dataesr/scientific-paragraphs-categorization huggingface dataset:
Extracting developmental progression from transcriptomes has recently become a topic of intense research, especially after the advent of single-cell RNASeq. Many algorithms have been developed that learn developmental trajectories from large scale bulk, single cell, or whole-organism transcriptomic data and sort samples along those trajectories (e. g. Slingshot  16  , DPT  17  , Monocle  18  , BLIND  19  ). However, a major drawback of these trajectory-learning algorithms is that they require large amounts of samples to learn the developmental trajectory from the data. Moreover, they only provide dataset specific ranks or pseudo-times, making it difficult to compare results across datasets or conditions. To overcome these limitations, we developed RAPToR (Real Age Prediction from Transcriptome staging on Reference), a computational method that, instead of learning developmental trajectory from the data, exploits available time series gene expression data as reference to determine the absolute developmental age of even a single sample from its transcriptome with high precision.
We implemented RAPToR in R (available at https:// github. com/LBMC/RAPToR) providing references to stage C. elegans, D. melanogaster, D. rerio, and M. musculus development from gene expression. We show that RAPToR can stage samples of one species using another species as reference, and can also capture tissue-specific development from whole-organism data. Finally, we show that inferred age allows quantification of a perturbation effect on developmental speed and of perturbation-specific effects on gene expression even when the perturbation is completely confounded by development.
To overcome this limitation, we interpolate reference gene expression (Fig. 1d ) with respect to time in a dimensionally reduced space (Fig. 1e , Sup. Note 1), generating interpolated expression profiles between original reference time points (Fig. 1f ). The sample age estimate is simply the time point of maximum Spearman correlation between the interpolated reference and the sample gene expression (Fig. 1g ). We then compute the estimate confidence interval by bootstrapping on genes (Fig. 1h , methods). We implemented this strategy in RAPToR, an R package where we provide functions to interpolate references and stage samples. Moreover, we already provide high resolution interpolated references to stage the most commonly used animal model organisms exploiting existing time series data on roundworm embryonic and larval development  [20] [21] [22]  zebrafish embryonic and larval development  23  , mouse  24  , and fly  25  embryonic development (Sup.
RAPToR age estimates are robust to genetic variation in gene expression. Variable genetic background is another potential confounder for RAPToR so we tested RAPToR performance on expression data for over 200 C. elegans recombinant inbred lines (RILs) that shows extensive genetic variation in gene expression  11. This dataset was already staged by a trajectory-learning approach and found to span mid-larval to young adult stage  13  , a period with vast expression changes both in the soma (molting) and the germline (spermatogenesis, oogenesis). RAPToR age estimates closely match those previously found (R²=0. 94, Sup. Fig. 8 ). However, we noticed that some gene expression dynamics are advanced and others delayed compared to the reference (Fig. 3a , Sup. Note 1). Shifts between soma and germline developmental time (soma-germline heterochrony) are easily induced by environmental and physiological changes in C.
Quantifying differential expression due to differences in development Often, when perturbations strongly impact developmental speed and controlling for age between experimental groups is challenging, development and variable of interest are completely confounded. In this scenario, detecting perturbation specific effects by including age as a model covariate is not feasible. However, not accounting for confounding developmental variation can lead to misleading conclusions as purely developmental expression changes are attributed to the perturbation of interest. To show an example of this, we reanalyze a dataset comparing young adult C. elegans that developed through dauer state (post-dauer) to controls that did not  37. The authors found a down-regulation of spermatogenesis-associated genes and an upregulation of oogenesis-associated genes from which they concluded that post-dauer animals have reduced spermatogenesis and increased oogenesis.
Discussion We presented here RAPToR, a computational strategy to accurately stage samples from their genome-wide gene expression profile. Unlike trajectory-based methods, RAPToR exploits existing reference time-series data to stage each sample separately, providing several advantages: first, it eliminates the need for large datasets to infer developmental trajectories; second, it provides absolute developmental times that are comparable across data sets, conditions, genetic backgrounds, profiling technologies and other covariates; third, with RAPToR outliers have no impact on the staging of other samples. While RAPToR staging is limited by the existence of reference time-series data, reference interpolation allows precise staging well beyond the resolution of the original reference data, enabling the use of sparse time series as references.
Data accessibility All the data used in this study were previously published and deposited in public databases or accessible by request to the authors. The data from Sémon et al. is, at the time of writing, awaiting publication  31. The full list of datasets and accession numbers is given in Supplementary Table  7. The code to download and (pre)process the data, perform the analyses and generate the figures of this paper can be found at https://gitbio. ens-lyon. fr/LBMC/qrg/raptor-analysis.
Data pre-processing Probe or gene IDs of datasets were converted to standard IDs (WBGene IDs for C. elegans, FBgn IDs for D. melanogaster, Ensembl IDs for D. rerio and M. musculus). When multiple probes or IDs matched a single standard ID, they were mean-aggregated for microarray, sum-aggregated for RNA-seq counts. IDs with no standard ID match were dropped. For RNA-Seq datasets, TPM data was used when available, or computed from raw counts using the transcript lengths from the Ensembl biomart (v99). No remapping of the transcriptomes was done, aside from the M. musculus tooth data (see below). No background correction was applied to microarray data. Samples were considered of poor quality and discarded when the 99 th percentile of the distribution of their Spearman correlation coefficients with others samples fell below a threshold defined below for each dataset.
RAPToR implementation Our method is implemented in an R package : RAPToR (v1. 1. 4), which can be downloaded and installed from the following url. https://github. com/LBMC/RAPToR Functions for staging samples, plotting results, interpolation and building references are included in the package. Detailed vignettes on general usage, reference building and showcases are also provided with the package. Auxiliary R data-packages include references for C. elegans (embryonic, larval and young adult to adult development, https://github. com/LBMC/wormRef), D. melanogaster (embryonic development, https://github. com/LBMC/drosoRef), D. rerio, (embryonic and larval development, https://github. com/LBMC/zebraRef) and M. musculus (embryonic development, https://github. com/LBMC/mouseRef).
Reference interpolation Let X (m × n) be the gene expression matrix of m genes by n samples. The matrix is first gene-centered such that X0 = X -rowMeans(X). We then use ICA ('ica' function, 'icafast' library v1. 0. To interpolate the components, we fit Generalized Additive Models (GAMs) to handle non-linear dynamics through splines with the 'gam' function in the 'mgcv' package (v 1. 8. 31) using a single model formula for all components selected by Cross-Validation (CV) as following: CV training sets are built with 80% of samples, with proportional representation of any covariate group (e. g. batch). The model is evaluated using the average relative error, mean squared error (MSE), and average root MSE  40. We compared GAMs fitted with different splines (cubic, thin plate, duchon), and chose the model with minimal CV and prediction errors.
Tissue-specific staging and quantification of soma-germline heterochrony Microarray intensities of the Recombinant Inbred Lines (RILs) profiles  11  were first normalized within arrays with LOESS using the 'normalizeWithinArrays' function of the 'limma' library. Arrays corresponding to pooled mixed stage controls were then discarded. Samples were discarded when the 99 th percentile of the distribution of their Spearman correlation coefficients with others samples fell below 0. 95 , leaving 193 samples for analysis. The reference used to stage the samples is the "Cel_larv_YA" reference  21  of the wormRef package (see Sup. Table  1 ). The number of interpolated time points in the reference was set to 1000. Samples were first staged using the entire available gene set to obtain the global estimates, then with somatic and germline specific gene sets to obtain the corresponding tissue-specific estimates: the somatic gene set corresponds to the oscillatory genes denoted "osc" in Hendriks et al.
26. The "germline" gene set corresponds to the union of "germline_intrinsic", "spermatogenesis_enriched", and "oogenesis_enriched" gene sets defined in Reinke et al. 22. Estimating somatic age, required the use of the global estimate as prior (due to gene expression oscillations generating multiple correlation peaks), with the prior standard deviation set to 10 for all samples. Germline age estimates required no prior. To compare expression dynamics between reference and RILs, we kept the overlapping genes between the non-interpolated reference and the samples, quantile-normalized both datasets together, and performed an ICA ('ica' function of 'icafast') extracting 46 components, explaining 95% of the variance in the joined data. A two-sided hypergeometric test was used to evaluate the enrichment of the components in soma, oogenesis and spermatogenesis genes selecting genes above 1.
Quantitative Trait Loci (QTL) analysis on soma-germline heterochrony The multivariate QTL analysis on soma-germline heterochrony among RILs defined as (soma age) -(germline age) was performed by Random Forest (RF) regression  44  with or without batch as a covariate. Each RIL was genotyped at 1455 SNP markers  11. Redundant markers were filtered out from the selected 193 RILs, missing values for the remaining 1105 markers are imputed with the 'rfImpute' function and random forest regression was fit with 5000 trees using the 'randomForest' function; both functions are from the 'randomForest' package (v4. 6. 14). The RF Selection Frequency (RFSF) was used as importance measure, adjusted for selection bias  44  which was estimated by fitting 500 forests of 10 trees to gaussian noise. We estimated the null probability distribution of RFSF through 100 trait permutations, calculated empirical pvalues and adjusted them for FDR.
We also did staging on a second reference interpolated as above but using the first 2 instead of 8 components. For both interpolated references, the number of interpolated time points was set to 500. Further analysis is restricted to the overlapping set of orthologs between worm and fly datasets (3194 genes). We ranked genes by Spearman correlation between the C. elegans embryo time series and their matching timepoints in the second D. melanogaster reference. We then selected the 10% genes with highest correlation (319 genes) and staged the C. elegans samples once more on the second D. melanogaster reference, evaluating staging performance with Spearman correlation and the R² of a linear model between chronological age and estimated age. Hierarchical clustering the top 10% genes in the original D.
Drug dose response on developmental delay in C. elegans. Expression profiles of young C. elegans adults exposed to drugs  35  were staged on the "Cel_larv_YA" reference  21  from the wormRef package (Sup. Table  1 ), with 500 interpolated time points in the reference. We estimated global, soma-specific, and germline-specific ages (see Tissue-specific staging). For each age type, we then subtracted the age of the control sample within each replicate of each drug assay to compute the developmental difference by treatment group. We fit a linear model with drug, dose, and interaction on the age differences to assess the significance of the effects.
Increasing statistical power in differential expression analyses WT and pash-1ts C. elegans samples 36 were staged on the "Cel_YA_2" reference  22  from the wormRef package (Sup. Table  1 ), with 500 interpolated time points in the reference. The second replicate of the first wild-type time point (wt_h0. 2) was omitted from further analysis due to its extreme developmental displacement and lack of comparable mutant sample. d-f , RAPToR staging exploits existing reference time-series expression data (d). This data is first decomposed into principal / independent components which are interpolated with respect to time (e). Interpolated reference is then reconstructed by matrix product of interpolated components and gene loadings (f). g-h, For each sample, a correlation profile is built by computing genome-wide Spearman correlation with every time point of the reference (g).
Fundings M. F. is supported by  INSERM. Work in M. F. 's lab is supported by a grant from the  Agence Nationale pour la Recherche  ( ANR-19-CE12-0009  " InterPhero "),  Université de Lyon  ( IDEX IMPULSION G19002CC ) and  ENS-Lyon  (Projet emergent 2019). R. B. PhD fellowship is funded by the french ministry of research.
Background In the 21st century, a reliable health information system (HIS) must rely on a digitalized decision-making information system (DMIS), which is "a bundle of subject-oriented, integrated, time-evolving, and nonvolatile data designed to aid managers in their decision-making"  [1]. The performance of such a DMIS depends on its ability to manage data from heterogeneous sources, and it uses a data warehousing approach to help the HIS perform well. Thus, it is important to consider transactional information systems in the development of a DMIS (Figure  1 ). In any organization, the quality of the choice of interventions to be undertaken is linked to the visibility that decision makers have of the performance of their organizations. For any health system, the decision-making level needs to rely on a DMIS, which, by means of an extraction, transformation, and loading (ETL) approach, can process data from the transactional systems  [2].
HIS Implementation in SSA Countries The implementation of HISs in SSA has been largely supported by the recent version of District Health Information Software 2 (DHIS2; Health Information Systems Programme)  [5]. The latter was developed under the leadership of the University of Oslo in Norway  [6, 7]. The management of health data through the DHIS2 platform is now effective in many SSA countries  [8]. However, despite this, the performance of information systems remains problematic in many SSA countries, including in Gabon. Indeed, in most health facilities, aggregated data are collected manually by data managers  [9] , who are in charge of integrating these aggregated data into the DHIS2 platform, contextualized according to the country and health system. Nevertheless, this approach to manual upstream data processing often results in a large number of human errors, including incomplete data.
To rectify this predicament, some health system officers have decided to use the e-Tracker DHIS2 technology to digitize all data management tasks  [7]. However, despite such initiatives to help improve the quality of data for decision support, the difficulties in getting integrated information systems that take into account individual data are still present. In most resource-limited countries, specifically in SSA, the implementation of decision support tools is largely supported by the DHIS2 system and its data warehouse (DW) platform. Unfortunately, this platform is not focused on data processing at the operational level, particularly at the level of primary data sources (consisting of transactional systems and where data are produced). However, the DHIS2 platform facilitates the collection of aggregated data at the level of the health department, health region, and central programs that are vertically oriented (HIV, tuberculosis, malaria, etc).
Gabon and Its Health System Context Located in Central Africa, Gabon is a country with a surface area of 267,667 km 2 and an estimated population of 1,811,079 as of 2021. It is bordered by Equatorial Guinea, Cameroon, Congo Brazzaville, and the Atlantic Ocean, which extends for about 800 km along its coast (Figure  2 )  [13]. The health system in Gabon comprises 3 types of stakeholders: public, private, and para-public. The entire system is structured as a pyramidal organization, with health districts at the bottom, regions in the middle, and the national level at the top, which coordinates and centralizes all health care activities and data in the country (Figure  3 ). When the digital infrastructure is adequate, health care institutions are typically digitalized with a reliable internet connection, computers, software, and health care management software.
Overview The implementation of any HIS requires the consideration of all the processes that contribute to the production of data and the dissemination of information. This makes it possible to highlight the sequence of activities enabling the transformation of data into information, information into knowledge, and knowledge into action  [14]. Thus, to define the different relationships between the data structures, in this study, data were first collected from semidirective interviews with the different health actors involved in these different processes. Following data collection, a model of DMIS based on 2 modeling methods was proposed. First, the process method aims to break down activities step by step to study their functioning and their interactions to improve the organization of the overall system.
Analysis of the Different Interactions in the Data Production Process and Modeling of the DMIS Owing to the fact that operational systems such as HIS feed DMIS with data, it was vital for the purposes of this study to analyze the numerous activities that occur within the health care facilities, where the primary data are produced. This allows for a realistic depiction of the many interactions among the actors involved in data production. This description emphasizes the sequence of medical care activities that generate diverse sorts of data. The operational level, which represents the numerous information sources, was now digitalized, and the data integration requirements were easily met by designing an ETL procedure. The latter ensures that the data are processed and loaded into a specific DW  [17].
Data Production Process The production of data that were fed into the DMIS was based on a set of activities that were associated with medical diagnoses performed in a given medical service (operational system). During the medical visit phase, the patient was identified as the central participant around whom the medical staff performed all activities associated with the medical consultation, which produced a set of data. These data consisted mainly, but not exclusively, of 3 types of records: • Sociodemographic data, which were information related to age, gender, nationality, place of residence, and date of consultation of the patient. In addition, the records indicated the name and role of the health provider. • Economic data, which were information associated with the health insurance status, occupation, and copayment rate of the patient.
With regard to health insurance, it was systematically indicated whether the patient received health coverage from the government. • Clinical data, which included vital signs, medical diagnoses, drugs prescribed, and types of laboratory tests prescribed. Traditionally, when the system is not digitalized, all raw data are recorded on paper-based forms. Data processing at all levels becomes problematic. Indeed, at all decision-making levels, data are processed and aggregated manually (Figure  4 ). In this case, their semantic structuring, at least to give them a semantic coherence, might be done in a very subjective way. Manual matching is sometimes carried out subjectively by data managers or statisticians who proceed to perform the semantic matching of local medical concepts with other standard concepts of terminological reference systems such as the International Classification of Diseases (ICD)-10 for diagnoses and the Anatomical Therapeutic Chemical for drugs.
Conceptual Modeling of the DMIS The design of the DMIS considered two aspects: (1) the data production processes (bottom-up approach) and (2) the needs of the users (top-down approach). This led to the identification of 5 decision-making levels for the construction of the DMIS (Table  1 ). Conceptually, this model is based on a relational database model (Figure  5 ). The objective is to propose a DMIS that integrates all the different data for the relational online analytical processing (ROLAP) solution to be performed to allow a multidimensional analysis of the data. A digital interface was integrated into this relational database model, into which data managers enter data from the various hospital registers. This interface is simply a digital replica of the different hospital registers, using the same items, except for the data on patient identity.
The data entered are detailed at the finest possible level of granularity and stored in the various relational tables of the tool. Then, a Structured Query Language (SQL) query was integrated to execute and generate a data table (a fact table) from which all multidimensional analyses can be performed. The model summarized in Figure  5  integrates all the processes contributing to the performance of an HIS, from data collection to the dissemination of useful information for decision support. In this conceptual data model, each decision-making level was organized as a relational table. The tables were then linked together by logical relationships. These relationships made it possible to identify all the existing correlations among the data stored in different tables. This later facilitated their extraction and loading into a table generated using SQL queries.
RenderX To address this issue, an algorithm was defined to help the semantic matching of data and was integrated into a tool that enables the structuring of data and facilitates their automatic processing. The proposed tool provides a graphical user interface, allowing the user to map an unstructured term with a structured term of ICD-10 from other platforms of assistance to coding or semantic annotation based on medical ontologies. This was done to match medical terms with each other and strengthen their understanding by gaining access to more detailed knowledge elements, such as definitions and synonyms  [17]. A match score between 0 and 1 was automatically generated to evaluate the quality of a given match. This score was calculated from the total number of matches established between a given local term and a given standard term.