dataesr/scientific-paragraphs-categorization huggingface dataset:
reconstruction package) and SPIHIM (a data collection), to respond to the need for reproducibility and open access in single-pixel imaging. The SPIHIM collection currently contains 140 hypercubes that are natively 64 × 64 × 2048 in size with a spectral resolution of 2. 3 nm and a spatial resolution that can be adjusted between 182. 4 µm and 15. 2 µm using a digital zoom. It also contains the hypercubes reconstructed at resolution of 128 × 128 × 2048 by a data-driven reconstruction algorithm. The SPIHIM dataset can be exploited for spectral imaging studies in general, but also for more specific studies. In particular, the OpenSpyrit ecosystem should provide a benchmark for single-pixel reconstruction algorithms. In the future, the data collection is expected to continue growing and, in a similar manner, the SPYRIT package should integrate novel reconstruction algorithms.
Background: Exploring the function or the developmental history of cells in various organisms provides insights into a given cell type's core molecular characteristics and putative evolutionary mechanisms. Numerous computational methods now exist for analyzing single-cell data and identifying cell states. These methods mostly rely on the expression of genes considered as markers for a given cell state. Yet, there is a lack of scRNA-seq computational tools to study the evolution of cell states, particularly how cell states change their molecular profiles. This can include novel gene activation or the novel deployment of programs already existing in other cell types, known as co-option. Results: Here we present scEvoNet, a Python tool for predicting cell type evolution in cross-species or cancer-related scRNA-seq datasets.
ScEvoNet builds the confusion matrix of cell states and a bipartite network connecting genes and cell states. It allows a user to obtain a set of genes shared by the characteristic signature of two cell states even between distantly-related datasets. These genes can be used as indicators of either evolutionary divergence or co-option occurring during organism or tumor evolution. Our results on cancer and developmental datasets indicate that scEvoNet is a helpful tool for the initial screening of such genes as well as for measuring cell state similarities.
The understanding of cell biology emanates from describing cells by their functions, their gene expression, interactions with their environment, and their lineage relationships. The emergence of single-cell RNA sequencing (scRNA-seq) began a new age of transcriptomic research, extending our understanding of cell heterogeneity and dynamics. Highly detailed atlases of cell types were produced for many tissues and organisms, in normal or pathological conditions  [3] [4] [5] [6] [7]. Comparing those highly divergent datasets would allow asking key questions regarding the conservation of core genetic programs in poorly-related cellular contexts, the origins of cellular diversity and its evolutionary mechanisms, or the transcriptional paths leading to disease. However, data received from various biological conditions and various organisms is entangled by technical and biological batch effects which vastly complicates their comparison  [8, 9].
Currently, there is a lack of a specific tool that uses closely or distantly-related scRNAseq datasets as input to study the potential co-option and evolution of gene programs between different organisms during development and differentiation, or between tumor cells at different stages of tumor progression. Kun Xu et al. [12]  used Monocle  [13]  and scVelo  [14]  to study transcriptome dynamics of malignant cells between the primary tumor and lymph node metastases. They also used NATMI  [15]  for the generation of the cell-to-cell receptor-ligand network where edges are generated based on the expression of the ligand in one cell type and of its related receptor in another cell type. However, this latter strategy is not designed to study co-option in cancer which is a crucial mechanism forcing the molecular changes that propel tumor progression  [16].
In another work, Pandey et al. use scRNA-seq to study the evolutions of neuronal types by comparing cell types in larva and adult zebrafish. They utilized Random Forest to generate a model for each cell type and predict cells with each model to build a confusion matrix mapping cell types by the number of cells predicted with each model  [17]. Yet, this strategy is not wrapped into a framework and is not designed to extract genes that are characteristic of cell type transitions. These genes could be considered the drivers of genomic individualization, which is the process of diversification of cell types during evolution. These genes might play a role in functional segregation, divergence, co-option of gene modules, and de novo gene emergence. Thus, we present scEvoNet, a method that builds a cell typeto-gene network using the Light Gradient Boosting Machine (LGBM) algorithm  [18]  overcoming different domain effects (different species/different datasets) and dropouts that are inherent for the scRNA-seq data  [19].
This tool predicts potentially co-opted genes together with genes characteristic of each cell state during development across species. Recently we showed the ability of a similar LGBM-based classifier to detect neural crest cells in distantly-related scRNA-seq datasets  [20]. Despite technical batch effects (datasets were made in different laboratories with different technologies) and biological batch effects (datasets were from two evolutionarily distant organisms and at different developmental time points), we have achieved a high AUC score of 0. 95 for classifying zebrafish cells with our frog-based NC model  [20]. Here, we have expanded this method: scEvoNet applies to a variety of applications, e. g. , between different time points during a given organism's development, between species, and when comparing primary tumor and metastasis.
Implementation The workflow of scEvoNet is illustrated in Fig. 1A. scEvoNet takes (1) an expression matrix, and (2) a list of cell labels as input data per organism/time point of interest. For each cell type provided by a user, scEvoNet generates an LGBM binary classifier (one Fig. 1  scEvoNet scheme. A scEvoNet takes a list of clusters and a matrix of expression for each sample as input. For each sample, it generates an object with cell type classifiers and top important features for each cluster from the provided set of clusters. In the final step, the tool builds a confusion matrix and a network of genes associated with each cell type. B We use the LGBM algorithm to produce a classifier for each cell type. To smooth the data in order to deal with the batch effects we apply the sigmoid function and use only the top important features to create the final cell type classifiers cell type vs all other cells) in two steps (Fig.
1B ). The LGBM itself is a gradient boosting framework which is based on a tree learning algorithms. Firstly, it generates a model considering all genes in the dataset. For the obtained model of the particular cell type, scEvoNet selects the top 3000 important features (cell types-related genes) and uses only them to re-train the final cell type model which will be used for the generation of the cell types confusion matrix. The basis for choosing the top 3000 important features (cell types-related genes) for retraining the final cell type model is to achieve a smaller batch effect. The batch effect refers to the variation in gene expression caused by technical differences in the scRNA-seq data, such as different sets of genes being expressed as a result of the drop-out effect. By reducing the number of genes used in the model, we aim to reduce the impact of the batch effect on the accuracy of the model.
3000 genes were chosen as the optimal number because it strikes a balance between minimizing the batch effect and still maintaining a sufficient number of genes to accurately classify cell types. Thus, this is part of the domain adaptation that we perform to make the resulting model less dependent on the number of genes that are missing in another dataset to which the model will be applied (e. g. , 30% of the genes considered crucial for training a NC classifier on frog data were absent from the zebrafish dataset  [20] ). Additionally, to reduce the effect of the different biological domains between datasets, and to reduce the effect of the scRNA-seq data sparsity, we apply a sigmoid function that smooths expression units in a more flexible manner than simple binarization, which has been shown to keep enough information for scRNA-seq data analysis  [21].
For the model training, we use early stopping to avoid overfitting with 10 rounds which determines the actual number of estimators in the regressions. Overfitting occurs when a model becomes too complex and begins to fit the noise in the training data instead of the underlying pattern, leading to poor generalization performance on unseen data. Early stopping helps to mitigate this issue by preventing the model from learning the noise in the training data. Next, scEvoNet uses each cell type model to predict cells from both datasets. This way we get a confusion matrix with cell type to cell type comparison. In the next step, scE-voNet builds a network where the nodes are cell types or genes so that cell types can only connect to genes. To do this, firstly we extract top features (genes) which are important for each cell type (both positively correlated and negatively correlated).
Feature importance refers to the evaluation of the contribution of each feature to the prediction accuracy of the model. It measures how much a feature affects the impurity of the target variable, by quantifying the average decrease in impurity caused by splits of the data based on that feature. Once we identified the important features for each cell type, we combine all the cell type-related important features (genes) into one main network with all cell types and cell type-related genes. This strategy is similar to GRNboost2  [22]  which outperformed many other tools in a recent benchmarking study  [23]. GRNboost2 generates a gene-gene network similarly, whereas scEvoNet extends it to all cell types in two datasets. Furthermore, scEvoNet implements a shortest path search in order to generate a subnetwork of interest.
Results First, we applied scEvoNet to identify core characteristics during the evolution of the neural crest (NC) cells using two different vertebrate organisms. The NC is a multipotent and migratory cell population unique to vertebrates and essential notably for pigment, peripheral and enteric nervous system, and craniofacial structures formation  [24]. For the input data, we used whole embryo scRNA-seq datasets for the Xenopus tropicalis, a non-amniote tetrapod vertebrate, at an early developmental stage (late gastrulation stage 12, and neurulation stages 13 (neural plate) and 14 (neural fold)) and Mus musculus, a mammal, at a similar developmental stage (late gastrulation stage 8. 25)  [20, 25]. Before using scEvoNet, we validated how one NC-based classifier trained on the Xenopus dataset will recognize NC cells in the whole embryo mouse dataset and obtained the result of a 0.
89 AUC score (Fig. 2A, B ). The NC classifier demonstrated the ability to accurately identify neural crest cells in a dataset from a different organism with a low rate of false positives. This is significant because each classifier is utilized to generate a gene program, and the more accurately the cells of a given type are classified, the more accurate the gene program's signature will be. Next, using published cell type annotations for these two whole embryo scRNA-seq datasets, we run scEvoNet and obtained the confusion matrix (Fig. 2C ). In this dataset of extended complexity, the highest similarity score for frog NC remains the mouse NC. Thereafter, we built a network of cell types and related genes. To identify genes that are highly conserved and cell type-specific in the evolution of the NC we selected a sub-network that consists of the shortest paths from Xenopus NC to mouse NC with the top 3 close cell types according to the confusion matrix.
The third group includes genes that are associated between the frog NC and mouse NC, and shared with the mouse NC-related cell types (as defined with confusion matrix of similarities): mafb, cldn6 for the neural plate; zic3, tfap2a for the midhindbrain. Thus, our tool was not only able to construct a matrix of similar cell types that can be used to study cell types similarities, but also defined three groups of genes that may have diverse roles in a cross-species transformation of the molecular profile of neural crest cells. Next, we applied scEvoNet to a human breast cancer metastasis dataset  [12]. We selected a patient with available datasets for the primary tumor and the lymph node Fig. 2  The development of the specific cell type between frog and mouse. A First UMAP represents highlighted annotated neural crest cells in the whole embryo dataset, second UMAP represents predicted neural crest cells with our classifier, and third UMAP represents predicted scores of our classifier (the predicted scores represent the probability of a given data point belonging to the NC class).
g. , B cells, plasma cells, immune cells, macrophages, dendritic cells, and tumor cells. Next, to study cancer cell evolution, we used scEvoNet to discover common cluster-specific genes between the most distant malignant clusters from the primary tumor (cluster p_cancer_cells_cox6+) and metastasis (cluster m_cancer_cells_gapdh+). We found 14 genes that were directly connected to both cell types (Fig. 3C ), among them malat1, levels of which inversely correlate with breast cancer progression and metastatic capacity  [32] , and b2m an important marker involved in carcinogenesis, invasion, and metastasis  [33]. Among this list of genes directly connecting two tumor cell types were several mitochondrial genes. Although a common hypothesis relates the expression of mitochondrial genes to sample or data processing artifacts, growing evidence supports the importance of mitochondrial genes in cancer metastasis  [34].
The clustering was performed similarly to that used in the analysis of the earlier breast cancer dataset. We have identified the same number of neuronal types, 15 in total for each dataset. Firstly, we generated a confusion matrix of cell types and verified the association of comparable clusters as outlined in the original study (Fig. 4A ). Generally, our findings aligned with the previously published results, including, for instance, the association of the larval kiss1 + cluster with multiple kiss1 + clusters in the adult sample (Fig. 4A , green stars). Furthermore, we established a link between the cluster of tubb5 + immature neurons and the rpl3 + tubb5 + adult neurons, as reported in the original article (Fig. 4A , blue arrows; Fig. 4B, C ). Our analysis also confirmed the close proximity of habenula clusters in larva and adult Zebrafish expressing tac3a (1a and 3y clusters, Fig.
4A , red arrows). To sum up, the first step of scEvoNet effectively reconfirmed the results of a comprehensive comparison of clusters between the larva and adult phases. Moreover, our method of displaying the similarity between cell types is more advanced, as it enables a user to compare clusters simultaneously in two datasets which provides a more comprehensive view and allows user to assess the similarity between clusters by comparing them to those of the same type/stage within and across datasets. Furthermore, bipartite network (generated with the second step of scEvoNet) analysis revealed that, for instance, rgma gene contributes significantly in a subgroup of cells that also co-express tac3a in larval stages (cluster 3y) and a cluster in adults that co-express gng8 and gng2 (cluster 6a; Fig.
4D ). Simultaneously, expression of rgma decreases in a related 1a cluster in the adult samples (Fig. 4B, C ). This change in expression pattern of rgma suggests that the gene may have been re-used (or maintained) from its original function to a new one in the development of the nervous system across different stages (from larva cluster 3y to adult cluster 6a). It is also possible that the rgma gene is involved in multiple processes during nervous system development, and its expression level and pattern are regulated by different factors in different stages. Further research is necessary to fully understand the role and function of the rgma gene in the development of the nervous system. Therefore, scEvoNet demonstrates the potential of our tool to investigate the dynamics of gene program utilization by exploring how different genes programs are connected through various time points.
Conclusion The evolution of cell types and gene programs is one of the main focuses of developmental biology and is crucial for a better understanding of the origin of particular functions. For the moment, there is a lack of computational tools to address this question using the abundant scRNA-seq data publicly available databases. We found only one existing approach which is not wrapped into a usable framework (e. g. , R/Python package) and has only one application (cell states comparison) so it cannot be used to extract genes that are linked to cell types transitions such as co-opted genes or genes conservatively important for several cell states  [17]. In this manuscript, we present scEvoNet, a method for analyzing the evolution of cell states from highly sparse scRNA-seq data. Our study demonstrates the feasibility of using this method to examine transitions between different species, stages, and from primary tumors to metastasis.
With this tool, we re-discover a canonical gene signature that remains conserved through evolution, and also predict species-specific genes and new candidates associated with similar cell types. Our findings may indicate the co-option of genes or shared programs in closely related cell types. It also suggests the potential use of an immune escape mechanism in breast cancer metastasis, which has previously been shown in another cancer type. Yet, one limitation is that scEvoNet does not match gene sequences and only works with labels provided by the user, which can reduce the number of genes to be found between different cell types in cross-species comparison. scEvoNet has the potential to greatly advance the field of developmental biology by allowing for the study of cell type evolution and gene program switching.
Funding This project received funding from  European Union 's  Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie Grant  agreement No  860635 , NEUcrest ITN (to AHMB);  Agence Nationale pour la Recherche  ( ANR-21-CE13-0028 ; to AHMB) and  Institut Universitaire de France  (to AHMB);  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ).
Code availability List the following: Project name: scEvoNet. Project home: https:// github. com/ monso ro/ scEvo Net. Operating system(s): Mac OS, Linux. Programming language: Python 3. Other requirement: lightgbm ≥ 3. 0. 0, pandas ≥ 0. 24, networkx ≥ 2. 5. License: MIT license. Any restrictions to use by non-academics: MIT license.
Artificial neural networks are emerging as key tools to model brain processes associated with sound in auditory neuroscience. Most modelling works fit a single model with brain activity averaged across a group of subjects, ignoring individual-specific features of brain organisation. We demonstrate here the creation of personalised auditory artificial neural models directly aligned with individual brain activity. We used a deep phenotyping dataset from the Courtois neuronal modelling project, where six subjects watched four seasons (36 hours) of the Friends TV series in functional magnetic resonance imaging. We fine-tuned SoundNet, an established deep artificial neural network, to achieve substantial improvement in predicting individual brain activity, including but not limited to the auditory and visual cortices.
The past decade has seen a push towards deep, individual-focused neuroimaging datasets. These have opened the possibility of directly aligning ANNs with individual brain responses, thus providing a more personalised understanding of auditory processing. This approach not only enables the characterization of individual-specific features of brain organisation but may also enhance the generalisation of learned representations to downstream tasks. In the present study, we aim to align a pretrained auditory ANN with individual brain data and study the subsequent impact on auditory task performance, using a rich, deep fMRI dataset from the Courtois NeuroMod project  (Boyle et al. , 2020).
Individual brain models Cognitive neuroscience has seen an uptick in the use of deep, individualised datasets, such as the myConnectome project, wherein an individual underwent rigorous scanning for 18 months  (Poldrack et al. , 2015) , and the Natural Scenes Dataset  (Naselaris et al. , 2021) , featuring exhaustive imaging of up to 40 hours per subject (N=10). The NSD in particular was established with a unique emphasis on tailoring artificial neural networks (ANNs) to individual brain patterns  (Gu et al. , 2022). This approach aims to not only train ANNs to predict brain responses accurately at the individual level, but also capture idiosyncrasies in brain organisation. Exploring this concept further, Seeliger and coll. developed a multilayer convolutional network trained on 40 hours of fMRI data from a single individual watching episodes of the television show Dr.
Courtois NeuroMod Despite these advancements towards individual brain models, in particular in the field of vision, we are not aware of attempts to train auditory ANNs with individual brain data and assess its impact on auditory task performance. In this work, we take advantage of the sample collected by Courtois NeuroMod  (Boyle et al. , 2020) , the largest deep fMRI dataset to date, featuring few subjects (N=6) with over 100 hours of fMRI per subject in their 2022 release. The Courtois NeuroMod dataset features a wide range of tasks across multiple domains, and includes several movie watching datasets featuring a complex soundtrack. This dataset thus opens the possibility to build individual models of sound processing.
Materials and Methods fMRI data Participants Six healthy participants (aged 31 to 47 at the time of recruitment in 2018), 3 women (sub-03, sub-04 and sub-06) and 3 men (sub-01, sub-02 and sub-05) were recruited to participate in the Courtois Neuromod Project for at least 5 years. All subjects provided informed consent to participate in this study, which was approved the ethics review board of the "CIUSS du centre-sud-de-l'île-de-Montréal" (under number CER. Three of the participants reported being native francophone speakers (sub-01, sub-02 and sub-04), one as being a native anglophone (sub-06) and two as bilingual native speakers (sub-03 and sub-05). All participants reported the right hand as being their dominant hand and reported being in good general health. Exclusion criteria included visual or auditory impairments that would prevent participants from seeing and/or hearing stimuli in the scanner and major psychiatric or neurological problems.