dataesr/scientific-paragraphs-categorization huggingface dataset:
Developmental profile analyses We used wild-type Ae. albopictus mosquitoes from San Gabriel Valley, located in the Los Angeles County, CA, for RNA extraction. Mosquito rearing, total RNA isolation, and RNA-seq were carried out as previously described  [63]. RNA-seq libraries were aligned to AalbF2 using STAR aligner  [104]. Gene models were downloaded from NCBI (GCF_006496715. 1_Aalbo_primary. 1_genomic. gtf) and quantified with feature-Counts  [105]. Transcripts per million (TPM) and fragments per kilobase million (FPKM) values were calculated from count data using Perl scripts. All sequencing data has been made publicly available at NCBI SRA under BioProject PRJNA563095 (genomic) and PRJNA563095 (transcriptomic).
Funding The authors would like to thank the following for their financial support of research:  European Research Council  ( ERC-CoG 682394  to  M. Bonizzoni ;  ERC-CoG 615680  to R. P. van Rij);  Italian Ministry of Education, University and Research  ( FARE-MIUR  project  R1623HZAH5  to  M. Bonizzoni  and  Dipartimenti Eccellenza Program   2018-2022  to  Department of Biology and Biotechnology "L. Spallanzani," University of Pavia );  Human Frontiers Science Foundation  (Research Grant number  RGP0007/2017 ) to  M. Bonizzoni  and R. P. van Rij;  National Institutes of Health  ( R21AI135258  to  M. Sharakova ;  1R01AI151004-01  and  1DP2AI152071-01  to  O. S. Akbari ;  R01AI32409  to  A. Caccone );  Defense Advanced Research Project Agency (DARPA)   Safe Genes Program Grant  ( HR0011-17-2-0047 ) to O.
Availability of data and materials All the data that support the genome assembly described in this study have been deposited in the NCBI repository and can be accessed with the BioProject accession ID PRJNA530512  [106]. Alternative haplotypes discarded from the primary assembly can be accessed with the BioProject accession ID PRJNA535494 (https://www. ncbi. nlm. nih. gov/ bioproject/535494). The publicly available Ae. albopictus data analyzed during the current study are available from the NCBI BioProject repository under the following accession IDs: PRJNA484104 (https://www. ncbi. nlm. nih. gov/bioproject/484104) for the Foshan strain WGS data  [11] ; PRJNA484104 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA484104) and PRJNA562979 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA562979) for the wild samples WGS data  [12] ; PRJNA475859 (https:// www.
ncbi. nlm. nih. gov/bioproject/PRJNA475859) for the Foshan RNA-seq data  [13] ; PRJNA563095 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA563095) for the RNA-seq data pertaining to the developmental transcriptome profiles  [63] ; PRJNA607026 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA607026) for the small RNA-seq data used to analyze piRNA and miRNA expressed by Ae. albopictus  [12]. The scripts and pipeline for the identification of novel viral integrations described in this study are available on GitHub (https://github. com/epischedda/ViR).
Application to Merging Vortices We now apply the previous methodology to a classical GFD problem, namely, the merger of like-signed vortices. We choose this as a first test case because this scenario is usually identified with the upscale cascade of rotating flows. In this sense, we know what the answer should be, broadly speaking, and so can judge the utility of the procedure. In addition, we can examine what new information is provided by this approach. For this purpose, we used the recently developed MOM6 model (https://github. com/NOAA-GFDL/MOM6). This model includes a number of modern features: most importantly, it can be deployed in a purely isopycnal configuration. There are relatively minor modifications to the kinetic energy equation in this setting, all of which are detailed in Appendix D.
The smaller square in panel (c) encloses the region over which averaging takes place (see Equation  33 ). Closing budgets as we are attempting to do here requires high accuracy, especially in the solution of the adjoint equation. MOM6 does not have an adjoint option, so the following procedure was adopted. Output from the merger experiment of horizontal velocity components and thickness were cataloged at intervals of 1 day, from a simulation that used time steps of 300 s. The computational domain was doubly periodic and f -plane, so we developed a spectral model for the adjoint equation in MATLAB. We adopted a comparable 300-s time step for the adjoint equation which, of course, required knowledge of the model state at comparable temporal resolution. This was obtained for any given one 1-day interval by using 6 days of MOM6 data centered on that day to interpolate via cubic splines the velocities and thickness to the required adjoint model times.
For the present calculation, nx = n = 70 and grid spacing dx, d = 5m is isotropic. In fact, this does not account for the island, inside of which are several of the regularly spaced grid points but which represent locations inaccessible to the fluid. It is necessary to remove those points, resulting in slightly smaller matrix, the regular, pentadiagonal structure of which is then interrupted. This matrix can be fed into a standard eigenvalue extraction routine; we used the function "eig" in MATLAB. The gravest and 10th-most gravest eigenmodes appear in Figure  A2 ; these are associated with eigenvalues k 2 1 ≈ 10 -4 m -2 on the left and k 2 10 ≈ 1. 4 × 10 -3 m -2 , or equivalently L k ≈ 200 m (left) and L 10 ≈ 52 m (right). It is easy to associate a length scale of 200 m with the former, but the latter also measures comparable length scales while being spatially considerably more complex.
The hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here, we propose a novel method, PenDA, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and is robust to normalization effects.
Applying the method to lung cancer cohorts, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up-or down-regulation. Based on the individual information of deregulation given by PenDA, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated to survival. In particular, we identified 37 biomarkers whose up-regulation lead to bad prognosis and that we validated on two independent cohorts. PenDA provides a robust, generic tool to extract personalized deregulation patterns that can then be used for the discovery of therapeutic targets and for personalized diagnosis. An open-access, user-friendly R package is available at https://github. com/bcm-uga/penda.
Rankcomp  [15, 16]  uses pairs of genes with a stable, relative order in a reference dataset to infer deregulated genes in individual samples  [17] [18] [19]. This method, based on ranking, avoids the problem of normalization between samples, but results in very high false discovery rates (above 20%, see Methods). Alternative methods, like DEGseq  [20] , NOISeq  [21]  or Gfold  [22] , exploit paired samples from the same patient (one control versus one malignant) to perform differential analysis. However, such matched samples are usually rare (for example, in the case of cancer, a single sample from the tumorous biopsy is usually available for one patient). Above all, it is not clear if the variabilities observed between paired samples are due to actual deregulation, to intrinsic inter-sample heterogeneities, or to technical biases.
For example, in lung cancer, correlations between paired tumorous and normal samples are similar than between tumors of two different patients, and are only slightly higher than between a tumorous sample and an unmatched normal tissue  (Fig 1A). To overcome all these limitations, we developed PenDA, for Personalized Differential Analysis, a rank-based method, robust to batch or normalization effects, that uses information extracted from a reference dataset to infer the deregulation status of genes in individual samples of interest. For illustrating the power of the method, we focused on lung cancer, which is the first cause of cancer-related death world-wide  [23]  and represents a major public health issue. In particular, we studied two datasets provided by The Cancer Genome Atlas (TCGA) for two of the most common histologies of non-small-cell lung cancers (NSCLCs): adenocarcinoma (ADC  [24] ) and squamous cell carcinoma (SQCC  [25] ).
For example, ADC is generally classified into three subtypes according to transcriptional and histopathological data: terminal respiratory unit (TRU or bronchoid), proximal inflammatory (PI or squamoid) and proximal proliferative (PP or magnoid). These subtypes differ by gene expression but also by clinical behaviors like the stage-specific survival  [24, 36, 37]. Recently, Chen et al  [27]  combined various molecular information (DNA methylation, copy number alteration, mRNA, miRNA and protein expression) to define 6 molecular subtypes of ADC that partially overlap with the standard classification and that show correlation with survival rate, immune profiles or cigarette exposure. By using our method PenDA on the TCGA datasets for ADC and SQC, we illustrated how personalized differential analysis can bring additional information compared to previous studies about inter-individual heterogeneity, can help to find gene classifiers for molecular subtypes and may be used to infer biomarkers related to prognosis.
A robust algorithm to infer if genes are differentially regulated in individual samples Description of the method. PenDA is a rank-based method that allows to infer if the expression of any gene in a given sample of interest is deregulated compared to a set of reference samples (see Methods for details). The fundamental assumption behind the algorithm is that a gene is seen as deregulated in an individual sample if its local ordering compared to other genes with similar expressions is perturbed, as similarly stated by the RankComp method  [15]. Briefly, PenDA starts by inferring a reference of relative ordering in control samples: for every gene g, it constructs two lists L(g) and H(g) of genes whose expression is lower and higher respectively than that of g in almost all the samples of a given reference dataset (Fig 1B  top  and 1C ).
To limit the consequences of this effect on the detection of deregulation, PenDA iteratively applies the previous scheme until convergence by excluding at each iteration the current set of deregulated genes from every L and H lists (S1 Fig). In the cases where the L(g) or H(g) lists are empty, we used the percentile method (see Methods for details) to evaluate the deregulation of g (S2 Fig). Impact of method parameters and of the dataset properties on performance. To test and validate our method, we generated a realistic simulated dataset where we controlled the identity of deregulated genes and the direction (up or down) of deregulation. Based on the RNA-seq profiles of 18,000 genes in normal and tumorous samples of two lung cancer cohorts (adenocarcinoma: ADC, squamous cells: SQCC) of the TCGA database  [24, 25] , we simulated 10 tumorous samples each having on average 30% of deregulated genes (see Methods for details).
Note that to avoid any bias in the analysis, simulations were not based on the same principle that governed the PenDA method, ie, the relative order of gene expressions. Rather, each in silico tumor was generated by randomly choosing a normal sample and a list of deregulated genes was randomly assigned. Then, the perturbed gene expressions of these genes were obtained by adding to the normal levels random values typical of the differences in gene expression between tumorous and normal samples as observed in the actual dataset. We first aimed at testing the method on this dataset by varying the two parameters of the algorithm: l the restricted size of the L(g) and H(g) lists, and h the detection threshold based on the |L u (g)|/|L(g)| and |H d (g)|/|H(g)| ratios (see above). We used the 97 non-tumorous lung samples of the TCGA dataset to determine L(g) and H(g) and then, apply the PenDA method to the 10 simulations.
We then evaluated how PenDA performance depends on the intrinsic properties of the investigated datasets. We determined L(g) and H(g) using different numbers of non-tumorous samples and run PenDA on the same set of 10 simulations. We observed that the method is very robust regarding the size of the reference datasets, achieving very high efficiency even for a limited number of control samples (Fig  2B ). Next, we kept the reference pool fixed but varied the number of investigated genes from 100 to 18,000 and applied PenDA to the simulated dataset restricted to the corresponding limited set of genes (Fig  2C ). We remarked that the reliability of the method is an increasing function of the number of genes, achieving very good performance for numbers higher than ~3,000. Indeed, a large number of genes augments the capacity of L(g) and H(g) lists to integrate genes that may be sensitive to changes in relative ordering.
Finally, we tested the effect of the percentage of deregulated genes in the simulated datasets that may affect the current sizes of L and H lists during the iterations of the method. Fig 2D showed that the predictive power of PenDA is relatively insensitive to this quantity, performance slightly declining for very high percentage. All these quantitative analyses illustrate that the method is very robust regarding parameters and dataset properties fine-tuning. In particular, PenDA remains performant even for a small number of reference datasets. Comparison with other individual-based methods. We next sought to compare PenDA with other existing methods that also allow personalized diagnosis of gene deregulation. Using the same set of 10 simulations introduced before, we generated ROC curves (see Methods) for 4 alternative methods (Fig  3 ): 2 versions of the rank-based method RankComp  [15, 16] , a simple percentile method based on outlier detection and DESeq2  [38] , the popular algorithm for detecting differential expression at the population level but used here on an individual basis.
We observed that PenDA outperforms these methods, in particular in the limit of high specificity (FPR< = 5%) where PenDA could reach very high sensitivity (TPR> = 90%) even for a limited number of control samples (Fig  3B ). Surprisingly, outcomes of the RankComp methods were very dependent to the number of control samples and even lead to better results for smaller control datasets. Note that basing our definition of deregulation on relative rankings The PenDA package. The PenDA method is available as a R package at https://github. com/bcm-uga/penda. The penda vignette (vignette_penda, S1 Text) runs the PenDA pipeline (S3 Fig) on the samples of interest. It takes as an input two dataframes corresponding to the reference dataset of control samples and the dataset to investigate. It first filters for genes whose expressions are very low in every samples.
Functional profiling indicated that SQCCs SU genes are enriched in cell cycle processes, DNA replication and keratinocyte differentiation. Interestingly, a significant proportion of SQCCs and ADCs SD genes are related to angiogenesis and signal transduction processes (S7 Fig). As an illustration of the 'super' regulation patterns, we examined more closely three characteristic genes: the SC gene CAPS, the SU gene ESRP1 and the SD gene RILPL2. CAPS encodes for a calcium binding protein, ESPR1 is an epithelial cell-type-specific splicing regulator and RILPL2 is a rab-interacting lysosomal protein. In Fig 5D  -5F , we plotted for these three genes the distribution of gene expression (normalized RNA-seq counts) within the control dataset, the ADC and the SQCC cohorts. Interestingly, for the CAPS gene, we do observe a difference in mean expression of the gene between cancer tissues and control whereas no differential expression was detected at individual level.
We compared the three ADC subclasses obtained with our approach with the six ADC genomic subtypes previously identified by Chen et al, using a multiplatform-based approach on the TCGA-LUAD dataset  [27]. Class II ADC patients are mainly associated with AD1, AD2 and AD3 subtypes, whereas the majority of class III ADC patients is distributed among AD4 and AD5 subtypes (Fig  6E ). Similarly, class II and class III ADC patients did not directly relate to the integrated ADC molecular subtypes defined by the pioneer work of The Cancer Genome Atlas Research Network  [24]  (S8A Fig). Interestingly, the same hierarchical clustering analysis using the same genes but with normalized counts did not clearly highlight the three ADC subtypes identified with the PenDA differential expression matrix (S8B Fig).
Since all these genes are committed toward up-regulation in class II patients, we tested if the up-regulation of these genes would be a good predictor of cancer survival. We added up the level of individual deregulation of the 37 genes (values equal to -1, 0 or 1, for each gene) to quantify the total deregulation score associated with those genes. Then we defined three groups using the 1 st and the 3 rd quantile of the score distribution. Analysis of the 5-years survival curve in the ADC LUAD-TCGA dataset showed a significant difference between groups, with a worst prognosis for patients that display up-regulation of most of the genes (score � 34, Fig  7D ). To validate our selected set of 37 genes as robust biomarkers, we applied the PenDA method on expression data (Affymetrix Human Genome U133 Plus 2.
0 Array) of an independent adenocarcinoma cohort from the Grenoble Hospital (85 patients, GSE30219  [4] ) (see Methods). We then investigated the 5-years survival curve of the three groups predicted using 36 genes (all genes were analyzed in the Grenoble Hospital cohort, except FAM72D not measured by the array). Coherently with the results observed in TCGA-LUAD ADC cohort, patients up-regulated for many genes (score � 15) have a worst prognosis (cox p-value = 5. 2. 10 -4 , Fig  7E ). Thus, using the PenDA method, we identified 37 biomarkers predicting a bad outcome when they are all up-regulated. Altogether, these results suggest that PenDA method is a powerful approach to discover new biomarkers in cancer.
org/10. 1371/journal. pcbi. 1007869. g006 individual level on simulated datasets. Applied to non-small-cell lung cancer expression data, our method showed that gene deregulation varies in a continuous manner between patients. When frequently deregulated, genes tend to commit to specific deregulation patterns (up or https://doi. org/10. 1371/journal. pcbi. 1007869. g007 down regulation). We observed that a small proportion of genes exhibits unusual 'super' deregulation pattern (always down, up or non-deregulated). Personalized differential analysis succeeds to properly cluster adenocarcinoma and squamous cells lung cancer histology. More specifically, clustering analysis leads to the identification of 37 biomarkers that efficiently predict 5-years survival in two independent adenocarcinoma cohorts.
The method is available as an open source R package called penda. We provide user guidelines so that penda could be installed and run by users with limited computational experience. To ensure reproducibility of analysis, the penda vignette provides a summary of used parameters ready to be included in the method section of publications using PenDA. PenDA is robust against different techniques of transcriptome analysis and against batch effects. Notably, the biomarkers that we identified on the ADCs TCGA cohorts based on an RNA-seq technology was validated on an independent ADC cohort where gene expressions were measured with microarrays. Another advantage of the method is that it is easily generalizable to other types of data like transcript expression, DNA methylation, proteomics, etc. For instance, several methods have been recently developed and benchmarked for the inference of isoform abundance from RNA-seq data  [46].
However, classical differential expression analytical tools (on RNA-seq count data) are based on gene features and are not optimized for the estimate of transcripts abundance data. Thus, testing for individual differential isoform abundances with PenDA would be an interesting challenge. The PenDA approach could also be adapted for single cell analysis  [47]  to leverage the understanding of single cell expression and to quantify intra-sample heterogeneity at the single cell level. The current PenDA method has however several limitations. First, though our method does not depend on replicates to identify individual deregulation, it relies on a control cohort that is supposed to reliably define a 'normal' ranking. Therefore, it is crucial to properly define suitable control datasets. Second, PenDA individual expression analysis requires the use of genome-wide transcriptomic data.
In the future, we would like to explore the possibility to define a set of super conserved genes that could serve as internal reference for 'partial' PenDA analysis on sparse qPCR data. Third, our method is not suitable for genes with low expression levels in all samples, which are currently removed by filtering in the first step of the analysis. The aim of population differential analysis is to detect consistently up or down regulated genes, in average. The PenDA method was based on the concept that individual level analyses are complementary of population approaches. Applying DESeq2, one of the most common DE analysis software, to the ADC and SQCC TCGA cohorts, highlighted similarities and differences for the genes with specific deregulation patterns identified by PenDA (super-conserved, super-up-regulated, super-down-regulated) (S10A and S10B Fig).
Data and preprocessing Two datasets of gene expression (HTSeq-Counts) were downloaded from The Cancer Genome Atlas program (https://portal. gdc. cancer. gov/). The datasets contain tumor ('01' barcoded samples) and control ('11' barcoded samples) tissues from two non-small cells lung cancers: lung adenocarcinoma (LUAD or ADC) and lung squamous cell (LUSC or SQCC). Patients with prior malignancies and replicated samples were removed from the analysis. We kept 1026 samples: 455 ADC tumors, 473 SQCC tumors and 98 control tissues consisting of normal adjacent lung tissue samples (50 from the ADC cohort, 48 from the SQCC cohort). For further analysis, we selected 19177 protein coding genes (hg38 reference genome). This corresponds to protein-coding genes of the base RefSeqGene (https://www. ncbi.
The percentile method The percentile method consists in finding if the expression value of a gene in a test-sample is an outlier of the distribution of expression for the same gene within an ensemble of reference samples. More precisely, for each gene g, we determined p l and p u respectively the x and (100-x) percentiles of the distribution of expression E(g,S) for g within the ensemble of normal samples {S}, where x, given in %, is a user-tunable parameter. Then, a gene g in tumor sample T with an expression E(g,T) was considered as differentially expressed in that sample if E(g, T)<p l /f (down-regulation) or E(g,T)>p u � f (up-regulation), with f�1 a user-defined factor allowing to expand the window of normal expression. A ROC curve analysis obtained by varying x and based on the simulated datasets (see below) suggested that using a factor f~1.