dataesr/scientific-paragraphs-categorization huggingface dataset:
In a first step, the thresholds (quartiles and median) and the order of decisions were established from FACS data. These FACS data included expression of each CAF marker in the four CAF (CAF-S1,2,3,4) cellular subpopulations, sorted from fresh HGSOC samples (prospective cohort of HGSOC patients, N = 22). Threshold values were then transposed to IHC data, using a learning set of tumors containing both non-activated and activated CAF (Retrospective cohort, N = 60). Maps of CAF subsets. IHC staining from consecutive sections were scanned on Philips Ultra Fast Scanner. ×10 images of each of the five CAF markers (CD29, FAP, PDGFRβ, SMA, FSP1) and of CD3 staining from the same tumor areas in representative tumors were further analyzed. Images were first aligned by applying an elastic transformation using a Fiji software plugin (bUnwarpJ).
The plugin uses landmarks, defined by the user, and haematoxilin and eosin (H&E) staining from the different sections to compute the best correlation between images. The software next applies an elastic transformation to all images to align tissue structures at cellular level. Epithelial cells were masked to facilitate stroma visualization. Images were next divided into tiles (square of 225 μm 2 defined as the approximate size of a cell), allowing identification of each cell by a tile. Each tile was then annotated according to specific position in the section. After color deconvolution, intensity of DAB staining for each tile and for each marker was measured by densitometry analysis (ImageJ software). CAF identity per tile was determined by using an algorithm developed by the team (see paragraph above), which takes as input DAB intensities of CAF markers measured within each tile.
For functional assays, cells were maintained during a maximum of ten passages, for avoiding them entering into senescence. At least ten different CAF-S1 primary cell lines, isolated from HGSOC, were analyzed and used for functional experiments. qRT-PCR from HGSOC and cell lines. For gene expression analysis, total RNA isolation was performed using miRNEasy kit (Qiagen #217004) according to the manufacturer's instructions. RNA concentrations were determined with a NanoDrp apparatus (NaNodrop Technologies, Inc. ). One microgram of total RNA per sample was reverse transcribed using an iScript Reverse Transcription Kit (Bio-Rad #1708840). qRT-PCR was next performed using Power SYBR Green PCR Master Mix (Applied Biosystems #4367659) on a Chromo4 System (Bio-Rad) with primers at 300 nM final CXCL12 primers were designed using the PrimerQuest tool (Integrated DNA Technologies) to span two exons.
We analyzed CXCL12β expression levels among different primary cell types by analyzing publicly available database on Gene Expression Omnibus resources under the accession number GSE49910. Data have been generated from Affymetrix U133plus2. 0 array. To have access to CXCL12α and CXCL12β mRNA levels, we considered both 203666_at and 209687_at probesets. We calculated the average of expression of the two probesets in the different primary cell types analyzed. They are included in the GSE49910 data set, both control, and treated, infected or stimulated cells. We have considered in our analysis, only control primary cells, devoid of any treatment. CXCL12 in situ hybridization. RNAscope ® Technology is a commercially available cutting-edge in situ hybridization (ISH) technology that utilizes a branched or "tree" in situ method to obtain ultrasensitive, single transcript detection.
Cell analysis was performed on LSRFORTESSA (BD Biosciences). At least 5×10 5 events were recorded. Compensations were performed using single staining on anti-mouse IgG and negative control beads (BD Biosciences #552843) for mouse antibodies, on AbC Total compensation beads (Molecular Probes #A10513) for rat antibody and on ArC reactive beads (Molecular Probes #A10346) for Live/Dead staining. Data analysis was performed using FlowJo version X. Cells were first gated based on forward (FSC-A) and side (SSC-A) scatters (measuring cell size and granulosity, respectively) to exclude debris. Single cells were next selected based on SSC-A versus SSC-W parameters. Dead cells were excluded based on their positive staining for Live/Dead. Cells were then gated on CD45 + , CD3 + , CD4 + cells and next examined for their CD25, FOXP3, and CXCR4 expressions (see Supplementary Table  1  for references of each antibody and Supplementary Fig.
Data shown in this paper are generally represented as mean ± SEM (unless otherwise specified) from at least three independent experiments. All along the study, the graphical representation of the data and statistical analyses were done using R environment (https://cran. r-project. org) and GraphPad Prism software. Barplots or scatter plots are represented with mean ± standard error of the mean (SEM) from at least three independent experiments, as indicated in the figure legends. Statistical tests used are in agreement with data distribution: Normality was first checked using the Shapiro-Wilk test and parametric or non-parametric two-tailed test was applied according to normality respect. When normal distribution was observed, equality of variances was then tested using Bartlett's test. If variances between groups were similar, Student's t-test was performed.
Acknowledgements We are grateful to  Fariba Nemati  and  Lisa Maranghoni  from the preclinical investigation laboratory at the  Institut Curie  for expert technical assistance in establishing PDX mouse models,  Renaud Leclere  and  A. Nicolas  from the experimental pathology platform at the  Institut Curie  for help and advice,  O. Renaud  and the PICT platform at the  Institut Curie  for technical assistance with microscopy and  O. Mariani  from  CRB Institut Curie  for her help in preparing the mRNA and protein samples from patient tumors. The results presented here are in part based upon data generated by the  TCGA Research Network : http://cancergenome. nih. gov/. We are grateful to all members of the animal facilities of  Institute Curie  for their helpful expertise. A. -M. G. was supported by funding from the  Cancéropôle Ile-de-France  and  Ligue Nationale Contre le Cancer.
Data availability. RNAseq data from CAF-S1 and CAF-S4 fibroblast sorted from HGSOC samples have been deposited at the European Genome-Phenome Archive (EGA) at EMBL-EBI under the accession number: EGAS00001002184. The results shown here are in part based upon data generated by the TCGA Research Network and available in a public repository from the http://cancergenome. nih. gov/ website. The authors declare that all the other data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request.
25 RBP4, SHROOM3, PDPN, FGF9, PTK7, 2. 80E-5 0. 04 IGF1, GLI2, CXCL12, GLI1, FOXP2, MYCN, WNT2, PTHLH, WNT4, GPC3, CTGF, PDGFRA, TGIF1, ROBO2, HS6ST1, LOX, CYR61 GO:0006928~cell motion 35 5. 17 NRP2, CTHRC1, IL16, PTGS2, SPOCK1, 5. 61E-5 0. 09 GLI2, EPHB3, CXCL12, EPHB2, WNT2, UNC5B, CTGF, ANG, SEMA3C, ROBO2, SCARB1, ROBO3, SCG2, EGR2, KIF5C, IGF1, CERCAM, NTN1, SLC9A10, COL5A1, ELMO1, SMO, SEMA6A, LAMA1, VEGFC, CD34, VCAN, BMPR1B, GAP43, MYH10 KEGG_Pathway-Term Count % Genes P value FDR hsa04512:ECM-receptor interaction 16 0. 26 COL3A1, HSPG2, ITGB5, COL5A1, 4. 18E-7 4. 89E-4 COL4A6, CHAD, SDC3, LAMA1, SDC1, ITGB8, COMP, COL6A3, COL1A2, COL1A1, COL11A1, THBS2 hsa04360:Axon guidance 18 0. 30 ABLIM1, PLXNC1, LIMK2, LIMK1, ABLIM3, 5. 84E-6 6. 82E-3 LRRC4C, EPHB3, NTN1, CXCL12, EPHB2, SEMA6A, UNC5B, SEMA7A, SRGAP3, SEMA3C, ROBO2, EFNA4, ROBO3 Enrichment was performed using DAVID web software based on Gene Ontology and KEGG databases.
17 ADCY3, ADCY4, ADCY1, NUDT4, ADCY5, 2. 48E-6 0. 004 metabolic process NUDT4P1, ADCY6, GUCY1A2, GUCY1A3, GUCY1B3 GO:0006936~muscle contraction 16 3. 87 MYL4, ACTC1, ACTA2, CALD1, UTRN, 3. 69E-6 0. 006 VIPR1, GJC1, EDNRB, ACTG2, SSTR2, AGT, MYH11, MYOM1, CACNA1C, HRC, SGCA GO:0050880~regulation of blood 10 2. 42 CAV2, ACTG2, EDNRB, CAV1, ACTC1, 3. 87E-6 0. 007 vessel size P2RX1, ACTA2, AGT, GUCY1A3, HBB GO:0014706~striated muscle tissue 14 3. 38 CAV2, ACTC1, CAV1, TBX2, UTRN, RXRG, 5. 01E-6 0. 008 development CACNB2, PTEN, GJC1, PLN, MYH11, RARB, MKL2, AGRN KEGG_Pathway-Term Count % Genes P value FDR hsa04270:Vascular smooth muscle 21 5. 08 ADCY3, ADCY4, ADCY1, ROCK1, ACTA2, 4. 00E-11 4. 55E-8 contraction PPP1R12B, ADCY5, CALD1, ADCY6, MRVI1, PRKG1, KCNMB1, ACTG2, GUCY1A2, PPP1R12A, MYH11, GUCY1A3, GUCY1B3, CACNA1C, MYLK, PLA2G5 Enrichment was performed using DAVID web software based on Gene Ontology and KEGG databases.
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive fatal neurodegenerative disease affecting 1 in 350 people. The aim of Project MinE is to elucidate the pathophysiology of ALS through whole-genome sequencing at least 15,000 ALS patients and 7,500 controls at 30X coverage. Here, we present the Project MinE data browser (databrowser. projectmine. com), a unique and intuitive one-stop, open-access server that provides detailed information on genetic variation analyzed in a new and still growing set of 4,366 ALS cases and 1,832 matched controls. Through its visual components and interactive design, the browser specifically aims to be a resource to those without a biostatistics background and allow clinicians and preclinical researchers to integrate Project MinE data into their own research.
Currently, the Project MinE initiative has sequenced 4,366 ALS patients and 1,832 age-and sex-matched controls. Project MinE is a largely crowd-funded initiative. As such, we are committed to sharing data and results with the scientific and healthcare communities, as well as the public more broadly. Data sharing within the genetics community facilitated large-scale genome-wide association studies and ignited initiatives such as the Gene Atlas, LDhub GWAShare Center, and MRbase, places where people can share, explore and analyze data with few restrictions  5, 6. In this same spirit, we aim to share raw sequence data, provide results from our analyses, and facilitate interpretation through integration with existing datasets to serve researchers and the public across disciplines. We, therefore, created the Project MinE databrowser (databrowser.
projectmine. com). We integrated multi-level association statistics, metadata, and public resources including gnomAD, GTEx and ClinVar in an intuitive and flexible framework  [7] [8] [9]. These data are freely available through the browser for any research initiative. We aim for the data to serve several purposes, including providing a backbone for new gene discovery, serving as a costless replication dataset, and aiding clinical interpretation of individual ALS patient genomes or specific genetic variants.
Results Dataset. The databrowser currently comprises 4,366 ALS cases and 1,832 age-and sexmatched controls whole-genome sequenced and quality control processed as part of the broader Project MinE effort. Quality control and association analysis. The quality controlled dataset includes 6,198   individuals and describes more than 105 million SNVs and indels. In this sample we have limited power to detect genome-wide significant association in a single variant framework and as a result we did not find any variants reaching genome-wide significance. In our rare-variant burden framework we find that the excess of disruptive and damaging variants at MAF < 1% in the canonical transcript of NEK1 in ALS patients compared to controls reaches exome-wide significance (p = 2. 31 × 10 -7 , odds ratio = 3.
Transcript details. Here we describe the elementary transcript details for the gene of interest. This includes the Ensembl transcript ID, Ensembl Gene ID, number of exons and genomic coordinates as described in the GRCh37 build. Coverage information (Fig. 3a ). To illustrate whether a particular gene/transcript or exon has been adequately covered to detect variation, we have included a graphical representation of average depth of coverage. This graph also includes the coverage information from the ExAC database to illustrate the difference in coverage between genome-and exomesequencing. Optionally, the coverage across introns can be visualized. Genic burden results (Fig. 3b ). Burden testing, by definition, aggregates many variants. This approach can increase statistical power to find an association, but can obscure which variant(s) are driving a potential association.
Group and individual level data sharing. The summary statistics for the latest GWAS, WGS single variant association and all WGS burden analyses can be downloaded directly. Access to individual-level data can be requested by providing a digital form with a brief research proposal (https://www. projectmine. com/research/data-sharing/). Duplicate and relatedness checks. We have created sumchecks for each individual in our dataset. Sumchecks are hashes which have been created, based on a small subset of SNPs, which allow for the identification of duplicates without sharing the genetic data itself. If researchers wish to check duplicates with our dataset, they can simply request the sumchecks, create hashes for their own data and compare the hashes. The code to generate the hashes and the list of SNPs used is available on the Project MinE Bitbucket.
Discussion Both research and clinical work increasingly rely on open-access databases to find newlyassociated variants and interpret genetic findings when counselling patients  12. Therefore, sharing de-identified data is instrumental to ensuring scientific and clinical progress, and patient-derived data should not be regarded as intellectual property nor as trade secret  13, 14. Also, most genetic browsers are based on healthy individuals, or unselected individuals who might carry specific rare genetic variants which hampers adequate comparison to a sample of patients from another geographical region. With exactly this in mind, we developed a unique, publicly-available, disease-specific databrowser which serves as a transparent framework for sharing data and results in ALS genetics. The Project MinE Databrowser contains an unprecedented amount of WGS data from ALS patients, more than doubling the currentlyavailable exome based databases, and provides (meta)data in far greater detail.
The intuitive design facilitates interpretation of robust statistical association analyses by presenting detailed metadata and through integration with population-based observations, biological/functional context and literature. As a result, we make our data and results accessible to a broad public of diverse backgrounds and for any research initiative. The databrowser provides an easy framework for other consortia who are generating similar genetic data and results in ALS and other diseases. The data has already provided a backbone for new gene discovery and variant interpretation in ALS. For example, subsets of the current dataset have been incorporated in previous publications which identified C21orf2, NEK1 and KIF5A  1, 15, 16. The resource will continue to grow as the Project MinE consortium does, and will thus increasingly allow for more reliable identification of true positives  17, 18.
The growth in both sample size and ancestral diversity will increasingly reflect the ALS mutation spectrum and yield increasingly accurate estimations of effect sizes in the general population. The browser can also offer researchers quick, easy to access to a reliable dataset for significant improvement in statistical power without financial burden. One of the major goals of the databrowser is to allow cross-disciplinary interrogation and interpretation of the data with minimal effort. We enable this through the intuitive display of individual variant level data, statistical results and through the integration with databases including GTEx and gnomAD. The databrowser ensures transparency and continued reevaluation of established associations, vitally important for clinical laboratories to make appropriate variant classifications  18.
Furthermore, we aim to facilitate the design of functional experiments by showing which variants, might be driving a genic burden signal and if these are located in specific exons and therefore specific protein domains. Project MinE is largely crowd-funded and the ALS-community is highly engaged in the scientific progress in our field. Consequently, we feel an obligation to give something back to the community and promote data sharing in general. We hope that our databrowser will inspire similar efforts in other fields. The Project MinE databrowser is a light-weight and opensource R script that can easily be adapted to serve other consortia and thus share similarly important data. Further, we aim to improve data sharing by encouraging fellow researchers to gain access to individual-level data by submitting an analysis proposal to the consortium.
Methods Sample selection and WGS. The first batch of samples (1,935 cases and controls collected in the Netherlands) were sequenced on the Illumina HiSeq 2000 platform  4. All remaining samples (4,644 cases and controls) were sequenced on the Illumina HiSeq X platform. All samples were sequenced to ~35X coverage with 100bp reads for the HiSeq 2000 and ~25X coverage with 150bp reads for the HiSeq X. Both sequencing sets used PCR-free library preparation. Samples were also genotyped on the Illumina 2. 5M array. Sequencing data was then aligned to GRCh37 using the iSAAC Aligner, and variants called using the iSAAC variant caller; both the aligner and caller are standard to Illumina's aligning and calling pipeline.
Data merging and initial site filtering. Per individual, WGS data was stored in both BAM and Illumina gVCF format. These gVCFs contain single-nucleotide variants (SNVs), short insertions and deletions (indels), and large structural variants (SVs). To begin quality control, we merged all sample into a single file using the Illumina 'agg' tool v0. 3. 4   (https://github. com/Illumina/agg). 'Agg' first generates metadata across all samples, typically batched into groups (e. g. , n = 50) to minimize CPU time, and then proceeds to extract genotypes for all samples across all sites containing at least one non-reference allele in the full casecontrol dataset. This process results in a VCF of all samples and all variants with minor allele count ≥ 1. The resulting merged VCF contained all possible sites, regardless of whether or not they passed the Isaac pipeline set of variant filters.
We used this set of markers to calculate inbreeding in two ways: first, by calculating inbreeding coefficients using Plink 1. 9 (--het, Supplementary Fig. 3 ); and secondly, by calculating the ratio of heterozygous to homozygous non-reference genotypes per sample. In the first instance, we removed samples > 6 sd from the full sample distribution (Supplementary Fig. 3 ). In the second instance, we filtered samples for inbreeding coefficients on a cohort-by-cohort basis and excluded individuals > 6 sd from the cohort distribution (Supplementary Fig. 4 ). We estimated kinship coefficients (i. e. , relatedness) using the KING method, as implemented in the SNPRelate package in R. As samples were ascertained from a number of countries, we used the KING method, as it calculates kinship in the presence of potential population stratification (a potential confounder in other identity-by-descent approaches, such as that implemented in Plink).
In some instances, research groups had intentionally ascertained related samples. We identified all pairs of related individuals (kinship > 0. 0625). Of these, several pairs included one sample appearing to be related to several other samples in the data (likely due to sample contamination; Supplementary Fig. 3 ). Samples related to > 100 other samples in the data were dropped; true related pairs were left in the data. For burden testing, we excluded these related samples (kinship coefficient > 0. 0625). For single variant association analysis, we used a linear mixed model in GCTA, including a genetic relationship matrix and the first 20 principal components (PCs), alleviating the need to exclude related samples. Lastly, we used principal component analysis (PCA) implemented in EIGENSTRAT to visualize potential structure in the data, induced by population stratification or other variables (Supplementary Fig.
4 ). Projection onto the HapMap 3 populations indicated that the samples were primarily of European ancestry, though some were of African or East Asian ancestry, while other samples appeared to be admixed. PCA across the dataset alone revealed structure induced not only by population but also by sequencing platform/calling algorithm (e. g. , principal component 2, Supplementary Fig. 5H ). However, because of a balanced casecontrol ratio in both batches, we observed a very small effect of platform/calling algorithm when including it as a covariate in association testing. A summary of all sample QC, including thresholds and removed samples, is provided in Supplementary Table  2. Variant-level quality control. To clean variants, we first inferred a set of QC thresholds from the set of SNVs falling on chromosomes 1-22 and then extrapolated these thresholds to filter all variants, including indels and variants on the sex and mitochondrial chromosomes.
7 8 ). From these visualizations of the data, we could infer the following QC thresholds and remove: variants with total depth < 10,000 reads (i. e. , ~1. 53X per sample) or > 226,000 reads (i. e. , ~34. 8X per sample), variants with missingness > 5%, and variants with a passing rate < 70%. We did not filter variants on minor allele frequency or QUAL score. Scripts used for performing data merging and sample-and variant-level quality control are available through the Project MinE BitBucket (https://bitbucket. org/ProjectMinE/databrowser). Scripts include calls to PLINK, bcftools, SnpSift, EIGENSTRAT, and SNPRelate, as well as the relevant command-line options used for the QC steps described here. Association analyses. The main association analysis consists of several rare-variant burden analyses for an association with ALS risk.
For quality control we have performed single variant association analysis using a mixed linear model, including a genetic relationship matrix and the first 20 PCs, as implemented in GCTA  20. We set genome-wide significance in single variant association analyses at p < 5 × 10 -9 21 , to account for the increased number of independent SNVs tested in sequence data. We performed rare-variant burden tests using firth logistic regression in R, adjusting for the first 10 PCs, sex and platform  22, 23. Variants for the rare-variant burden tests have been aggregated on multiple levels; gene, protein superfamilies, pathways, druggable categories and exome-wide. Genic regions were defined as all transcripts in the GRCh37. p13 version of Ensembl Biomart  24. Higher level aggregation for burden analysis was performed by creating genesets.
These genesets are based on: (a) protein superfamilies  25  ; (b) drugable categories as defined by the drug-gene interaction database  26  ; and (c) pathways downloaded from GSEA, using curated genesets v6. 1 from KEGG, BioCarta or Reactome  27, 28. We tested genes or genesets when we could identify ≥ 5 individuals with ≥ 1 variant. We used three definitions for 'rare': minor allele frequency cutoffs 1% and 0. 5%. and variants not observed in ExAC  7. We classified variants based on their functional annotation (disruptive, damaging, missense-non-damaging, and synonymous , and described previously  10  ). Briefly, frame-shift, splice site, exon loss, stop gained, stoploss, startloss and transcription ablation variants were regarded as disruptive variants. We defined damaging variants as missense variants (resulting in an amino-acid change) predicted as damaging by all of seven methods: SIFT, Polyphen-2, LRT, Mutation Taster, Mutations Assessor, and PROVEAN  10.