dataesr/scientific-paragraphs-categorization huggingface dataset:
Number of badger and fox reads varied among samples (mean = 5409, SD = 4822). These reads were used to confirm predator identity. Among the 71 fox samples, we identified 15 samples that contained Martes. After applying the filtering pipeline, 13 of these samples did not contain any fox reads, ultimately supporting the assumption that they might be faeces deposited by marten rather than fox. The remaining two samples with marten sequences contained both fox and marten, and their origin could not be ascertained. These two samples were excluded from further analyses. Among the 95 badger samples, we identified one that contained Vulpes sequences and no badger sequences. This sample was included in the analysis for the fox, with a final dataset of 57 fox samples available for analysis. Due to low sample size, we excluded the 13 marten samples from statistical analysis of diet comparison, but reported on the composition of these samples separately, and included the dietary information in the analysis of applicability of dietary approaches in biodiversity assessment.
Six out of the 42 species (and one order without species identification; Zingiberales) were most likely not present in the study area. However, for all these species, either the genus or family was likely to be present in the sampling area, suggesting that the lack of an appropriate reference in the sequence database might have led to the identification of an incorrect, but closely related, species. Additionally, 13 of the prey species not recorded by field observations might represent later introduction as edible plants (e. g. the American cranberry, Vaccinium macrocarpon) or invasive species (e. g. the alien moss, Orthodontium lineare). However, for many of the potentially introduced species, limited information on distribution is available and it was therefore not possible to assess whether these detections were legitimate (Supplementary Table  6 ).
While some of the taxa identified by the eDNA metabarcoding only might be attributed to misassignments due to the absence of an appropriate reference sequence in the EMBL database, others might result from recent introductions since the last survey, like in the case of edible or horticultural plants, or natural invasions, such as alien moss or coleoptera and gastropod species. Our order-level analysis furthermore suggests that certain components of biodiversity, and therefore their associated ecosystem function, could be systematically underestimated by field surveys. A number of reasons may explain this pattern. First, common species, such as slugs or common house fly, may not be noted during field surveys due to their common occurrence (and, thereby potentially not considered important for biodiversity).
Acknowledgements We thank biologists involved in data collection and  Delphine Rioux  for help in the laboratory. This project was funded by  Aage V. Jensen Naturfond ,  15. Juni Fonden , and  Aalborg Zoo Conservation Foundation  (AZCF, Grant Number  3-2017 ). LI received funding from the  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Action  (Grant Agreement No. 656697 ).
Results Identification of two retinoblastoma molecular subtypes with distinct clinical and pathological features. We analyzed a series of 102 enucleated retinoblastomas (Supplementary Data 1). To investigate the existence of different retinoblastoma molecular subtypes, we combined three genomic approaches, mRNA expression, DNA methylation, and somatic copy-number alterations (SCNAs) in a subset of 72 of the 102 retinoblastomas. All three datasets were available for 53 of the 72 tumors, and at least two of the three datasets were available for all 72 tumors (Supplementary Data 1). Within each of these three omics datasets, we calculated several partitions of the samples in k clusters (k-partitions), for various values of k, through unsupervised hierarchical clustering, using varying numbers of features and different linkages (see "Methods" section).
3b ). We searched for hallmark gene sets associated with stemness (Supplementary Data 3). The hallmarks positively correlated with stemness included E2F targets, MYC targets V2, MYC targets V1 and G2/M checkpoint (Fig. 3d , lower panel and Supplementary Data 3). These hallmarks were the same as those identified in cluster 2 (cluster of genes overexpressed in subtype 2). The hallmarks negatively correlated with stemness included interferon-alpha response, interferon-gamma response, and complement (Fig. 3d , lower panel and Supplementary Data 3), and were the same as those identified in cluster 1. 1 (cluster of genes overexpressed in subtype 1 and associated with the tumor microenvironment). We also assessed the relationship between stemness and the abundance of the various immune cells, as estimated with the Microenvironment Cell Population (MCP)-counter score  37.
4c ). All subtype 1 tumors were close to iPS cell-derived retinal organoids at a late time point of differentiation (d173). Subtype 2 tumors were spread out from d84 to d173 retinal organoids. To explore further the heterogeneity in terms of cone differentiation in retinoblastoma, we studied by immunohistochemistry the distribution of an early photoreceptor marker (CRX), and a later marker specific to the cone lineage (ARR3). We also assessed the expression of one ganglion cell marker (EBF3). Immunohistochemical staining was performed on paraffin-embedded samples of subtype 1 (n = 9) and subtype 2 (n = 25) retinoblastomas (Supplementary Data 4). Two examples of each tumor subtype are presented in Fig. 4d. As expected, in the peritumoral normal retina, the transcription factor CRX was expressed in all cells of the outer nuclear layer (ONL), whereas ARR3 was expressed in a subset of cells in the ONL.
6b, c  and 7 ). We assessed TFF1 protein expression by immunohistochemistry, in 55 of the tumors from our initial series of 102 classified retinoblastomas (18 subtype 1 and 37 subtype 2 tumors). Expression of TFF1, CRX, and ARR3 are shown for representative tumors of subtypes 1 and 2 in Fig. 6a. Subtype 1 tumors displayed little or no TFF1 expression (QS ≤ 50; QS, quick score), whereas most subtype 2 tumors displayed high levels of expression (QS > 50; Fig. 6a, b , Supplementary Data 6). We then analyzed TFF1 expression in the additional series of 112 primary tumors with HRPFs including 19 metastatic cases (Garrahan series). TFF1 expression could be evaluated in 18 of the 19 primary tumors that subsequently developed metastasis. All 18 cases were positive for TFF1 (QS > 50), in contrast to the non-metastatic cases (p = 0.
00033) (Fig. 6b  and Supplementary Data 6), Fig. 3  Transcriptomic differences between the two retinoblastoma subtypes. a Volcano plot with genes significantly upregulated in subtype 1 (n = 26) (gold) and subtype 2 (n = 31) (blue). The genes related to cone-cell and neuronal/ganglion-cell differentiation are indicated (in gold and blue, respectively), together with the most highly differentially expressed genes in each subtype. b Hierarchical clustering of the significantly differentially expressed genes identified three main gene clusters. c Upper panels: Gene sets from the GOBP collection enriched in clusters 1. 1, 1. 2, 2 in hypergeometric tests. Results are presented as networks of enriched gene sets (nodes) connected based on their overlapping genes (edges). Node size is proportional to the total number of genes in the gene set concerned.
Among the 112 patients, 19 subsequently developed the metastatic disease (9 females and 10 males). The median time from retinoblastoma diagnosis to metastasis was nine months (range: 4-65 months). Additional clinical characteristics are included in Table  2  and Supplementary Data 6. Formalin-fixed paraffin-embedded tissues from the 112 tumors were analyzed. For seven metastatic patients, the metastatic sites were also available. Additional retinoblastoma sample for single-cell RNA sequencing. One additional sample (RBSC11) was studied by single-cell RNA-seq. The sample was obtained from an enucleated patient >18 months of age with a unilateral non-hereditary form of retinoblastoma who did not receive treatment prior to enucleation. Fetal retina. Fetal retinas were obtained from medical abortions.
Human iPSCs. Total RNA was extracted from human iPSCs using the Nucleospin RNA II kit (Macherey-Nagel), according to the manufacturer's instructions. Gene expression arrays. RNA of 59 samples (see Supplementary Data 1) were hybridized, in two batches, to Affymetrix Human Genome U133 plus 2. 0 Array Plates (Santa Clara, CA) according to Affymetrix standard protocols. Raw CEL files were RMA  78  normalized using R package affy 1. 60. 0. Batch effects were corrected with the help of the Bioconductor package SVA 3. 30. 1. The arrays were mapped to genes with a Brainarray Custom CDF (EntrezG version 23)  79. Independent component analysis in k = 3 independent components (IC) was performed using R package MineICA 1. 24. 0 (JADE method)  80, 81. The genes with high negative (< -2. 5) or positive contributions (>2.
5) to IC were analyzed through pathway enrichment analysis (hypergeometric tests), seeking specifically signatures related to potential contamination by stromal cells. Genes with high positive contributions to IC #1 were found highly enriched in markers of stromal cells, and were discarded from clustering analyses. DNA methylation arrays. Sixty-six DNA samples (Supplementary Data 1) were hybridized on Infinium HumanMethylation450 BeadChip arrays (Illumina, San Diego, CA). Four microliters of bisulfite-converted DNA were used for hybridization, following the Illumina Infinium HD Methylation protocol  82. Data were processed using preprocessIllumina and getBeta functions in R package Minfi 1. 28. 4  83. Probes were annotated using the R package IlluminaHumanMethyla-tion450kmanifest 0. 4. Probes located in Chromosome X and Chromosome Y were discarded from subsequent analyses.
SNP arrays and BAC-CGH arrays. Ninety-five retinoblastomas were analyzed using SNP arrays or BAC-CGH arrays (Supplementary Data 1). Seventy tumor samples were analyzed on high-density SNP arrays. The B allele frequency and logratio signals were smoothed and analyzed using the Genome Alteration Print (GAP) algorithm (http://bioinfo-out. curie. fr/projects/snp_gap/)  84. Twenty-five tumor samples were analyzed on BAC-CGH microarrays. These arrays consisted of 3510 or 5323 clones covering the human genome with an average resolution of 850Kb or 560Kb; they were designed by the CIT-CGH Consortium (INSERM U830, Institut Curie, Paris) and IntegragenTM  85. Hybridized slides were scanned and the scan data was pre-processed using R package MANOR 1. 36. 0  86  to correct for local spatial bias and continuous spatial gradient.
Cells were sequenced at a mean depth of 100000. For quality control and quantification of cDNA and library, BioAnalyzer (Agilent BioAnalyzer High Sensitivity chip) was used. Additional RNA quantification, DNA methylation, and mutation analyses NanoString® codeset design and mRNA quantification. A codeset of 22 target genes was custom-designed and manufactured by NanoString® (Supplementary Data 4). One hundred nanograms of total RNA extracted from each sample was assessed on the Gen2 nCounter Analysis System from NanoString® Technologies at the Genomics Platform of the Curie Institute following the manufacturer's instructions. Samples were hybridized with multiplexed NanoString® probes containing a biotinylated capture probe and a reporter probe attached to a fluorescent barcode specific for each transcript, according to the nCounter codeset design (NanoString, Seattle, WA, USA).
Hybridized samples were then purified and immobilized in a sample cartridge on the nCounter Prep Station for data collection, followed by quantification of the target mRNA in each sample using the nCounter Digital Analyzer (NanoString®). Data were normalized according to NanoString guidelines with nSolver 4. 0. Briefly, the background was subtracted using the geometric mean of negative controls provided by NanoString®. The matrix was log-transformed (base 2) for further analysis. Pyrosequencing. Forty-seven retinoblastoma samples were analyzed by performing pyrosequencing of the 9 selected CpGs (Supplementary Data 1 and Data Analysis section (Array-based methylation signature)). Bisulfite treatment of genomic DNA (500 ng) was performed using the EZ DNA Methylation kit (Zymo Research). Primer design for each CpG target was performed using the PyroMark Assay Design software 2.
0. 2 (Qiagen) and pyrosequencing reaction was performed using PyroMark Q24 instrument (Qiagen). Primers used are provided in Supplementary Data 7. Pyrograms obtained were analyzed using the PyroMark Q24 software 2. 0. 6. 20 (Qiagen) and methylation status was calculated at each CpG of interest. Targeted sequencing. Targeted sequencing of the exonic regions of RB1, BCOR, and ARID1A was performed by IntegraGen SA (Evry, France) on 23 samples from the series of 102 retinoblastomas not subjected to whole-exome sequencing (Supplementary Data 2). The Fluidigm Access Array microfluidic system was used. PCR products were barcoded, pooled, and subjected to Illumina sequencing on a MiSeq instrument as paired-end 150-bp reads. Sanger sequencing. Primer design was performed using Primer3 plus software  89.
For the cohort of 102 retinoblastomas included in this study, automated immunostaining for CRX, ARR3, EBF3, Ki-67 (Supplementary Data 4), and TFF1 (Supplementary Data 6) was performed on the available paraffin-embedded samples with Autostainer 480 (Lab Vision) at Institut Curie. The following antibodies were used: anti-CRX (Abcam, ab140603; 1:300 for AFA/Bouin fixed tissue and 1:500 for formalin-fixed tissues), anti-ARR3 (Proteintech Group, 11100-2-AP; 1/300 for AFA/Bouin fixed tissue and 1/500 for formalin-fixed tissues), anti-EBF3 (Abnova Corporation, H00253738-M05; 1/800), anti-Ki-67 (Abcam, ab1558; 1/2500), and anti-TFF1 (Sigma-Aldrich, HPA003425; 1/1000). Additional information about the conditions used is described in Supplementary Data 4. For each slide, staining was assessed by eyeballing independently by two specialists (authors: NS and PF) blind to molecular subtype classification, taking into account the intensity (I) as null (0), mild (1), moderate (2), and strong (3), and the percentage (P) of tumor cells with stained nuclei for CRX and EBF3 and stained cytoplasm for ARR3 and TFF1.
01 for the test of variance). We observed both for transcriptome and methylome that the (intra-omics) consensus partition with k = 2 clusters was more stable than solutions with k > 2 clusters. We thus selected k = 2 clusters for these two omics. The DNA copy-number data yielded 5 clusters. Cluster-of-clusters and centroid classification. To identify a common samples' partition across all three genomic platforms (transcriptome, methylome, copy number), we used a cluster-of-cluster approach. Based on the three unsupervised consensus partitions previously obtained from the three omics datasets (one consensus partition per omics data set), we first built a (samples × samples) coclassification matrix, with values ranging from 0 to 1, with 0 corresponding to a pair of samples that never co-classify in any genomic data set, and 1 corresponding to a pair of samples that always co-classify in all three genomic datasets.
To identify CNAs using WES data, we calculated the log ratio of the coverage in each tumor and its matched normal sample for each bait of the exome capture kit with a coverage ≥ 30× in the normal sample. Log-ratio profiles were then smoothed using the circular binary segmentation algorithm, as implemented in the Bioconductor package DNAcopy 1. 50. 1  91  (default parameters except min. width = 4, undo. splits = sdundo, undo. SD = 1. 5). The most frequent smoothed value was considered to be the zero level of each sample. Segments with a smoothed log ratio >zero + 0. 15 or <zero -0. 15 were considered to have gains and deletions, respectively. High-level amplification and homozygous deletion thresholds were defined as the mean ± 5 s. d. of log ratios in regions of normal copy number. Visual inspection of the profiles allowed to validate recurrent focal amplifications and homozygous deletions.
Methylation analysis Array-based methylation signature. From the methylome array data (n = 66), we selected the most differentially methylated CpGs between the two retinoblastoma subtypes (clusters of clusters) based on statistics of the Wilcoxon test. Out of the top 50 hypermethylated CpGs and top 30 hypomethylated CpGs of subtype 2 retinoblastoma (by p-value), top 7 hypermethylated and top 7 hypomethylated CpGs by the difference of beta value were selected for pyrosequencing. 5 of them did not perform well in pyrosequencing. This method led to the selection of 9 CpGs significantly differentially methylated that have been analyzed by pyrosequencing for sample classification. Seventeen samples from the initial series were analyzed by pyrosequencing for validation of the nine-CpG-based classifier (9 subtype 1, 8 subtype 2); from these samples, we derived subtype 1 and subtype 2 centroids based on these 9 CpGs.
2 (4520 higher in subtype 1, 2087 higher in subtype 2) (Supplementary Data 2). Differential gene expression and pathway enrichment analysis. Differential gene expression analysis was performed by Limma R package 3. 40. 6  100  to compare the expression of 20,408 genes between 26 subtype 1 and 31 subtype 2 tumors. 6207 genes were found differentially expressed (adjusted p-value < 0. 05) (Supplementary Data 3). Three main gene clusters were identified by hierarchical cluster analysis (mean centering of genes, 1 -Pearson's correlation coefficient as distance and average linkage). Visualization using heatmaps was performed with the R package ComplexHeatmap 2. 1. 1. Pathway enrichment analysis was performed by R clus-terProfiler package 3. 12. 0 101. Enriched gene sets from GOBP (Gene Ontology Biological Process) with adjusted p-value < 0.
01 were selected for CytoScape (3. 7) EnrichmentMap (2. 1. 1) analysis  102. Gene sets tested (GOBP and HALLMARK) were from the Molecular Signatures Database (MSigDB, version 6. 2)  103. Evaluation of stemness by transcriptome. Stemness indices in retinoblastoma were evaluated as described in Malta et al. 32. Briefly, the weight vectors of 12,955 genes were obtained by Malta et al. as a stemness signature to identify pluripotent stem cells from progenitor cells in PCBC (Progenitor Cell Biology Consortium) transcriptomic data set. 12,364 genes were available in our data set. After meancentering, the expression matrix, Spearman's correlation with the stemness signature vectors were calculated for each sample of retinoblastoma and then scaled to the range of 0 to 1 as the stemness indices. The other three stemness indices were estimated using three stemness gene signatures (Miranda et    33, 35, 36  by ssgsea function of R package gsva 1.
30. 0. Boxplots were generated using R package ggpubr 0. 2. 0. Pathway meta-score. Pathway meta-scores were calculated as the average expression of the genes involved in one selected pathway and then centered and scaled. Analysis of two independent transcriptomic datasets. We applied the nearestcentroid approach (with Pearson's metric and a minimal threshold of 0. 1) using the transcriptomic centroids calculated from our datasets to classify two publicly available transcriptomic datasets (GSE59983 and GSE29683). In the Kooi et al. 's series  18  (n = 76), 46 subtype 1 samples and 28 subtype 2 samples were identified, 2 samples were unable to be assigned a subtype. In the McEvoy et al. 's series  16  (n = 55), 24 subtype 1 samples and 22 subtype 2 samples were identified among the 48 samples, 2 samples were unable to be assigned a subtype.
Some samples (n = 7) were excluded from clustering analysis due to the high contamination of retinal pigmental epithelial (RPE) cells. We examined the average expression of an RPE gene signature (from Liao et al. 104  , n = 83/87 genes present in the data) and removed the suspected outlier samples (n = 7) by Interquartile rule (suspected outliers are the samples when their average expression of RPE signature > Q3 + 1. 5 IQR or < Q1 -1. 5 IQR). Phylogenetic analysis of retinoblastoma and retinal organoids. Gene expression data of 8 genes related to cone-cell differentiation (OTX2, CRX, THRB, RXRG, PDE6H, GNAT2, ARR3, GUCA1C) were assessed by NanoString in 67 retinoblastomas (23 subtype 1 and 44 subtype 2) and 18 retinal organoids at 6 time points after induction from iPSCs were used in phylogenetic analysis.
Phylogenies were inferred by the minimal evolution algorithm 105 using fastme. bal function in R ape package 5. 3 applied to Euclidean distance matrix based on these 8 gene expressions. Single-cell transcriptome analysis RBSC11 retinoblastoma. Sample demultiplexing, alignment to the reference genome (GRCh38, Ensembl 84, pre-built Cell Ranger reference version 1. 2. 0), quantification and initial quality control (QC) were performed using the Cell Ranger software (version 2. 1. 1, 10× Genomics). Genes that were expressed in more than 3 cells and cells that expressed more than 500 genes and less than 5% of mitochondria genes were retained (n = 1198). The median numbers of genes and UMI counts per cell were 2911 and 7749, respectively. Normalization and clustering were performed using Seurat package version 2.
3. 4. UMI counts were normalized by NormalizeData function with logNormalize method, by a scaling factor of the median UMI count. UMI counts were then scaled to regress out the effect of UMI counts. Variable genes were found with FindVariableGenes function with logVMR function. Genes with an average expression more than 0. 0125 and <8 and with dispersion more than 0. 5 were considered as variable genes for principal component analysis (PCA). Cell clusters were identified by FindClusters function with shared nearest neighbor (SNN) method modularity optimization-based clustering algorithm  106  , using the first 20 principal components. The parameter Resolution in the FindClusters was set between 0. 4 and 1. 4 and finally set to 0. 6 for it provided a better biological interpretation. Cluster markers were identified by FindAllMarkers function.
Briefly, the expression of genes that expressed in more than 10% of cells in one cluster were compared with the expression of these genes in all other clusters, using Wilcoxon rank-sum test and corrected with BH correction. The procedure was repeated for all clusters. Genes upregulated in each cluster with more than 0. 2 fold were considered as cluster markers. Pathway analysis of cluster markers was performed by R clusterProfiler package  101. Gene sets tested were from the Molecular Signatures Database  103  (HALLMARK and BioCarta) and from Supplementary Data 3 (Cell type markers_Lu data and Selected cell type markers). Correlation to bulk mRNA expression profiles of purified cell types was performed by R SingleR package 1. 0. 1  107. The expression profile of each cell was compared with the expression profiles of a data set that contains 713 microarray samples classified to 38 main cell types and further annotated to 169 subtypes  108.
Copy-number variations (CNVs) were inferred from the single-cell gene expression by InferCNV package 0. 8. 2, using normal retinal organoids derived from hiPSCs as reference. Normal developing retina (Lu et al. 38  , data). Normal retina scRNA-seq data from Lu et al. 38  were retrieved from GEO Omnibus database GSE138002. We retrieved the final filtered count matrix (GSE138002_Final_matrix. mtx. gz), gene annotations (GSE138002_genes. csv. gz), and cell annotations (GSE138002_Final_barcodes. csv. gz). The latest includes, for each cell, the UMAP coordinates and the retinal cell type annotation computed by Lu et al. that was used for our analysis. Normalization of the UMI counts and identification of markers for each cell type was done with Seurat as described for the retinoblastoma sample.