dataesr/scientific-paragraphs-categorization huggingface dataset:
The laboratories primarily involved in this work are: Grand Accélérateur National d'Ions Lourds (GANIL), Institut de Recherche sur les Lois Fondamentales de l'Univers (CEA, IRFU), Institut de Physique Nucléaire d'Orsay (IPNO), Institut Pluridisciplinaire Hubert Curien (IPHC), Centre de Sciences Nucléaires et de Sciences de la Matière (CSNSM), Argonne National Laboratory (ANL), KU Leuven, Laboratoire de Physique Corpusculaire de Caen (LPC), and the Institut des NanoSciences de Paris (INSP). S3 has been funded by the French Research Ministry, National Research Agency (ANR), through the S3 EQUIPEX (EQUIPment of EXcellence) reference ANR-.
Known structural motifs are highlighted in the third column in green (Walker motifs and R-finger) and purple (DNA-binding motifs). All source data for the chemical-shift values of columns 1 and 3 are listed in the Source Data file very crowded spectral regions. Stiffening of the corresponding amino acids is thus the most likely explanation for the large number of newly appearing resonances in the ADP:AlF 4 -state and this state is clearly the one showing the highest rigidity. The residues involved in the structural and dynamic rearrangements during the hydrolysis cycle are indicated on the HpDnaB X-ray structure (only the CTD is shown) in column four of Fig. 2 : residues with significant CSPs (≥0. 2 ppm) are plotted in orange, those with minor CSPs (<0. 2 ppm) in green, residues newly appearing in pink and disappearing ones in steel blue.
The assignments of HpDnaB are taken from reference 21 and extended in this work (see Source Data file). 31 Pdetected experiments were acquired at 11. 74 T in a Bruker 3. 2 mm probe using spinning frequencies of 12. 0-17. 0 kHz. The spectra were referenced to 85% H 3 PO 4. All experimental details are provided in the Source Data file. Reporting summary. Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was supported by the  Swiss National Science Foundation  (Grant  200020_159707  and  200020_178792 ), the  French ANR  ( ANR-14-CE09-0024B ), the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) within the  Université de Lyon program Investissements d'Avenir  ( ANR-11-IDEX-0007 ), and the  ETH Career   SEED-69 16-1. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement n°741863, FASTER).
Data availability Data supporting the findings of this manuscript are available from the corresponding authors upon reasonable request. The following PDB structures were used in this study: 4ZC0 and 4NMN. The source data underlying Figs. 2, 3  and 4  and Supplementary Figs S7, S8a, S9a and S12 as well as the chemical-shift values for apo DnaB and DnaB:ADP:AlF 4 -:ssDNA and all experimental NMR parameters are provided as a Source Data file.
For calculating the similarity kernel matrices K 1 and K 2 , we first perform z-score normalization on the gene expression levels and the methylation rates and then calculate the inner product for the elements in the cell-by-cell similarity matrices. As in the simulations, we embed the two domains into a latent space of dimensionality p = 5. We first plot the Principal Component Analysis (PCA) projection of the single cells based on their gene expression levels and their methylation rates separately (Figure  2A ). In this plot, when we connect the two dots corresponding to the same cell, we observe that each cell tends to be projected to two different locations in the latent space. Accordingly, the average fraction of data points closer than the true match is 0. 49. We then run MMD-MA algorithm on this dataset and plot the PCA projection of the 61 single cells in terms of gene expression and methylation rate in the shared space recovered by MMD-MA (Figure  2B ).
MMD-MA's performance is robust to variations in hyperparameters Running the MMD-MA algorithm requires specifying several hyperparameters. We investigated the robustness of the learned embedding relative to variations in these hyperparameters. As noted previously, in all of our studies the dimensionality p of the latent space has been set to 5 even though the correct number should be p = 2 in the first two simulations, p = 3 in the third simulation, and is unknown for the Methyl-Expr data set. We observe that MMD-MA algorithm can still align the two manifolds even when the dimensionality parameter p is misspecified. The trade-off parameters λ 1 and λ 2 determine how much the three terms contribute to the overall objective function. In this work, we set these trade-off parameters by monitoring whether the three terms have comparable magnitudes or whether one particular term dominates in the converged solution.
RESULTS In order to better visualize the spatial distribution of the two TADs associated with the HoxD cluster (Fig. 1A ), we modeled their structures in 3D by using Hi-C matrices  [34]  for both distal and proximal E12. 5 limb bud cells (Fig. 1B ) and the TADkit scripts package as a 3D modeling viewer  [36]. In the wild-type condition, the HoxD cluster contained a strong boundary and was thus positioned between the two regulatory domains T-DOM and C-DOM, in both distal and proximal limb cells (Fig. 1B ). In both tissues, the region called CS38-41 (Fig. 1 , red disk) established a weaker boundary between two sub-TADs in T-DOM. The structure and separation between the two regulatory domains were generally conserved between the two cell types, although with some minor differences. We applied the same 3D modeling viewer to Hi-C datasets obtained with limb bud cells from the HoxD Del (1-13)d9lac mutant mouse stock (hereafter Del(1-13)d9lac), which contains a deletion including the HoxD cluster  [34]  (see Additional File 1).
In this mutant, the deleted DNA was substituted by a Hoxd9lac reporter transgene while the Evx2 and Lnpk genes remain present. In the absence of the HoxD cluster, C-DOM and T-DOM were still observed as independent structures despite a substantial shortening of the distance separating them (Additional File 1B-C). A clear spatial contraction was nevertheless scored between C-DOM and the first sub-TAD in T-DOM until region CS38-41 (Additional File 1B, C, red disk). We next used the HoxD del(attP-Rel5)d9lac (hereafter Del(attP-Rel5)d9lac) Hi-C datasets from mutant limbs lacking ca. 350kb of DNA including the HoxD cluster plus flanking regions (Fig. 1A, C ). With this large deletion, the two TADs merged into one single structure (Fig. 1C ) regardless of the cell type considered (distal or proximal), indicating that the TAD boundary had been entirely deleted.
In this stock, the same Hoxd9lac transgene could be used as a transcriptional readout. The consolidation of T-DOM and C-DOM into one single structure was obvious up to the CS38-41 region, whereas the most telomeric located sub-TAD in T-DOM was somewhat less engulfed (Fig. 1C ). We also computed an eigenvector analysis and distributed the interacting domains according to the first eigenvector values. We concluded that the position of the HoxD locus in compartment A, as well as the general compartment distribution along chromosome 2, were virtually identical between distal and proximal cells, when both the wild type and the del(attP-Rel5)d9lac datasets were considered (Additional File 1D).
3D Modeling of Hi-C datasets Hi-C original datasets from wild type, HoxD del (1-13)d9lac and HoxD del(attP-Rel5)d9lac were obtained from  [34]  (GEO accession: GSE101715). Three-dimensional modeling of the normalized 40kb binned Hi-C matrices was performed by means of the model_and_analyze. py script from the TADbit v0. 2. 0. 58 software  [36]  in chr2: 73800001-75760000 (wild-type coordinates mm10). We generated 500 models for optimization and 5000 for modelling and we did not filter out matrix columns showing no interactions. We visualized the model with TADkit using the Virtual Research Environment (https://vre. multiscalegenomics. eu). Region CS38-41 (wildtype coordinates in mm10, chr2:75120051-75165771) was used as a reference mark in the 3D reconstructed Hi-C model.
RNA extraction, RNA-seq and qPCR Limb tissue was dissected, placed in RNAlater (Invitrogen) and directly frozen at -80ºC until further processing. After genotyping, RNA was extracted from individual samples using RNAeasy Micro Kit (QIAGEN). For RNA-seq, a total of three different biological replicates per tissue and genotype were processed. Libraries were prepared from 100ng of total RNA using the TruSeq Stranded mRNA protocol and sequenced on a HiSeq 2500 machine (100-bp reads, single end). Sequencing data were treated using the facilities of the Scientific IT and Application Support Center of EPFL. The gtf file used for STAR and cufflinks, based on Ensembl version 94 annotations, is available on figshare  [58]. Adapters were removed using cutadapt (v1. 16) and aligned onto mm10 using STAR  [59]  with ENCODE parameters.
DESeq2 analysis was performed with default parameters and genes with absolute log2 fold change above 1. 5 and p-value below 0. 05 were considered as significant (  [60]  version 1. 22. 1). For HoxD del(attP-Rel5)d9lac , three biological replicates were used for each genotype and tissue. For HoxD inv(attP-Itga6) only one sample was used per tissue and genotype. Track profiles show the mean of the coverage of uniquely mapped reads normalized to the number of uniquely mapped reads. They were obtained with the UCSC browser. For qPCR, purified RNA was retrotranscribed with Promega GoScript Reverse Transcriptase (Promega). Custom SYBR probes were used for quantitative real-time PCR (qPCR) in a QuantStudio 5 384-well block machine. Island3 primers were Forward: TTCCATCACAGGAGAGTCGTTG and Reverse: AGGTGGGAACATGGACTGAAAG.
4C-seq experiments The limb samples used in this study were dissected from E12. 5 forelimb buds for all wild type and mutant lines. Samples were processed as in  [34]. Briefly, cellular suspensions were filtered and fixed using a 2% Formaldehyde/10% FBS/PBS solution for 10 minutes. NlaIII (NEB) was used as a first cutter and DpnII (NEB) as a second cutter. DNA libraries were prepared using twelve to fourteen independent PCR reactions with 100ng DNA on each. Sequencing was performed by multiplexing several independently barcoded viewpoints. 4C-seq data were analyzed using the HTSstation web interface  [62]. They were normalized to the distribution of reads on a 10Mb window and profiles were smoothened using a window of 11 fragments. 4C-seq data from wild type tissue was taken from GEO (GSE101717).
unionbedg. In order to quantify the gain or loss of chromatin marks in the C-DOM, T-DOM, and in the CS38-41 region, the number of reads falling into their respective intervals (chr2:73914154-74422050 for C-DOM, chr2:74781516-75605516 for T-DOM, and chr2:75120051-75165771 for the CS38-41 region) were assessed after duplicate removal by picard (http://broadinstitute. github. io/picard/ version 2. 18. 14)using the multiBamSummary function from deeptools [68]. For the C-DOM, the reads falling into the region of the artifactual peak, which is due to a PCR contamination (chr2:74207282-74208158), were excluded in all datasets. The counts were normalized to the number of reads in the input bam file and the significance was assessed by the function wilcox. test in R (https://www. R-project. org).
Acknowledgements We thank  Leonardo Beccari ,  Aurélie Hintermann ,  Andréa Willemin ,  Jozsef Zákány  and other members of the  Duboule laboratories  for insightful comments and discussion. We also thank  Bénédicte Mascrez ,  Sandra Gitto  and  Thi Hanh Nguyen Huynh  for their help with mice breeding and genotyping, as well as  Mylène Docquier ,  Brice Petit  and  Christelle Barraclough  from the  Geneva Genomics platform (University of Geneva ).
The sieve residues were re-dried at 40 C and weighed to determine, by difference, the mud fraction. Benthic foraminifera were picked using a wet brush from the dried residues under an Olympus SZ40 dissecting microscope. Digital photographs of the new species were taken using an Olympus PEN Lite E-PL3 camera attached to an Olympus SZX12 dissecting microscope. Selected specimens were mounted on a stub, coated with Au-Pd and examined in a FEI Quanta 200 scanning electron microscopy (SEM). The maximum and minimum dimensions of 20 selected tests, including those of the mica plates and the agglutinated rings, were measured using the Axiovision Rel. 4. 8. program. This program was also used to measure the maximum and minimum thickness of the agglutinated ring and the test in lateral view. In addition, the area of each mica plate was calculated using the ImageJ program.
Six individuals of the new species were selected for detailed analysis of the size distribution of the mineral grains and their chemical compositions. The size of the grains was evaluated by means of image analysis using the software Fidji and R on the flat surface that was exposed by the removal of one of the large plate-like mineral grains that define the shape of the test. After threshold adjustment and binarization of the image, grains were separated prior to being counted using watershed transformation tools (https://imagej. net/Interactive_Watershed). The area of each grain was automatically determined and these data used as the basis for statistical treatment. The methods used for the determination of the chemical composition of the mineral grains agglutinated in the foraminiferal tests largely followed the ones of Armynot du  Châtelet et al.
(2013b). All the observations were performed by an environmental scanning electron microscope with an energy-dispersive spectroscopy device FEI Quanta 200 (ESEM-EDS). This technique is directly applicable on unpolished foraminiferal surface and allows us to determine the grain shapes and to identify their mineralogical composition. The specimens were positioned on stubs and carbon-coated to improve the quantification of chemical elements and to produce quality pictures. The EDS data allow a quantification and qualification of the chemical elements distributed within the surface of the agglutinated test (Armynot du  Châtelet et al. , 2013a Châtelet et al. , , 2013b;; Armynot du Châtelet, Frontalini & Guillot, 2014). As a first step, the foraminiferal tests were visualized using secondary electrons to determine their general shape, and then, as a second step, imaged for mapping selected chemical elements, namely S, Al, S, Cl, Fe, I, Ti, Ca, Mg and K.
The measurement and mapping were carried out using a 20 kV beam. An image analysis based on the chemical maps allows us to characterize the mineral diversity whereas the mineral nature was constrained on point analyses' results. In order to visualize the distribution and elemental composition of the mineral grains, specimens were reconstructed as 3D models from SEM images using SEM photogrammetric techniques  (Eulitz & Reiss, 2015). Specimens were glued at the end of a 50-mm-thick copper wire and carbon coated. Within the chamber of the SEM, specimens were rotated to get surface (backscattered electron) and chemical (secondary electron) images every 20. Photogrammetric reconstruction of 3D models was carried out using Visual SFM software. The electronic version of this article in portable document format will represent a published work according to the International Commission on Zoological Nomenclature (ICZN), and hence the new names contained in the electronic version are effectively published under that Code from the electronic edition alone.
ACKNOWLEDGEMENTS We thank  Raehyuk Jeong  and  Jisu Yeom  for field work to collect samples on May 2016,  Jaehyun Kim  for field work to collect the new species on May 2018. We appreciate very much to academic editor,  Joseph Gillespie , and two reviewers,  Michael Kaminski  and  Anna Waśkowska  for their critical comments on the earlier version of manuscript.
We show that souporcell not only outperforms these new methods, but also surpasses demuxlet on both cell assignment and doublet accuracy. Furthermore, souporcell is the only tool that explicitly models and estimates the amount of ambient RNA in the experiment, which is a major confounder of scRNAseq analysis with regard to both expression and genotype. Although a tool for ambient RNA quantification exists  3  , it requires prior knowledge in the form of one or more well expressed genes known to not be expressed in a particular cell type. Souporcell is freely available under the MIT open source license at https://github. com/wheaton5/souporcell. To cluster cells by genotype, we first must measure the allele information for each cell. To achieve the most accurate clustering, it is imperative that the variant calls and allele counts are measured accurately.
2m ). We found that all tools performed well down to the minority cell cluster comprising only 1. 2% (50 cells) of total cells (Fig. 2m ), but only souporcell and vireo were able to correctly identify all minority sample singletons as their own cluster down to 0. 6% of all cells. Again, demuxlet's poor ARI was due primarily to extremely high levels of false positive doublets (Fig. 2k ). We then compared souporcell's genotype and ambient RNA co-inference to vireo and sc_split versus the variants called from whole genome sequencing data. As previously mentioned, the total variants available for genotyping differed between tools (Fig. 2b ). In scRNAseq data most variants have very low coverage per cluster compared to what would be generated from WGS data, thus the genotype accuracy is significantly lower than one would attain with genome sequencing.
We generated three datasets containing six genetically distinct strains of P. falciparum (methods) sampling 1893-2608 cells with median UMIs of ~1000 (1/25th of the HipSci samples). Analysis of the expression profile of one of these (see Fig. S3  for the others) reveals that the genotypes are evenly distributed across the Plasmodium intra-erythrocytic cycle (Fig. 3b ) while being well separated in normalized loss cluster space (Fig. 3c, d ). The ARI for each method (Table  S4 ) on the three Plasmodium data sets show superior performance for souporcell across the board, with sc_split suffering on all datasets and vireo performing poorly on one, which had an ARI versus demuxlet of 0. 24. This sample was more difficult due to sample skew caused by a clonal expansion of one of the six strains.
Discussion Here we have presented souporcell, a novel method for clustering scRNAseq cells by genotype using sparse mixture model clustering with explicit ambient RNA modeling. Our benchmarks show that souporcell can outperform all other currently available methods, including those that require genotypes a priori. Using more realistic and challenging test cases than previous studies, we show that souporcell is robust across a large range of parameters, and more so than any other currently available method. Moreover, souporcell is highly accurate for challenging datasets involving closely related maternal/fetal samples, and varying mixtures of Plasmodium falciparum strains. Due to the advantages that mixtures give to scRNAseq experiments in ameliorating batch effects, improving doublet detection, and allowing for ambient RNA estimation, souporcell enables donor multiplexing designs to be used more easily than was previously possible, including in situations when no WGS or genotyping data are available.
Remapping We remap reads due to several different artifacts, described below. We first take the STAR aligned bam and create a fastq file from it using pysam and a custom python script (available at https://github. com/wheaton5/souporcell /renamer. py) while placing the UMI and cell barcode information in the read name for later use. We map these reads to the reference genome using minimap2 version 2. 7-r654 with parameters -ax splice -t 8 -G50k -k 21 -w 11 --sr -A2 -B8 -O12,32 -E2,1 -r200 -p. 5 -N20 -f1000,5000 -n2 -m20 -s40 -g2000 -2K50m --secondary=no, but have seen similar accuracy with the RNAseq aligner HiSat2  21. We resupply the cell barcode tags and UMI tags to the bam using pysam and a custom python script (available at https://github. com/wheaton5/souporcell/retag. py ) and sort and index the bam file with samtools.
All steps are now encapsulated into a simple pipeline script and provided as a singularity container for easy installation. We identified three different artifacts introduced by the STAR alignments resulting in false positive variants as well as reference bias that causes reads that do not support the reference allele to appear as though they do. The first artifact is due to the way STAR handles spliced reads when the read does not match the reference well. STAR will take such a read and introduce multiple splice events that force it to fit the reference in a statistically spurious fashion. We have observed cigar strings such as 8M129384N12M50238N77M where the read matches one location well with 77 matches plus mismatches in one location. Instead of soft clipping the initial 20 bases that did not have a statistically significant alignment, STAR introduced two large splicing events to match very short regions (8 and 12 bases) to the reference.
Variant Calling Souporcell Variant calling consists of two steps. First we identify candidate SNPs using freebayes (version v1. 3. 1-17-gaa2ace8) with parameters -iXu -C 2 -q 20 -n 3 -E 1 -m 30 --min-coverage 6 --max-coverage 100000 --pooled-continuous. If one wished to use known common variant sites, one could skip this step and provide that vcf to the following step. In the second step we count alleles for each cell using the program vartrix (available at https://github. com/10XGenomics/vartrix (release version 1. 1. 3)) with parameters --umi --mapq 30 --scoring-method coverage which gives us two sparse matrix outputs which represent the UMI allele counts per cell for each locus. For souporcell, we limit the loci considered for clustering to the ones with at least n cells (default =10) supporting each allele.
Vireo Vireo recommends running cellSNP ( https://github. com/huangyh09/cellSNP version 0. 1. 6) on the STAR aligned bam with parameters --minMAF 0. 1 --minCOUNT 100 limiting the analysis to loci with at least 100 UMIs and 10% minor allele fraction which are the settings we used throughout our analysis. For vireo donor clustering we use their R package, cardelino version 0. 3. 8.
Sc_split Sc_split recommends using freebayes (the version we ran test on was v1. 3. 1-17-gaa2ace8) on the STAR aligned bam with parameters -iXu -C 2 -q 20 and then filtering for SNPs with a quality score >=30. We used bcftools for filtering with the command bcftools filter -e 'QUAL<30'. Then this vcf is used along with the matrix. py script in sc_split with the filtered vcf, the STAR aligned bam, and the cell barcode file as input to get the allele counts for each cell. For sc_split donor clustering we used git commit hash 52face6a4c1b291651bdf9b56328d168c7cb1fa6 cloned from master at https://github. com/jon-xu/scSplit on April 21, 2019.
Libraries were multiplexed and sequenced at a rate of one library per lane of a Hiseq 4000 (Illumina), acquiring 150 bp paired-end reads. Supp Figure  1. HipSci data sparsity, replicates, and genotyping. a, Distribution of the number of cells expressing a variant as well as b, the distribution of the number of alleles observed per cell that were used in souporcell clustering for HipSci mixture replicate 1 (replicates 2 and 3 are very similar, so not shown). c, Expression PCA of HipSci mixture replicate 2 (4832 cells) colored by genotype clusters from souporcell. d, and e, PCAs of the normalized cell-by-cluster loss matrix of HipSci mixture replicate 2 also colored by genotype cluster. f, Expression PCA of HipSci mixture replicate 3 (5144 cells) colored by genotype clusters. g and h, PCAs of normalized cell-by-cluster loss matrix of HipSci mixture replicate 3 colored by genotype cluster.