dataesr/scientific-paragraphs-categorization huggingface dataset:
To focus the downstream analysis to the physical positions corresponding to the analyzed tissue, we used an in-house R script taking as entry an image of the DNA array scanned with the TRICT filter after the reverse transcription step. Indeed, this image reveals the presence of the fiducial borders and the cDNA within the tissue. Specifically, we upload to R a croped image within the fiducials (imager package) and we use the "px. flood" function to retrieve the pixels associated to the tissue. Finally we applied a pixel to gexel coordinates conversion prior to cross this information with the outcome of SysISTD. The "tissue-focused" matrix presenting read counts associated to spatial coordinates in columns, known transcripts in rows was processed with our previously described tool MULTILAYER  6, 7.
For human brain organoids processed in Figure  1 , tissue partitioning has been performed with ≥ 4 contiguous gexels per pattern; Tanimoto Similarity threshold: 25%; while for the 3-dimensional tissue reconstruction in Figure  2 , tissue partitioning has been performed ≥ 3 contiguous gexels per pattern; Tanimoto Similarity threshold: 10%. To compare contiguous sections, the "batch option" of MULTILAYER has been used, allowing to compute spatial classes in addition to spatial communities. Spatial classes, Spatial communities and tissue relationships were visualized within Cytoscape. 3-dimensional view of upregulated genes across sections were generated with an in-house R script (library "rgl"). Prior visualization, spatial gexels across sections were aligned relative to the center of the left human brain organoid (L-hBG) and the angle between the horizontal axis and a vector between the centers of the L-hBG and the R-hBG.
Pseudo coordinates were computed by rotating tissues by the aforementioned angle and by centering the zero coordinate to the center of the L-hBG. Cell/tissue type enrichment analysis has been performed with EnrichR  13  with the ARCHS4  14  , CellMarkerAugmented  15  and Panglao 16 databases. These databases are incorporated within MULTILAYER, allowing to perform the aforementioned enrichment analysis directly within this tool.
Acknowledgements We thank all the members of our laboratories and the  Genoscope sequencing  platform for discussion. This work was supported by the institutional bodies  CEA ,  CNRS , and  Université d'Evry-Val d'Essonne , the  Genopole Thematic Incentive Actions funding  ( ATIGE-2017 ), '' Fondation pour la Recherche Medicale' ' (FRM; funding  ALZ-201912009904 ) as well as the  Institut National du Cancer  (INCa: Funding  2020-181 ).
B. APPLICATIVE LAYER Although Bitcoin was built to support its cryptocurrency, most blockchains are now transversal and built to support an array of applicative use cases (e. g. ,  [12] ,  [13] ). The foundational aspect of which is the Smart Contract, a piece of code which lives on the blockchain and runs exactly as it is programmed, with no possibility of change. Smart Contracts are developed in different languages on different blockchains, e. g. , Solidity on Ethereum or Michelson in Tezos (Michelson being conceptually low level, developers typically use higher level languages such as SmartPy  [19] ). These Smart Contracts can be combined with frontend User Interfaces (UI) to create Decentralized Applications (dApps). dApps differ from legacy apps in that they have a trustless, verifiable, and tamper-proved behavior, complete data integrity, perfect privacy, and do not have any downtime.
B. RM-TLSC ETHEREUM IMPLEMENTATION In this example, we use the popular ERC721 NFT standard  [15] , although any other would function similarly. The complete Smart Contracts are available alongside this paper, including a complete Truffle framework alongside a test NFT and web3py  [49]  testing scripts. We also provide the code and further testing on the Remix Integrated Development Environment (IDE). The Remix project contains two executable testing scenarios, one going through the complete lifecycle of the token successfully, the other highlighting fraudulent behavior not being tolerated by the RM-TLSC. The experiments ran in this subsection used the settings summarized in Table  3. Let us now dive into specifics. The Smart Contract stores three kinds of variables: the ones relevant to the owner status, the token, and the royalty information.
C. RM-TLSC TEZOS PROOF OF CONCEPT To illustrate the flexibility of this solution, we setup a proof-of concept implementation on a less populated infrastructure: Tezos  [54]. Tezos is a developing yet established infrastructure with a lively update cycle, active marketplaces with different stances on royalty management, and low gas costs. We use SmartPy  [19] , an online IDE for Tezos Smart Contracts available through a Python library. This code is accessible alongside the Ethereum implementation. The experiments ran in this subsection used the settings shown in Table  4. A test scenario illustrating a basic utilization of the RM-TLSC, including attempted unauthorized actions, is available to run via SmartPy's ''Run Code'' button. This scenario illustrates the Initialization, Trading, and Termination phases, and is shown in Figure  12.
GEGVIC is designed as a modular pipeline that combines a variety of widely used available methods distributed in three principal modules (Gene Expression, Genomic Variation and Immune Composition), which run independently and include several visualization tools. This open-source software is also presented as a graphical user interface (GUI) using the Shiny framework (GEGVICShine) to eliminate the coding barrier for non-R users and enable comprehensive analyses of tumor samples via one-click features. Conclusions. In summary, GEGVIC provides a simple, powerful and highly flexible workflow for researchers to process and interpret tumor transcriptomic and genomic data while decreasing or eliminating coding burden and facilitating efficiency for inexperienced bioinformatics users.
Background Research in oncology increasingly relies on high-throughput technologies to dissect the complex biology of tumors and the tumor microenvironment (TME). DNA sequencing (DNA-seq) and RNA sequencing (RNA-seq) have become central tools in the cancer field, where they are used to uncover the molecular mechanisms underlying the disease, as well as the dynamics of tumors and TME based on their genomic and transcriptomic profiles. Indeed, the application of such omics technologies to primary and metastatic tumor analyses has already resulted in notable advances in our fundamental understanding of the biological landscapes of most tumor types, with numerous discoveries relevant to cancer diagnosis and treatment  (1, 2). The steady progresses of sequencing technologies and the rapid accumulation of massive amounts of omics data have catalyzed the development of a plethora of bioinformatics tools for the analysis of complex, high-dimensional datasets that are continuously being developed and improved  (3, 4).
Omics data inform on many dimensions of cellular systems and biological processes which can be differently inferred from genomic or transcriptomic data. This justifies the wide range of bioinformatic techniques being developed to answer complex cancer-related questions, ranging from how tumors evolve to how to better predict clinical outcomes (3). However, the most common starting-point for cancer studies from high-throughput datasets is based on defining a set of different molecular features for phenotypes of interest from bulk genomic and/or transcriptomic readouts. This includes the quantification and description of the somatic mutational landscape, the identification of differentially expressed genes between groups (Differential Gene Expression or DGE analysis) along with the signaling pathways they target, and the assessment of the immune cell composition and density in the TME.
In order to simplify and improve the accessibility of such multidimensional (genomic and transcriptomic) bioinformatics analyses, we created a modularized R package named GEGVIC (Gene Expression, Genetic Variations and Immune cell Composition). This package integrates the most frequently used approaches to analyze human or mouse transcriptomic and genomic data in cancer research. GEGVIC can be executed by functions as an R package or as a graphical user interface (GUI) using the Shiny framework (GEGVICShine). GEGVIC has been developed with the aim of facilitating the execution of comprehensive analysis of tumor samples and their microenvironment's immune composition. By eliminating or decreasing the coding burden for inexperienced bioinformatics users, our package allows them to focus on the biological questions and data interpretation.
Implementation and applicability GEGVIC comprises three main modules that run independently: 1) Gene Expression, 2) Immune Composition and 3) Genomic Variation (Figure  1 ). Modules 1 and 2 use transcriptomic data whereas module 3 uses genomic data. Inputs consist of minimally processed transcriptomic (read counts table) or genomic mutations (maf format table) data, which are the universal format files produced by most alignment and variant calling pipelines. A metadata file containing one or more categorical variable of interest for each sample (e. g. , treatment or response status, absence/presence of a genomic alteration, high/low aneuploidy burden) is also required to define groups or conditions. As an R package, the tool can be executed function-by-function or, alternatively, module-by-module, by using a single module-specific function.
Plots can be customized by modifying specific parameters incorporated within the functions. As a web-based tool, users may select their modules of interest, after which they are intuitively guided through the different steps necessary to perform the analyses and customize the data plots. Results from the web-interface are the same as the ones obtained from the R-package, with the additional advantage that the tables generated with the GUI are interactive. The software is also adapted to the analysis of mouse (Mus musculus) data. Although some methods integrated in our tool were designed to work solely with the human genome, GEGVIC automatically converts mouse gene symbols to their human orthologues through the functionalities of the BioMartR package  (12). A comprehensive description of the tool is provided herein, where each module is detailed specifying the utility of each function from the R package and the outputs generated.
Gene Expression module (GE) This module uses the functionalities provided by DESeq2  (13) , with the default parameters for data normalization recommended for bulk RNA-seq. The two main analyses included are (a) the computation of the Differential Gene Expression (DGE) between groups of interest and (b) the subsequent pathway enrichment analysis of genes found by the DGE (Figure  1A , Supplementary Figure  1 ). Before the computation of the DGE, GEGVIC facilitates a first data exploration by performing a principal component analysis (PCA) using the gene expression counts through the function ge_pca(). This function follows DESeq2 steps to normalize counts and transform them using the variance stabilizing transformation (VST). The DGE analysis comparing the selected groups of interest is computed using the ge_diff_exp() function.
The function allows the user to select which logFC shrinkage method to apply (see Supplementary Material). Multiple comparisons are possible when variables contain more than two levels. The output is saved in an object (named results. dds), which can be consulted by accessing the results table and/or visualized using the ge_volcano() function. By default, the ten most significantly up-and down-regulated genes are highlighted. If needed, the ENSEMBL or NCBI gene identifiers can be directly converted to HGNC symbols by running the ge_annot() on the results. dds object and by specifying the Biomart database of interest. The reference genomes from the two last versions of human and mouse genomes (GRCh37 and GRCh38; GRCm38 and GRCm39, respectively) are built in the tool, but the user may also incorporate other versions by previously downloading them.
Next, to perform the functional enrichment analysis from the genes identified by the DGE, GEGVIC provides two complementary methods: Gene Set Enrichment Analysis (GSEA)  (14)  and Gene Set Variation Analysis (GSVA)  (15). The GSEA uses permutation approaches to establish whether a specific pre-defined gene set is significantly enriched in one of the comparing groups of interest (supervised comparison analysis). In contrast, the Gene Set Variation Analysis (GSVA) estimates the variation of pathways' activity over a dataset by translating, for each sample, the gene expression information to gene set enrichment scores, which allows to evaluate the degree and/or type of clustering between samples in an unsupervised manner. Both methods require a collection of gene sets provided by the user, which must be encoded within a single file following the GMT (Gene Matrix Transposed) format.
We recommend using collections from the Molecular Signatures Database (MSigDB), one of the most comprehensive databases of gene sets for performing functional enrichment analysis  (16). For further information see Supplementary Material. To perform the GSEA, the ge_gsea() function takes the functionalities of clusterProfileR  (17) , with the option of modifying the adjusted p-value cutoff if needed. The top regulated gene sets (20 up-and 20 down-) according to the Normalized Enrichment Score (NES) are represented as a bubble plot. To facilitate the interpretation of all the significant information obtained, the same gene sets are grouped by similarity and plotted using the GSEAmining R package  (18). These plots include: (i) a dendrogram clustering the gene sets; (ii) a word-cloud of biological terms enriched at each cluster; and (iii) the top three genes in the leading-edge analysis included within the enriched gene sets present in the cluster.
To perform the GSVA, the ge_single() function uses the GSVA R package  (15). The GSVA results are presented in a form of a clustered heatmap highlighting the groups of interest and the enrichment score of each gene set. This unsupervised clustering of samples using biological pathways instead of genes is complementary to the PCA and can be used to determine the biological relationship between the samples and groups. In both cases, output table can be saved in specific objects (gsea. res or gsva. res) and consulted or exported afterwards.
Immune cell Composition module (IC) This module allows to infer the cellular composition and status of immune infiltrates in the TME for each sample and to compare the results obtained for specific groups of interest (Figure  1B , Supplementary Figure  2 ). Since TME cell deconvolution from bulk transcriptomes is technically challenging and the current algorithms vary in their advantages and pitfalls  (19) , GEGVIC includes different methodological approximations that generate a wide range of comparable and/or complementary results at the same time, giving a more complete and confident picture to the user. Note that before running these analyses, the RNA-seq count data must be transformed to transcripts per million (TPM) using the ic_raw_to_tpm() function. First, an estimation of immune cell fractions for each sample can be obtained by performing the comprehensive analysis provided by the immunedeconv R package  (20)  and incorporated in our tool through the ic_deconv() function.
This package includes six commonly used computational methods (quanTIseq  (21) , TIMER  (22) , MCP-Counter  (23) , xCell  (24) , EPIC  (25)  and CIBERSORT  (26) ) that are run automatically at once except for the CIBERSORT, which requires a previous download and storage of the source code in a user-defined path that needs to be specified (see Supplementary Material or GitHub guide for further information). Results are saved in the ic. pred object, which is a table containing the raw score/abundances assigned to each cell type by each method for each sample. These approaches differ in their scoring strategies as well as the number and names of immune cell populations they examine, making direct comparison and interpretation of results difficult. For instance, xCell predicts 39 immune cell types including a wide range of very specific subtypes, whereas TIMER only predicts 6 general cell types.
Although all results are available through the ic. pred object, GEGVIC also incorporates the ic_plot_comp_samples() function, which allows the integration and visualization of the data to facilitate a first global interpretation of the immune composition of the analyzed datasets. The function groups the immune cell subtypes in seven major categories (B-Cells, Macrophages, mDendritic cells, Neutrophils, NK-Cells, T-CD4+ and T-CD8+ cells), and plots the distribution of the scores obtained for each comparison group and method for each immune category (Supplementary Material). The statistical method for comparison of means ('t-test', 'wilcox. test', 'anova' or 'kruskal. test') can be defined by the user according to the type of data and number of groups to be compared. Besides this, given that CIBERSORT, EPIC and quanTIseq also calculate the absolute cell fractions of each immune cell population within each sample, our tool enables to plot these results through the ic_plot_comp_celltypes() function.
Genomic Variations module (GV) Unlike the two previous modules, this one uses DNA-seq data in order to complement the transcriptomic results with the assessment of specific genomic mutations (Figure  1C , Supplementary Figure  3 ). Taking the standard maf files obtained from any DNA variant caller as input, the two main analyses comprised here consist of: (a) an overview of the mutational landscape identified in the studied cohort and comparison groups by using the functionalities of maftools  (28) ; and (b) a mutational signature analysis of each sample provided by the deconstructSig package  (29). In terms of mutational landscape, two key aspects include: (i) the characterization of those alterations found at higher frequency than expected in a given dataset or comparison group, which are generally interpreted as 'driver' events essential for neoplastic transformation  (30) ; and (ii) the impact of accumulating mutations in the tumor genome (Tumor Mutation Burden or TMB), which may be indicative of genomic instability and has also been linked to immunotherapy response  (31, 32).
To facilitate the analysis of these aspects, GEGVIC incorporates two functions. The first one consists of the gv_mut_summary(), which calculates the number of genomic variants present in each sample and the frequency of each mutation in the study cohort. To display the results, the function generates an oncoplot containing a given number of the top recurrently mutated genes (defined by the user), accompanied by several barplots providing general information on the type of variants detected in the dataset. Within the oncoplot, the samples are sorted by the established comparison groups, facilitating the comparative assessment of their mutational landscape. The second one, the gv_mut_load() function, plots the distribution of mutational load (i. e. , the total number of mutations in each sample) for each group of interest and performs the statistical comparison of these distributions using a user-defined method.
Results To exemplify the applicability of GEGVIC in a biologically relevant scenario, we used the publicly available genomic and transcriptomic data from the TCGA colorectal cancer dataset (COAD-READ), and we established the annotated microsatellite instability (MSI) / microsatellite stability (MSS) tumor status as comparative variables. A random subset of this data is available as a demo input within the package. MSI tumors represent a well-defined subset of colorectal carcinomas (CRC) with special molecular etiology and characteristic clinical features, including an increased survival rate and higher sensitivity to immune checkpoint blockade  (34, 35). They are characterized by a hypermutated phenotype caused by incompetent DNA mismatch-repair (MMR), and typically present high levels of immune cell infiltration  (35) (36) (37).
Consistently, the mutational analysis from the Genomic Variation module showed (a) a significantly higher mutational load of MSI tumors compared to the MSS tumors (Figure  2a ); (b) the oncoplot illustrating the genetic alterations typically associated with CRC (e. g. APC, TP53, SMAD4 and PIK3CA), where is it also possible to visualize the expected lower proportion of APC mutations and the higher proportion of BRAF V600E mutations in MSI-related tumors, as well as their enrichment in TTN mutations, a pancancer biomarker of high tumor mutation burden (38) (Figure  2b ); and (c) the enrichment of signatures 6 and 15, associated with deficiencies of MMR  (39) , in the MSI tumors (Figure  2c  and 2d ). In terms of transcriptomic data, the expression analysis from the Gene Expression module provides (a) the PCA obtained from the gene-count matrix which discriminates the MSI and MSS groups (Figure  3a ), directly indicating the global biological distinction between both tumor subtypes; (b) the volcano plot highlighting the top-10 significantly upregulated and the top-10 signficantly downregulated genes in MSI in comparison with MSS (Figure  3b ); and (c) the expected enrichment of terms and pathways related to immune system modulation in MSI tumors, observed from both GSEA and GSVA analyses, as well as the downregulation of processes related to lipid metabolism that is also in consistence with previous findings (40) (Figure  3c  and 3d ).
Finally, the Immune Composition module allowed to better explore to which extent the immune landscape of the TME differs between MSI and MSS tumors (Figure  4 ). Interestingly, CRC patients with an MSI phenotype typically display a high levels of T-lymphocyte infiltration and strong antitumor immune response, which has been associated with the high mutational burden and neoantigen load  (41, 42). Instead, MSS CRC harbors a disrupted immune response in the TME, characterized by low infiltrated T cells and reduced immune cytotoxicity  (43). Consistent with these observations, the application of the GEGVIC integrative strategy to the immunodeconv results revealed a significant enrichment in CD4+T / CD8+T lymphocytes and macrophages in the MSI tumors (Figure  4a )  (44, 45). Similarly, the results from the IPS analysis indicated that the higher immunogenicity of the MSI samples can be explained by the higher proportion of immune effector cells (i.
Discussion In recent years, scientific research has witnessed a rapid evolution of high throughput technologies, which in turn led to a shift in focus towards data analysis over experimental procedures. High-throughput technologies and workflows have become an integral component of biological research, leading to considerable expansion of the bioinformatics field. The increasing availability of multi-omics datasets opens new opportunities for complex integrative data analysis, providing for a more thorough assessment of complex biological phenomena. However, the analysis of such large datasets can prove challenging, and it is becoming crucial to ensure that the ability to examine this type of data is not limited to individuals with advanced computer skills. With sequencing technologies becoming more ubiquitous in the experimental labs and clinical settings, it is vital to provide access to both easy-to-use and comprehensive open-source tools to overcome the barrier of 'big data' analysis and enable wet-lab as well as clinical scientists to analyze common omics data derived from their own experiments.