dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement The authors would like to thank the probands families for their support and consent to the study. JG was supported by the  Cerebral Palsy Alliance ,  National Health and Medical Research Council Senior Research Fellowship. CvE was supported by the  Hospital Research Foundation Fellowship, Cerebral palsy Alliance Research Foundation. The work by ToP and IT was made possible by the generous gifts to Children's  Mercy Research Institute and Genomic Answers for Kids program  at Children's Mercy Kansas City. ToP holds the Dee Lyons/Missouri Endowed Chair in Pediatric Genomic Medicine. We would like to thank the GA4K team and  Laura Puckett ,  Adam Walters , and  Margaret Gibson  for their work in library preparation and sequencing. We thank  Konrad Platzer  ( Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany ) for proof reading.
The National Center for Biotechnology Information (NCBI), https://www. ncbi. nlm. nih. gov/ UCSC Cell Browser (human cerebral cortex), https://cells. ucsc. edu/ Variant Effect Predictor (VEP) from ENSEMBL, https://www. ensembl. org/ DECIPHER, https://decipher. sanger. ac. uk/ GenBank, https://www. ncbi. nlm. nih. gov/genbank/ UniProt database, https://www. uniprot. org/ STRING database, https://www. string-db. org/ WebAutoCasC, https://autocasc. uni-leipzig. de/ GeneMatcher, https://genematcher. org/ gnomAD, https://gnomAD. broadinstitute. org/ MetaDome, https://stuart. radboudumc. nl/metadome OMIM, https://omim. org/ Figures Declarations Figure 1 Variants.
ACKNOWLEDGEMENTS We thank  R. Delanoue ,  M. Milán ,  P. Léopold , and  A. Teleman , for sharing flies. We acknowledge the  Bloomington Drosophila Stock Center (BDSC -NIH   P40OD018537 ), the  Vienna Drosophila Resource Center (VDRC) , the  Developmental Studies Hybridoma Bank , the  Montpellier Drosophila facility , and  FlyBase  for providing reagents and tools critical for our research.
Estimating the ratio of looped to non-looped conformations In a typical experimental dataset, only a portion of the configurations will exhibit a specific loop, while the complementary fraction will lack this feature. Consequently, we need to investigate how the log-spectral ratio depends on the probability of occurrence of a given loop, and whether it can provide any information about this probability. In Supplementary material D we derive an expression for the log-spectral ratio Λ (for fixed ι = ι 0 ) for mixed populations in terms of the probability p of having a loop of size θ. From this expression, we learn that the position of the maximum is independent of p, while its amplitude depends on it. Since we have acces to this maximal value of the log-spectral ratio from the Λ-plot, we may use it to know the looping probability p.
Indeed, inverting this formula yields p = π 2 8µ 0 1 -e -Λmax csc 2 π 2µ 0. (4) Crucially, this connection between the fraction of looped conformations and the strength of the maxima provides a means to estimate the fraction of looped conformations in the sample, offering valuable insights for biological datasets. cells of human chromosome 21. The examined genomic region spans from 34. 6 Mb to 37. 1 Mb, sampled at a genomic resolution of approximately 30 kb with a spatial resolution of less than 50 nm (roughly 0. 15 kb)  [21]. Two variants were analyzed: an untreated, wild-type variant, and an auxin-treated variant. Cohesin depletion, resulting from auxin treatment, leads to the removal of TADs at the population level  [3] , without altering the occurrence of TAD-like structures in individual cells.
It's interesting to note that Ref. [5]  suggests that flower-like looping is a fundamental mechanism in chromatin folding leading to hubs or cluster of interacting cis-regulatory modules including enhancers and promoters. This suggests that our algorithm is capable of detecting such structures. However, it's important to confirm that these loops are present simultaneously in unique configurations, rather than being a result of averaging across the entire dataset. To address this question, we need to determine in which specific samples a detected loop is present. This will be explored in the next subsection.
To independently gauge the presence of loops, our NN is fed with the spatial distances between pairs of points equidistant from the loop midpoint ι along the chain. In looped configurations these distances should exhibit a minimum at approximately half the loop's length, while in nonlooped ones, on average, they should show a linear increase with distance (see Figure  6b ). Once a loop is identified, by locating a maximum (ι, η) in the Λ-plot of FISH data, we use the trained NN on each individual conformation to ascertain whether it contains a loop at the specified position. For details, see Supplementary material E. The neural network approach offers the added benefit of allowing us to collect data at the singlecell level, enabling further analyses on segregated datasets. We emphasize that the Λ-plot is pivotal for the NN's applicability, since the NN can only be applied to one location at a time and is specifically trained for a single loop size.
As a first test, we compare the proportions of looped conformations determined by the neural networks to those obtained from the Λ-plot for each detected loop in the data sets. The results are presented in Figures  3e  and 3f. Hypothesis testing reveals that we can reject the null hypothesis of equal estimated proportions for all loops except for loops 14 and A3 (or 5, 14, A3, and A4) with 99% (or 95%) confidence. This strong agreement between the two methods underlines their reliability. The NN approach enables precise discrimination at the individual conformation level once a loop is detected in the population by the lambdaplot method. This, in turn, enables the separation of two distinct sub-populations: one with looped configurations and the other with non-looped configurations. For illustration, in Figure  4 , we present a comparison of average distance maps for the whole Auxin-treated dataset and those derived from its sub-populations -one with loop A3 (ι 0 = 51, η = 34) and the other without, as determined by our NN approach.
Further insights into chromatin architecture We can use the analogy of fBm to gain further insights into chromatin architecture features in TADs. Let's consider the two large TADs in the wild-type (from 0 to 50 • 30kb, region (1), and 50 • 30kb until 83 • 30kb, region (2)) and the entire region in the Auxin-treated dataset (Region 3) as a potential third TAD. If we treat these regions as non-looped, we can fit the internal end-to-end distance R(s) with a power law f (s) = A(s/30kb) H , for each of these regions. The fitted exponents H are given in the first row of Table  2. It's worth noting that these three values are quite close to each other, and their exponents are not significantly distant from 1/3, which is the typical exponent expected for the crumpled globule model  [24, 37]. However, our previous results allows us to potentially determine the effects of the presence of loops on the exponent H.
Our analysis, for instance, prompts a critical reevaluation of the crumpled globule model. We discovered that the region  (1)  ( (3) all conformations 0. 283 ± 0. 014 0. 314 ± 0. 014 0. 300 ± 0. 003 low loop content 0. 394 ± 0. 015 0. 418 ± 0. 016 0. 411 ± 0. 005 Table  2 : Values of the exponent H obtained by fitting R(s) = A(s/30kb) H for regions (1), (  2 ) and (3) (see main text) while considering all the conformations (upper row) or only conformations with two loops or less (for the wild type) or without any loops (for the Auxin-treated variant) (lower row). corresponding critical exponents of 1/3, frequently encountered in experimental data, may result from averaging looped and non-looped configurations within a dataset. This effect may have remained unnoticed, due to the lack of an efficient loop detection procedure.
Another intriguing finding involves the potential existence of clusters of adjacent loops, resulting in flower-like structures reminiscent of cisregulatory module hubs  [5]. The ability to examine loops at the single-cell level now allows for a quantitative investigation of correlations between different loops for the first time. Our results suggest that the presumed occurrence of neighboring loops forming a flower is the result of an average between looped and non-looped conformations, in the investigated dataset. Multiple future research endeavors are possible with the developed method, and we highlight some topics that warrant further exploration. First of all, the biological function of the detected loops requires further research. In particular, the abundance of loops in the Auxin-treated variant raises questions about their relevance and potential roles.
Expression quantitative trait loci (eQTL) analysis Expression quantitative trait loci analyses were performed using two previously-published datasets  [40, 44]. For Khamis et al. , 2019, a total of 100 organ donors and 103 living donors of European descent were included in the study, with genotyping data (2. 5M Omniarray Beadchip) from blood. We utilised the Genome-studio software to call the genotypes and QC thresholds for SNPs were as follows: a Hardy-Weinberg equilibrium > 0. 001, a minor allele frequency > 0. 05 and a call rate > 0. 95. Imputation was performed using Impute2 (v2. 3. 2) from the 1,000 genomes panel (phase 3). Following QC, > 8. 7M SNPs remained for further analysis. Ethnicity was tested using principal component analysis from 1,000 genomes and confirmed the European descent of subjects, therefore, population structure was not adjusted for.
Acknowledgements G. A. R. was supported by a  Wellcome Trust Investigator award  ( 212625/Z/18/Z );  UKRI-Medical Research Council (MRC)   Programme grant  ( MR/R022259/1 ), an  NIH-NIDDK  project grant ( R01DK135268 ) a  CIHR -JDRF Team grant  ( CIHR-IRSC TDP-186358  and  JDRF 4-SRA-2023-1182-S-N )  CRCHUM  start-up funds, and an  Innovation Canada John R. Evans Leader Award  ( CFI 42649 ). We thank  Dr Jorge Ferrer  ( CRG, Barcelona ) for helpful discussion and advice.
2. The overall structure was predicted using ColabFold. 33  The chromophore was positioned using PyMol (Version 2. 5. 4 Schrödinger, LLC. ) to superimpose the structure of mTFP1 (PDB ID 2HQK)  23    Table  1. One-photon photophysical properties of T-GECO1 and mTFP1, 28 measured as purified proteins (n = 3, averaged). Extinction coefficients were obtained using Strickler-Berg formula. 28.
Acknowledgments This work was supported by grants from the  Canadian Institutes of Health Research (CIHR ,  FS-154310  to REC) and the  Natural Sciences and Engineering Research Council of Canada (NSERC ,  RGPIN 2018-04364  to REC). Two-photon characterization work (MD) was supported by the  NIH   BRAIN grant   U24 NS109107  ( Resource for Multiphoton Characterization of Genetically-Encoded Probes ). Two-photon Ca 2+ imaging of AP and all-optical data (IB and VE) were supported by the  ERC Advanced  (Grant  ERC-2019-AdG ; award no. 885090 ), the  Medical Research foundation FRM  ( FRM-FDT202204015069 ) and the  Axa research foundation. AA and REC wrote the manuscript. AA, SS, MD, LZ, JZ,  S-YW , and YS designed and performed experiments and analyzed the data. IB and VE designed experiments for AP imaging under two-photon excitation.
Acknowledgements We are grateful for technical assistance from  Melanie Fuchs , and for the support of the  Virus Core Facility (VCF) , the  Advanced Medical Bioimaging Core Facility (AMBIO)  and the  Microscopy Core Facility (MCF)  at the  Charité-Universitätsmedizin Berlin. We are appreciative of ongoing input and structural support from both  Dietmar Schmitz  and  Craig Garner. Finally , we are grateful for diverse project funding from the  DFG  (individual projects  261102178  and  431572356 , as well as collaborative research grants  EXC 257 ,  CRC 665 ,  CRC 958 ,  EXC 2049 ). Both Hanna L. Zieger  and  Bettina Schmerl  were additionally supported financially through the  PhD fellowship program  of the  Charité-Universitätsmedizin Berlin.
All these data provide the unprecedented opportunity to reveal how different molecular layers interact through complex regulatory mechanisms to define cell identity. Several methods, co-analysing single-cell omics data to elucidate the regulatory mechanisms that encode cellular identities, have been recently developed  [8] [9] [10] [11] [12] [13] [14]. The output of these methods are Gene Regulatory Networks (GRNs), corresponding to graphs linking Transcription Factors (TFs) with their inferred target genes and/or peaks  [15] [16] [17]. The GRNs are obtained by all methods performing TF-peak-gene associations based on binding motif databases (e. g. JASPAR  18  ), then filtered through scRNA and scATAC data analysis. All these methods ignore intra-omics cooperation between biological macromolecules, which is crucial in biology.
Indeed, TFs can cooperate in the regulation of gene expression by forming dimers and multiple DNA regions can co-regulate the expression of the same gene. In addition, state-of-the-art methods only consider TF-gene interactions present in binding motifs databases and miss all those interactions that are not reported there. Furthermore, all these methods infer GRNs by integrating scRNA and scATAC data, thus ignoring all other complementary layers of molecular regulation (e. g. methylation, proteome). Finally, many methods require either paired data, or perform cell pairing before GRN inference  [11] [12] [13] [14]. This is a major limitation, as paired single-cell multi-omics data are still rare and performing cell pairing in dataset profiled from different cells forces a decrease in the size of one of the two datasets thus reducing the richness of its information content.
Here we introduce HeterogeneoUs Multilayers for MUlti-omics Single-cell data (HuMMuS), a flexible tool based on Heterogeneous Multilayer Networks (HMLNs) to reconstruct regulatory mechanisms from multiple single-cell omics data. HuMMuS considers not only inter-omics interactions (e. g. peak-gene, TF-peak), as done by the state-of-the-art, but also intra-omics ones (e. g. peak-peak, gene-gene, TF-TF) thus allowing to capture cooperation between biological macromolecules. This inclusion of intra-omics interactions allows HuMMuS to explore new TF-gene interactions not present in binding motif databases. In addition, HuMMuS is a flexible framework, that can be used both for paired and unpaired single-cell multi-omics data or easily extended to deal with additional omics data, thus not limiting the regulatory mechanisms analysis to only scRNA and scATAC, as it is currently done in the state-of-the-art.
We extensively benchmarked HuMMus with respect to the state-of-the-art on four independent datasets of scRNA and scATAC. This benchmarking included the prediction of TF targets, TF binding regions, regulatory regions and the association of its communities with known biological processes. Finally, by applying HuMMuS to unpaired scRNA, scATAC and scnmC data from mouse cortex, we showed that its GRN allows to accurately cluster scRNA profiles and to identify regulators relevant to mouse brain cortex. HuMMus is available at https://github. com/cantinilab/HuMMuS as R package, together with a tutorial for its usage.
HuMMuS a new tool for molecular mechanisms reconstruction from single-cell multi-omics data We developed HeterogeneoUs Multilayers for MUlti-omics Single-cell data (HuMMuS), a new tool for regulatory mechanisms inference from single-cell multiomics data (Figure  1 , https://github. com/cantinilab/HuMMuS). 19, 20. As summarized in Figure  1 , we reconstruct HMLNs composed of three layers: The TF layer, containing unlinked TFs, the scATAC layer containing peak co-accessibility information inferred from scATAC data and the scRNA layer encoding transcriptional regulation inferred from scRNA data. For all details on the layers' construction see Methods. Of note, we here focused on this combination of omics data to not advantage HuMMuS by the additional information provided by other single-cell omics data.
HuMMuS is based on Heterogeneous Multilayer Networks (HMLNs Thanks to the use of a HMLN structure, HuMMuS has multiple advantages with respect to the state-of-the-art. First, it captures not only inter-omics interaction (e. g. peak-gene, TF-peak), as done by the state-of-the-art, but also intra-omics ones (e. g. peak-peak, gene-gene, TF-TF). This allows HuMMuS to capture cooperation between biological macromolecules and use it to predict, for example, TF-gene interactions not present in binding motifs databases. In addition, HuMMuS is a flexible framework, that can be used both for paired and unpaired single-cell multiomics data or easily extended to deal with additional omics data, thus not limiting the regulatory mechanisms analysis to only scRNA and scATAC, as it is currently done in the state-of-the-art.
In the following we extensively benchmark HuMMuS against CellOracle and Pando 10,11 , being the most famous published works in the field. Interestingly, CellOracle is the only existing method considering some cooperation at the peaks level. In addition, we included GENIE3 in the benchmark as a baseline for performances when considering scRNA alone. All the benchmarking is performed on four test cases (see Methods and Supp Table  1 ): two datasets (called in the following Chen and Liu) of human Embryonic Stem Cells (hESCs), jointly profiled for scRNA and scATAC (i. e. paired data), and two unpaired scRNA and scATAC datasets of mouse Embryonic Stem Cells (mESCs) (called in the following Düren and Semrau). For details on HuMMuS layers structure in these four datasets see Supp Table  2. Of note, in Düren and Semrau, being the data unpaired, the scRNA and scATAC information has been profiled from different cells all extracted from mESCs.
Of note, even when pairing the cells in the two unpaired datasets, the performances observed for HuMMuS are not affected (see Supp Figure  1 ). To then test whether the observed performances were driven by a subgroup of TFs or consistent for a high number of them, we computed the number of TFs having a significant number of targets in their top predicted targets (see Methods for details). As shown in Figure  2C , overall, all methods get few TFs with a significant amount of correctly predicted targets. At the same time, also in this case, HuMMus gets best performances in all four test cases. Taken together these two results suggest a high potential for HuMMus in TF targets prediction.
We then focused on the regulatory regions associated with each gene. As shown in Figure  3C , in HuMMuS we predicted the peaks having a regulatory role on a gene based on RWRs starting from the gene and filtered the obtained ranking at (100%, 80%, 60%, 20%). For CellOracle instead, the peaks associated to a gene by its model were considered and filtered with different correlation thresholds: 0. 05, 0. 2 and 0. 8, with the last being the default one. The obtained predictions were finally compared with a ground truth composed of gene-regulatory regions associations available from different databases  [23] [24] [25] [26] [27] [28] [29]. For all details on the analysis, see Methods. GENIE3 and Pando have been excluded from this analysis as they did not provide an output allowing for this type of evaluation.
Regarding the community detection, as the Louvain algorithm depends on the resolution parameter, we here run it with resolution varying in the range 0-2 and choose for each method the resolution giving best performances and a reasonable number of communities ( 10). See Methods for details on the analysis, Supp Table  4  for performances across different resolution values. Figure  4B  shows the results of the comparison. Regarding the number of communities corresponding to the best enrichment performances, all methods vary in a range of 10-30 communities, depending on the test case and the database under analysis. Concerning the enrichment in pathways and Gene Ontologies, in three out of four test cases (Liu, Duren and Semrau), HuMMuS gets the highest percentage of enriched communities in most of the databases.
As a first observation, the activity of the obtained regulons, computed according to  41, 42  , is able to correctly cluster the cells according to their area of origin in the mouse cortex (see Figure  5B ). This suggests that the regulons identified by HuMMuS can nicely recapitulate the known heterogeneity present between the analyzed cells and already reported in  38, 43. We then focused on the regulons strongly associated with each of these cell populations, considering only the top five differentially active regulons per cell population (Figure  5C , Methods for details). Of the obtained 34 regulons, 76% of their TFs have an already reported association with either neurons, cortex, or brain (see Supp Table  5 ). In particular, five of them (Esx1, Pgr, Nr3C1, Smad1/5, Mnt) are reported in the Bgee database as expressed in the brain  44   64  ) are reported in publications specifically referring to the mouse cortex.
Overall, the main advantages of HuMMuS are the ability to capture intra-omis cooperation between biological macromolecules, its flexibility allowing it to easily integrate additional omics or prior information (e. g. pathway databases) and to work with both paired and unpaired data. For simplicity, we here only explored inter-layer links based on databases. However, such links could be improved in concrete biological applications considering interlayer links derived from experimental evidence (e. g. resulting from ChiP-seq experiments instead of generalistic motif databases). In addition, cooperation between TFs is not here considered to not favor HuMMuS over other methods in the benchmarking. However, protein-protein interaction data and ChiP-seq data could be included in the TF layer of HuMMuS as a proxy of TF-TF cooperativity.
TF layer TFs expressed in the scRNA data and having a known motif according to JASPAR or cisBP databases  18, 68  were included in the TF layer. In the presented results, we did not include TF-TF interactions in the TF layer of HuMMuS, to make a fairer comparison with state-of-the-art methods. However, the option to add links in the TF layer is provided in the released version of HuMMuS. scATAC layer scATAC data are used in this layer to infer cis-regulatory interactions using Cicero  69. Cicero provides co-accessibility scores between peaks within given windows of the genome. We used 500kb as genomic window size for both human and mouse data, as done in  10, 69. In addition, Cicero requires to define pseudocells, by averaging groups of N cells. In the following we used N=50, corresponding to the default Cicero value, with the only exception of the Liu dataset, where too few cells were present, thus requiring N=10.
TF-peak bipartite To associate TFs to potential binding regions we used the function AddMotifs from the Signac package  71  and based on motifmatchr  72. This function can be however substituted by the users with others, if needed. TF binding-motifs were obtained from JASPAR and cisBP databases  18, 68. JASPAR motifs were obtained through the JASPAR2020 R package  73. cisBP motifs already reformatted and deduplicated were accessed through chromVARmotifs R package  74. To find overlap between TF binding motifs and scATAC-seq peak coordinates, elements were mapped on the genomic sequences from BSgenome. Hsapiens. UCSC. hg38 and BSgenome. Mmusculus. UCSC. mm10 for human and mouse, respectively. The obtained network is unweighted.