dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter interaction was shown to be necessary for invasiveness. Additional examples illustrating the discovery potential of LR maps can be found in the literature  (17) (18) (19) (20) (21). Those results highlight the need for a systems biology tool that would assist investigators in portraying cellular networks by inferring confident putative LR interactions for follow-up validation. SingleCellSignalR is the first such tool available in R. It relies on a comprehensive database of known LR interactions, which we called LRdb. It also introduces a new regularized product score aimed at adapting to variable levels of depth in single-cell datasets, i. e. the prevalence of censored read counts or dropouts. LRdb is the result of integrating and curating existing sources plus manual additions; to the best of our knowledge, it is the largest database of this kind.
LRdb contains human genes, but we can accommodate murine datasets by translating murine genes to their human orthologs. An application example illustrates this functionality on mouse skin data. Several authors proposed tools for mapping LR interactions  (18, (22) (23) (24) (25) (26)  from scRNA-seq data. They exploited different reference lists of potential LR interactions and LR interaction scoring schemes to rank those interactions. A systematic comparison of scoring sensitivity and selectivity, and tool features is presented.
LRdb--a curated database of ligand-receptor interactions We decided to favor interpretable inferences that are supported by the literature or experimental data. Accordingly, we compiled the content of existing databases that contain such LR pairs: FANTOM5 (6), HPRD  (27) , HPMR  (28) , the IUPHAR/BPS Guide to Pharmacology  (29)  and UniProtKB/Swissprot  (30)  annotations related to families of ligands or receptors covered in the previous databases. We also extracted LR pairs, with respective participants annotated as ligand or receptor in GO  (31) , from Reactome (32) pathways. We required GO Cellular Compartment (GOCC) annotation 'receptor complex' (GO:0043235) for receptors, and 'extracellular space' (GO:0005615) or 'extracellular region' (GO:0005576) for ligands. Reactome pathways were downloaded from Pathway Commons  (33).
SingleCellSignalR implementation The software package was implemented in R, following Bioconductor standards. Basic usage examples are provided in Supplementary Material and the package documentation. In principle, we encourage users to apply advanced data normalization, clustering and cell-type calling tools  (12, 34) , and to submit preprocessed count matrices to Sin-gleCellSignalR to perform LR interaction inference and visualization. For convenience, we implemented simple data preparation steps enabling users to start from a raw read count matrix. We normalize individual cell transcriptomes according to their 99th read count percentile. In case a cell has its 99th percentile equal to zero, it is discarded. Normalized read counts (+1 to avoid zeros) are log-transformed. Clustering can be obtained either by chaining principal component analysis for dimension reduction and K-means  (35) , or by using the advanced SIMLR model  (36).
Datasets Different single-cell datasets were used to illustrate and benchmark SingleCellSignalR. MELANOMA is a metastatic melanoma dataset covering several patients  (40) , SMART-Seq2 protocol, GEO GSE72056. 10xPBMC is a peripheral blood monoclonal cell dataset from healthy donor (41), Chromium 2 protocol. 10×T is a pan T-cell dataset from healthy donor (42), Chromium 2 protocol. 10×PBMC and 10×T were downloaded from 10× Genomics web site. HNSCC is a head and neck squamous cell carcinoma (primary and metastatic) dataset  (16) , SMART-Seq2 protocol, GEO GSE103322. PBMC is a second, deeper peripheral blood monoclonal cell dataset  (43) , SCRB-seq protocol, GEO GSE103568.
48, No. 10 PAGE 4 OF 12 given couple of cell types. That should imply that the interaction is likely to exist but it might result in poor P-values for real LR interactions that are shared by many couples of cell types. For instance, in tumors, it is common that many immune cell populations express immune checkpoints and their ligands, e. g. PD-1/PD-L1, forming an immunosuppressive micro-environment. In such a case, many combinations of cell types would involve the PD-1/PD-L1 interaction resulting in bad P-values if an insufficient number of other cell populations, not expressing these molecules, would be present in the dataset. To address the points above, we introduced a regularized product score: LRscore = √ lr μ + √ lr , where μ = mean(C) and C is the normalized read count matrix. In this empirical score, the mean μ acts as a scaling factor, and the square roots are meant to keep the lr products and μ on the same scale.
The LRscore is bounded by 0 and 1, independent of the dataset depth. Now, to define a score threshold is not a simple task since, by essence, all LR pairs listed in LRdb are correct in a particular context, i. e. defining false LR interactions must rely on external knowledge specific to the couple of cell types considered each time. Ideally, single-cell transcriptomes would be available for a tissue where all possible LR pairs would have been tested experimentally, e. g. by immunofluorescence, eventually including functional evidence. Such data are obviously not available. To estimate an appropriate LRscore threshold and to compare LRscore to other scoring schemes nonetheless, we decided to use an ad hoc benchmark that would be unbiased regarding the scoring schemes and accurate enough biologically.
We already mentioned LR pair scores equal to the product lr or the mean l+r 2 as well as their P-values. A further proposed scheme was to select in each cell type, those genes whose expression was specific. Subsequently, whether the reference database would contain pairs of such genes was checked  (22, 24). In the case of Zhou et al. (22) , gene selection relied on a z-score calculation. To obtain a ROC curve, we varied the coefficient applied to the standard deviation at that gene selection stage. Another selection-based tool  (24)  did not report any score. It is thus not included here but covered in the tool comparison below only. scTensor  (23)  applied non-negative Tucker decomposition to model ligand and receptor read counts as sums over the many LR pairs they might contribute to. Despite considerable efforts, we could not use it beyond its pre-packaged example.
It is therefore absent from the presented comparison. We applied the two benchmarks above to five datasets covering several cell types in T ref and in P ref ('Materials and Methods' section). Every couple of cell types, both in T ref or P ref , yielded ROC curves such as the example featured in Figure  2A  for 10×PBMC data against T ref. Areas under the curves (AUCs) over all the datasets and all the cell-type couples represented in T ref are presented in Figure  2B ; individual curves and dataset-specific AUCs are in Supplementary Figures  S2-6. We see that the best AUCs were achieved by LRscore, the product, and the average. As expected, P-value calculations yielded inferior performance. Zhou et al. selection mechanism was also inferior to the best scores. We next asked how variable a threshold aimed at cutting at a certain FP rate would be; we imposed 5% FPs (Figure  2C  and Supplementary Figures  S2-6  for datasetspecific plots).
Naturally, due to its design, LRscore thresholds turned out to be much more stable. In Figure  2D , we show how a threshold common to all the single-cell datasets could be determined, imposing <5% FPs in 75% of all the ROC curves of all the five datasets. We found LRscore > 0. 4. Repeating the analysis for the proteomics reference P ref gave the results in Figure  2E  and F (and Supplementary Figures  S7-11 ) that are very similar. The computation of a common threshold to achieve <5% FPs in 75% of the ROC curves resulted in LRscore > 0. 6 (Supplementary Figure  S12 ). This more stringent threshold can be explained by a potential lower sensitivity (∼10 000 proteins) compared to FANTOM5 transcriptomics and by the obvious differences between transcriptome and proteome. These considerations indicate that a universal threshold that would guarantee, e.
g. 5% FPs cannot be determined, but score regularization is already a major step toward more rigorous cutoffs. To illustrate this, we imposed LRscore > 0. 5 to all the datasets against both T ref and P ref. From Figure  2G , we see that we managed to maintain the FPs in a reasonable range, and, most importantly, the variability of the FP rate within each dataset is modest. That is, a threshold can be set and FP rates on LR interactions between any combinations of two cell types remain comparable. Repeating this analysis with the product score and common threshold 10 -2 found as above, we note large discrepancies between datasets.
Representing ligand-receptor interactions After scoring and application of a threshold, Single-CellSignalR outputs LR interactions in various formats. 10×PBMC raw UMI counts were submitted to our default pipeline, which clustered cells in six populations: B cells, T cells, regulatory T cells (Tregs), neutrophils, cytotoxic cells and macrophages (Figure  3A ). A summary chord diagram can be generated that indicate the number of LR interactions between each cell population couple (Figure  3B ). Chord diagrams for individual interactions between two populations are possible as well (Figure  3C ). Inspection of the expression of the ligand and the receptor involved in a LR interaction is implemented in mixed or dual 2D projections (Figure  3D-E ). Chord diagrams are convenient for looking at specific couples of cell types but not to account for the whole dataset.
Comparison with other tools The general characteristics of SingleCellSignalR and related tools appear in Table  1. iTALK  (25)  infers LR pairs from a compilation of public LR databases using a product score limited to the most abundantly expressed genes. It requires a read count matrix, where cell type calling was performed beforehand. iTALK includes a feature to deal with multiple datasets (time course, different conditions) such that variability and trends in ligand and receptor expression across datasets can be added to the LR plots. CellPhoneDB  (26)  is both an online tool and a Python package that can be downloaded. It scores LR pairs after P-values of the mean score. A modification is operated in case of a multimeric receptor and/or ligand, requiring that all the subunits are expressed.
This unique and biologically sound feature might cause false negatives due to dropouts in scRNA-seq data. Kumar et al. (18)  employed a product score. Instead of simple data shuffling, they applied a statistical test (Wilcoxon) to assess that over several tumors, the score was significantly different from zero. That procedure cannot be reproduced when analyzing a single dataset. Zhou et al. (22)  proposed to select LR pairs based on specific expression of the ligand and the receptor in two cell subpopulations, referring to a database of known LR pairs. Specific gene expression was tested, requiring that the average expression of a gene, in a subpopulation, was above its mean expression over the whole count matrix plus three standard deviations. scTen-sor  (23)  aims at inferring LR pairs through non-negative Tucker decomposition, explaining the observed ligand and receptor read counts as sums of the contributions of all the interactions they would engage in.
It exploits a database of potential LR pairs generated from STRING interactions and Swissprot annotations (secreted/membrane) automatically, which yields a considerable number of putative LR pairs and does not cover many known interactions (Figure  1C ). One attractive scTensor feature is the LR reference available for multiple organisms. PyMINEr (  24 ) is a Python program that can perform k-means plus clustering based on log-transformed and normalized data. Cell-type calling is based on subpopulation specific gene pathway enrichment; application of characteristic gene signatures is not available. By contrast, SingleCellSignalR relies on gene signatures to perform cell-type calling (see Supplementary Methods for details and Supplementary Figure  S15  and Table  S7  for an illustration). It is possible to prepare and cluster data externally with PyMINEr.
LR pairs between two cell subpopulations are inferred from pairs of genes found to be specific to each subpopulation, in interaction in STRING  (57) , and respectively classified as receptor and ligand in GO. There is no scoring per se, only this selection process. PyMINEr further infers a so-called co-expression graph, linking genes correlated over the whole read count matrix. This graph can be exploited to project different data such as the expression of genes in one specific subpopulation. Like scTensor, PyMINEr relies on automatically generated LR references from STRING. As explained above, we could not use scTensor beyond its prepackaged example dataset (58) despite considerable efforts. We nonetheless tried to obtain some elements of comparison by processing the same data with SingleCellSig-nalR.
We found two reliable LR pairs whereas scTensor returned 14 pairs, none in common, see Supplementary Table  S2. Not being able to test scTensor on more examples did not allow us to perform a real comparison. We analyzed 10×PBMC data with PyMINEr and observed a substantial discrepancy with our results. Without imposing any threshold on PyMINEr output (no guidance provided by the authors), PyMINEr typically retrieves 10-20 times more paracrine interactions, and twice the number of pooled paracrine and autocrine interactions, with little interactions with our LRscore > 0. 5 results (Supplementary Tables  S3  and 4 ). The ROC curve analysis we conducted and such enormous lists of interactions inferred by PyMINEr suggest contamination with FPs. Obviously, PyMINEr output should be imposed a threshold in practice, which would reduce the FPs but would not improve the overlap with the known interactions in LRdb.
We next submitted the same 10×PBMC data to CellPhoneDB website. The smaller LR reference database, strict rules regarding multimeric ligands or receptors, and the impact of a re-shuffling-based P-value calculation caused reduced sensitivity compared to our solution by a factor 8. 5 on average (Supplementary Table  S5 ). Inspection of CellPhoneDB output showed that the interactions we missed were absent from LRdb and imported in CellPhoneDB from sources that we did not consider, such as InnateDB, MINT, IntAct, MatrixDB or manual curation. At last, we installed iTALK R package and applied it to 10×PBMC data as downloaded from iTALK GitHub  b This number may vary since the reference interaction list is generated from STRING automatically. c In case a complex is considered, all subunits must be selected in a significant interaction with the partner.
d The authors added a Wilcoxon test to assess that the LR pair score was different from zero in multiple tumors. repository. As for PyMINEr, iTALK scores and orders the LR interactions but does not propose any cutoff. Contrary to PyMINEr, iTALK relies on a selective database of LR pairs. We hence decided to consider the 118 top-scoring pairs of its output since LRscore > 0. 5 gave us an average of 118 selections. Results are in Supplementary Table  S6  and we observe a good overlap with our larger selections. Dissecting the data showed that all of iTALK unique LR interactions were caused by LR pairs with deprecated HUGO gene symbols; they were present in LRdb with the current symbols. For CellPhoneDB and iTALK, it was not possible to clearly separate paracrine from autocrine, and we hence compared results on the pooled paracrine and autocrine predictions.
CONCLUSION In the nascent topic of intercellular network mapping, Sin-gleCellSignalR is an R software package that facilitates the transformation of complex data into higher-order information. The package comes with a large variety of graphical representations and export formats to accommodate users and to support the downstream analysis of data. Of particular note are the abilities to represent a complete intercellular network and to import the latter in systems biology tools such as Cytoscape, and to explore receptor downstream signaling by integrating Reactome and KEGG pathways. For the first time, we discussed the question of the significance of inferred LR interactions. We showed that Sin-gleCellSignalR regularized score achieves better control of the FP compared to other solutions, independent of the retained single-cell platform.
Sin-gleCellSignalR is open to other software packages; UMI or read count matrices prepared with other tools can be imported, or preparatory steps can be accomplished with SingleCellSignalR built-in procedures. Although Single-CellSignalR was designed for scRNA-seq data, it could be used with emerging single-cell proteomics technologies such as CyTOF  (59)  or SCoPE-MS  (60). At last, our R library does not require advanced R skills from users.
FUNDING Labex  EpiGenMed Postdoctoral Fellowship  [ ANR-10-LABX-12-01  to J. C. , S. C. A. ];  Fondation ARC pour la PAGE 11 OF 12 Nucleic Acids Research, 2020 ,  Vol. 48 , No. 10 e55 Recherche sur le Cancer [PJA 20141201975 to J. C. ];  Région Occitanie , programme Recherche et Société(s), and  European Union (FEDER)  to the project biomarqueursMETCP (to J. C. ). Funding for open access charge: Inserm.
Automated methods that can identify white matter bundles from large tractography datasets have several applications in neuroscience research. In these applications, clustering algorithms have shown to play an important role in the analysis and visualization of white matter structure, generating useful data which can be the basis for further studies. This work proposes FFClust, an efficient fiber clustering method for large tractography datasets containing millions of fibers. Resulting clusters describe the whole set of main white matter fascicles present on an individual brain. The method aims to identify compact and homogeneous clusters, which enables several applications. In individuals, the clusters can be used to study the local connectivity in pathological brains, while at population level, the processing and analysis of reproducible bundles, and other post-processing algorithms can be carried out to study the brain connectivity and create new white matter bundle atlases.
The proposed method was evaluated in terms of quality and execution time performance versus the state-of-the-art clustering techniques used in the area. Results show that FFClust is effective in the creation of compact clusters, with a low intra-cluster distance, while keeping a good quality Davies-Bouldin index, which is a metric that quantifies the quality of clustering approaches. Furthermore, it is about 8. 6 times faster than the most efficient state-of-the-art method for one million fibers dataset. In addition, we show that FFClust is able to correctly identify atlas bundles connecting different brain regions, as an example of application and the utility of compact clusters.
Introduction Diffusion Magnetic Resonance Imaging (dMRI) is an in-vivo and noninvasive technique that estimates the structure of white matter (WM) through the measurement of water molecules diffusion  (Basser et al. , 1994; Le Bihan and Iima, 2015). The main trajectories of WM can be reconstructed using tractography algorithms based on local orientation fields estimated from dMRI. The generated datasets consist of a 3D representation of the main WM fiber tracts  (Basser et al. , 2000). Streamline deterministic tractography follows the preferred direction of water diffusion in each voxel to reconstruct trajectories or lines represented by a sequence of point coordinates in 3D space. These lines are called "streamlines" or simply "fibers", though they do not represent real neural fibers but an estimation of the main trajectory of WM fascicles.
Tractography datasets contain fibers belonging to well known anatomical bundles, and also a set of bundles, mostly short association bundles, which have not yet completely described. They also have noisy fibers and artifacts, coming from dMRI intrinsic limitations and uncertainty, producing an incomplete reconstruction of the fibers  (Maier-Hein et al. , 1349). The application of clustering methods has helped to develop methods for the study of deep white matter bundles (DWM), in particular, the construction of deep white matter bundle atlases  (O'Donnell and Westin, 2007; Guevara et al. , 2012). More recently, the study of superficial white matter (SWM) or short association fiber has been carried out, with the study of reproducibility of these bundles and the construction of two SWM bundle atlases  (Guevara et al.
, 2017; Garyfallidis et al. , 2018). The size increment of tractography datasets from new high quality MRI databases, and various analysis that can take advantage of clustering results, has imposed the challenge to develop high quality and optimized fiber clustering methods. This work proposes a new method for the clustering of large tractography datasets. The main goal is to develop an efficient clustering to group fibers into compact and regular clusters, representing the whole brain WM structure. The representation must be of good quality, i. e. the clusters must be compact along all the fibers, so that several analyses can be performed, using as input the resulting clusters or cluster centroids. A special interest is its use for the study of short association bundles and their segmentation, as well as of subdivisions of long anatomical bundles.
Tractography dataset We used the ARCHI database  (Schmitt et al. , 2012)  that contains high quality MRI acquisition sequences of a 3T MRI scanner with an antenna of 12 channels (Siemens, Erlangen). The MRI protocol included the acquisition of T1 images at 1 mm isotropic spatial resolution using a MPRAGE sequence  (Brant-Zawadzki et al. , 1992)  and the following parameters: 160 slices, TH ¼ 1:10mm, TE=TR ¼ 2:98=2300ms, TI ¼ 900ms, deflection angle FA ¼ 9; matrix ¼ 256x240; RBW ¼ 240Hz=pixel. The protocol also included a B0 fieldmap to correct for susceptibility artifacts, and a single-shell HARDI SS-EPI sequence  (Mansfield, 1977)  with an isotropic spatial resolution of 1. 7x1. 7x1. 7 mm 3 , with the following parameters: 60 optimized diffusion directions, b ¼ 1500s=mm 2 , 70 slices, TH ¼ 1:7mm, TE ¼ 93ms, TR ¼ 14; 000ms, FA ¼ 90, matrix ¼ 128x128, RBW ¼ 1502Hz=pixel, echospacing ES ¼ 0:75ms, partial Fourier factor PF ¼ 6=8; GRAPPA ¼ 2  (Griswold et al.
, 2002). All data was processed with the BrainVISA/Connectomist-2. 0 software  (Duclap et al. , 2012). The HARDI dataset was corrected for artifacts using a threefold strategy. First, signal dropouts and spikes are detected and corrected. Then, geometrical distortions induced by susceptibility effects are corrected using a further fieldmap calibration providing a non linear deformation to be applied along the phase axis. Finally, a joint correction for eddy currents and motion is applied to each diffusion-weighted volume. This processing corrects each diffusion direction using the rotation stemming from the rigid motion of each DW volume  (Dubois et al. , 2014). The diffusion-weighted (DW) processing pipeline includes the calculation of the analytic q-ball diffusion model  (Descoteaux et al.
, 2007). Also, a robust brain white matter propagation mask is created, relying on a T1-weighted segmentation  (Guevara et al. , 2011c). Whole-brain streamline deterministic tractography was calculated with one seed per voxel, from all the voxels of the propagation mask, in forward and backward directions, with a tracking step of 0. 2 mm and a maximum curvature angle of 30 ∘. Resulting tractography datasets contain about one million fibers per subject. As a post-processing step, all the fibers were resampled using 21 equidistant points, as in  (Guevara et al. , 2011a (Guevara et al. , , 2012)).