dataesr/scientific-paragraphs-categorization huggingface dataset:
Among them, somatic mutations as well as chromosomal copy number aberrations have been identified, with loss of heterozygosity (LOH) being the most effective in predicting progression to oral cancer  [10]. Furthermore, alterations in gene expression of OPL have been shown early during oral tumorigenesis and have been associated with malignant transformation, leading to possible gene expression profiles predicting oral cancer-free survival (OCFS). This predictive model was shown to improve the clinicopathologic determinants of oral malignant transformation  [11]. To the best of our knowledge, in OPLs only four datasets of gene expression are at present publicly available and only one of them includes a significant sample size with complete clinical annotations  [12] [13] [14] [15]. In the latter, case material was deeply analysed at molecular level in a subsequent paper  [16].
OPL Dataset Eighty-six oral premalignant cases, defined as OPL and enrolled in a randomized chemoprevention trial at University of Texas MD Anderson Cancer Center (Houston, TX 77030, USA), were profiled by microarray gene expression  [20]. Lesions included in the trial had: clinical and histologic evidence of measurable or assessable OPLs (leukoplakia and/or erythroplakia); histologic examination showing dysplasia or extensive leukoplakia with hyperplasia and symptoms (i. e. , pain), cosmetic cases (e. g. , leukoplakia of the lips), or high-risk location (i. e. , soft palate, floor of mouth, ventral tongue, and alveolar ridge). A normalized data matrix was retrieved from NCBI Gene Expression Omnibus repository (ID: GSE26549), where raw data were processed by quantile normalization and RMA algorithm; then, expression values were log 2 transformed  [21, 22].
Gene Expression-Based Molecular Subtyping In a previous study on a meta-analysis of 8 datasets including 527 HNSCC tumors and 138 normal tissues, we used a data structure decomposition approach using the Disease-Specific Genomic Analysis (DSGA) tool  [23]. Gene expression profiling depicts the average transcriptomic landscape weighted by the cell type proportions present into bulk tissue specimens  [24] ; DSGA method allows simplifying the expression data in order to highlight the pathologic component embedded into the data by isolating the disease-like and the normal-like portions for every gene. Taking into account the expression data of normal tissues, DSGA builds a healthy state model that allows each tumor tissue to be decomposed as the sum of two components: (i) the normal component; (ii) the disease component (i.
e. , residuals). As a result, the disease component assessed the extent to which each tumor deviates from the normal state and was used for the identification of our six molecular subtypes. Subsequently, we defined the topological connections among HNSCC samples, in order to study the structures into the high-dimensional data, recognizing shapes and patterns leading, eventually, to the identification of meaningful subgroups. To this purpose, we applied PAD, a topological approach that discloses the geometry into the data and provides an easily accessible picture of data relationships  [18]. PAD incorporates Mapper, a mathematical tool that identifies local clusters and then defines the interaction between these clusters by connecting them to form a graph whose shape captures the topology present into the data  [25].
The distance from the normal state is summarized by the PAD score. Since our subtyping model in HNSCC evaluates the deviation of each tumor from the continuous range of normal phenotypes, its application on OPLs with oral cancer development as clinical endpoint is conceivable. Based on the molecular subtyping model developed in HNSCC, the topological distances (i. e. , PAD score) in the GSE26549 cases were predicted by least-angle regression (LARS), an algorithm for fitting linear regression models to high-dimensional data (i. e. , gene expression). LARS is useful in predicting quantitative traits: it avoids the over-fitting issues of least-squares linear regression when the number of variables (i. e. , genes) is large compared to the number of cases, introducing cross-validated squared predictions  [26].
LARS was applied using a dedicated BrB-plugin that develops a linear model for predicting a continuous response variable based on gene expression measurements, imposing 0% as error threshold and 10-fold cross-validation for model selection  [27]. As described in  [17] , Prediction Analysis for Microarrays (PAM) algorithm  [28]  was applied to project our classification to GSE26549. PAM, a nearest shrunken centroid method, is a popular classification method for high-dimensional data. The approach relies on a classification rule based on the scaled distance between the expression profiles of new samples and class centroids. In deeper detail, PAM shrinks the class centroids towards the overall means and embeds a variable selection mechanism  [29]. The degree of shrinkage allows minimizing the cross-validated error rate on the training set  [30].
Tumor Microenvironment Analysis Immune and stroma cell abundance in OPLs was estimated through the xCell R package  [33]. xCell is based on single-sample GSEA to estimate the abundance of 64 immune cell types, including adaptive and innate immune cells, hematopoietic progenitors, epithelial cells, and extracellular matrix cells using a novel compendium of 489 gene sets. We focused on 26 cell types provided by the xCell tool corresponding to the "parent" cell types and on three summary scores (immune, stroma and microenvironment score).
Variance Partition Analysis There are likely many variables that contribute to variability in OPL between individuals. To assess the contribution of the prognostic molecular signatures and clinical features as a source of data variance, we adopted a visualization framework. Thus, the variancePartition package was used in order to evaluate the proportion of gene expression variance explained by both the demographic/clinical (i. e. , age, gender, smoking, alcohol, histology) and the molecular covariates  [39]. The variancePartition package uses linear mixed model based statistical methods to quantify the contribution of multiple sources of variation. This method is instrumental in providing a way for comparing the relative effects of molecular and demographic/clinical drivers on gene expression.
Bioinformatics Analysis and Data Visualization Bioinformatics analysis was performed using R version 3. 6. 3 [40] , and BrB-ArrayTool version 4. 6. 0, developed by Dr. Richard Simon and the BRB-ArrayTools Development Team, available at http://linus. nci. nih. gov/BRB-ArrayTools. html  [41]. The R/Bioconductor package ComplexHeatmap  [42, 43]  was used to visualize data and to assess whether there was association between the expression values of 102 genes in the 86 OPL samples and clinical-molecular features, including molecular subtype membership, gender, histology, and smoking and alcohol habits. Genes (rows) were chosen by PAD analysis and clustered by Pearson's correlation distance, while samples (columns) were ordered by increasing PAD score. Regarding the published signatures, the sum of the expression values weighted by the regression coefficients of each gene entering into the signatures was assessed to compute the risk scores for 13-gene model and MPRS.
When the algorithm was not specified (i. e. , hypoxia metagene and lipid metabolism-related signature), the samples were ranked by the z-score based on the genes. The GSVA R package was used to estimate the z-scores of a gene set over the samples in an unsupervised manner  [44]. For biomarker cutoff determination, we used the Cutoff Finder R package (available at http: //molpath. charite. de/cutoff) that allows optimizing a cutoff point taking into account the presence of complex distributions in the data using the time interval for oral cancer development as endpoint  [45]. Kaplan-Meier survival analyses for both the six molecular subtypes and the four prognostic signatures were realized through the R package survival  [46]. The ggplot2 package  [47] , was used to represent the OCFS curves and a follow-up of 8 years was chosen as a limit because of the very low number of events occurring after that time period.
Six Molecular Subtypes The OPL dataset disposed of 86 patients' gene expression data including histologically proven hyperplasia (63%) and dysplasia (37%) cases; among the latter, 21 were mild, 8 moderate and 2 severe. The median follow-up time was 6. 08 years and 35 (40. 7%) patients developed OSCC over the course. Cases were stratified according to a classification based on the six molecular subtypes  [17]. As a result, 2 cases (2. 3%) were classified as Cl1-HPV, 10 (11. 6%) as Cl2-Mesenchymal, 11 (12. 8%) as Cl3-Hypoxia, 21 (24. 4%) as Cl4-Defense Response, 11 (12. 8%) as Cl5-Classical, 31 (36. 0%) as Cl6-Immunoreactive. The six subtypes applied to the OPL dataset evidenced differences in terms of OCFS probability (log-rank test; p = 0. 0053). In detail, clusters that in the context of HNSCC appeared to have a worst survival (Cl2, Mesenchymal; Cl3, Hypoxia; Cl5, Classical), displayed the same pattern with a 5-year OCFS of 70% (95% CI: 46.
7-100), 30. 7% (95% CI: 12-78. 3) and 45. 5% (95% CI: 23. 8-86. 8), respectively. Conversely, a higher 5-year OCFS probability of 100%, 69. 9% (95% CI: 52. 3-93. 4) and 82. 6% (95% CI: 69. 7-97. 8) was observed in Cl1, Cl4 and Cl6 (HPV-like, Defense Response, Immunoreactive), respectively (Figure  1 ). Considering the small sample size of the dataset, we decided to perform the subsequent analyses comparing subtypes with low OCFS (high risk: Cl2, Cl3, Cl5) against subtypes with high OCFS (low risk: Cl1, Cl4, Cl6). Figure  1. Six molecular subtypes. The six molecular HNSCC (head and neck squamous cell cancers) subtype classifications were applied to the OPL (oral premalignant lesions) dataset. The dataset included 86 patients, having a median follow-up of 6. 08 years. The six clusters evidenced differences in terms of probability of oral cancer development (p = 0.
Progression Analysis of Disease To unravel the topological characteristics hidden in the OPL gene expression data and to identify the relevant connections among them we associated the oral premalignant cases to PAD linear progression. Expression profiles belonging to the OPL dataset showed a linear progression starting from molecular features close to the normal state and ending with samples distant from the normal state, thus suggesting an accumulation in alterations through different stages of tumor progression. We imputed the topological distances from normal state highlighting 102 genes and Figure  2  shows the heatmap of the genes associated with PAD. OPL cases were ranked based on the topological transformation disclosing a significant association with subtype membership (p = 6. 6 × 10 7 ) with Cl6-Immunoreactive and Cl4-Defense response having profiles close to the normal state, and Cl3-Hypoxia being the most distant.
Prognostic Value of Oral Cavity Signatures The 13-gene model originally developed in Lohavanichbutr et al. [48]  was revised in Chen et al. [34]  focusing on AJCC stage I/II cases, with the inclusion of clinical features (i. e. , age and gender) along with the 13-gene signature: the final model resulted in improvement in the prediction of 2-year oral cavity cancer-specific survival. The Chen's stage I/II model was tested in the OPL dataset. The model was structured for predicting the survival of HPV-negative OSCC patients; however, it proved to perform well also in the context of OPL setting (Figure  3A ). The 13-gene model significantly stratified patients with higher and lower risk (HR = 2. 71, 95%CI: 1. 36-5. 41, p = 0. 0034). Additionally, the prognostic molecular signatures related to metabolisms were applied.
Hu et al. [37]  reported a signature associated with obesity that included genes related to lipid metabolism. This signature assesses the expression of three genes (TGFB1, SPP1, SERPINE1) in OSCC. The model was tested in the GSE26549 dataset in order to understand its role in identifying the risk of oral cancer development. The three gene lipid metabolism-related signatures were able to identify two different risk trends, stratifying patients with lower or higher risk (HR = 4. 18, 95%CI: 1. 73-10. 1, p = 0. 00057) (Figure  3B ). Moreover, we tested the six metabolic pathway signatures (MPRS) identified by Xing et al. [35]  to foresee prognosis in HNSCC patients. The metabolism-related signature was able to stratify patients with lower and higher risk (HR = 4. 16, 95%CI: 1. 76-9. 82, p = 0.
Molecular research in the setting of OPL is the necessary step to implement new preventative strategies  [54]. We dissected the molecular pathways of a publicly available OPL dataset profiled by gene expression, and we investigated whether and to what extent the molecular portraits of HNSCC are already present in OPL. Our six HNSCC subtype stratification were identified after bioinformatics topological decomposition, transforming expression data of tumor tissue as a sum of the normal component that mimics healthy tissue, and the disease component that measures the deviation from normal state  [17]. Considering the uniqueness of our approach in assessing the distance to normal state, we investigated the performance of our HNSCC subtype stratification in OPL. Having as endpoint OCFS and based on our subtypes stratification, we were able to identify three clusters at higher risk of oral cancer development, namely Hypoxia, Mesenchymal and Classical clusters, and the protective effect of immune-related clusters (i.
, age and gender) providing improved performance in early stage tumors  [34]. Considering its value in stage I/II stage oral cancer, we tested the 13-gene model in OPL, demonstrating its significant performance in defining high-risk oral premalignant lesions. Taken together, our results highlight the linear progression in OPL that can be disclosed by gene expression data and summarized with pathways or signatures developed in the tumor state. Although we identify gene expression based models associated with OCFS, the relevance of these models in accounting the variability of expression data has to be assessed. We exploited a tool implementing a statistical method to quantify the contribution of multiple sources of variation; clinical parameters (i. e. , gender, age, smoking, alcohol habits, histology) and biological features (i.
A comprehensive molecular knowledge of OPL will offer the possibility to explore new algorithms of interaction and mutual reinforcements among different therapeutic weapons such as surgical treatment, and systemic prevention by immunotherapy and targeted therapy. Until now, among molecular markers, the most effective in predicting oral cancer risk is LOH, which may appear in different histological grades of dysplasia. Patients carrying OPL with LOH at 3p14 and/or 9p21 plus LOH at another locus have an expected 3-year risk of developing oral cancer of 35%  [60, 61]. However, no direct implication may be derived from the discovery of high-risk OPL, as neither a different follow-up nor any systemic therapies are available. We acknowledge the limitations of our study, mainly consisting of the analysis of a limited series of OPLs in which variance partition analysis proved that clinical, pathological, and molecular features of OPLs are able to explain only a limited portion of data variability.
As OPLs are easily accessible lesions for clinical visits and monitoring, the concept of chemoprevention has been an attractive topic  [62]. However, at present, the literature data are very scant in this field and the analyzed series is the only one having recruited prospective cases within the frame of a clinical study with strict inclusion criteria, with available well-annotated clinical data and with an appropriate follow-up. In addition, as the process of carcinogenesis can require a long period of time and can be quite unpredictable, our data seem to suggest an extension of OPL follow-up. In conclusion, we strongly believe that the here presented findings will contribute to a more rational design of biological and clinical studies, hopefully enabling identification of adequate prognosticators in OPLs and molecular pathways to be addressed for reduction of the risk of malignant transformation.
Such Application Programming Interface (A. P. I. ) allows to handle communications requests during asynchronous computations. Correct termination is ensured by adding partial snapshot operation in accordance with the same principle than the one presented at the end of subsection 3. 2. 2. Finally, to produce a unique code for both overlapping and asynchronous schemes the top front-end of Jack2 provides two classes, which manage communication requests and convergence detection. The management of communications and detection of convergence, in both cases synchronous or asynchronous, is achieved following the same pattern. Then function pointers are used according to the communication mode without using "if "conditions. For pointto-point communications Jack2 is configured to activate several MPI reception requests on each same reception buffer defined by the user application; then processes can use more recent data for last updated local components, instead of only benefiting from data received at the beginning of the computation phase, allowing more flexibility during the computation.
Introduction Large sets of patient-matched multi-omics data have become widely available for large-scale human health studies in recent years, with notable examples including the The Cancer Genome Atlas (TCGA; The Cancer Genome Atlas Research Network and others, 2013) and Trans-omics for Precision Medicine (TOPMed) program. The increasing emergence of multi-omic data has in turn led to a renewed interest in multivariate, multi-table approaches  (Meng and others, 2016)  to account for interdependencies within and across data types  (Husson and others, 2017). In such large-scale multi-level data, there is often limited or incomplete a priori knowledge of relevant phenotype groups for comparisons, and a primary goal may be to identify subsets of individuals that share common molecular characteristics, design therapies in the context of personalized medicine, or identify relevant biological pathways for follow-up.
Pathway relevance ranking  (Verbeke and others, 2015)  integrates binarized tumor-related omics data into a comprehensive network representation of genes, patient samples, and prior knowledge to calculate the relevance of a given pathway to a set of individuals. A pathway-centric supervised principal component-based analysis implemented in pathwayPCA  (Odom and others, 2019)  performs gene selection and estimates latent variables for association testing with respect to binary, continuous, and survival outcomes within each set of omics data independently. Pathifier  (Drier and others, 2013)  instead seeks to calculate a personal pathway deregulation score (PDS), based on the distance of a single individual from the median reference sample on a principal curve; this principal curve approach is analogous to a nonlinear principal components analysis (PCA), but can be applied only to a single-omic dataset (e.
g. , gene expression). For both PARADIGM and Pathifier, clusters of scores across pathways are shown to correlate with a clinically relevant clustering of patients. Here, we extend the basic philosophy of the Pathifier approach to multi-omics data, using an innovative application of a Multiple Factor Analysis (MFA), to quantify individualized pathway deviation scores. In particular, we propose an approach called padma ("PAthway Deviation scores using Multiple factor Analysis") to characterize individuals with aberrant multi-omic profiles for a given pathway of interest and to quantify this deviation with respect to the sampled population using a multi-omic consensus representation. We further investigate the following succession of A. Rau and others questions. In which pathways are high deviation scores strongly associated with measures of poor prognosis? For such pathways, which specific individuals are characterized by the most highly aberrant multi-omic profile? And for such individuals, which specific genes and omics drive large pathway deviation scores? By providing graphical and numerical outputs to address these questions, padma represents both an approach for generating hypotheses as well as an exploratory data analysis tool for identifying individuals and genes/omics of potential interest for a given pathway.
According to the desired focus of the analysis, data can be structured either with molecular assays (e. g. , RNA-seq, methylation, miRNA-seq, copy number alterations) as tables (and genes as features within omics), or with genes as tables (and molecular assays as features within genes). The MFA weights balance the contributions of each omic or of each gene, respectively. In this work, we focus on the latter strategy in order to allow different omics to contribute to a varying degree depending on the chosen pathway. In addition, we note that because the MFA is performed on standardized features, simple differences in scale between omics (e. g. , RNA-seq log-normalized counts versus methylation logit-transformed beta values) do not impact the analysis. More precisely, consider a pathway or gene set composed of p genes (Figure  1A ), each of which is measured using up to k molecular assays (e.
g. , RNA-seq, methylation, miRNA-seq, copy number alterations), contained in the set of gene-specific matrices X 1 ,. , X p that have the same n matched individuals (rows) and j 1 ,. , j p potentially unmatched variables (columns) in each, where j g ∈ {1,. , k} for each gene g = 1,. , p. Because only the observations and not the variables are matched across data tables, genes may be represented by potentially different subset of omics data (e. g. , only expression data for one gene, and expression and methylation data for another). In the first step, these data tables are generally standardized (i. e. , centered and scaled). Next, an individual PCA is performed using singular value decomposition for each gene table X g , and its largest singular value λ 1 g , which corresponds to the variance of the first principal component, is calculated (Figure  1B ).
padma R software package The proposed method described above has been implemented in an open-source R package called padma, freely available at https://github. com/andreamrau/padma. Padma notably makes use FactoMineR  (Lê and others, 2008; Husson and others, 2017)  to run the MFA; heatmaps in the following results were produced using ComplexHeatmap  (Gu and others, 2016). All of the analyses in this paper were performed using R v3. 5. 1. In addition, all R scripts used to generate the results in this work may be found at https://github. com/andreamrau/RMFRJLA_2019.
Description of TCGA data and pathway collection We illustrate the utility of padma on data from two cancer types with sufficiently large multi-omic sample sizes in the TCGA database: invasive breast carcinoma (BRCA), which was chosen as individuals have previously been classified  (Paquet and Hallett, 2015)  into one of five molecular subtypes (Luminal A, Luminal B, Her2+, Basal, and Normal-like), as well as lung adenocarcinoma (LUAD), which was chosen for its high recorded mortality. The multi-omic TCGA data were downloaded and processed as described in  Rau and others (2018) ; in particular, all associated scripts can be found at https://doi. org/10. 5281/zenodo. 3524080 and additional details are provided in the Supplementary Methods. In this study, pre-processed and batch-corrected multi-omic data for BRCA and LUAD included gene expression, methylation, copy number alterations (CNA), and microRNA (miRNA) abundance for n = 504 and n = 144 individuals, respectively.