions of BMI across the four cohorts in Table 42 is applied to evaluate the relationship between genetically regulated gene expression and a phenotype. The 2SLS approach is described for this setting in26 and summarized in Fig. A standard two-stage least squares 2SLS) approachSLS appro44. Results for all three biased sampling scenarios are included for completeness although the results are expected to be similar as these are the first stage modeling results. In all cases, the model intercept as well as both SNP level coefficients considered are biased downward. As shown in Fig. The results of the first stage analysis based on 2,000 simulations are provided in Fig. The results of the second stage analysis are given in Table 8; however, this result indicates that selective sampling in the context of integrated analysis can further reduce coverage substantially.Coverage defined as the percentage of simulations in which the 95% confidence interval for the expression-trait association parameter covers the true population level value, is estimated to be: 75.0%, 69.5%, 63.1% and 46.4% for the four respective sampling scenarios. Coverage is expected to be less than 95% even under settings of no sampling bias (RS), as 2SLS is based on single mean imputation, as described for example in27 in the first-stage model fitting procedure using known and estimated sampling weights (see Methods) using data simulated according to scenario 2 in silico simulations that mirror characteristics of established cohorts arising from a range of different populations revealed the potential for systemic bias that can result from selective sampling. Importantly, as seen in the case studies, the magnitude of bias relative to the true parameter value for association between expression and a trait can be large, and the direction of this bias can be positive or negative, depending on the specific sampling mechanism. This degree of bias represents a grave matter, particularly in light of growing concerns over reproducibility and generalizability in genomics research. In practice, for most integrated -omics analysis involving independent datasets, the sampling mechanisms are not known although, in many cases, it will be clear that the populations from which the samples arise are dissimilar. Inverse probability weighting is one well-established approach used in single cohort analysis to address bias and our application suggests that it partially mitigates the errors introduced by selective sampling. In the case that the sampling weights are unknown, they can be estimated using observed covariate values. The precision of these estimates will impact the degree to which IPW attenuates the impact of sampling bias. Overall, this research suggest that judicious approaches to address pervasive sampling biases are critically required to ensure validity and generalizability of transcriptome association findings based on integrative analysis.In summary, integrative analysis approaches that leverage independent data resources are increasingly popular as they offer substantial opportunity for novel discovery using existing datasets. However, consideration of fundamental design principles relating to sampling is imperative to ensure the validity and reproducibility of findings in this context. In the present investigation, 10 are designed to assess the health and nutritional status of adults and children in the United States through a combination of interviews and physical examinations. We use the NHANES 2015-2016 Demographics and Examination Data (https://wwwn.cdc.gov/nchs/nhanes/) with a combined dataset of n = 9544 individuals aged 0 to 80 years . The median age is 27 [IQR = ] and the median BMI is 25.20 kg/m2 [IQR = ]. We limit this sample to include only White/non-Hispanic and Black/non-Hispanic individuals aged 21 − 70 years for consistency with the GTEx project data (see below). The NHANES data are used as the basis for our simulation as it is a sample that is expected to be representative of the general US population.The Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) data9 is an established and comprehensive public resource that includes whole genome sequencing (WGS) and cell and tissue-specific gene expression across 54 non-diseased sites. Our analysis is based on publicly available dbGaP Accession phs00424.v7.p2 data . The GTEx cohort is composed of n = 752 post-mortem donors aged 20 to 70 years . The median age is 56 [IQR = ] and the median body mass index (BMI) is 27.26 kg/m2 [IQR = ]. Information on exclusion criteria are described in9. Clinical conditions of relatively high prevalence in this cohort as compared to the US Adult population include Chronic Respiratory Disease or Chronic Lower Respiratory Disease (19.0%), Cerebrovascular Disease (9.6%), Ischemic Heart Disease (19.2%), Hypertension (55.5%), Renal Failure (13.4%) and Diabetes mellitus type II (21.8%). The heterogeneity of this cohort is further evidenced by the presence of conditions ranging from Schizophrenia (2.7%) and Major Depression (8.5%) to Arthritis (8.7%) and Pneumonia (9.6%). Individuals missing ethnicity (44.7%) are excluded from analysis.The Genotype-Tissue Expression (GTEx) projecthttps://repository.niddk.nih.gov/studies/cric/). The cohort used for analysis is composed of n = 3939 individuals with CRI aged 21 to 75 years . The median age is 59 years [IQR = ], n = 1908 (48.4%) have diabetes mellitus, and the median BMI is 30.87 kg/m2 [IQR = ]. We again limit analysis to individuals aged 21−70 years for consistency.The Chronic Renal Insufficiency Cohort (CRIC) Study is an ongoing observational study to characterize risk factors for progression of chronic kidney disease (CKD) and cardiovascular disease (CVD) among individuals with chronic renal insufficiency (CRI) ] and the median BMI is 23.32 kg/m2 [IQR = ].The Genetics of Evoked Response to Niacin and Endotoxemia (GENE) study is an NIH-sponsored investigation of the genomics of inflammatory and metabolic responses during low-grade endotoxemiayi = γ0 + xiγ1 + ϵi and xi = f(zi) + δi, where ϵi⊥δi, yi is a quantitative trait, xi is cell or tissue-specific expression for a single gene, and zi is a vector of SNPs for individual i = 1, …, n. We let f(zi) be a linear function given by α0 + zi1α1 + zi2α2 with additive effects of each of two SNPs and minor allele frequencies of 0.20. Parameter values are selected to result in an average expression equal to the mean whole blood (WB) interleukin 1β (IL-1β) μ1 = 10.39) and a distribution of natural log BMI that is consistent with the observed distribution for White/non-Hispanic women in the NHANES cohort (μ2 = 3.36): α1 = α2 = 0.06; α0 = μ1 − 0.4*(α1 + α2) = 10.23; γ1 = 0.15; σδ = 1.6; and σϵ = 0.065. A population of size n = 100, 000 is generated.Data are simulated according to a composite model of association given by n = 2250 observations are sampled and randomly divided into two groups with n1 = 750 and n2 = 1500. For the biased sampling analysis (scenarios 2−4), n1 observations are sampled from the population with replacement using individual level sampling probabilities given by wi is defined as the proportion of GTEx observations that fall in the same decile of BMI distribution in the population as individual i. Additionally, for scenarios 3 and 4, n2 observations are sampled from CRIC and GENE respectively using similarly defined weights based on the corresponding cohorts. IPW is applied to scenario 2 using inverse probability weights for each individual equal to 1∕pi where pi is as defined above. IPW-known uses the true values of pi used in sampling while IPW-estimated uses estimated values pi based on predicted BMI. Estimates are derived by first fitting a model for BMI in the observed NHANES data with age and weight as predictor variables. A predicted BMI for each individuals in the GTEx data is then calculated based on the observed age and weight in GTEx and the fitted model from the NHANES data. Finally, inverse probability weight estimates are defined as above where predicted BMI values are used in place of observed values.For the unbiased analysis (scenario 1), https://www.r-project.org/). Code and associated documentation is available at: https://github.com/andrea-foulkes/twas-transport.All analyses were performed using R version 3.5.2 (Supplementary Information.


P < 0.01). In the training phase, these candidate CpGs were further narrowed down to a 15‐CpG‐based signature by conducting least absolute shrinkage and selector operator (LASSO) Cox regression in the training set. In the validation phase, the 15‐CpG‐based signature was verified using two different internal sets and one external validation set. Furthermore, a nomogram comprising the CpG‐based signature and TNM stage was generated to predict the 1‐, 3‐ and 5‐year overall survival in the primary set, and it showed excellent performance in the three validation sets . This study showed that our nomogram has a precise predictive effect on the prognosis of TNBC and can potentially be implemented for clinical treatment and diagnosis.DNA methylation is an important biological regulatory mechanism that changes gene expression without altering the DNA sequence. Increasing studies have revealed that DNA methylation data play a vital role in the field of oncology. However, the methylation site signature in triple‐negative breast cancer (TNBC) remains unknown. In our research, we analysed 158 TNBC samples and 98 noncancerous samples from The Cancer Genome Atlas (TCGA) in three phases. In the discovery phase, 86 CpGs were identified by univariate Cox proportional hazards regression (CPHR) analyses to be significantly correlated with overall survival ( There is an increasing incidence of BC throughout the world, and it has become the main cause of mortality and morbidity in females.DNA methylation is an important biological regulatory mechanism that changes gene expression without altering the DNA sequence.However, to the best of our knowledge, few studies have investigated the prognostic value of methylation sites in TNBC. Therefore, this study was designed to investigate overall survival (OS)‐related CpGs in TNBC. First, we selected 86 candidate CpGs in 120 training samples; we then validated those CpGs in 38 testing samples and 37 external validation samples. Finally, a nomogram incorporating the prognostic risk model and clinicopathological features was developed. Overall, our nomogram has a precise predictive effect on the prognosis of TNBC and may potentially be implemented for clinical treatment and diagnosis.22.1https://gdc‐portal.nci.nih.gov/). A total of 164 TNBC samples and 98 normal breast samples with methylation data (Platform: Illumina Infinium Human Methylation 450), mRNA‐Seq data (Platform: Illumina HiSeq 2000 RNA sequencing) and clinical information were downloaded for further analysis. Baseline clinicopathological data, including age, sex, race, menopausal status, margin status and American Joint Committee on Cancer (AJCC) stage, were derived from TCGA clinical data. It is worth noting that the 50‐gene signature test (PAM50)Breast cancer datasets, including BC methylation, mRNA expression profiles and clinical information, were downloaded from The Cancer Genome Atlas (TCGA)  both methylation level and survival data were available; (b) OS time was more than 1 month; and (c) histologically confirmed invasive TNBC. A total of 158 TNBC samples with complete survival information were obtained. Following the methods of random sampling at a ratio of 70:30, the 158 TNBC samples were separated into the training set (n = 120) and test set (n = 38). To avoid the reduction in statistical test efficiency and the bias caused by the direct exclusion of missing values, we used multivariate interpolation to estimate the missing values2 fold change (FC)| ≥ 0 were defined as differentially methylated sites (DMSs). Then, univariate Cox proportional hazards regression (CPHR) analysis was performed to select significant DMSs correlated with OS. Finally, the 86 DMSs most related to OS with P < 0.01 were selected for least absolute shrinkage and selector operator (LASSO) Cox regression in the training set to narrow down the candidate CpGs using the R package glmnet.Three phases were used to investigate the OS‐related methylation sites in TNBC patients. In the discovery phase, LIMMA package has been used to carry out normalization and the Mann–Whitney U‐test was used to compare methylation differences between the TNBC and normal samples, sites with DNA methylation levels with false discovery rate (FDR) <0.05 and |logThe risk score was calculated as follows:15 methylation sites were found to have nonzero coefficients in the model, and the optimal cut‐off value of −11.6 was derived from the time‐dependent receiver operating characteristic (ROC) curve using the Youden index. The samples with a risk score greater than −11.6 were divided into the high‐risk group, and the remaining samples were divided into the low‐risk group. Finally, the 15‐CpG‐based clinicopathological nomogram was built according to the results of the multivariate Cox analyses.In the validation phase, we validated our nomogram in three different cohorts. The area under the curve (AUC) based on the time‐dependent ROC analysis2.3According to the risk scores based on the 15‐CpG signature, 158 TNBC samples were divided into high‐ and low‐risk groups. The limma and DESeq2 R packages were used for differentially expressed gene selection. Genes with were recognized as significantly differentially expressed. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed by the clusterProfiler R/Bioconductor package.2.4U‐test and the Pearson chi‐square test were performed to compare the associations of continuous and categorical variables, respectively, between the training set and testing set. Univariate and multivariate CPHR analyses were used to identify the predominant prognostic factors of OS (P < 0.05). Kaplan–Meier survival curves were compared using the log‐rank test. The limma R package was used to nominalize the data, and the DESeq2 R package was used to identify differentially expressed genes. The ggplot2 R package was used to plot the volcano plot and heat map. P < 0.05 (two‐sided) was considered statistically significant.All statistical analyses were conducted using R version 3.6.1. The Mann–Whitney 33.1P > 0.05). The detailed baseline clinical features of the TNBC and normal samples are also shown in Table Our research flow chart is shown in Figure . Six samples were excluded because the OS time was <30 days. Therefore, a total of 158 TNBC samples from 888 BC samples from TCGA were included. The primary cohort was divided into a training set and a testing set at a ratio of 70:30 with the method of random sampling. The detailed baseline clinical features of the training and testing sets are shown in Table 3.22FC| > 0) were identified (Appendix P < 0.01)  (Appendix P = 0.029) (Appendix P = 0.007) , T3‐T4 stage (P = 0.00076), N0 stage (P = 0.033), N1‐N3 stage (P < 0.0001), AJCC I‐II stage (P < 0.0001) and AJCC III‐IV stage (P < 0.0001) patients  in the different groups were found . The differentially expressed genes were selected by the limma R package. Hierarchical clustering analysis was used to show the expression levels of the genes most related to the risk scores as heatmaps Figure . A totald Figure . The resd Figure . To estid Figure . The ove4P < 0.001). These epigenetic markers may play a key role in diagnosing BC.In the era of precision medicine, a method based on molecular markers to accurately predict the survival of patients with cancer is urgently needed. A previous study showed that methylation data play an important role in the prognosis of BC patients.Different researchers have identified distinct markers, indicating that there are no comprehensive biomarkers to predict the prognosis of BC. Moreover, different subtypes of BC have different methylation profilesIn our research, DNA methylation data and survival data were acquired from TCGA to build a novel CpG‐based prognostic model. First, 86 CpGs were found to be significantly associated with OS by using univariate CPHR analysis. Subsequently, these OS‐related CpGs were narrowed down to 15 candidates by LASSO Cox regression analysis in the training set. Eventually, the 15‐CpG signature was validated in three internal sets and an external set. Our data identified that patients with TNBC can be separated into two groups with high or low‐risk scores based on the 15‐CpG signature, and the patients with high‐risk scores were significantly correlated with poor prognosis.To build the best prognostic nomogram, some clinical characteristics were also analysed. In our study, only N stage, M stage and AJCC stage were significantly associated with OS. To avoid the occurrence of multicollinearity,The occurrence of human diseases is caused by a complex regulatory network. Multisite methylation as a biomarker is more specific and sensitive than single‐site methylation. In this study, a total of fifteen methylation sites were identified as a prognostic signature, which corresponded to 11 protein‐coding genes . Previous studies have identified that CDK14 and ANKRD9 are correlated with cancer. CDK14 is located on chromosome 7 and participates in the occurrence and development of various malignancies, including hepatocellular carcinoma,Some candidate markers, such as IRX3, PDX1 and SCN2A, have not been found to be associated with tumours. However, their reported functions are similar according to our GO and KEGG pathway analyses. IRX3 is located on chromosome 16 and plays multiple roles in the pattern formation of vertebrate embryos.Our results also identified 191 genes that displayed significant differential expression (FDR < 0.05 and |log2FC| ≥ 1) between the two groups divided by risk score. In the GO and KEGG pathway analyses, these genes were mainly enriched in biological processes such as the regulation of catecholamine secretion and transport.There are limitations in our study. First, it has been reported that the prevalence of TNBC is different in different races.In summary, we built a nomogram including the 15‐CpG signature and AJCC stage to predict prognosis in TNBC patients. The performance of the nomogram was verified in different validation sets. Therefore, our nomogram may potentially be implemented to predict the prognosis of patients with TNBC.On behalf of all authors, the corresponding author states that there is no conflict of interest.Yang Peng: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Software (lead); Visualization (lead). Lin Shui: Data curation ; Formal analysis ; Funding acquisition ; Software ; Visualization . Jian Xie: Methodology ; Project administration . Shengchun Liu: Funding acquisition (supporting); Resources (supporting); Writing‐review & editing (supporting).Fig S1Click here for additional data file.Tab S1Click here for additional data file.Tab S2Click here for additional data file.Tab S3Click here for additional data file.Tab S4Click here for additional data file.Tab S5Click here for additional data file.App S1Click here for additional data file.App S2Click here for additional data file.App S3Click here for additional data file.App S4Click here for additional data file.App S5Click here for additional data file.App S6Click here for additional data file.App S7Click here for additional data file.App S8Click here for additional data file.


Neisseria meningitidis is a major cause of meningitis and septicaemia. A MenB vaccine (4CMenB) was licensed by the European Medicines Agency in January 2013. Here we describe the blood transcriptome and proteome following infant immunisations with or without concomitant 4CMenB, to gain insight into the molecular mechanisms underlying post‐vaccination reactogenicity and immunogenicity. Infants were randomised to receive control immunisations (PCV13 and DTaP‐IPV‐Hib) with or without 4CMenB at 2 and 4 months of age. Blood gene expression and plasma proteins were measured prior to, then 4 h, 24 h, 3 days or 7 days post‐vaccination. 4CMenB vaccination was associated with increased expression of ENTPD7 and increased concentrations of 4 plasma proteins: CRP, G‐CSF, IL‐1RA and IL‐6. Post‐vaccination fever was associated with increased expression of SELL, involved in neutrophil recruitment. A murine model dissecting the vaccine components found the concomitant regimen to be associated with increased gene perturbation compared with 4CMenB vaccine alone with enhancement of pathways such as interleukin‐3, ‐5 and GM‐CSF signalling. Finally, we present transcriptomic profiles predictive of immunological and febrile responses following 4CMenB vaccine. A randomised clinical trial evaluates transcriptomic and proteomic profiles following infant concomitant 4CMenB vaccination, compared with control vaccines alone. A novel framework is provided for both understanding and predicting vaccine immunogenicity and reactogenicity. Neisseria meningitidis is estimated to cause 0.5–1.2 million infections and 50,000–135,000 deaths worldwide each year  infants completed this study Fig . All infCD177, a neutrophil‐specific gene involved in neutrophil activation, was the leading contributor to (i.e. correlate with) PC 2  analysis of blood transcriptome RNA‐sequencing data from all study time points showed clustering of early (< 24 h) post‐vaccination samples on the first and second PCs Fig . AnalysiPC 2 Fig . Full blPC 2 Fig . The neuIn this study, the greatest number of differentially expressed genes  compared with baseline (pre‐vaccination at 4 months), when the two vaccine groups (test and control) were combined, was seen 24 h post‐vaccination . Moreover, when all DEGs in either study vaccine regimen were considered, they generally showed similar regulation  were significantly differentially regulated (FDR < 0.05) in infants who received 4CMenB compared with those who received the control vaccines alone  differing between vaccine treatments are displayed in Fig While most transcriptional regulation was similar in both the study vaccine arms, three genes  at 24 h post‐vaccination Dataset . MoreoveSELL) was statistically significantly differentially regulated (FDR < 0.05) between febrile and afebrile infants, 4 h post‐vaccination   with observed MenB‐specific SBA titres in an test set of individuals  > 0.7 on both training and withheld test set Dataset . The modrile Fig . We alsorile Fig . The topIn order to validate genes significantly differentially regulated 24 h post‐immunisation in infants, we proceeded to immunise mice with control vaccines, on their own or in combination with 4CMenB (test group), or with 4CMenB only and performed RNA‐sequencing on RNA derived from the peripheral blood taken at 24 h after the second dose. Mice in the test group exhibited greater perturbation of the transcriptome (695 DEGs vs PBS controls), compared with those receiving the 4CMenB vaccine alone (268 DEGs vs PBS controls) or control vaccines alone  and pro‐inflammatory signalling pathway components were differentially regulated between the 4CMenB‐containing combinations and the control vaccines Fig . PathwayCell deconvolution analysis was performed with these samples to infer the composition of immune cells from whole blood gene signatures , contained within the OMV component of the 4CMenB vaccine, has been identified as a potential source of vaccine reactogenicity  is upregulated, 4 h after vaccination, in peripheral blood from infants who experience fever within 24 h of vaccination compared with those who remained afebrile. The upregulation of SELL suggests that post‐vaccination fever may be associated with enhanced leucocyte extravasation and neutrophil recruitment to the site of inflammation (i.e. vaccination site)  was more common in infants vaccinated with 4CMenB, given concomitantly with control (PCV13 + DTaP‐IPV‐Hib) immunisations, than in infants who received control immunisations alone (Gossger ENTPD7 (24 h), IGHG3 (day 7) and IGLV9‐49 (day 7). The first of these, ENTPD7, was significantly upregulated 24 h post‐vaccination in infants who received 4CMenB compared with infants who received the control vaccines alone. ENTPD7 encodes an ectonucleotidase, which is an ATP hydrolysing enzyme. Extracellular ATP modulates multiple immune cell functions, via purinergic receptors, and is tightly regulated by ectonucleotidases  in their regulation: et al, et al, We observed a raised CRP 24 h post‐vaccination in both the test  and control , compared with the normal reference range for this plasma protein (0–5 mg/l). Moreover, CRP levels were higher in the test group than the control group, 24 h post‐vaccination (FDR = 0.01). Importantly, an elevated CRP (> 5mg/l) was common 24 h post‐vaccination—CRP levels and neutrophil counts are commonly used as diagnostic markers in suspected sepsis—these findings needs to be taken into consideration when assessing febrile infants following vaccination , correcting for neutrophil counts did not remove the difference in expression seen between the concomitant 4CMenB group and the control group  , may ameliorate the reactogenicity of the OMV‐containing vaccines  has been found to be upregulated similarly in mice immunised with native and lpxL1 mutant OMV‐containing vaccines, suggesting that non‐endotoxin pyrogens, such as peptidoglycan, may also contribute to OMV reactogenicity , we observed similar trends in gene expression profiles between the species  but when given concomitantly with other childhood vaccines, fever rates are increased  at 7 days post‐vaccination  of the most important contributors to the baseline gene signature predictive of MenB‐specific SBA titres have also been shown to be differentially expressed in monocytes stimulated with LPS  conducted by the Oxford Vaccine Group, University of Oxford. One hundred and eighty‐seven healthy infants aged 8–12 weeks who had not yet received their routine infant immunisations were enrolled. Infants were randomly assigned (1:1) to a control group . Ethnicity is a factor that is known to influence baseline gene expression characteristics, and contemporary gene expression analysis methods frequently exclude data from different ethnic groups (to reduce data heterogeneity) from downstream analysis   performed the full blood counts (FBC).The clinical biochemistry laboratory  performed the C‐reactive protein (CRP) assessment using spectrophotometry.The body temperature of participants at the time of their 4‐month immunisations was recorded by using a wireless continuous temperature monitoring system (iButton®). The iButton® is a non‐invasive device for human skin temperature measurements, that has the capacity to record a temperature reading each minute over a 24 h period. In this study, the iButton® was applied to the infant's abdomen and used to record temperatures in the first 24 h post‐vaccination. Fever was defined as any iButton® recording of ≥ 38°C within the first 24 h of vaccination. The chi‐square test was used to compare the iButton® fever rates between the control vaccine and 4CMenB + control vaccine groups. Time to first fever in both groups was illustrated using Kaplan–Meier failure curves, and the log‐rank test was used to compare the groups. In the transcriptomic analysis, only iButton® datasets with < 90 min of missing data were considered “complete” and included in the reactogenicity analysis (to minimise misclassification).et al, E. coli LPS  in alum , ultrapure E. coli LPS  in alum, E. coli peptidoglycan  adsorbed on alum, combination of alum‐adsorbed peptidoglycan and LPS, alum only or no immunisations at all  was collected into a reduced volume PAXgene™ RNA stabilisation reagent . Total RNA was extracted using the Blood RNA Kit , using the automated protocol . The ribodepleted and globin depleted fraction were selected from the total RNA using Ribo‐Zero™ Gold rRNA removal Kit . RNA was converted to cDNA, second‐strand cDNA synthesis incorporated dUTP. The cDNA was end‐repaired, A‐tailed and adapter‐ligated and prior to amplification, samples underwent uridine digestion (to ensure strand‐specific sequencing). The prepared libraries were size selected, multiplexed and quality‐controlled before 100bp paired‐end sequencing (HiSeq4000). The multiplexing blocking strategy is available with the raw sequencing data . Sequencing was conducted at the Wellcome Trust Centre for Human Genetics .Homo sapiens) genome build GRCh38 (https://ccb.jhu.edu/software/hisat2/index.shtml), using HISAT2 (version 2.0.5) . To remove genes with very low counts across most libraries, only genes with an abundance of more than three counts per million in nine or more samples were carried forward. Genes assigned a “gene biotype” of ribosomal RNA (rRNA), sex chromosome genes, mitochondrial RNA or haemoglobin were excluded from downstream analysis. Human leucocyte antigen typing of RNA‐sequencing data using HISAT‐genotype (version 1.0.1‐beta) was used to check correct pairing of pre‐ and post‐vaccination samples  files were aligned against the whole human . The Human cytokine/chemokine panel (cat # HCYTOMAG‐60K‐23C) and human CVD panel 3 (cat # HCVD3MAG‐67K) were used to measure the following plasma proteins: L‐selectin, epidermal growth factor (EGF), transforming growth factor alpha (TGF‐α), granulocyte colony‐stimulating factor (G‐CSF), granulocyte–macrophage colony‐stimulating factor (GM‐CSF), fractalkine (FKN), interferon‐γ (IFNγ), GRO, interleukin‐1α (IL‐1α), interleukin‐1β (IL‐1β), interleukin‐2 (IL‐2), interleukin‐3 (IL‐3), interleukin‐4 (IL‐4), interleukin‐5 (IL‐5), interleukin‐6 (IL‐6), interleukin‐8 (IL‐8), interleukin‐10 (IL‐10), interleukin‐13 (IL‐13), interleukin‐17A (IL‐17A), interleukin‐1 receptor antagonist (IL‐1RA), IP‐10/CXCL10, tumour necrosis factor alpha (TNF‐α) and soluble CD‐40 ligand (sCD40L).Samples and standard curves were run in duplicate. Mean fluorescence intensity (MFI) was read on a MagPix®  instrument. MFI was converted to concentration based on standard curves via the xPonent version 4.2 software  using default settings (logistic 5P weighted curve). Average concentrations were calculated for samples run in duplicate. A two‐sample Wilcoxon rank sum test was applied to compare post‐vaccination plasma protein levels between the concomitant 4CMenB and control vaccine groups.M. musculus) GRCm38 reference assembly and annotation  using STAR v2.6 . Samples were depleted of α‐ and β‐globin messenger RNA (mRNA) transcripts using a GLOBINclear™ Mouse/Rat Kit, . Polyadenylated mRNA transcripts were selected by oligo (dT) beads, uridine digested, converted to complementary DNA, amplified and labelled using a TotalPrepTM‐96 RNA Amplification Kit . The prepared libraries were size selected for 75 bp fragments and multiplexed before paired‐end sequencing using an Illumina HiSeq4000 , at the Wellcome Trust Centre for Human Genetics . Reads were aligned to the mouse  employing XGR, using the functional categories of Gene Ontologies   value; statistical testing for module expression was evaluated using the “tmodCERNOtest” function, which is a non‐parametric test working on gene ranks  within the training dataset were removed from both datasets, which were then scaled and centred. SVR was used to tune model parameters, using 25 bootstrapped iterations of the training dataset with log10 MenB SBA titres as the outcome measure, utilising the R package “caret”  MenB SBA titres based on pre‐vaccination gene expression profiles. For this purpose, we utilised a radial basis function kernel support vector regression (SVR), a support vector machine (SVM) that is appropriate for regression analysis .AppendixClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Dataset EV6Click here for additional data file.Dataset EV7Click here for additional data file.Dataset EV8Click here for additional data file.Dataset EV9Click here for additional data file.Dataset EV10Click here for additional data file.Dataset EV11Click here for additional data file.Dataset EV12Click here for additional data file.Dataset EV13Click here for additional data file.Dataset EV14Click here for additional data file.Dataset EV15Click here for additional data file.Dataset EV16Click here for additional data file.Review Process FileClick here for additional data file.


TP53 gene mutations are very common in human cancer. While such mutations abrogate the tumor suppressive activities of the wild‐type (wt) p53 protein, some of them also endow the mutant (mut) protein with oncogenic gain of function (GOF), facilitating cancer progression. Yet, p53 may acquire altered functionality even without being mutated; in particular, experiments with cultured cells revealed that wtp53 can be rewired to adopt mut‐like features in response to growth factors or cancer‐mimicking genetic manipulations. To assess whether such rewiring also occurs in human tumors, we interrogated gene expression profiles and pathway deregulation patterns in the METABRIC breast cancer (BC) dataset as a function of TP53 gene mutation status. Harnessing the power of machine learning, we optimized a gene expression classifier for ER+Her2‐ patients that distinguishes tumors carrying TP53 mutations from those retaining wt TP53. Interestingly, a small subset of wt TP53 tumors displayed gene expression and pathway deregulation patterns markedly similar to those of TP53‐mutated tumors. Moreover, similar to TP53‐mutated tumors, these ‘pseudomutant’ cases displayed a signature for enhanced proliferation and had worse prognosis than typical wtp53 tumors. Notably, these tumors revealed upregulation of genes which, in BC cell lines, were reported to be positively regulated by p53 GOF mutants. Thus, such tumors may benefit from mut p53‐associated activities without having to accrue TP53 mutations. In ER+Her2‐ breast cancer (BC), p53 tumor suppressor gene mutations are associated with bad prognosis, relative to tumors that retain wild‐type (wt) p53. Applying machine learning to a large BC dataset reveals a subset of ER+Her2‐ tumors (pseudomutant p53 tumors) which, despite retaining wt p53, display gene expression and pathway deregulation profiles typical of p53‐mutated tumors, correlating with bad prognosis. BCbreast cancerCorrcorrelationFNfalse negativeFPfalse positiveFSWforward selection wrapperGLgene‐probe listsGOFgain of functionGRgene‐probe rankingLSVMlinear support vector machineMLmachine learningmutmutantNGSnext generation sequencingPDSpathway deregulation scorePMpseudomutantROCreceiver operating characteristicSSSanger sequencingwtwild‐type1et al., et al., One out of eight women is likely to develop breast cancer (BC) during her lifetime  p53 protein with oncogenic gain of function (GOF)  label of either wt or mut p53. False‐positive (FP) samples, which are labeled as mut even though they are wt, are designated as PM.2.2et al., TP53 status (see Data TP53 mutation status was from sequencing: from next generation sequencing (NGS) when available . Samples that were suspected to harbor germline mutations (n = 15) were excluded from the analysis , are summarized in Table Probe‐level expression data from the METABRIC dataset , whereas the Her2+ and ER‐Her2‐ groups exhibit the opposite misbalance; this also motivated us to focus on the ER+Her2‐ subtype. Analyses that try to compensate the learning algorithm for imbalanced training sets introduce several uncontrolled parameters. To avoid this, we used learning processes that do not correct for imbalance. In the ER+Her2‐ group, lack of compensation for the prevalence of wt samples gives rise to a conservative classifier, with a bias toward a wt call. Hence, we have high confidence in the classification of those wt TP53 samples whose transcriptome deviated so strongly from wt behavior, that a mut call was generated; confidence in identification of such ‘false positives’(FP) is essential for fulfilling the aim of this analysis. With this said, we also ran learning processes that did compensate for sample misbalance   were retained. For each iteration and each GL/GR combination, we generated a list of the top‐ranked N ≤ 300 probes. Ranking features is a notoriously unstable process—two slightly different training sets may produce very different lists of top‐ranked genes  used to fit these data must be reduced drastically to avoid overfitting. We started from two gene‐probe lists (GL): (a) knowledge‐based, of p53‐related genes . For each k, we required to have at least C = 70 valid classifiers, for which the classification error was calculated for the training set and for the validation set, otherwise we stop at k‐1. Finally, the training and validation errors were averaged over the valid classifiers and plotted as a function of the number of probes used. These plots, together with considerations of robustness, were then used to determine the optimal learning process  and k*, the number of probes used, to yield the optimal set of subclassifiers.Step 4 ‐ Model selection: For every iteration, each learning process  was applied using the top‐ranked Notably, the total number of classifiers that were constructed is on the order of 2×4×3×300×100 = 720,000 .k* probes of each of the 70 ≤ C ≤ 100 subclassifiers to produce C predicted p53 labels; the majority vote of these constitutes the final deduced p53 status.Step 5: The final master classifier uses, for a given sample, the expression levels of the Step 6: testing the master classifier. The samples of the test set were submitted to the master classifier and the test error was calculated. Tumors identified as mut are referred to as ‘Positive’; the error is the ratio of /.: FP samples from the test set were labeled PM. For the learning set, we relied only on misclassification of the validation samples, using the following rule: identify the iterations in which a wt sample was assigned to the validation set; if in the majority of these iterations, it was classified as mut—it is labeled PM.Step 7: identification of the PM samples2.5et al., TP53 ER+Her2‐ samples, for which expression was measured using RNA‐seq. The manner in which the TCGA data were handled to account for different representations of genes in TCGA and METABRIC is described in Data The classifier derived on the basis of METABRIC data was tested on TCGA BC data , a Pathway Deregulation Score (PDS), as described , gene ranking (GR), and ML, 100 iterations were performed. Classifiers based on k gene probes were constructed by adding one probe at a time, according to their ranking. Note that for ER+Her2‐, we stopped at k = 138 probes; beyond this, the number of valid classifiers was < 70. For each k, GL, GR, and ML combination, the average and standard deviation of the error  over the 100 iterations was stored. These error curves, together with considerations of their robustness  plotted as a function of gene‐probe number k, and the mean receiver operating characteristic (ROC) curves of the optimal classifiers, are presented in Fig. k* =62 probes, representing, on average, 49.3 ± 0.1 genes (mean ± SEM); this yielded a mean ± SEM validation error of 0.116 ± 0.002 , our classifier is strongly biased toward wt calls and against mut calls; as a result, out of the 51 mut TP53 test samples, 22 were falsely classified as wt . When the imbalance is corrected, this error rate is reduced to 12/51. Appendix P‐values of 0.043 and 0.1464 (hypergeometric test) for the final classifier and the imbalance‐corrected classifier, respectively, raising the interesting possibility that, in some tumors, mutp53 proteins retain partial wtp53 activity.The final wt/mutp53 call, for a previously unseen sample , is obtained as a majority vote of the 100 subclassifiers Data . The tesTP53 training samples), so that in total 44 out of 1122 (about 4%) wt TP53 ER+Her2‐ tumors were identified as PM . Altogether, we found 30 such additional cases  was then applied Fig. . RemarkaTP53 by both methods. Only for four was there a discrepancy; they were called wt by NGS but mut by SS. Of the remainder 10 wt TP53 cases, eight were sequenced only by NGS and two only by SS . Thus, at least by PDS assessment, the PM tumors appear to behave as even ‘more mutant’ than true mutants.Next, we looked for pathways displaying significantly different mean PDS in PM vs the rest of the wt les Fig. . Intereset al., GRB2, were significantly differentially expressed. Growth Factor Receptor Bound Protein 2 (GRB2) is an adaptor protein that links growth factor receptors to the Ras signaling pathway. As seen in Fig. Deregulation of cancer‐related pathways can cause wtp53 to adopt mut‐like behavior (Furth 3.6P = 1.5 × 10−5) in the PM group than in the wtp53 group, resembling those of mutp53 samples . Thus, additional mechanisms are likely to contribute to the PM behavior. These might involve deregulation of cancer‐relevant signaling pathways; for example, it was previously shown that deregulation of the Hippo pathway can alter the functionality of wtp53  gene‐probes representing 24,369 genes, for 1928 tumor and 144 normal samples.Click here for additional data file.Appendix S2. TP53 labels, the mutation types and breast cancer subtypes , protein change, survival and treatment information and clinical parameters.Click here for additional data file.Appendix S3. List of probes used for each of the final 100 subclassifiers.Click here for additional data file.Appendix S4. List of False Negative cases.Click here for additional data file.Appendix S5. Mutations identified in other sequenced genes.Click here for additional data file.Appendix S6. List of main genes and gene probes used in the classifier for each breast cancer subtype.Click here for additional data file.Data S1. Pre‐processing.Data S2. Imbalanced Learning.Data S3. Forward Selection Wrapper and Orthogonal Matching Pursuit.Data S4. Robustness measurements for both genes and lists.Data S5. TCGA pre‐processing.Data S6. Results of the learning process for ER‐Her2‐ and for Her2+.Data S7. Additional error curves for ER+Her2‐ tumors.Data S8. Additional information about pseudomutant classification for ER+Her2‐ samples.Data S9. Comparison of the number of ER+Her2‐ pseudomutant (PM) samples using varying combinations of ML, GL and GR methods.Data S10. Discrepancy between NGS and Sanger sequencing for TP53 mutations for the PM samples.Data S11. Comparison of the expression of the main gene‐probes, used by the final classifier, between the wtp53 and mutp53 groups.Table S1. Samples suspected to have germline mutations (n = 15).Table S2. Robustness of the final model’s gene lists (ER+Her2‐ using p53 related genes). The mean and std of the intersection and the total number of genes appearing among the 100 lists of top 62 ranked probes are displayed.Table S3. The final constellation of ML*, GR*, GL* and k* for ER‐Her2‐ and Her2+and the corresponding error rates.Table S4. The number of ER+Her2‐ samples that were falsely classified at least once during the training phase and the validation phase of the final classifiers. The numbers of mut and wt samples, and percentage of mutants at the training stages (without test data), are shown in the three right columns.Table S5. PM samples (from ER+Her2‐) that were recognized by the final classifier both METABRIC and TCGA datasets.Table S6. Chosen parameters for the final imbalance‐corrected model for ER+Her2‐ and its corresponding errors , with the number of models having all required genes in the TCGA (out of the 100 subclassifiers).Table S7. Discrepancies between NGS and Sanger sequencing for the 44 pseudomutant (PM) samples.Table S8. About 4% incidence of PM tumors was obtained independently of the GL, GR and ML methods used. The number of FP samples and the percentage of the FP group, revealed using 6 different combinations of ML, GL GR is presented below for ER+Her2‐ subtype. The first line displays the results of the final classifier that was used in the paper.Fig. S1. Number of appearances of each gene amongst the 62 top ranked probes, chosen from the p53 related gene list to be used in at least one final subclassifier, during the 100 iterations for ER+Her2‐ tumors.Fig. S2. Accuracy and error rates for the chosen models for ER‐Her2‐ and Her2+tumors.Fig. S3. Additional error rates for ER+Her2‐ tumors, each based on 100 iterations (and therefore 100 error rates).Fig. S4. The number of wtp53 ER+Her2‐ samples from the validation set, which were misclassified as false positive (mutp53 classification) at least once (n = 81).Fig. S5. Mean expression of the main gene probes (see SI appendix 10) used by the final classifier for predicting p53 state, for ER+Her2‐ subtype in METABRIC (panels A&B) and TCGA (panels C&D) cohorts, shown for the PM, wt and mutp53 groups.Fig. S6. Mean expression of the main gene probes (see SI appendix S10) used by the final classifier for predicting p53 state, for ER‐Her2‐ subtype in METABRIC (panels A&B) and TCGA (panels C&D) cohorts, for the wt and mutp53 groups.Fig. S7. Mean expression of the main gene probes (see SI appendix 10) used by the final classifier for predicting p53 state, for Her2+subtype in both METABRIC (panels A&B) and TCGA (panels C&D) cohorts, for the wt and mutp53 groups.Fig. S8. Comparison of TP53 mRNA expression in the different groups of ER+Her2‐ tumors and in normal human breast tissue.Fig. S9. ER+Her2‐ PM patients tend to present with a higher number of positive lymph nodes than wtp53 patients and have a lower probability for relapse‐free survival.Click here for additional data file.


In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets, in which the number of features is more than 100,000 whereas the number of samples is as small as about 100, hence constituting a typical large p small n problem. The proposed TD-based unsupervised FE outperformed other conventional supervised feature selection methods, random forest, categorical regression , penalized linear discriminant analysis, and two unsupervised methods, multiple non-negative matrix factorization and principal component analysis (PCA) based unsupervised FE when applied to synthetic datasets and four methods other than PCA based unsupervised FE when applied to multiomics datasets. The genes selected by TD-based unsupervised FE were enriched in genes known to be related to tissues and transcription factors measured. TD-based unsupervised FE was demonstrated to be not only the superior feature selection method but also the method that can select biologically reliable genes. To our knowledge, this is the first study in which TD-based unsupervised FE has been successfully applied to the integration of this variety of multiomics measurements.The large  For these cases, the number of instances is typically smaller than the number of features [p small n” problem has relatively rarely been investigated comprehensively, although there are many studies to tackle “large p small n” problem [The term “big data” often indicates a high number of instances as well as features ,2. Typicfeatures ,10,11. I problem ,13,14,15p small n” problem is relatively rare and not as important. Nevertheless, dependent on the domains, the “large p small n” problem will inevitably become typical. For example, in genomic science, the typical number of features is as many as or more than that of genes -based unsupervised feature extraction (FE) [m-mode tensor is associated with more than two suffix whereas matrix is associated with two suffix, row and column. In this method, a smaller number of representative features, referred to as singular value vectors, are generated with linear combinations of the original large number of features, without considering labeling. These singular value vectors are attributed to both samples and original features. We have investigated the singular value vectors attributed to samples. Finally, the selected singular value vectors attributed to the original features are used to evaluate the importance of the original features; because the singular value vectors are the linear combinations of original features, their coefficients of linear combinations can be used to evaluate the importance of individual original features. The original features with larger absolute values of coefficients are selected. As a result, we can avoid many difficulties in the “large p small n” problem; for example, we can perform standard discriminant analysis when there is a higher number of features than instances.Nonetheless, Taguchi has proposed a very different method to the typical machine-learning methods that are applicable to large ion (FE) . m-mode p small n” problem: random forest (RF), penalized linear discriminant analysis (LDA) [p small n” dataset. The results for the synthetic and real datasets demonstrate the superiority of our proposed method when compared against the five conventional methods in feature selections.In this paper, we propose a TD-based unsupervised FE formalism, applied to synthetic data that imitate typical genomic datasets. The proposed method is first compared with five conventional methods applicable to the “large is (LDA) , categoris (LDA) . These sIn this study, we need to deal with categorical data set. Since the labeling is neither binary nor numeric, no standard regression-based approaches are applicable. Thus, the methods applicable are restricted to either supervised methods that can deal with categorical labeling, e.g., tissues, or unsupervised methods that do not require labeling and do not consider sample annotations at all. The above six methods were selected as representative methods that can satisfy the above requirements.To our knowledge, this is the first study in which TD-based unsupervised FE has been successfully applied to the integration of this variety of multiomics measurements. The performance of TD-based unsupervised FE is highly data-type-dependent, since we cannot intentionally generate singular value vectors of interest. When singular value vectors generated by TD is not of interest, we cannot do anything since TD does not have tunable parameters. In this sense, it is challenging to estimate how many types of multiomics datasets  can be successfully integrated with this method.The types of multiomics data  that areA recent review  listed hAs can be seen, the number of samples are from jth kind of omics data  attributed to ith gene of sth biological replicates of kth organism. In this case, for kth patients, M kind of omics data were measured using microarray or next generation sequencing technology measuring variables attributed to N genes at once. Thus, the multiomics measurements are naturally formatted as a tensor without any special reformat.The way by which multiomics data is formatted as a tensor as follows. Suppose that The primary difference between PCA based unsupervised FE and TD based unsupervised FE is as follows. When condition of experiments is restricted to one, e.g., “patients vs healthy control”, PCA is very useful. However, if it is a combination of multiple ones, e.g., “patients vs healthy controls” and “multiple tissues”, since tensor is more reasonable format than matrix, TD based unsupervised FE is more suitable method than PCA based unsupervised FE.x. The In the synthetic dataset, we assumed that there are 81 samples composed of three tissue subclasses (each class has three replicates) and three disease subclasses . That is, the 81 samples are composed of all possible pairs of one of the nine tissue samples (= three tissues × three replicates) and one of the nine disease samples (= three diseases × three replicates). Typically, the three tissues are supposed to be, for example, heart, brain, and skin, while the three diseases are supposed to be a few exclusive diseases, and healthy controls; this means that we do not have any biological knowledge of which combinations are more likely to be associated with one another. The number of genes, which are features, is as many as j and k are assumed to be two independent omics measurements while replicates are supposed to be biological replicates in real experiments.The synthetic data set intentionally model the simplest cases of integrated analysis of multiple omics data set. All performances reported in this study is averaged value over one hundred independent trials.The real dataset consists of multiomics measurements of prostate cancer retrieved from the Gene Expression Omnibus (GEO) , using GThe values in each file were averaged over 25,000 DNA sequence intervals, giving a total number of 123,817, as j or k belong to the Jth tissue or Kth disease subclass, respectively. j or k belong to the Jth multiomics measurement group or Kth tissue .ith interval of the jth omics measurement group of the kth tissue   was thenG with larger absolute values with those identified singular value vectors attributed to samples. For example, suppose that p-values are attributed to the ith feature as [x. p-values are corrected by the BH criterion and features associated with adjusted p-values less than 0.01 are selected.The first step is to check which singular value vectors attributed to sample subclasses (ature as (6)Pi=Pχn latent variable vectors attributed to is and samples, respectively, and there are no non-positive components in Q and n is an integer smaller than any N or nmf function in the NMF package of R.Suppose that we have multiple matrices sharing the same number of rows, i.e., When MNMF is applied to the synthetic dataset, it is applied to the matrix i whereas PC loading applied to either p-values are attributed to is as [ℓth PC score that corresponds to the PC loading of interest. p-values are corrected by BH criterion and is associated with adjusted p-values less than 0.01 are selected.Although the process was fully described in the recently published book , we brieto is as (10)Pi=Pp-value . RF results in as many as 11,278 genomic regions having non-zero importance. This number is almost eight times larger than the number of regions selected by TD-based unsupervised FE (1447). One of the evaluations of RF performance is its accuracy of discrimination, as RF is a supervised method. RF should have selected the minimum features that can successfully discriminate between the 16 classes. The error rate given by RF was as small as 0.35 . This inIn spite of the comparisons presented in the above, one might wonder that RF can achieve comparative performances if top ranked restricted number of genomic regions are intentionally selected. In order to this, we selected 1447 top ranked regions (they are as many as those selected by TD based unsupervised FE) and identified as many as 1267 gene symbols included in these genomic regions. These 1267 genes are uploaded to Metascape to evaluate them biologically.Next, we attempted to apply PenalizedLDA to the real dataset, but found that we could not. To perform LDA, all in-subclass variance must be non-zero. This condition was not fulfilled for the real data. Hence, PenalizedLDA cannot be employed to process real datasets.p-values less than 0.01 was as high as 106,761 among all 123,817 features. This indicates that categorical regression selected almost all features and is thus clearly ineffective. The reason categorical regression failed, despite being relatively successful  and identified as many as 962 gene symbols included in these genomic regions. These 1267 genes are uploaded to Metascape to evaluate them biologically.p-values to genes, as there is no suitable null hypothesis by which we can compute the p-values.Finally, MNMF  can compete with TD-based unsupervised FE when applied to a real dataset.In addition, the CPU time required of PCA and TD-based unsupervised FE are also the shortest compared to the other four methods when applied to the real dataset . From thi independent of other index, Unlike “genomic regions + samples” structure used in traditional unsupervised learning that requires a matrix representation, Our TD employs “genomic regions + samples + tissues + biological replicates” structure that requires a four-axes tensor representation. Reported results via enrichment analysis show the superiority of our TD, attributed to taking into account the relationships among genomic regions, samples, tissues, and biological replicates at once. One might wonder why TD based unsupervised FE is more coincident with subclasses than other supervised or unsupervised methods. It is simply because of the nature of tensor. As can be seen in Equations  and 5),,5), usinp small n problem. The proposed method outperformed the conventional supervised feature selection methods of RF, categorical regression, PenalizedLDA, and MNMF, and was demonstrated to be superior not only in feature selection but also in selection of biologically reliable genes. In this study, a TD-based unsupervised FE formalism is successfully applied for the first time to a variety of multiomics measurements for both synthetic and real datasets that represented a typical large


Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data.cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature.Here, we propose a novel approach for predicting gene expression at the single-cell level using https://github.com/SchulzLab/Triangulate.Our proposed method allows us to identify distinct TFs that show cell type–specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via  Single-cell sequencing has become a powerful tool to study gene expression patterns in different cellular contexts, such as cell differentiation, complex tissues, and disease. It is an open question how to best use single-cell RNA sequencing (scRNA-seq) data to infer cell-specific transcriptional regulatory programs.Many methods have been developed that use gene expression data of pooled cell samples (bulk) to infer cell-specific transcription factor (TF) regulation. These methods often use the idea to decompose or associate variance in measured gene expression data with putative TF target gene sets to infer TF activity. To name a few examples, such approaches include network component analysis  and methAs scRNA-seq data protocols are becoming more widely adopted, novel methods have been developed that learn TF regulation by making use of the large number of cells obtained in current experiments (see overview ). For exSCENIC  is a widLater, Suo et al.  exploiteOne of the appealing aspects of the SCENIC approach is that it is able to infer the TF activity per cell. However, because scRNA-seq data are noisy, this inference is challenging and, as stated above, negative associations cannot be made on a single-cell level in this way.A widely adopted approach to overcome noise in challenging machine learning applications is the use of multi-tasking. In the context of bulk RNA-seq analysis several regression approaches that associate regulatory features with gene expression in a multi-tasking framework have been proposed .In this work, we introduce TRIANGULATE, a tree-guided multi-tasking approach for inferring gene regulation in single cells. This work is conceptually similar to SCENIC  because in vitro differentiated hepatocyte-like cells (HLC), a data set of human skeletal muscle myoblasts (HSMMs), and the third a data set of normal and tumour samples from T cells of a patient with liver cancer. We inspected the coefficients of these models to identify interesting sets of features that best explain the gene expression in single cells. In addition, we compared the MTL results with standard univariate response regression models. These results indicate that the MTL models that integrate the information among all single-cell gene expressions not only produce more interpretable models but also often lead to higher accuracy.We trained our models on 3 single-cell gene expression data sets, a data set comprising primary human hepatocytes (PHH) and n genes, arranged at the rows, and p TFs, arranged at the columns. We generate the TF data in 3 different ways, as described below. In addition, we use the scRNA-seq data as the response variable for our statistical models. After applying the filtering steps described below, we apply a log2 transform to all feature and response matrices prior to the model-fitting phase.In this section, we explain how the feature and response matrices were generated for our statistical models. We define TRAP  was run Using TEPIC version 2.0 , the binChIP-seq data for 123 TFs of the HepG2 cell line were downloaded from ENCODE, considering files processed by ENCODE's uniform processing pipeline. ChIP-seq read counts were measured in ChIP-seq peaks overlapping a 3-kb window defined around the gene’s TSS (mapped against genome hg38) to be combined with the HLC/PHH single-cell data for model training. We refer to these features as “ChIP-seq" features.Fig. We generated scRNA-seq data for 657 HLCs differentiated from induced pluripotent stem cells/PHH (HLC/PHH) as described in the In addition, we obtained the HSMM data from . It is wk to 2 for the HLC/PHH and 1 for the HSMM data. We use and compare the 2 variants of the gene expression data set, imputed and unimputed.We used the scImpute method  on 2 scRRRID:SCR_016339)  is a vector of size p, holding the affinity values in the ith row. Next, we define a threshold t based on the third quartile computed over vari’s ∀i ∈ {1, ⋅⋅⋅, n}, as a cut-off to decide whether the genei should be kept:We further reduced the gene set by completely removing all the affinities computed for the genes for which the variance in their feature space (TF affinities) was less than the third quartile of the variances measured for each gene. More precisely, given the In addition, we removed the TFs whose corresponding gene expression was zero.Similarly, we applied these filtering steps on the other 2 feature set-ups, dynamic and ChIP-seq.Here, we describe 2 distinct statistical learning frameworks, single-task learning (STL) and MTL. The MTL approach is further categorized into ordinary MTL (OMTL) and tree-guided MTL (TRIANGULATE).We partitioned the data into training (60%) and test (40%) sets. Five-fold cross-validation was performed on the training set to select the best hyperparameters for all models. The TF and gene expression data are normalized to have zero mean and unit variance. We use Pearson correlation computed between the predicted expression and measured expression values on the same test set to assess performance for all models.We trained individual regression models with elastic-net regularization through a 5-fold cross-validation model selection scheme, exploring the α parameter within the range of 0 and 1 with step size of 0.1 using the glmnet package in R .n observations (samples) and p features. Let k tasks. We look for an appropriate coefficient matrix X and Y with the error term ϵ as described in the following formula:Let B coefficient matrix. In this section, we describe several MTL set-ups used in this study to understand the performance of different formulations and also downstream interpretation of the results.There are various ways to obtain the optimal values for the B* denotes the optimal coefficient matrix, α is a tuning parameter that controls the magnitude of the coefficients through the L2 norm regularization, and iy is a vector of size n holding the response values of the ith task. βi denotes the coefficients corresponding to the ith task (column) of matrix B. Similarly, βj denotes the jth row of matrix B.To optimize a multi-task regression model with elastic-net regularization, the following objective function is used:Given the optimization formula, we trained an MTL model with elastic-net regularization using the R glmnet package , where tT with V vertices. Each leaf node of T is associated with a task and the internal nodes reflect the groupings of the tasks. This tree structure can be inferred directly from the data or may be available as prior knowledge beforehand. Within this tree, each node v ∈ V is associated with a weight vw, typically representing the depth of the subtree rooted at node v. The optimization formula for tree-guided MTL isIn the ordinary MTL scenario all tasks share the same relevant features. However, it is possible that a subset of highly related tasks may share a common set of relevant features, whereas weakly related tasks are less likely to be affected by the same features. An improvement was proposed by Kim and Xing  to addreted in R  to trainThe gene expression matrix is used to infer the tree structure of the tree-guided MTL models. For a sanity check of the models, we created a randomized gene expression matrix, to contrast the models trained on the real data with the random data. We generated several trees derived from the gene expression data to guide the optimization of the tree-guided MTL models. Fig. HC-tree: We used the BuildTreeHC function of the LinearMTL package using the complete linkage and 1 − Pearsoncorrelation as the dissimilarity measure to apply hierarchical clustering on the real single-cell expression data. The clustering tree is then used to guide the tree-guided MTL models.M-tree: Coordinates of the single cells in the reduced dimension space (a matrix of size 2 × number of cells) derived from the Monocle model trained on the real data. Using the BuildTreeHC function, as described in HC-tree, we generated the tree structure from the coordinates in the reduced dimension space.S-tree: We constructed another tree that serves as a baseline for our tree-guided MTL models. The tree structure forms a star shape, with a root and as many child nodes as the number of cells. More precisely, let k be the number of cells. Then, the star tree has k + 1 nodes, labelled by 0, 1, ⋅⋅⋅, k, where 0 represents the root and the remaining nodes represent the k cells. Every non-root node has 1 and only 1 edge connecting it to the root. Clearly, the root has immediate links to other nodes, i.e., degree of k. This tree structure is considered baseline because it does not suggest any particular grouping of the cells, as they all are uniformly connected to the root.R-tree: To generate appropriate random data, we shuffled the gene expression profile of each single cell. Given that the genes are arranged in rows and cells in columns, for each column, we shuffled the gene expression values across the genes and then used this shuffled gene expression matrix as the input to the Monocle tool. Finally, we trained the tree-guided MTL model using the M-tree structure described above. Replacing the gene expression matrix in Fig. The implementation of our workflow can be accessed via the GitHub repository .Because for the static features, several hundreds of TFs were included in the set and visualizing this many TFs makes the interpretation difficult, we decided to shrink this set by selecting those that pass a certain criterion. Essentially, for a given TF arranged in the rows of the coefficient matrix, we compute the sum of absolute regression coefficients for that TF across all cells. If this value is higher than our predefined threshold of 0.5, we keep that TF, or discard it otherwise, for visualization of TRIANGULATE result heat maps.We define the TF-expr-cor as the Spearman correlation between the cell-specific expression of a TF and the inferred TF activity per cell (coefficients) of the TRIANGULATE model. We used a permutation approach to obtain a significance estimate for the correlation values.First, we used the R-tree model, which is based on permuted expression values, to obtain an estimate of the regression matrix This we can compare to an actual model. For example, using the HC-tree model, we computed the TF-expr-cor values. For our analysis  denotes the log2-transformed TPM values of TFi measured in single cells, i in cell j, and n is the number of TFs.To illustrate the interesting TFs only, we further reduced the TF set by keeping those where the sign of correlation agreed with the sign of sum of coefficients across cells for a given TF.RRID:SCR_017247)  and Cardio-Pulmonary Institute (CPI) [EXC 2026], and the DFG SFB/TRR 267 Noncoding RNAs in the Cardiovascular System.F.B.A. and M.S. conceived and designed the project. F.B.A. implemented the TRIANGULATE workflow and produced all figures/plots; F.S. generated the TF feature data; K.K. ran SCENIC on the HLC/PHH and T-cell data sets; F.B.A. analysed the SCENIC results and produced the TF-expr-cor analysis; T.H. implemented the linearMTL package; D.F., G.G., K.L., P.N., J.H., and J.W. were involved in generation and/or primary processing of the HLC/PHH single-cell gene expression data. F.B.A. and M.S. wrote the draft manuscript. All authors approved the final manuscript.giaa113_GIGA-D-20-00071_Original_SubmissionClick here for additional data file.giaa113_GIGA-D-20-00071_Revision_1Click here for additional data file.giaa113_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa113_Reviewer_1_Report_Original_SubmissionWei Chen -- 4/28/2020 ReviewedClick here for additional data file.giaa113_Reviewer_1_Report_Revision_1Wei Chen -- 8/27/2020 ReviewedClick here for additional data file.giaa113_Reviewer_2_Report_Original_SubmissionLongqi Liu -- 5/23/2020 ReviewedClick here for additional data file.giaa113_Supplemental_FileClick here for additional data file.