dataesr/scientific-paragraphs-categorization huggingface dataset:
Layers used for each architecture are selected as a trade-off between memory storage and time-consuming (deeper layers provide a coarser representation of the image, therefore, extracting less descriptors) and discrimination performance. Table  1  lists the layers used and the number of descriptors considered for the analysis. Tables  2 3 4 show the numerical results of DP and SENS of the features selected by each comparative method (columns 2-7) and the DM tool (first column), by using the networks and the layers listed in Table  1 , for case studies #2-#4. METHODS 1-6 are applied as follows. First, features are extracted by applying a given network and layer over the original dataset of training images. Then, features are ranked according to the criterion included in methods (1-5) or directly selected using method 6.
After ranking, features are selected by taking the first Nsel ranked features, where Nsel is the number of features selected using DM tool. By extracting also the same features over the modified dataset of images (obtained after applying the proper tests according to the imaging modality), we then computed the SENS and the new DP that are listed in Table  2. In this way, we may directly evaluate the importance of using the sensitivity criterion in conjunction with the DP value, especially in comparing DM approach with the METHOD 5 that actually uses a DP-like ranking criterion, but evaluate on the original dataset and without the support of the sensitivity evaluation procedure. Average values of SENS and DP are listed in the table, while values in brackets represent the standard deviation computed over the set of features selected.
With this in mind, we selected a case study from the recently published LIVEcell labelled dataset  13. In particular, with the aim to present a practical application in line with the examples used in this work, we selected BT-474 cells, breast cancer cells grown in rafts. The task was to recognize the cancer growth factor after 4 h. We then compared cells at day 0 with cells visualized after 4 h and applied the DM platform to select most discriminant and robust deep features for the task. Figure  8  shows five examples of cells from each group. The annotations allow us to locate each cell and extract a Region of Interest (ROI) around it. According to the simulation results shown in Fig. 7 , we selected the CNN Densenet201 with average pooling layer 'average_pool5′ that exhibited the highest average DP values for all three tests.
In order to create a challenging scenario, we performed the classification task of discriminating cells at day 0, hour 0 from cells at day 0, hour 4. To make the task realistic, avoiding overtraining of the model, we applied the perturbation tests 1-3 (luminance, movement, and out-of-focus) to the images in the test set, as provided by the LIVEcell dataset. We extracted all the 5554 ROIs annotated in the training dataset and test the 1912 ROIs from the test set after applying the random perturbations described in the method. The features extracted through the DM toolbox were then fed to an SVM with a linear kernel  20   As it can be clearly noted, the opportunity to select the features, not only allows higher classification performance in terms of ACC, F1-score, and AUC but also strongly reduces the time required by the model to be trained.
001 initial learning rate. Training took about 33 h on Matlab 2022b, using the GPU NVIDIA GeFORCE RTX and Intel Core i7, 9th generation. Also, we randomly selected 5000 training data from 16662 for computational time constraints. We then applied the tuned network to the test set used in the previous experiments. Results achieved in terms of ACC are shown in Figs. 9-11 as the cyan line. In addition, we also applied the DM feature selection criterion to the deep features extracted by the fine-tuned DENSENT201 network. Results are represented by the black lines. As it can be noted, the fine-tuned network is not sensible to the feature selection procedure, probably due to the tuning step. On the other hand, the generalization capability of the fine-tuned network remains quite low. Although results are comparable in terms of ACC and F1 scores, a training time of 33 h and the need to retrain the network in the presence of new perturbation tests,   as needed, make DM tool the outperforming approach considering also that its reduced set of features with respect fine-tuned network is utilized as input for the classification models.
Conclusions The greatest goal for all those who develop data analysis techniques for medical applications is to see their work used in a real context. Unfortunately, the transition from the excellent results obtained in the laboratory, for example, in a hospital, is anything but easy. In the real measurement scenario, many variables are not controlled, and their variations can lead to not acceptable performances. Such an issue is even more critical when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this work, we have introduced a software platform called Deep-Manager, that counteracts this limitation by analyzing the performance and sensibility of each feature to different disturbances.
Methods Deep-Manager platform allows users to perform specific sensitivity tests to their own images dataset to select the most appropriate features for the specific classification task. Sensitivity tests aim to detect which features extracted from ad hoc algorithms (handcrafted) or from a pre-specified Deep Learning network through transfer learning approach are more sensitive to external quantities and phenomena that are acquisition-specific. Among the existing vast panorama of acquisition devices and experimental set-up, with the aim to prove the effectiveness of the proposed method, we selected three of the most used practical contexts in the field of biological image analysis: Deep-Manager 1. 0 available artifacts. For the modality IM-ACQ-1 we implemented "Brightness artifact", "Stage multi-positioning artifact", "Out-of-focus artifact".
We consider here DP equal to the maximum value between 1-AUC and AUC, being AUC a way to quantify the discrimination capability of a descriptor in a binary classification problem  17. With respect to the AUC value, the DP of each feature has been chosen due to its invariance to the "feature-to-label" direct or inverse relationship. In other words, either high or low AUC values (both indicative of highly discriminant capability) correspond to high DP values. The software applies a threshold value th DP to classify descriptors according to the DP values and a threshold value th SENS to classify descriptors according to the sensitivity values. In light of this, descriptors are classified into different regions: high DP and low SENS (those selected) having DP higher than th DP and sensitivity lower than th SENS (cyan markers in Fig.
For details, see the original biological publication  10. In this use case, we demonstrated how deep features' discrimination capability of the T-cell cytotoxic effect in lung tumors is influenced by image alterations and compared results with those achieved using standard features selection approaches and diverse deep learning architectures. Case Study #5. Phase contrast TL microscopy static images of BT-474 breast cancer cells from the recently presented public dataset LIVECell. With the aim to demonstrate the potential of selecting deep features through the proposed DM tool, we selected a case study from the recently published LIVECell labeled dataset  13. In particular, with the aim to present a practical application in line with the examples used in this work, we selected BT-474 cells, breast cancer cells grown in rafts.
2D transmission light time-lapse microscopy, 3D phase-contrast time-lapse microscopy, and 3D fluorescence time-lapse microscopy. The implemented sensitivity tests are therefore thought for those contexts. However, the list of possible tests of the Deep-Manager platform could be enlarged in the future to other fields such as histopathological imaging or indirect immunofluorescence. For this reason, in the remainder, we will indicate the present release as Deep-Manager 1. 0 version. Link: https://github. com/BEEuniroma2/Deep-Manager. All the data required to reproduce the figures are in the Supplementary Data 1 file. Deep-Manager 1. 0 considered imaging modality. Regarding the imaging modality the actual version of DM-tool considered three distinct imaging modalities (Supplementary Notes 1. 1): IM-ACQ-1 (2D TL time-lapse microscopy, Supplementary Notes section), IM-ACQ-2 (3D phase-contrast TL time-lapse microscopy), IM-ACQ-3 (3D fluorescence time-lapse microscopy).
Data availability The authors declare that data supporting the findings of this study are available within the paper [and its supplementary information files]. Any other support and request can be submitted at the corresponding author email: martinelli@ing. uniroma2. it or filling the form that can be found at https://web. bee. uniroma2. it/our-contacts/. Images belonging to the LIVECell dataset can be downloaded at https://sartorius-research. github. io/LIVECell/. See also ref. 13.
Omics technologies has opened new possibilities to assess environmental risks and to understand the mode(s) of action of pollutants. Coupled to dose-response experimental designs, they allow a non-targeted assessment of organism responses at the molecular level along an exposure gradient. However, describing the dose-response relationships on such high-throughput data is no easy task. In a first part, we review the software available for this purpose, and their main features. We set out arguments on some statistical and modeling choices we have made while developing the R package DRomics and its positioning compared to others tools. The DRomics main analysis workflow is made available through a web interface, namely a shiny app named DRomics-shiny. Next, we present the new functionalities recently implemented.
, 2025 , DESeq2, Love et al. , 2014 , EdgeR, Robinson et al. , 2010). The analysis of such data typically starts with a pairwise differential analysis to the control followed by an enrichment procedure to identify GO (Gene Ontology) terms or KEGG (Kyoto Encyclopedia of Genes and Genomes) biological pathways of differentially expressed items (e. g. contigs, proteins)  (Dubois et al. , 2019 , Murat El Houdigui et al. , 2019 , Meier et al. , 2020 , Zhan et al. , 2021). In a second time, some perform a DR modeling on differentially expressed items to estimate a BMD per item and summarize the sensitivity of each pathway for example by the median of BMDs of corresponding pathways  (Meier et al. , 2020 , Zhan et al. , 2021). Studies implementing DR (multi-)omics approaches sometimes aim at a mechanistic understanding of adverse effects (Adverse Outcome Pathway perspective -AOP).
They could identify potential Modes of Action of pollutants (MoAs) at the molecular level, that generally need to be validated in a second step using targeted experiments  (Andersen et al. 2018). Some authors already used our R package DRomics ("Dose Response for Omics") on transcriptomics and/or metabolomics data on different organisms and pollutants, to help the understanding of the adverse effects, by identifying most sensitive pathways  (Larras et al. , 2020; Gust et al. , 2021; Vokuev et al. , 2021). DRomics was also used on community transcriptomics and metabolomics data to provide insights into mechanisms of pollution-induced community tolerance  (Lips et al. , 2022; Larras et al. , 2022). And recently,  Song et al. (2023)  showed, using DRomics, how DR modelling and estimation of points of departure at several omics and apical levels can be mapped to an AOP network.
Those applications of DRomics especially motivated us to develop new R functions and a new shiny application to help the biological interpretation of DR modeling of omics data. The purpose of this paper is to present the new version of the DRomics R package and its two companion interactive web applications, DRomics-shiny and DRomicsInterpreter-shiny. We first review the software available to tackle a DR analysis of high-throughput omic data and explain how DRomics distinguishes from other tools. Then, we present the various functionalities we added to DRomics from its first version published in 2018 and especially explain the way it can be used to make sense of DR (multi-)omics data in environmental risk assessment.
The main characteristics and functionalities of those five tools are summarized in Table  1. DRomics is the only tool which is composed of both an R package and a free web application (see Table  1 ). We chose to develop it in the R language among others i) to facilitate the interoperability with the Bioconductor packages that implement state-of-the-art bioinformatics methods and ii) to add a shiny application (interactive web app. straight from R) that can be launched both from the package and from a server freely accessible without registration. The companion DRomics-shiny application was thought for users who do not want to work in the R environment, but also to help new users to take the package in hand. For that purpose, the R code of the whole performed analysis is provided in the last page of the shiny application.
While developing DRomics, we were cautious to limit as much as possible the dependencies to other statistical tools and to only depend on well-maintained R packages. Notice that the installation of BMDx is currently impossible because of dependencies to non-maintained R packages. DRomics was at the root designed to be able to analyze data from typical DR design, favoring the number of doses over the number of replicates per dose, or even for datasets with no experimental replicates. This situation of DR approach with no replicates is met in some field studies (one dose per sample) and in some screening studies as illustrated in  Rollin et al. (2023). This is the reason why, for the selection of significantly responsive items, we did not use classical methods based on comparison of means at different doses, which cannot be applied without replicates, such as one-way ANOVA, William's trend test  (Williams, 1971)  or the ORIOGEN method  (Peddada et al.
rproject. org/web/packages/DRomics/vignettes/DRomics_vignette. html#models or  Larras et al. , 2018). BMDExpress and DRomics results were compared in the case study of a microarray dataset, as reported in the supporting information of  Larras et al. (2018). DRomics models were shown to give a better description of data (smaller AIC values) and more repeatable and conservative BMD estimations for biphasic responses. Unlike the other tools (Table  1 ), DRomics does not only provide a BMD estimation, but also a characterization of the DR response in four classes (increasing, decreasing, U-shape, bell-shape) which we thought may be of great interest in an AOP perspective, when the DR analysis not only aims at defining BMD values but also at making sense of DR (multi-)omics data in environmental risk assessment.
Thus, functions of the DESeq2 and limma packages are internally called within the package to normalize and/or transform omic data and to implement the trend tests for selecting significantly responsive items. The call to additional R functions can be added for example to correct omics data for a potential batch effect (see an example using ComBat-seq in the package vignette: https://cran. r-project. org/web/packages/DRomics/vignettes/DRomics_vignette. html#batcheffect). Various functions were also added to the package (highlighted in bold type in Figure  1 ) to respond to user requests. Among them we can cite PCAdataplot() for a visualization of data after the importation step and detection of potential outlier samples or batch effect, targetplot() to visualize the response of targeted items whatever they are selected or not in the DRomics workflow, bmdboot() and bmdplot() to compute and visualize confidence intervals on BMD values using bootstrap.
New functions and the new shiny application to help biological interpretation In toxicology, while working on sequenced and well-annotated organisms, items (e. g. genes) can be functionally annotated prior to DR analysis. BMDExpress integrates this annotation step for 6 model species on the basis of GO or reactome databases, and FastBMD for 14 model species on the basis of GeneID, GO, Reactome or KEGG databases (see Table  1 ). Such an annotation of all the items whatever they respond or not to the dose gradient exposition, enables the classical enrichment analysis  (Wu et al. , 2021). This analysis consists in highlighting gene sets/pathways which are the most overrepresented among the responding ones. In ecotoxicology, one often works on non-model organisms or even on samples from environmental communities (freshwater biofilm for example -  Creusot et al.
• for selected groups/pathways, all the DR curves represented as curves (using the curvesplot() function, see a multi-level example on Figure  3 , right part), Those functions can also be used to compare the response at one molecular level but measured under different experimental conditions (different time points, different pre-exposure scenarios, in vitro/in vivo, etc. ). The selectgroups() function was also developed to help the user to focus its interpretation on the most represented and/or the most sensitive biological groups (see an example in the package vignette: https://cran. r-project. org/web/packages/DRomics/vignettes/DRomics_vignette. html#selectgroups).
Acknowledgements The authors thank colleagues and students whose requests and/or discussions contributed to the recent evolution of DRomics, especially  Olivier Armant ,  Ellis Franklin ,  Sandrine Frelon ,  Sophie Prud 'homme,  Mechthild Schmitt-Jansen  and  Philippe Veber. We also want to thank the referees for their helpful suggestions that contributed to the improvement this manuscript. Preprint version 4 of this article has been peer-reviewed and recommended by Peer Community In Ecotoxicology and Environmental Chemistry (Cosio 2023, https://doi. org/10. 24072/pci. ecotoxenvchem. 100105 ).
Data, scripts, code, and supplementary information availability Scripts and code are available online: DRomics is written in R, freely available from CRAN and encompasses two shiny applications (https://cran. r-project. org/web/packages/DRomics/). Those shiny applications are also freely available online. The homepage https://lbbe. univ-lyon1. fr/fr/dromics gathers links to all resources related to DRomics, e. g. the two shiny applications, a cheat sheet and a complete vignette to start with DRomics. The github https://aursiber. github. io/DRomics/ hosts DRomics stable as well as development versions. Data used in this paper are embedded in the DRomics package.
, 2007 Philips et al. , 2019 Toxicology free standalone application that must be locally installed yes (GitHub) Java, C, Fortran 2007 2020, BMDExpress 3 in prep. https://github. com/auerbac hs/BMDExpress-3 not mentionned Software References Main application area Platform Open source Programming languages Date of the first version Date of last revision (as on 02/02/2023) Dependencies to other statistical R packages Types General Import/ Visualisation of data.
sapiens, M. musculus, C. no lupus familiaris, R. norvegicus, D. PCA + Density melanogaster, D. rerio yes optional (by default yes, > 2) GO, reactome optional (by default no) ANOVA, William's trend test, not found Oriogen Shapes of proposed models User selection of models Proposed criterion for best fit model choice Characterization of the response Toxicity threshold provided Method for computing confidence intervals on the toxicity threshold Includes the annotation step Preprocessing of transcriptomic data (microarray/RNAseq) supported by the Available organisms for annotation software Proposed plots to check imported data Selection of significant responses to the dose gradient Filter on the fold change for selection Annotation available databases Control of the FDR for this selection Functions to compare the responses at Proposed methods for selection different experimental levels (multi- omics, diff.
Harmonizing cell types with respect to transcriptomics identity and nomenclature across the single-cell community and assembling them into a common framework is an essential step towards building a standardized Human Cell Atlas. Here we present CellTypist v2. 0 (a new version of our automated annotation tool, https://github. com/Teichlab/celltypist), where we develop a predictive clustering tree-based approach to resolve cell type differences across datasets that have different naming conventions, annotation resolution, and technical biases. CellTypist v2. 0 accurately quantifies cell-cell transcriptomic similarities and enables robust and efficient cross-dataset meta-analyses. Cell types are placed into a relationship graph that hierarchically defines shared and novel cell subtypes. Application to multiple immune datasets.
confirms its sensitivity and specificity by recapitulating expert-curated cell annotations. We also apply CellTypist to datasets from eight diseases which all lead to pulmonary fibrosis, and reveal underexplored relationships between healthy cell types and a variety of diseased cell states. Furthermore, we present a workflow for fast cross-dataset integration guided by the harmonized cell types at different levels of annotation granularity. Finally, we apply CellTypist to 12 tissue atlases from 38 datasets, and provide a deeply curated cross-tissue database (www. celltypist. org/organs) with more than 3. 6 million cells and 250 cell types. This is complemented by a large collection of machine learning models for automatic cell type annotation across human tissues.
Introduction The past decade has seen an accumulation of single-cell genomics datasets profiling a variety of tissues at different developmental stages. By integrating these resources, international consortia such as the Human Cell Atlas (HCA) have begun to chart a standardized reference map in the human body  1, 2. A challenge in assembling distinct cell atlas datasets is that different laboratories use their own definitions of cell types, often resulting in inconsistencies in the naming schema. This challenge is being partially met by the ongoing Cell Ontology database effort, which aims at unifying cell type labels across communities  3. Other contributions come from the NIH Human BioMolecular Atlas Program (HuBMAP)  4  project and its ASCT+B tables that name and interlink major anatomical structures, cell types and biomarkers  5  , and the Cell Annotation Platform (a collaborative effort funded by Schmidt Futures, https://celltype.
info). However, standardizing cell types across datasets, denoted here as the process of cell type harmonization, has intrinsic challenges beyond nomenclature. These include the variability in cell quality caused by various technical confounders, presence of shared versus novel cell types across datasets, and the discrepancy in annotation resolution due to differences in cell sampling depth and in laboratory-specific annotation strategies. A number of label transfer methods have been developed to match cell types across datasets  [6] [7] [8] [9] [10] [11] [12] [13]. A limitation of these methods is that they are only applicable to two datasets (reference and query). Aligning multiple datasets simultaneously is still arduous as it requires multiple pairwise comparisons and brings challenges in information integration and consistent visualization.