dataesr/scientific-paragraphs-categorization huggingface dataset:
, for which the same 5 QTLs are simulated). The red dotted line indicates a baseline of 0. • Figure  3 : Neighborhood posterior inclusion probabilities across simulation settings. Panels represent combinations of heritability (columns; h 2 = 0. 8 to 0. 1) and type of data used (rows; 50k or 50k custom). Bars represent average (across 5 QTLs × 10 independent datasets) neighborhood PIP values for the four BayesR effect size classes: null (grey), small (blue), medium (yellow), and large (red). • Figure  4 : Neighborhood MAP rule for QTL mapping across simulation settings. Number of true QTL windows (out of 5 QTLs × 10 independent datasets simulated for each scenario, corresponding to a total of 50) correctly assigned to the medium (yellow) and large (red) effect size class using the neighborhood MAP rule.
Panels represent data type (columns; 50k and 50k custom) and heritability (rows; h 2 = 0. 8 to 0. 1). The small effect class is not represented because it was empty across all simulation configurations. • Figure  5 : QTL window mapping using the neighborhood inclusion probability across different effect sizes and LD strengths for h 2 = 0. 5. Neighborhood inclusion probabilities 1-PIP for each of the 50 simulated QTLs (heatmap rows) for the 50k (left) and 50k custom (right) data across scenarios (heatmap columns). QTLs are sorted in descending order according to their LD, as measured by D ′ (left annotation, with deeper reds representing larger values). QTL windows that are represented by white to red cells are correctly detected using the neighborhood non-null MAP. • Figure  6 : Genome-wide posterior estimate of the proportion of genetic variance per SNP for a single dataset with h 2 = 0.
5. Posterior estimates of the per-SNP proportion of genetic variance across all p = 46, 178 SNPs for one of the simulated independant datasets. Panels represent a given simulation setting for percentage of genetic variance per QTL (columns; k = {1%, 2. 5%, 5%}) and data type (rows; 50k versus 50k custom). Points represent individual SNPs, and are colored according to their true effect class (null, polygenic, in the neighborhood of a true QTL, and true QTL). The same five QTLs appear in each panel; true QTLs are only present in the 50k custom data. • Figure  7 : QTL window mapping using three different criteria across simulation settings. Number of true QTL windows (out of 5 QTLs × 10 independent datasets simulated for each scenario, corresponding to a total of 50) corrected identified using the CIPi ranking (top 150), Vi (top 10), and MAP non-null i neighborhood criteria.
QTL windows that are represented by white to red cells are correctly detected using the neighborhood non-null MAP. Neighborhood MAP rule for QTL mapping across simulation settings. Number of true QTL windows (out of 5 QTLs 10 independent datasets simulated for each scenario, corre-sponding to a total of 50) correctly assigned to the medium (yellow) and large (red) effect size class using the neighborhood MAP rule. Panels represent data type (columns; 50k and 50k custom) and heritability (rows; h2 = 0:8 to 0. 1). The small effect class is not represented because it was empty across all simulation con gurations. Figure 4 Neighborhood MAP rule for QTL mapping across simulation settings. Number of true QTL windows (out of 5 QTLs 10 independent datasets simulated for each scenario, corre-sponding to a total of 50) correctly assigned to the medium (yellow) and large (red) effect size class using the neighborhood MAP rule.
Figure6: Genome-wide posterior estimate of the proportion of genetic variance per SNP for a single dataset with h 2 = 0. 5. Posterior estimates of the per-SNP proportion of genetic variance across all p = 46, 178 SNPs for one of the simulated independent datasets. Panels represent a given simulation setting for percentage of genetic variance per QTL (columns; k = {1%, 2. 5%, 5%}) and data type (rows; 50k versus 50k custom). Points represent individual SNPs, and are colored according to their true effect class (null, polygenic, in the neighborhood of a true QTL, and true QTL). The same five QTLs appear in each panel; true QTLs are only present in the 50k custom data.
Figure7: QTL window mapping using three different criteria across simulation settings. Number of true QTL windows (out of 5 QTLs × 10 independent datasets simulated for each scenario, corresponding to a total of 50) corrected identified using the CIP i ranking (top 150), V i (top 10), and M AP non-null i neighborhood criteria. Panels represent data type (rows; 50k and 50k custom) and heritability (columns; h 2 = 0. 1 to 0. 8).
Average (across all simulation scenarios and independent datasets) of the posterior mean cardinality of each BayesR SNP effect class (null, small, medium, large) for three parameterizations of the prior large effect class variance. For a given dataset, each class size (#) is computed as the posterior mean of the number of SNPs assigned to each class across iterations, and V j is the posterior estimated cumulative variance of each class j. Additional FilesAll code used to simulate and analyze the data, as well as the scripts to implement BayesR are available on GitHub (https://github. com/fmollandin/BayesR_Simulations). The repository is divided into three parts:• Simulations Fortran source code of the software used to simulate data based on real genotypes. An example of parameters and description of their use are also provided.
• bayesR The modified version of BayesR (available at https://github. com/syntheke/bayesR),including recovery of the estimated per-SNP effects for each iteration, which in turn facilitates the estimation of per-SNP posterior variances. An example of the use of this software is also provided. • codes R Partial R scripts used to analyze the BayesR model output and visualize the corresponding results. Scripts to reproduce all figures presented in the article are also included.
Neighborhood MAP rule for QTL mapping across simulation settings. Number of true QTL windows (out of 5 QTLs × 10 independent datasets simulated for each scenario, corresponding to a total of 50) correctly assigned to the medium (yellow) and large (red) effect size class using the neighborhood MAP rule. Panels represent data type (columns; 50k and 50k custom) and heritability (rows; h 2 = 0. 8 to 0. 1). The small effect class is not represented because it was empty across all simulation configurations.
Availability of data and materials The Montbéliarde genotyping data on which simulations are based originate from a private French genomic selection program and were funded by the users (breeding companies and breeders). They are thus proprietary data that cannot be publicly disseminated to the scientific community. All code used to simulate and analyze the data, as well as the scripts to implement BayesR are available on GitHub (https://github. com/fmollandin/BayesR_Simulations).
• bayesR The modified version of BayesR (available at https://github. com/syntheke/bayesR),including recovery of the estimated per-SNP effects for each iteration, which in turn facilitates the estimation of per-SNP posterior variances. An example of the use of this software is also provided. • codes R Partial R scripts used to analyze the BayesR model output and visualize the corresponding results. Scripts to reproduce all figures presented in the article are also included. Prior large # null # small # medium # large V null V small V medium V large class variance 0. 5% 40783. 25 5054. 51 300. 44 39. 80 0 25. 55 14. 89 9. 60 1% (default) 40568. 94 5256. 72 336. 12 16. 23 0 26. 65 6. 91 10. 15 2% 40501. 21 5307. 33 361. 08 8. 38 0 26. 81 17. 97 5. 45.
Neighborhood MAP rule for QTL mapping across simulation settings. Number of true QTL windows (out of 5 QTLs × 10 independent datasets simulated for each scenario, corresponding to a total of 50) correctly assigned to the medium (yellow) and large (red) effect size class using the neighborhood MAP rule. Panels represent data type (columns; 50k and 50k custom) and heritability (rows; h 2 = 0. 8 to 0. 1). The small effect class is not represented because it was empty across all simulation configurations. 50k 50k 50k custom 50k custom 50 40 h²= 0. 8 h²= 0. 5 h²= 0. 3 h²= 0. 1 Mean inclusion probability Number of assigned QTLs (MAP) 0. 00 20 30 0. 25 0. 00 0. 25 0. 50 1. 00 30 20 0. 75 0. 50 0. 75 1. 00 0 10 0 10 20 30 40 50 40 50 50k h 2 = 0. 3 50k custom h 2 = 0. 8 h 2 = 0. 5 0. 00725 Figure 3: Neighborhood posterior inclusion probabilities across simulation settings.
018 0. 02 0. 025 0. 03 0. 035 0. 04 0. 05 0. 00725 0. 009 0. 01 0. 011 0. 013 0. 015 0. 018 0. 02 0. 025 0. 03 0. 035 0. 04 0. 05 Figure 4: D' % genetic variance per QTL 50k custom). Bars represent average (across 5 QTLs × 10 independent datasets) neighborhood PIP values for the four BayesR effect size classes: null (grey), small (blue), medium (yellow), and large (red).
INTRODUCTION Technologies to reliably amplify and sequence the mRNA content of single cells have progressed dramatically. By quantitatively measuring the expression levels of thousands of genes per cell, single-cell RNA sequencing (scRNA-seq) enables an unbiased classification of cell types and fine characterization of functional cell states  (1, 2). The bioinformatics analyses of scRNA-seq datasets allow the identification of new cell types and functional subsets  (3) , and may reconstruct cellular differentiation or activation dynamics a posteriori from snapshot data  (4) (5) (6) (7). All scRNA-seq protocols are based on four common steps: (i) single cell isolation, (ii) reverse transcription (RT) of mRNA, (iii) amplification of cDNA, and (iv) preparation of next-generation sequencing libraries.
The progeny of a single T or B cell can thus be accurately identified through identical (for TCR) or very similar (for BCR) V-J junctional sequences in their TCR or BCR chain genes, respectively. Integrating single-cell immunoglobulin heavy chain (IGH) sequencing with low-throughput gene expression analysis by single-cell qPCR already revealed important features of memory B cell diversification  (16)  and B cell lymphoma evolution  (17). Methods which enable the parallel analysis of repertoire sequence and whole transcriptome gene expression in single B or T cells are required to deeply investigate the multiple aspects of lymphocyte biology and malignancy. Experimental and computational approaches to infer TCR and BCR sequence from scRNA-seq datasets of T and B cells have been developed, relying either on data produced by plate-based full-length sequencing (Smart-seq2)  (18) (19) (20) (21) (22)  or droplet-based 5 ′ -end sequencing (10× Genomics)  (23).
FB5P-seq Bioinformatics Workflow The FB5P-seq data are processed to generate both a singlecell gene count matrix and single-cell BCR or TCR repertoire sequences when analyzing B cells or T cells, respectively. After extracting the well-specific barcode and UMI from Read2 sequences and filtering out low-quality or unassigned reads, we use two separate pipelines for gene expression and repertoire analysis (Figure  2 ). The transcriptome analysis pipeline was derived from the Drop-seq pipeline  (26). Briefly, it consists of mapping all Read1 sequences to the reference genome, then quantifying, for each gene in each cell, the number of unique molecules through UMI sequences. After merging the data from all 96-well plates in the experiment, we filter the resulting gene-by-cell count matrices to exclude low-quality cells and normalize by total UMI content per cell (see section Materials and Methods).
For the extraction of BCR or TCR repertoire sequences from FB5P-seq data, we have developed our own pipeline based on de novo single-cell transcriptome assembly and mapping of reconstituted long transcripts (contigs or isoforms) on public databases of variable immunoglobulin or TCR genes. We identify and select contigs corresponding to productive V(D)J rearrangements in-frame with an identified constant region gene. In cases where multiple isoforms are identified for a given chain (e. g. , IGH) in a single cell, we assign the most highly expressed isoform and discard the other one(s). In early validation experiments, our pipeline was equally efficient and accurate as RT-PCR followed by Sanger sequencing for IGH variable region analysis (data not shown), with the major advantage of retrieving complete variable regions and large portions of constant regions of both IGH and IGK/L, or TCRA and TCRB, from the same scRNA-seq run.
FB5P-seq Quality Metrics on Human Tonsil B Cell Subsets To illustrate the performance of our scRNA-seq protocol, we obtained non-malignant tonsil cell suspensions from two adult human donors, referred to as Tonsil 1 and Tonsil 2. Based on surface marker staining, we excluded monocytes, T cells, and naïve B cells and sorted memory (Mem) B cells, germinal center (GC) B cells, and plasmablasts or plasma cells (PB/PCs) for FB5Pseq analysis (Figure  3A ). We processed the Tonsil 1 and Tonsil 2 samples in two separate experiments, generating libraries from five and six plates, respectively. The libraries were sequenced at an average depth of ∼500,000 reads per cell (Table  S1 ). After bioinformatics quality controls (see section Materials and Methods), we retained more than 90% of cells in the gene expression dataset (Table  S1 ).
In Tonsil 1 and Tonsil 2 B cell subsets, the BCR reconstruction pipeline retrieved at least one productive BCR chain for the majority of the cells (Figure  3G ). Consistent with the high expression of BCR gene transcripts for sustained antibody production, we obtained the paired IGH and IGK/L repertoire for the vast majority of PB/PCs. In Mem and GC B cells, we obtained paired IGH and IGK/L sequences on ∼50% of the cells and only the IGK/L sequence in most of the remaining cells. The superior recovery of IGK/L sequences was likely because the expression level of IGK/L was about two-fold higher than that of the IGH in our FB5P-seq data (data not shown). Altogether accuracy, sensitivity, gene coverage, and BCR sequence recovery highlighted the high performance of the FB5Pseq method for the integrative analysis of transcriptome and BCR repertoire in single B cells.
FB5P-seq Analysis of Human Tonsil B Cell Subsets As a biological proof-of-concept, we further analyzed the Tonsil 1 and Tonsil 2 datasets. T-distributed stochastic neighbor embedding (t-SNE) analysis on the gene expression data discriminated three major cell clusters. Tonsil B cells clustered based on their sorting phenotype (Mem B cells, GC B cells, or PB/PCs) and did not cluster by sample origin (Figure  S1A ). Cell cycle status further separated the cycling (S and G2/M phase) from the non-cycling (G1) GC B cells (Figure  S1B ). The expression levels of surface protein markers recorded through index sorting were consistent with the gating strategy of Mem B cells (CD20 + CD38 lo CD10 -CD27 + ), GC B cells (CD20 + CD38 + CD10 + ), and PB/PCs (CD38 hi CD27 hi ) (Figure  4A , top panel). The expression of the corresponding mRNAs mirrored the protein expression (Figure  4A , bottom panel) but revealed numerous cells where the mRNA was undetected despite the intermediate or high levels of the protein.
FB5P-seq Analysis of Human Peripheral Blood Antigen-Specific CD4 T Cells To test whether our protocol is also effective in T cells, we applied FB5P-seq to Candida albicans-specific human CD4 T cells sorted after a brief restimulation of fresh peripheral blood mononuclear cells with a pool of MP65 antigen-derived peptides (29) (Figure  5A  and section Materials and Methods). C. albicans is a common commensal in humans known to generate antigenspecific circulating memory CD4 T cells with a T H 17 profile  (30). Similar to the B cell dataset, the T cell dataset displayed high per-cell accuracy (Figure  5B ) and an average of 1,890 detected genes per cell (Figure  5C ). Gene expression analysis showed an efficient detection of T cell marker genes (CD3E), activation genes (CD40LG, EGR2, NR4A1, and IL2), and TH17specific genes (CCL20, CSF2, IL22, IL23A, and IL17A) in those reactivated antigen-specific T cells (Figure  5D ).
As a consequence, FB5P-seq can successfully reconstruct BCR or TCR repertoire sequences from the scRNA-seq reads when sequencing libraries on a 75-cycle Illumina flow cell at a targeted read depth of 5 × 10 5 reads per cell, as had been done for the datasets presented here. In that configuration, the cost of performing FB5P-seq is roughly 5 e per cell (2. 5 e library preparation + 2. 5 e sequencing), which is significantly lower than Smart-Seq2 (30 e library preparation + 2. 5-5 e sequencing)  (11). In FB5Pseq, only one library preparation is required per 96-well plate, which drastically reduces the handling time when working with manual pipetting, as compared to generating one library per cell in Smart-Seq2. Yet because library preparation only requires a small amount of pooled cDNA, the full-length amplified cDNA from each single-cell can be archived and used for downstream applications such as single-cell antibody cloning  (34).
Single-Cell RNA-seq Single cells were FACS-sorted into ice-cold 96-well PCR plates (Thermofisher) containing 2 µl lysis mix per well. The lysis mix contained 0. 5 µl 0. 4% (v/v) Triton X-100 (Sigma-Aldrich), 0. 05 µl 40 U/µl RnaseOUT (Thermofisher), 0. 08 µl 25 mM dNTP mix (Thermofisher), 0. 5 µl 10 µM (dT)30_Smarter primer (8) (Table  S3 ), 0. 05 µl 0. 5 pg/µl External RNA Controls Consortium (ERCC) spike-ins mix (Thermofisher), and 0. 82 µl PCR-grade H 2 0 (Qiagen). For B cell subset sorting, the index sorting mode was activated to record the different fluorescence intensity of each sorted single cell. Index-sorting FCS files were visualized in FlowJo software, and compensated parameter values were exported in CSV tables for further processing. For visualization on linear scales in the R programming software, we applied the hyperbolic arcsine transformation on fluorescence parameters.
Single-Cell RNA-seq Data Processing We used a custom bioinformatics pipeline to process fastq files and generate single-cell gene expression matrices and BCR or TCR sequence files. Detailed instructions for running the FB5P-seq bioinformatics pipeline can be found at https:// github. com/MilpiedLab/FB5P-seq. Briefly, the pipeline to obtain gene expression matrices was adapted from the Drop-seq pipeline  (26) , relied on extracting the cell barcode and UMI from Read2 and aligning Read1 on the reference genome using STAR and HTSeqCount. For BCR or TCR sequence reconstruction, we used Trinity for de novo transcriptome assembly for each cell based on Read1 sequences and then filtered the resulting isoforms for productive BCR or TCR sequences using MigMap, Blastn, and Kallisto. Briefly, MigMap was used to assess whether the reconstructed contigs corresponded to a productive V(D)J rearrangement and to identify germline V, D, and J genes and the CDR3 sequence for each contig.
For each well, only ERCC-xxxxx which were detected (UMI ERCC-xxxxx > 0) were considered for calculating the accuracy. To estimate sensitivity (Figure  3C ), the percentage of wells with at least one molecule detected (UMI ERCC-xxxxx > 0) was calculated over all the wells from five or six 96-well plates corresponding to human B cells sorted from Tonsil 1 or Tonsil 2, respectively. The value for each ERCC-xxxxx gene was plotted against log 10 (#mol ERCC-xxxxx ). and a standard curve was interpolated with asymmetric sigmoidal 5PL model in GraphPad Prism 8. 1. 2 to compute the EC50 for each dataset. The normalized coverage over genes (Figure  3F ) was computed with RSeQC geneBody_coverage on bam files from 11 scRNA-seq 96-well plates corresponding to human B cells sorted from Tonsil 1 and Tonsil 2.
Single-Cell Gene Expression Analysis Quality control was performed on each dataset (Tonsil 1, Tonsil 2, and T cells) independently to remove poor-quality cells. Cells with <250 genes detected were removed. We further excluded cells with values below three median absolute deviations (MADs) from the median for UMI counts, for the number of genes detected, or for ERCC accuracy, and cells with values above three MADs from the median for ERCC transcript percentage. For each cell, the gene expression UMI count values were log-normalized with Seurat v3 (42) NormalizeData with a scale factor of 10,000. Data from B cells of Tonsil 1 and Tonsil 2 were analyzed together. Data from C. albicans-specific T cells were analyzed separately. Four thousand variable genes, excluding BCR or TCR genes, were identified with Seurat FindVariableFeatures.
ACKNOWLEDGMENTS We are grateful to  P. Barbry ,  K. Lebrigand , and  M. -J. Arguel  from  Institut de Pharmacologie Moléculaire et Cellulaire  for the initial discussions on method design and bioinformatics analysis. We thank all of the members of the  B. Nadel and P. Milpied laboratories at Centre d'Immunologie de Marseille-Luminy  for the useful discussions. We are grateful to the collaborators who trusted us with our method before publication. We thank the  Bioinformatics Core Facility of Centre d'Immunologie de Marseille-Luminy  for the helpful discussions and comments. We are thankful to  E. Mollaret  from  Institut Carnot CALYM  for organizing the tonsil samples transfer from the  CeVi biobank. We acknowledge HalioDX  and the  UCA Genomix platform  for sequencing. This manuscript has been released as a  Pre-Print  ( 44 ) at https://www.
FUNDING This work was supported by grants from  Fondation ARC ,  Cancéropôle Provence-Alpes-Côte d'Azur , and  ANR  ( JCJC MoDEx-GC ) to PM. This work was supported by institutional grants from  INSERM ,  CNRS , and  Aix-Marseille University  to the CIML. This work was granted access to the  HPC resources of Aix-Marseille Université  financed by the project  Equip@Meso  ( ANR-10-EQPX-29-01 ) of the program Investissements d'Avenir supervised by the  Agence Nationale de la Recherche.
Microtomography (µ-CT) Micro-CT allows studying the petrography of a sample in 3-D (e. g. ,  Ketcham 2005; Ebel et al. 2009; Beitz et al. 2013; Hezel et al. 2013; Hanna and Ketcham 2017) , largely nondestructive  (Sears et al. 2018 ) and noninvasive, and therefore seems to be the undisputed technique of choice for this study. This technique is nonetheless highly challenging for this kind of study, as µ-CT primarily images the density contrast between minerals. Olivine and pyroxene have very similar densities, with a span from 3. 27 to 4. 39 g cm À3 for forsterite and fayalite, and a span from 3. 20 to 3. 95 g cm À3 for enstatite and ferrosilite. We therefore performed a number of experiments with various measurement settings, as well as the two software packages ImageJ/Fiji and Avizo, to test whether the low-Ca pyroxene can be reliably discriminated from the olivine based on density contrasts.
2015). All samples studied are listed in Table  1. The resulting image stack was first processed with the Avizo software to correct for ring artifacts and reduce the noise as well as increase the contrast of the images using smoothing wave and enhancement filtering (cf. Li et al. 2017; Ni et al. 2017; Ferreira et al. 2018). As the olivine is slightly denser than the low-Ca pyroxene, olivine should appear slightly brighter than the pyroxene. Figure  1  displays a representative slice through Vigarano (CV3). It is almost impossible to unequivocally discriminate olivine from low-Ca pyroxene based on their respective different brightness. We also hoped to identify pyroxene by their characteristic 110 cleavages, which is principally very distinctive from the homogeneous appearance of olivine. However, the images were insufficiently resolved to unequivocally identify these cleavages.