dataesr/scientific-paragraphs-categorization huggingface dataset:
In vitro functional validation of Blimp1-associated module in human PDAC cells Blimp1 is one of the most overexpressed gene in pro-metastatic HMGA2+ PDAC cells  (Chiou et al. , 2017). In their study,  Chiou et al. analyzed the consequences of Blimp1 silencing in mice PDAC cells to unveil its function in disease progression. Based on in silico, in vitro and in vivo experiments, the authors concluded that Blimp1 acts as a driver of the metastatic ability of PDAC cells. In particular, they found that Blimp1 is a Hypoxia/Hif-regulated gene in human and murine PDAC which is in a good agreement with functions recovered in modules 2 and 5 raised by AMINE processing (Table  1  and Fig. 4 ). Surprisingly, Blimp1 was not included in these modules, but was indeed detected in a module of 4 genes (module number 169 with a p-value of 0.
05. Among the ten best modules, we found that modules 2 (p-value < 1. 01e-11) and 9 (p-value < 2. 83e-8) were associated to cytokine production and inflammatory process (Table  3  and Fig. 5B ) after functional enrichment. Next, in order to confirm the putative involvement of Blimp1 in epithelial cancer cell inflammatory process in vitro, we silenced BLIMP1 in MIA PaCa-2 cells, a human PDAC cell line, using siRNA silencing, (Fig. 6A ), and explored how it modified the profile of cytokine secretion using a cytokine profiling array. Indeed, we found that Blimp1 repression triggered the production of IL-18Bpa and angiogenin, two anti-inflammatory factors  (Lee et al. , 2014)  and reduced the secretion IL-6, a major proinflammatory interleukin  (Tanaka et al. , 2014)  (Fig. 6B ). Altogether, these results further validate AMINE as valuable method to detect relevant functional modules from large experimental datasets, and unveil a new function of Blimp1 in PDAC-related inflammatory process.
Discussion This paper proposes a new method for identifying gene modules that are activated as a result of a state shift caused by a biological experiment. Our method, called AMINE, uses as inputs, on the one hand, the ultimate result of any RNA-Seq analysis pipeline which is the differential expression of genes, and on the other hand, a network modeling the interactions between genes. Regarding the second point, the version of our method that is executable (at the address http://amine. i3s. unice. fr/) makes use of the protein interaction data provided by the STRING database for four model organisms: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens and Mus musculus. Thus, a user only needs to provide the differential gene expression data generated by the pipeline of his choice.
From a very simple interface (supplementary Fig. S5 ), he only has to specify the name of the organism analyzed, the file on which the data are located and the Id of the columns containing the genes' names, the p-values and optionally the fold changes to be able to launch the process. The address of the page containing the results is e-mailed to the user when the processing is completed. On the result page, the most significant modules are listed, however, all the modules found can be downloaded as an Excel document consisting of two sheets. The first sheet, named "list of modules" contains the list of all modules found. The results are presented in 4 columns containing the module number, the list of genes in the module, the s score of the module and the associated p-value. The second sheet, named "genes to modules", is composed of two columns: the first one contains the name of a gene and the second one, the module to which it belongs.
Despite the large number of methods developed over the last 20 years, AMINE identifies, with a higher accuracy than its competitors, modules created computationally on datasets intended to mimic the topology of biological networks. Extrapolating these results to a measure of accuracy on real datasets is very difficult. There is no method to ensure that good predictions on artificial data translate into good predictions on real datasets. However, we have made a special effort to ensure that our simulations are close to real datasets. The networks we generate, with the parameters presented in this article, are closer to a real interaction network than the networks used by some competing methods. However, these efforts run up against reality. Indeed, it is known that the interaction networks stored in public databases are both incomplete and contain erroneous interactions.
In their study, Von  Mering et al. (2002)  estimate that, for Saccharomyces cerevisiae, the protein-protein interaction data (PPI) reported in public databases account for only one third of existing interactions. This observation leads us to believe that methods based very precisely on the topology of networks are not to their advantage. We think first of all of the methods based on the identification of cliques. Their performance is more than questionable if we consider that a large part of the interactions between proteins are unknown. If we consider that the missing interactions are randomly distributed on the graph, we can estimate that all the paths on the graph are impacted in the same way and thus that the methods based on random walks could be the least affected. Intuitively, we can indeed argue that, in a graph on which a certain proportion of the edges have been randomly deleted, if, from a source node A, random walks allow on average to reach node B before node C, then, on the complete graph, node B will probably always be closer to node A than node C.
, 2002). The great advantage of our method is that it does not require any parameterization; it is not even necessary to indicate the number of modules to be identified or the size of the modules. We have checked that our method performs well on artificial datasets and compares favorably with existing methods that are the current state of the art. We then processed a real dataset from a study focused on PDAC, on which AMINE retrieved modules associated with functions involved in PDAC metastatic process. Moreover, we highlighted functions that could not be detected using traditional approaches consisting in analyzing only the most differentially expressed genes. Our studies show that AMINE can identify modules corresponding to functions not revealed by traditional approaches. Indeed, we found that Blimp1, one of the most up-regulated gene in highly metastatic cells, participated in the regulation of pro-inflammatory process, a result confirmed by in vitro by the silencing of Blimp1 in human PDAC cells.
Materials and Methods The method AMINE predicts active modules from data consisting of a background knowledge about gene interactions and measurements representing, in the specific context of a given experiment, indicators of the involvement of genes in the studied process. This concept of geneinvolvement is materialized by a p-value which quantifies, for each gene, the statistical significance of its variation (Fig. 7A ). Data about gene interactions and gene variations are merged to generate an attributed gene network in which genes are annotated with a numeric attribute representing the extent of their variation (Fig. 7B ). Mathematically, the dataset is represented as an attributed graph G=(V , E , λ) consisting of a set of vertices V (also called node, that symbolize the genes), a set of edges E ⊆ {(u , v ) ∈V 2 ∨u ≠ v } and a value function λ ( v ):V → R which associates a value p ∈ R to each vertex v ∈ V.
An induced subgraph of G is a subset S of the vertices of G together with those edges of G with both endpoints in S. Many active module detection algorithms focus on identifying induced subgraphs whose values associated with their nodes stand out from the values associated with the other nodes of the graph. We hypothesize that focusing heavily on the detection of connected sets of genes may not be optimal, given the fact that the interactions between genes described in the databases are still largely incomplete. For this, we adopt a definition of a module that is closer to the one used in cluster analysis: object that are grouped together (in a module) are more similar to each other than to those in other groups. The notion of similarity encompasses a component taking into account the distance on the graph between the vertices belonging to a module and a significant nearness between the values associated to these vertices.
Human Cytokine Array For the cytokine assay, the Proteome Profiler™ Human XL Cytokine Array Kit (R&D Systems, Minneapolis, MN, USA) was used. The array was carried out using 500µl of cell supernatants obtained by incubating MIA PaCa-2 in DMEM 0% FBS, 48h after siRNA (si-Ctrl or si-Blimp 1-2) transfection, following the manufacturer's instructions. For analysis of cytokine arrays, the intensity of each spot was measured using ImageJ software. Background was removed from all values, and they were normalized to the positive control spots. Comparison of several metrics associated, on the one hand, an artificial dense network generated with the extended Barabasi-Albert model using 3 initial nodes and setting parameters p and q to 0. 09 and 0. 7 respectively and, on the other hand, a subnetwork containing the same number of vertices extracted from the STRING database.
14e-06 1. 82e-06 transmembrane receptor protein tyrosine kinase signaling pathway GO:0007169 4. 04e-16 5 6. 17e-5 regulation of cell migration regulation of peptidyl-tyrosine phosphorylation GO:0030334 GO:0050730 4. 64e-07 5. 60e-07 angiogenesis GO:0001525 Module number P-value of the module Function/Signaling pathway Accession Id P -value of annotation regulation of interleukin-2 production GO:0032663 0. 0129 169 0. 044 regulation of alpha-beta T cell differentiation GO:0046637 0. 0129 Th17 cell differentiation mmu04659 0. 00038.
These capabilities have also led to an increased interest in DL in standard image-analysis workflows such as nuclear segmentation  5, 10, 11  , a common task that can be a significant challenge if done manually  12. However, the potentially game-changing capabilities of DL have, to date, remained out of reach for most researchers without a strong background in computer sciences. A classical DL pipeline requires a computer algorithm (called "artificial neural network" or "DL network" here for simplicity) to be trained on a training dataset to generate a "model" that can perform a specific task. The training step is crucial as it will dictate the specificity and performance of the model. Generally, there are two different approaches to this step, using either supervised or self-supervised training  13.
In the supervised approach, paired input and output images are required. The network learns by attempting to find a mapping from the input to the desired output images, on an image-by-image basis. In the self-supervised approach, networks learn implicit patterns in the data and therefore, do not require paired input and output images. For instance, in a supervised image restoration task, a DL network, such as content-aware image restoration (CARE)  7  , is trained using a dataset containing many examples of noisy and paired high-quality images. Once trained, the model can be used to denoise images similar to the noisy images encountered during training. Training these DL networks is computationally expensive and often requires coding and computational expertise. In many biomedical research laboratories, neither of these requirements are readily available, hindering the adoption of an ever-growing number of powerful DL methods.
To train DL networks, computer scientists typically set up local servers with high computational power or purchase expensive DL-ready workstations (Fig. 1a ). This constitutes a robust way to develop and deploy DL approaches but requires technical knowhow and financial commitment to set up and maintain. An alternative is to purchase computational resources provided by cloud services to train DL models. Trained DL models can then be used directly in the cloud or downloaded and used locally (Fig. 1a ). Several software packages taking these approaches have been developed for the bioimaging community, especially trying to simplify the interface with DL hardware (i. e. , U-Net 2,14 , cDeep3m  10  , DeepCell Kiosk  15  , DeepMIB  16  , NucleAIzer  17  , YAPiC (https://yapic. github. io/yapic/), ImJoy  18  , ilastik  19  , CellProfiler  20  and Noise2Void  8  and DenoiSeg Fiji plugin  11, 21  ).
Another solution is to take advantage of model "zoos" which provide trained DL models with high reusability and versatility potential (Fig. 1a ). These can be used directly to obtain predictions from new data using web interfaces or ImageJ plugins (i. e. , CellPose  22  , DeepImageJ  23  , StarDist Fiji plugin  5, 6  ). Using pretrained models alleviates the computational requirement of training and has therefore become popular for tasks such as cellular and nuclear segmentation  6, 17, 22. However, using pretrained models bears the risk of predicting incorrect structures and artefacts if they are used on unseen data too dissimilar to the data they were originally trained on  24, 25. This is also supported by observations that models perform best on datasets very similar to the training datasets  7, [25] [26] [27]  (see Supplementary Note 1 and Supplementary Fig.
1 ). Training will therefore often be necessary for users to achieve optimal performance of models on their own datasets. Given these considerations, there is a need for a tool that seamlessly enables users to train, validate and experiment with DL tools for various image-analysis tasks without the constraints of expensive resources and the potential drawbacks of pretrained models. Here, we developed ZeroCostDL4Mic, an entry-level, cloudbased DL-deployment platform (Fig. 1b ) that simplifies DL use for microscopy (Supplementary Movie 1). ZeroCostDL4Mic is a unified collection of self-explanatory Jupyter Notebooks, featuring an easy-to-use graphical user interface (GUI) (Supplementary Fig. 2 ) that requires only a web browser and a Google account for a user to run any of our DL-based tasks (Fig.
1c ). All calculations are performed in the cloud using Google Colaboratory (Colab for short), circumventing the need to purchase or install graphical processing units (GPUs) and associated software. Using ZeroCostDL4Mic does not require prior knowledge in coding. Researchers can, in a few mouse clicks and aided by a common workflow, install all needed software dependencies, upload their imaging data and run networks for training and prediction (Supplementary Movie 1). Within our framework, models are comprehensively evaluated for performance and reliability and can be directly applied to new data automatically. Models can also be ported to local machines to obtain predictions for which accessible interfaces are available (DeepImageJ 23 , StarDist  5, 6  , CARE  7  ). Additionally, ZeroCostDL4Mic guides researchers on how to generate the training data necessary for DL, allowing them to develop a deeper understanding of DL methods while experimenting with parameter optimisation.
Results The ZeroCostDL4Mic framework. The ZeroCostDL4Mic platform is built around the availability of cloud computing and the versatility of Jupyter Notebooks (Fig. 1b ). Jupyter Notebooks can efficiently and interactively run Python code, currently the default language to deploy DL applications. For cloud computing, we focused on developing the platform around Google Colab as it provides an appropriate range of resources for free, e. g. GPU, random access memory (RAM) and disk space. These online resources provide researchers interested in DL an entrance to the field without the need to purchase and maintain a local infrastructure dedicated to DL and allow the versatility to easily run multiple networks without reconfiguring the infrastructure. To make ZeroCostDL4Mic as easy to use as possible, we exploited the Jupyter Notebooks' code readability and Colab's integration of code input via a graphical user interface (GUI).
In each notebook, we also included two strategies commonly used to improve the training of DL models. Firstly, we added a range of data augmentation steps, using the Augmentor  32  or imgaug (https://github. com/aleju/imgaug) python packages that can be applied to the training data to effectively increase the diversity of the training data without requiring the user to produce and provide more data (Supplementary Note 3). Secondly, we also included the possibility to perform transfer learning  33  via the loading of a pretrained model as a starting model rather than initialising training with a blank model (Supplementary Note 4). This powerful approach allows the platform to benefit from the growing availability of pretrained models from model zoos without compromising on the quality of the performance of a model on the specific data type provided by the user.
2 3 4 5 6 7 8 9 (see Supplementary Table  1  for details of the dataset used and Supplementary Table  2  for training parameters and times). Installing the necessary libraries takes less than a minute. Training times vary depending on the network used and the assigned GPU (see Supplementary Table  3  for install times and training speeds), but providing a speed improvement ranging between 5× and 200× over central processing unit (CPU) computation time. Once a model is trained, and its performance validated, it can be applied to new data (see Supplementary Table  4  for inference speeds). Resources are typically sufficient to train models on real biological data and to provide the results presented in this paper. We do, however, also discuss strategies to overcome potential limitations (Supplementary Note 5).
Within ZeroCostDL4Mic, we implemented several DL-based image-analysis tools, including networks for image segmentation and object detection, image denoising and restoration, superresolution microscopy and image-to-image translations. In the following sections, we introduce each of the tasks and DL networks that are currently available on our platform, showcasing results obtained by using Colab. Image segmentation and object detection. Manual segmentation is a time-consuming and challenging task that typically requires expert knowledge and can be a bottleneck for studies that aim to quantify large datasets  12, 37. Hence, DL tools are of great interest in this field, as they can combine expert-level performance with highthroughput analysis  4. Within ZeroCostDL4Mic, we implemented two networks, U-Net 2,28 and StarDist  5, 6  , which perform state-ofthe-art image segmentation, both handling 2D and 3D imaging datasets.
To illustrate the applicability of our ZeroCostDL4Mic U-Net 2D and 3D notebooks, we trained U-Net networks to segment membranes from 2D electron microscopy (EM) images and mitochondria from 3D EM images (Fig. 2a , b and Supplementary Movie 3). To showcase our StarDist notebooks, we first trained a StarDist model to segment the nuclei of densely packed cell monolayers (Fig. 2c ). Interestingly, the trained model could detect extra nuclei that were missed when these images were manually labelled (Fig. 2c ). In addition, to facilitate the analysis of live-cell imaging data, the outputs generated by the StarDist notebook are directly compatible with the popular tracking software TrackMate which consequently enables automated cell tracking (Fig. 3d  and Supplementary Movie 4)  38. Object detection tasks have become of interest in microscopy studies as they allow the identification of multiple classes/objects in an image, e.
However, when performing live-cell imaging experiments, low laser intensities need to be combined with low expression of the molecule(s) of interest to ensure the physiological relevance of the phenomenon observed  40. Therefore, live-cell imaging experiments often lead to the acquisition of noisy images, and denoising strategies are becoming increasingly essential for the interpretation of data and its further analysis. Content-aware image restoration (CARE)  7  can denoise and improve the resolution of 2D and 3D images, using supervised training. To illustrate the applicability of our ZeroCostDL4Mic CARE notebooks, we trained a 3D CARE network to denoise live-cell structured illumination microscopy (SIM) imaging data (Fig. 4a , b and Supplementary Movie 6). To generate a suitable training dataset, both high and low SNR images of the actin cytoskeleton were acquired from fixed samples.
The network trained using these images was then used to restore live-cell imaging data (Fig. 4a, b  and Supplementary Movie 6). The approach employed here is especially useful as it can be very challenging to obtain high-quality live-cell imaging by other means while using SIM for extended periods of time  41, 42. Noise2Void 8 is a DL method capable of denoising microscopy images using self-supervised learning, therefore in the absence of a dedicated paired training dataset (Supplementary Movies 7-9). This has the advantage that the network can be trained directly on the data that needs processing and therefore makes this approach very easy and powerful to use. We first demonstrated the capabilities of our Noise2Void notebook to denoise the movie of an ovarian carcinoma cell migrating on cell-derived matrices (Supplementary Movie 7).
Here, a single Z stack (time point) is used to train Noise2Void, and the resulting model is applied to the rest of the movie (Fig. 4c  and Supplementary Movie 7). Next, we used the ZeroCostDL4Mic notebook to denoise data capturing the endogenous expression levels of a glioma cell endogenously labelled for paxillin-GFP, migrating on polyacrylamide hydrogel (Fig. 4d  and Supplementary Movie 9). In all the displayed examples, the Noise2Void models trained in ZeroCostDL4Mic performed very well and significantly improved the quality of the images. However, it is important to note that, in our hands, Noise2Void did not perform well when used to denoise the actin dataset used to train CARE (Fig. 4a ). This is likely due to the noise in this particular dataset not being homogeneous and containing imprinted patterns from the SIM reconstruction process.
This highlights the importance of testing a range of networks to identify which approach is most suited for a specific dataset, underlining the need for a platform integrating a broad range of networks. Super-resolution microscopy. Over the last two decades, superresolution microscopy (SRM) has enabled cellular structures to be observed down to the nanoscale  43. SRM methods often require the post-processing analysis of images, which can be aided by DL strategies  29, 44, 45. Within ZeroCostDL4Mic, we implemented Deep-STORM  29  , a DL network capable of reconstructing singlemolecule localisation microscopy (SMLM) data from dense Training Deep-STORM requires raw SMLM data accompanied by ground-truth localisation coordinates. Importantly, Deep-STORM can be trained using simulated data. For this purpose, we included a simulator within the ZeroCostDL4Mic notebook to directly generate SMLM training (and test) data that can be tuned to mimic the experimental data type that needs to be subsequently analysed.
Therefore, performing image-to-image translation using fnet and pix2pix requires the user to provide a paired dataset for training (Fig. 6a ). The fnet  26  network was developed to perform label-free predictions from bright-field and EM images. It is based on a U-Net architecture and training requires paired 3D stacks of two channels (e. g. fluorescence and bright-field images). To showcase the ZeroCostDL4Mic fnet notebook, we trained a fnet model to predict TOM20 mitochondrial labelling from brightfield images (Fig. 6b ). In contrast, pix2pix  30  uses GANs  53  to  of images from one domain and a set from the other without the need for any particular correspondence and, due to this, it provides unique flexibility in training. As for pix2pix, CycleGAN uses GANs to perform the image-to-image translation task and requires 2D images to train.