dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-SEQ mapping The sequencing reads raw data (FASTQ) were submitted to quality check and were trimmed for Illumina adapters sequences and lowquality bases using Trim Galore (version 0. 4. 5 with default parameters, [http://www. bioinformatics. babraham. ac. uk/ projects/trim_galore/]). Trimmed read pairs (>35 bp) were mapped to mouse reference genome (mm10 [https://www. ncbi. nlm. nih. gov/assembly/GCF_000001635. 20/]) using STAR  87  aligner (version 2. 5. 2b) with maximum multiple alignments of no more than 100, using the variables -winAnchorMultimapNmax 100 and -outFilterMultimapNmax 100. From BAM files, TEtranscripts 88 (version 2. 0. 3) was used to quantify both gene (uniquely aligned reads only) and transposable element transcript abundances (including both unique-and multi-aligned reads).
Microarrays transcriptomic data analysis Total RNA was extracted from mESCs as described for RNA sequencing analysis. Affymetrix GeneChip® Mouse Transcriptome Assay 1. 0 (MTA 1. 0) was used to perform gene expression analysis on 3 Pml -/-and 3 Pml +/+ mESC samples. Background correction, probe set signal integration, and quantile normalization were performed through Robust Multichip Analysis (RMA) algorithm, as implemented in the "R" Affymetrix package. Genes whose abs(log2(fold-change)) between comparative group was ≥0. 5 (with a p value cut-off of <0. 1) were selected. In addition, false discovery rate (FDR) was applied for multiple hypotheses testing using Benjamini-Hochberg correction. Genes with a FDRadjusted p value (adjusted p value) ≤0. 05 were finally accepted. Biological gene-pathway changes were scored using the Gene Set Enrichment Analysis algorithm (GSEA) using hallmark signature database (MSigDB, [http://www.
Acknowledgements This work was supported by grants from  Agence Nationale pour la Recherche, ANR SUMOPiv  (V. L. B. ); the  ERC, PML-Therapy  ( ADG-785917 ) (H. D. T. );  Fondation ARC  (V. L. B. ); ANRJC (P. T. ). We kindly thank the staff of the animal facility in  Research Institute of Saint-Louis (IRSL) ,  A. L. Maubert  for her help with mouse care. We are also grateful to image core microscopy facilities of  IRSL and College de France, Paris , in particular  N. Setterblad  for his helpful advice with incucyte analysis. We warmly thank  M. Tirard  and  N. Brose  ( Max Planck Institute of Experimental Medicine, Göttingen, Germany ) for the sharing of His6-HA-Sumo1 knock-in mice, and  Meng-Er Huang  ( Institut Curie, Orsay, France ) for his GRX1-roGFP2 construct. We thank  Pablo Navarro (Pasteur)  for providing mESCs.
Data availability The SUMO proteomic data generated in this study have been deposited in the ProteomeXchange Consortium, PRIDE  90  partner repository database, for APL data under the accession number PXD019609; for the mESCs data under the accession number PXD028865. The RNA seq data generated in this study have been deposited the EMBL-EBI database [https://www. ebi. ac. uk/] under the accession number E-MTAB-10153 and Affimetrix Microarrays transcriptomic data under the accession number E-MTAB-10151. Source data are provided with this paper.
The experiment consisted of eight replicates per treatment, and symptoms were recorded at 6 days post-infection. The symptoms developed in immature pears (i. e. , necrosis or bacterial exudates) were evaluated by measuring the diameter of necrotic lesions. The data were then analyzed using variance analysis (ANOVA), followed by Duncan's multiple range test (α = 0. 05), using SPSS statistical software (version 24. 0, IBM, Armonk, N. Y. , USA). All data were presented as mean values ± standard deviation (SD).
Data analysis. We first ran multiple independent linear regressions for each dependent SARS-CoV-2 measure (risk perception, impact estimation, and acceptance of quarantine). We also implemented separate linear regression models for each independent prosocial predictor (social cognition and moral dilemmas). Regarding moral behaviors, we assessed three types of measures including moral judgment, harm detection, and acceptance of the behavior. All models included demographics (age, sex, years of education, and SES) and mood (depression and anxiety symptoms) as covariables (see Supplementary Information S4 for a further description of the analyzed variables). In a subsequent analysis, we weighed the role of each group of independent variables in predicting dependent variables by running hierarchical regression models.
Thus, we introduced each independent and control variable following a step-by-step procedure and determined the change of R 2 and F measures due to each variable introduction. We employed R 2 and eta squared (n 2 ) as effect size measures for significant effects. Furthermore, we reported for each regression model the standard errors and the expected effect sizes using a post hoc sensitivity analysis. Results showed that with an expected power of 0. 95 and α level of 0. 05, the sample size we used was sufficient to detect a medium effect size (n 2 = 0. 13, critical t = 1. 98). We ran all statistical analyses using the JASP package version 0. 14. 1.
Methods: In the present paper, (i) we benchmark the performance of the lasso penalization and three variants (i. e. , ridge, elastic net, adaptive elastic net) on 16 cancers from TCGA after pre-screening, (ii) we propose a bi-dimensional pre-screening procedure based on both gene variability and p-values from single variable Cox models to predict survival, and (iii) we compare our results with iterative sure independence screening (ISIS). Results: First, we show that integration of mRNA-seq data with clinical data improves predictions over clinical data alone. Second, our bi-dimensional pre-screening procedure can only improve, in moderation, the C-index and/or the integrated Brier score, while excluding irrelevant genes for prediction. We demonstrate that the different penalization methods reached comparable prediction performances, with slight differences among datasets.
Background The roots of the 'P4' model of cancer medicine are based on prediction combined with personalization, prevention, and participation  [1]. Prediction of the best treatment for a given patient and prediction of clinical outcome, including overall survival, are both of growing interest. The '-omics' technologies now come with decreasing costs, which has made possible the molecular characterization of tumor samples of various subtypes, including gene expression  [2, 3]. As a result, there are growing numbers of knowledge databases that include molecular profiling of patient tumors, together with clinical information from patient follow-up. Survival analysis from transcriptome profiling of cancer patients in terms of messenger RNA (mRNA) expression is now emerging for clinical use  [4].
Transcriptome based tests such as MammaPrint ® and Oncotype DX ® are already used in clinical environment to assess risk of relapse of breast cancer, as well as Afirma ® gene expression classifier that is used to differentiate between invasive cancer and benign nodules  [5]. Also,  [6] [7] [8]  show that gene expression often provides the best survival prognosis compared with other omics. The Cox proportional hazard model  [9]  is one of the most popular approaches in medicine to link covariates to survival data. When considering the number of covariates, p (which can typically be 20,000 gene products), in relation to the number of patients in the databases, n, and so the number of events e (which can typically be only a few hundred), various issues occur due to the high dimensionality  [10] , which include the lack of stability of the selected genes  [11]  and over-fitting  [12].
In particular, the elastic net  [18]  and the adaptive elastic net  [19]  are regularization procedures that can overcome some stability issues of the lasso in the presence of highly correlated variables  [20] , and the ridge penalty allows control of the variance of the estimator. The lasso tends to select one variable in a set of correlated predictors  [21]. The elastic net and the adaptive elastic net result in lower mean-squared errors than the lasso and the ridge in the presence of highly correlated variables  [22]. Although there is no selection, the ridge regression has shown promise and reliability for survival prediction using high-dimensional microarray data  [23]. First, Bovelstad et al. [24]  compared the ridge and the lasso among other prediction models on three real datasets only.
Their results showed that the ridge penalty obtains the best predictions on the selected datasets. Then, Benner et al. [25]  compared the ridge, lasso, elastic net, and adaptive lasso penalties on simulated and two real cancer datasets. As practical conclusions, the authors advocated for the use of lasso or elastic net penalization, as they do not require an initial estimation step, and were among the best-performing methods in their simulations. They also suggest further research on gene pre-screening prior to the use of Cox model. Finally,  [26]  showed that the ridge, lasso and elastic net penalties perform equally well for low-dimensional settings with few events. Pre-screening methods should be considered as a statistical screening procedure to remove irrelevant genes. From this point of view, the aim is not to identify the most relevant variables, but to select a moderate size subset of variables that can be further reduced using penalized approaches  [24, 27, 28].
e, ridge, lasso, elastic net, adaptive elastic net) has been defined using mRNA-seq datasets of a large set of cancers of The Cancer Genome Atlas (TCGA, https:// www. cancer. gov/ tcga) with supervised and unsupervised pre-screening of the genes, and according to well-established evaluation metrics for prediction. In this context, the goals of this paper are to: (i) study the impact of pre-screening the genes based on their interquartile range (IQR) and p-value from a single variable Cox model on prediction accuracy, and propose a rationale toward setting a threshold; (ii) compare four multivariable Cox penalty methods (i. e. , ridge, lasso, elastic net, adaptive elastic net) after a prescreening step; (iii) compare the pre-screening methods we propose to a well-known algorithm, the iterative sure independence screening (ISIS)  [28].
Penalization methods for a sparse model We consider four alternative penalties: the ridge regression  [23] , the lasso  [17] , the elastic net  [18] , and the adaptive elastic net  [19]. Briefly, these methods consist of the addition of a penalty term to the log-pseudo-likelihood l(β) before the maximization: • The lasso • The elastic net • The ridge • The adaptive elastic net (a two-step procedure) 1 estimates the vector β0 by maximizing l with the ridge regression. 2 weights the elastic net penalty with the coefficient β 0 j , j ∈ {1,. , p} computed in step 1: with ŵj = 1/| β0 j |, j ∈ {1,. , p}. We used the package glmnet  [39]  to estimate Cox penalized models. We selected the parameter by minimizing β(lasso) = argmax β l(β) -||β|| 1 β(EN ) = argmax 𝛽 l(𝛽) -𝜆(𝛼||𝛽|| 1 + 1 -𝛼 2 ||𝛽|| 2 2 ) β(ridge) = argmax β l(β) -||β|| 2 2 β(AEN ) = argmax 𝛽 l(𝛽) -𝜆 p ∑ j=1 ŵj 𝛼|𝛽 j | + 1-𝛼 2 |𝛽 j | 2 , the deviance in the cross-validation process, on the training dataset.
• MCP γ is a tuning parameter of the MCP/SCAD penalty. We used the default parameter (3 for MCP and 3. 7 for SCAD). We used the package ncvreg  [41]  to compute the Cox models penalized by MCP or SCAD. Benner and colleagues show that SCAD depends on pre-selection procedures, and they advise lasso and elastic net as being the methods of choice  [25]. We will thus show most of the results with lasso-like penalties.
Prediction performance metrics We estimate the prediction performance of the models by 10 repetitions of a K-fold cross-validation ( K = 5 ). We compute the β vector of the Cox model on a training dataset (  4 5 of the patients), and from this estimated vector we define a risk score for each patient in the testing β(SCAD) = argmax 𝛽 l(𝛽) -𝜆||𝛽|| 1 , if ||𝛽|| 1 ≤ 𝜆 - 2𝛾𝜆||𝛽|| 1 -||𝛽|| 2 1 -𝜆 2 2(𝛾 -1) , if 𝜆 < ||𝛽|| 1 ≤ 𝛾𝜆 - (𝛾 + 1)𝜆 2 2 , if ||𝛽|| 1 > 𝛾𝜆 β(MCP) = argmax 𝛽 l(𝛽) -𝜆||𝛽|| 1 + 𝛽 2 2𝛾 , if ||𝛽|| 1 ≤ 𝛾𝜆 - 𝛾𝜆 2 2 , if ||𝛽|| 1 > 𝛾𝜆 dataset ( 1 5 of the patients). This risk score is called the 'Prognostic Index' (PI) and is defined for a given patient i as PI i = βT X i , with β the estimator of the coefficients in the Cox model, and X the gene expression vector. This procedure allows assessment of prediction performance by computing the C-index and the Integrated Brier Score (IBS) (Fig.
The Cancer Genome Atlas dataset Cancer acronyms, as provided by the TCGA consortium, are available in Supplementary Table  S1. First, we included cancers available in TCGA for which there were more than 75 patients with mRNA-seq and survival data. Then, we followed recent formal recommendations  [49]  to exclude PCPG dataset that has too few events and SKCM dataset that has a high ratio of metastatic samples sequenced. We used overall survival as disease-outcome, except when the authors recommend the use of progression-free interval (BRCA, LGG, PRAD, READ, TGCT, THCA and THYM). After these two steps, we retained 26 cancers. Finally, we computed the C-index estimates with the ridge regression method applied over all of the genes for these 26 cancers (Supplementary Fig. S2 ), as explained in Fig.
1 , without the pre-screening step. To focus on cancers for which the prediction exceed a minimal level of performance with RNA-seq data, we decided to retain only the datasets for which the median C-index is significantly higher than 0. 6 according to a one-sided Wilcoxon test at level 0. 01. At the end of this procedure, we retained 16 cancer subtypes (Table  2 ). Among them, UVM, ACC, and KIRP contain few events ( e < 50 ). Sup- plementary Fig. S3  shows that this may lead to a bias on the C-index toward a too optimistic value, resulting in an anti-correlation between the C-index and the number of events (cor = -0. 63, p-val = 0. 008). This anti-correlation disappears when selecting cancer subtypes for which the TCGA dataset contains more than 50 events (cor = -0. 18, p-val = 0. 55). However, we retain these 3 datasets as this bias is identical for all methods applied to a given dataset; therefore, the results remain comparable within a cancer subtype.
We obtained clinical and mRNA-seq datasets using the Broad GDAC FIREHOSE utility (https:// gdac. broad insti tute. org). We retained only expressed genes (i. e. CPM value higher than 1) for at least 1% of the patients. Then, we used a log2-CPM normalization (count per million) of the mRNA-seq count data using packages edgeR  [50]  and limma  [51] , and we standardized the data in the training dataset, and in the testing datasets using the mean and standard deviation values of the training data.
The independent dataset To further compare the validity of our prediction and the procedure used, we chose the clear cell renal cell carcinoma (ccRCC), for which the predictions are high (C-index = 0. 75), and the dataset is large enough to gather a large number of events (e = 175). We collected the expression data and the clinical data for Renal Cell Carcinoma (RECA-EU) from the ICGC repositories (https:// dcc. icgc. org), while selecting only ccRCC patients. We retained only expressed genes (i. e. CPM value higher than 1) for at least 1% of the patients. Then, we used a log2-CPM normalization (count per million) of the mRNA-seq count data using packages edgeR  [50]  and limma  [51]. To test the procedure on an independent dataset, we used TCGA:KIRC as a training dataset while ICGC:RECA dataset as the testing dataset, in a similar way as explained above.
Due to differences between datasets, we selected only genes that were found in both datasets. Therefore, we used 17,000 genes to learn the model on training subset of TCGA:KIRC and then tested both on the remaining patients from TCGA:KIRC or on all the patients in the independent ICGC:RECA dataset separately. We standardized the external testing dataset independently from the training set, by using the mean and standard deviation of the testing set. This standardizing procedure is different as for the K-fold validation, but was necessary to reach good performance in terms of IBS. We further comment this issue in the discussion section.
Bi-dimensional pre-screening procedure Supervised pre-screening: based on the single variable Cox model Single variable Cox pre-screening consists of allocating one p-value associated with the test ' β = 0 ' for each gene individually using the patients from the training dataset. We computed the p-values using the likelihood ratio test  [56]  implemented in the package survival  [57]. We corrected for multiple testing with the Benjamini-Hochberg procedure  [42]. We used six different thresholds (0. 01, 0. 05, 0. 1, 0. 2, 0. 5, 1), and only the genes with a corrected p-value below a given threshold were kept to estimate β j in a multivariable Cox model. This supervised pre-screening approach retains the genes that are individually associated with survival.
Unsupervised pre-screening: based on the interquartile range We used the interquartile range (IQR) applied to the gene expression of patients from the training dataset for unsupervised pre-screening. The IQR is a robust Fig. 2  C-indices obtained with clinical data alone (red), mRNA-seq data alone with ridge penalty and all the genes (blue, no bi-dimensional pre-screening), and clinical and mRNA-seq data together (purple) for the 16 cancers studied. We computed the C-indices by 10 repetitions of a K-fold cross-validation (K=5). To test whether the added value of mRNA-seq data for prediction is significant over clinical data alone, we computed the p-values of a one-sided Wilcoxon signed-rank test (purple stars at the top of each graphic, Benjamini-Hochberg correction for the 16 p-values). Red letters at the bottom of each graphics indicate the clinical data available (G: grade; T: tumor; N: node; M: metastasis).
Suggested screening algorithm In this study, we suggest to screen the genes in a bidimensional way, both on individual correlation with survival (i. e. p-value of the single variable Cox model) and variability of the genes among patients (IQR of VST data). For each pair of thresholds, we compute 50 C-indices (resp. 50 IBS) as explained in Fig. 1. Optimal thresholds are those that maximize (resp. minimize) the median C-index (resp. IBS), according to the performance metric of interest (i. e. C-index or IBS in this study). The pair of thresholds that maximizes prediction accuracy is referred to as the 'optimal case' , 'optimal pre-screening' or 'optimal thresholds'. Evaluation of prediction performances with the same cross-validation procedure and datasets used for model selection may lead to an optimistically biased evaluation.
To overcome this issue and measure the extent to which our bi-dimensional screening procedure improves predictions, we used a nested cross-validation procedure  [60]. Briefly, we simply re-ran the procedure with new sampling after having learned optimal thresholds, i. e. we learned new models on new training and testing datasets but with the optimal bi-dimensional screening threshold computed in the first run of 10 cross-validations (K=5). To evaluate if the prediction quality is improved by the pre-screening step, we compared the C-index (resp. IBS) distributions without screening (i. e. all the genes) and with the optimal pre-screening with boxplots. To help guide the comparisons, we also performed a one-sided Wilcoxon signed-rank tests, which is purely indicative as detailed in the discussion paragraph.
Simulation procedure In order to evaluate the pre-screening procedure in a controlled environment, compatible with the Cox model, we ran simulations. The Cox model assumes the time dependent hazard, for a given patient i, to be h i (t) = h 0 (t). exp(β T X i ). To compute the time depend- ent baseline h 0 (t) , we used the Cox-Weibull model, h 0 (t) = rst s-1  [61, 62]. The correlation structure of mRNA-seq data is complex and specific to each cancer  [63]. Thus, we used the real data from TCGA for mRNA expression X i. We chose ccRCC as the cancer of interest for the same reasons as for the validation on an independent dataset: high C-index and large number of events. We learnt a Cox survival model with Elastic Net penalty 10 times, leading to sets of selected genes of size 51 to 75. Out of these sets, we selected the 68 genes that are selected at least 7 times among the 10 models.
We then ran simulations only with this set of genes considered as the ground truth, and we use a normal distribution N (0, σ ) to compute β. We ran the simulations for different values of σ and chose σ = 0. 2 as it leads to the closest Kaplan-Meier curves between simulated and real data (Supplementary Fig. S7 ). We simulate the survival time for a given patient i by with (r, s) the parameters of the Cox-Weibull model, and U a variable following a standard uniform distribution  [64]. Finally, we simulated censored time C i following a uniform distribution between 0 and θ , this latter parame- ter determined to have the same censoring rate as for the real dataset (0. 67 for the TCGA dataset considered)  [65]. We ran only 3 different simulations scheme, due to the time required to run a single simulation, leading to 3 simulated datasets (each with 526 patients with the same gene expression as for the real datasets, but simulated survival and censored time).
We observed these two properties for all datasets (data not shown): genes selected by lasso likes penalization have all low p-values, but not all a high IQR. Fig. 3  C-index computed with different pre-screening thresholds for BRCA and the elastic net penalty. (A) Median C-indices obtained by 10 repetitions of a K-fold cross-validation (K = 5) for different pre-screening thresholds for corrected p-values on the x-axis and for interquartile range (IQR) on the y-axis. Box surrounded by gray: no pre-screening; box surrounded by blue: optimal case (highest median C-index); white numbers: number of genes after the pre-screening step. (B) Number of genes retained by unsupervised (purple) and supervised (lightblue) pre-screening in the optimal case (the blue square in (A)). (C) Boxplot of C-indices obtained with 10 repetitions of 5-fold nested cross-validation without pre-screening (gray box in (A) for elastic net) and in the optimal case for bi-dimensional pre-screening (blue box in (A) for elastic net) for the ridge, the lasso, the elastic net (EN), the adaptive elastic net (AEN).
S14  and S15 ). More precisely, calculating the median C-index among the 50 C-indices for each penalization method, MCP obtains the worst performance for 12 out of the 16 cancers studied, while ridge reaches the best performance for 9 out of 16. For IBS, AEN reaches the worst performance for 11 cancers, followed by MCP for 4 cancers. The best performances were again obtained by ridge (8 cancers) followed by lasso (5 cancers). Optimal bi-dimensional pre-screening shows improved performance on an independent dataset Figure  5  shows the performance of the model on the same TCGA dataset through cross-validation, and on an independent ICGC:RECA dataset. First, we observe on the independent dataset a strong reduction in performance on the C-index, which we interpret by the small proportion of patients with aggressive cancer in ICGC:RECA (14% of clinical stage III and IV patients in ICGC, compared to 39% in TCGA).
Interestingly, even though there is almost no improvement in the C-index after prescreening procedure is applied on TCGA (from C = 0. 71 [0. 702 ; 0. 72] to C = 0. 716 [0. 698 ; 0. 730]), the improve- ment is clear when the model learned on TCGA datase,t after the pre-screening procedure, is applied on the ICGC dataset ( (from C = 0. 545 [0. 535 ; 0. 553] to C = 0. 566 [0. 560 ; 0. 573]), Fig. 5A ). Besides, the improvement is clear and strong when measuring the performance with the IBS, both on TCGA as a test set, and on ICGC (Fig. 5B.