dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. Figure 1. Figure 2. 12 Figure 1. Description of the study site and the geometry and elevation of the SAFRAN reanalysis and the ALADIN RCM. The top right panel illustrates the spatial domain (FRB12) covered by the simulation, and the location of our study site is indicated by the pink box. In the main panel, SAFRAN massifs are delimited by the black contours for the Northern Alps and by the burgundy contours for the Southern Alps, and their centroids are indicated by the black stars. ALADIN grid points are represented by dots, with pink contour for the grid points closest to each SAFRAN massif centroid. Surface elevation of the SAFRAN reanalysis (50m-DEM from the Institut National d'Information Géographique et Forestière (IGN)) in France and from GTOPO30 (resolution of 30 arc seconds ≈ 1 km) outside of France, as well as the elevation of ALADIN is indicated by the color palette (in m above sea level (a.
Phylogenetic hypothesis and habitat coding The phylogeny used in all analyses was the maximum likelihood (ML) tree described in  Soltis et al. (2013). Briefly, this tree is a phylogeny of Saxifragales at the species level that contains 950 operational taxonomic units (OTUs) -36Á8 % of species-level diversity of the clade. The tree was obtained using a supermatrix approach and ML estimations. Branch lengths were later calculated using penalized likelihood as implemented in r8s  (Sanderson, 2002 (Sanderson, , 2003) )  with the time constraints described in  Jian et al. (2008). Determining the habitat characteristics of large taxonomic sets is problematic because sampling effort is necessarily different among species (i. e. not all species are equally well sampled across their range). Moreover, quantitative habitat characterizations of biological niches rely on distributional data stored in repositories such as the Global Biodiversity Information Facility (GBIF) and environmental characterizations based on modelled climatic values such as the BIOCLIM WorldClim variables.
Ancestral state reconstructions Habitat evolution in Saxifragales was reconstructed using the supermatrix-based tree and the discrete habitat codes described above. Ancestral habitat reconstructions were performed using a continuous Markov model of discrete character evolution (MkN;  Pagel, 1994; Lewis, 2001)  employing a Bayesian approximation with diversitree  (FitzJohn, 2012). Three different Markov chain Monte Carlo (MCMC) chains were started with priors of rate 0Á001, 0Á01 and 0Á1, respectively, and run for 100 000 steps with exponential prior distributions. Chain convergence was verified using the R package coda  (Plummer et al. , 2006). All chains converged within the first 1000 generations. Nevertheless, to be conservative, we discarded the first 10 000 steps of every chain and concatenated the last 90 000 steps for each run together to form the posterior probability distributions.
Our phylogenetic tree is almost fully resolved. However, diversitree requires dichotomous trees, so we randomly dichotomized the phylogeny, replacing polytomies with branches of length zero with the function 'multi2di' in the ape R package  (Paradis et al. , 2004). This transformation was relatively small, as only 106 of the 1877 branches had to be replaced and they were all terminal. Another limitation of diversitree is that it cannot incorporate polymorphic data at the tips. To investigate the possible consequences of dichotomizing the tree and the simplification of habitat data, we also reconstructed the ancestral habitat of Saxifragales using Bayesian stochastic mapping  (Bollback, 2006)  as implemented in the R package phytools  (Revell, 2012). We ran SIMMAP (StochastIc Mutational Mapping on Phylogenies;  Bollback, 2006)  analyses on the original tree for 10 000 iterations and considering both simple and polymorphic habitat coding for all extant species.
Additionally, lineagedependent rates of the three parameters were plotted on the tree to identify regions of the phylogeny associated with significant changes in macroevolutionary rates. Because BAMM requires a fully dichotomous tree with branches of length >0, we assigned all descendants of polytomies in the original tree a branch length of 1/100 000 of the shortest, non-zero branch. We conducted four independent runs of 1 000 000 generations and assessed convergence among chains within and across runs using the logLik estimate provided by the software  (Rabosky, 2014).
, 2011). We used GeoSSE to estimate the relative habitat specialization of lineages in every pair of habitats that were found to have taxa in common, but focused on the five boundaries of particular eco-evolutionary interest: forest/tundra, grassland/ forest, shrubland/forest, grassland/shrubland and shrubland/desert. Because GeoSSE models require that only two habitats be present in the tree, the original dichotomized tree was pruned as needed to leave only the data of the habitat pairs that were compared. GeoSSE analyses were run using an MCMC chain of 100 000 generations with a flat prior rate ¼ 0Á1 and discarding the first 2000 generations as burn-in. The significance of GeoSSE results was assessed by determining the proportion of samples from the posterior distribution for which a given result holds, i.
In general, MkN models resulted in mean transition rates <0Á01, the only exception being the transitions out of tundra and into forest and cliffs habitats (Table  1 ; Table  S2 ). SIMMAP analyses showed that habitat transitions in and out of forests and cliffs were relatively the most frequent (Table  1 ; Table  S2 ), indicating that forest and cliff habitats might have been crucial in the evolution of Saxifragales. Each of the seven habitats considered with their corresponding numeric code in parentheses and sets of habitats in which at least one species occurs. See Table  S1  for individual taxon assignment and habitat weights. n ¼ number of taxa assigned to habitat i using with simple/'polymorphic' (i. e. assigning taxa present in two or more habitats simultaneously to all the habitats) habitat coding.
ACKNOWLEDGEMENTS R. Fitz-John  and  E. Goldberg  provided invaluable advice with the diversification analyses. We are grateful to  C. Hinchliff  for his help with figure composition, to  E. Mavrodiev  and  D. Salazar  for help at various stages, and to  P. Soltis  for her input throughout this project. Analyses were run at the  Scientific Supercomputing Resources of the Universidad de Granada. This work was made possible by NESCent support in the form of a sabbatical awarded to D. E. S. and a post-doctoral fellowship awarded to  R. R. C. ( EF-0905606 ). R. R. C. has been further supported by grants ' TEE-OFF ' (MC-IIF-2011-300026  European Commission/Marie Curie Actions)  and 'Bet-hedging, trade-offs and the evolution of seed  dispersal and dormancy' (Talentia program ;  Junta de Andaluc  ıa/E-P7).
We set up a discovery series by curating Microenvironment Cell Population (MCP) transcriptomes from 81 public datasets derived using Affymetrix Human Genome U133 Plus 2. 0 microarrays (step 2). These transcriptomes were normalized  [13]  to allow their integration into a large meta-dataset which includes 1194 immune or non-immune microenvironment cell population samples; 742 tumor cell line samples were included as negative controls (Additional file 1: Table  S1 ). No hematopoietic tumor cell line was included as a negative control as the transcriptome of these cells is closely related to that of non-malignant hematopoietic cells  [14]. After manual curation, all the samples were annotated using 63 labels, of which 42 correspond to microenvironment cell populations and 21 represent cell lines from 21 nonhematopoietic cancer types (Fig.
We thus retained TMs specific for ten distinct populations: eight immune cell populations (T cells, CD8 + T cells, NK cells, cytotoxic lymphocytes, B cell lineage, monocytic lineage cells, myeloid dendritic cells, and neutrophils) and two non-immune stromal populations (endothelial cells and fibroblasts). The 81 datasets from the discovery set spanned 344 different culture conditions, purification methods, and cell treatments, which ensures that the selection of TM was not sensitive to experimental conditions. MCP-counter scores were defined as the log2 average expression of the TM for each population (step 6). We then validated MCP-counter (step 7).
Since, for a given cell population, the TM are expected to be coordinately expressed, we examined the correlation matrices of the TM on the three MCP datasets. We observed highly positive correlation matrices for all populations in the MCP datasets (Additional file 2: Figure  S6 ). Having assessed the reproducibility of this approach, we reduced the number of markers for four populations (neutrophils, fibroblasts, endothelial cells, B cell lineage) which had large TM sets (>90 TM) using information from the MCP validation series ("Methods"). We examined the correlation patterns of these final TM sets in 9408, 3548, and 6451 tumor transcriptomic samples (Additional file 1: Table  S6 ) obtained using Affymetrix Human Genome U133 Plus 2. 0, Affymetrix 133A, and Illumina HiSeq transcriptomic platforms, respectively (Additional file 1: Tables S7-S9).
Analysis of the microenvironment of normal and cancer tissues We applied MCP-counter to a first dataset of 505 samples spanning 47 non-pathological anatomical locations  [15]. Lymphoid organs (spleen, lymph nodes, tonsils, bone marrow) were, as expected, found to harbor a high number of immune cells, while the thymus featured the highest MCPcounter score for CD3 + T cells (Fig. 5a ). On the other hand, known "immune sanctuaries", such as testes, were correctly found to feature little abundance of immune cells. Tumor immunology is one of the natural applications of MCP-counter. We estimated the abundance of immune and non-immune cell populations in 19,407 samples spanning 32 non-hematopoietic human tumors, thus proposing a global analysis of non-malignant cell population abundances across human cancers (Fig.
4a ). In contrast, MCP-counter is specifically designed to compare the absolute abundance of a given cell population across multiple samples. MCP-counter scores linearly correlate with the corresponding cell population abundances across samples, but they are expressed in arbitrary units. These arbitrary units are dependent on the gene expression platform used to produce the data and one can only compare samples produced with the same gene expression platforms. Nonetheless, we showed that the relative cellular abundance across three large tumor datasets, totaling more than 19,000 tumors and obtained with three different gene expression platforms, are largely consistent (Additional file 2: Figure  S10 ), validating the use of MCPcounter to assess which samples are most or least infiltrated by each characterized cell population.
We notably illustrated its use to separately classify lung adenocarcinoma, colorectal, and breast tumors into microenvironmentdefined subgroups. In this setting, we were able to confirm the prognostic impact of three previously published microenvironment-based tumor classifications. These results suggest that MCP-counter may enable the identification of new multi-marker microenvironmental stratifications. MCP-counter relies on TM which have been identified in a dataset containing gene expression profiles of cancer cell lines from 21 different anatomic locations among its negative controls, ensuring applicability in a wide range of samples. This large diversity of control samples may, however, discard TM which would be relevant in a specific setting: for instance, the screening procedure discarded NCAM1 (CD56), a widely used marker of NK cells, as it is also expressed by nervous malignant cells and is thus unsuitable to quantify NK cells in brain samples.
Gene expression profile datasets We curated transcriptomic profiles from several types of samples from public repositories (microenvironment cell populations (MCP), non-hematopoietic human tumors, non-diseased human tissues, in vitro RNA mixtures) and obtained using different gene expression platforms (mainly Affymetrix HGU 133 Plus 2. 0, HGU 133A, HuGene 1. 0 ST, and Illumina HiSeq 2000). For survival analysis, transcriptomic profiles of non-hematopoietic human tumors with OS annotations were also included. Table  1  lists the types of samples curated, stratified by gene expression platforms, and points to the identifiers of the included samples.
Gene expression profile normalization Affymetrix Human Genome U133 plus 2. 0, Human Genome 133A, and HuGene 1. 0 ST arrays MCP datasets and tumor datasets from Affymetrix Human Genome U133 Plus 2. 0, Human Genome 133A, and HuGene 1. 0 ST arrays were normalized using the frozen robust multiarray average (fRMA) method, implemented in the fRMA R package (version 1. 18. 0). Unlike RMA, fRMA uses fixed estimates of probe-specific effects and variances, allowing a consistent normalization of gene expression profiles (GEP) from different series, provided that they were obtained on the same gene expression platform. GEP obtained with the Affymetrix Human Genome U133 Plus 2. 0, Human Genome 133A, and HuGene 1. 0 ST array platforms were thus normalized using the frma function of the frma Bioconductor R package using the preprocessing input vectors provided by the Bioconductor R packages frmahgu133plus2frmavecs version 1.
TCGA gene expression data Gene expression data from non-currently embargoed TCGA projects were obtained from TCGA data portal (https://gdc-portal. nci. nih. gov/). Only GEP obtained using Illumina HiSeq 2000 were retrieved. Already-normalized "level 3" data were downloaded separately for each project. The resulting *. rsem. genes. normalized_results files were then merged into a single pan-cancer expression matrix.
Other gene expression platforms For GEP obtained using other gene expression platforms, we used pre-processed GEP data as published by each study and available from the corresponding public repositories. Publicly available sample annotations MCP datasets: discovery series-Affymetrix U133 plus 2. 0 arrays) Samples were annotated according to the author-reported phenotypes, yielding 344 distinct labels (Additional file 1: Table  S1 ). We recoded these labels into 63 categories, including 21 cancer cell phenotypes (42 immune and stromal labels). MCP datasets: validation series-Affymetrix 133A and HuGene 1. 0 ST arrays) Samples were annotated according to the 63 labels used to annotate the discovery microenvironment series. The previously defined 63 categories did not fit the phenotype of a few samples and motivated the addition of 15 categories (Additional file 1: Tables  S4  and S5 ).
Non-diseased human tissues (GEO:GSE7307) Sample annotations were retrieved from GEO:GSE7307. Samples corresponding to diseased tissues, cell lines, or sorted immune cells were discarded, retaining only the non-diseased, cellularly heterogeneous samples. For clarity, some tissues were regrouped in broader anatomical locations (detailed in Additional file 1: Table  S14 ). Anatomical systems were manually added.
Non-hematopoietic tumor sample series From the three tumor datasets, only samples corresponding to primary tumors obtained from tumor resections with no neoadjuvant treatments and that did not use laser capture microdissection were retained for analysis (Additional file 1: Tables S6, S7, S8, S9, and S12). Annotations for samples retrieved from GEO and ArrayExpress were retrieved from the "Series matrix" files and "sdrf" files, respectively. Annotations for TCGA samples were retrieved from TCGA data portal (https://gdc-portal. nci. nih. gov/). Since annotations originated from many different groups, only a subset of the variables were retained for each series. The corresponding values were harmonized into a consistent ontology. The following list contains the final set of variables for which sample annotations were retained: sample identifiers; series identifiers; gene expression platform; cancer type; sample type (autopsy, biopsy, cell culture, surgery followed by laser capture microdissection, surgery); overall survival event and delay (months).
Removal of tumor sample duplicates Some tumor GEP are present in multiple public datasets and correctly labeled as "Reanalyzed". In this case, only the original sample was considered for analysis. To avoid unspecified duplicated GEP, we computed MD5 checksums for all uncompressed CEL files. Samples with identical MD5 checksums were considered duplicates. In this case, only the sample belonging to the oldest series was kept. Annotations present on the most recent sample instance and absent from the older sample instance were.
Of these 78 sample labels, some correspond to terminal leaves of this pyramid (e. g. , "Canonical CD4 Treg cells"), while others correspond to higher level nodes (e. g. , peripheral-blood mononuclear cells ("PBMC")). In addition to these 78 labels, 15 hematopoiesis or immunology-inspired categories that are not directly represented by samples but relevant for their organization in a structured pyramid (for instance "Lymphocytes") or as a potential cell population (for instance "antigen-experienced B cells") were added (Additional file 1: Table  S13 ). Categories corresponding to tumor samples were discarded for the identification of TM and only kept as negative controls, resulting in 68 categories available for screening. Having defined this set of 78 labels and 68 categories (53 categories are directly represented by labels, with 15 additional categories not directly represented in the dataset), we exhaustively encoded the relationships between labels and categories using three possible relationships (Additional file 1: Table  S13 ).
Reduction of TM sets for four populations For four populations (neutrophils, endothelial cells, fibroblasts, and B lineage cells), the number of TM identified in the discovery series was much higher (>90) than for the other cell populations (<25). To obtain more balanced marker sets, we performed the same selection process on the validation series, obtaining TM sets overlapping with those identified in the discovery series (Additional file 1: Table  S3 ). To reduce the number of markers, we thus took the intersection of the markers across the three microenvironment series (B lineage, fibroblasts), or the discovery and the HuGene 1. 0ST series (endothelial cells), or the discovery and the Affymetrix HGU 133A series (neutrophils). Of note, this filtering step was not performed for the figures where data from the MCP validation series were used (Fig.
Principal component analyses of microenvironment samples For each MCP series, we retained samples belonging to well-characterized hematopoietic lineages (T or NK cells, B cells, monocytic lineage, granulocytes, endothelial cells, fibroblasts) and cancer cell lines. Probe sets were filtered to retain only the those above the 95th variance percentile on each series. Principal component analysis was then performed for each series and data for the first principal two components (those explaining the most variance) are displayed (Additional file 2: Figure  S2 ; cell lineages are color coded).
Correlation profiles of TM in micrenvironment and tumor datasets For a given set of TMs, corresponding features were subsetted from the expression matrices of the three microenvironment series and three primary tumor series (Affymetrix Human Genome U133 Plus 2. 0, 133A, and TCGA). On these six matrices, Pearson correlation coefficients were computed for each pair of features (Additional file 2: Figures  S5-S7 ). This analysis is motivated by the following model: our definition of a transcriptomic marker is a feature that is expressed in one and only one cell population at an homogeneous level. Let (i) S be a tissue sample composed of n cell populations with proportions π i (i = 1. n), (ii) f be a feature and (iii) f S be its measured expression in sample S and (iv) f i its measured expression in a cell population i (i = 1.
Correlation between MCP-counter scores and known mRNA proportions MCP-counter scores were computed from the fRMAnormalized RNA-mixture microarray dataset and plotted against the known mRNA log-proportions. Pearson correlation coefficients and the corresponding tests against the t distribution were performed. For qPCR experiments, MCP-counter was ran using gene symbols as identifiers and the intersection of the "complete" TM sets for fibroblasts and endothelial cells and the gene assayed by qPCR as features.
Data deposition The transcriptome data of the 12 mixture samples has been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO series accession number GSE64385. Linear model correlating the cytotoxic lymphocyte MCPcounter score to NK and T cell abundances NK and CD3+ T cells are the two populations introduced in our mRNA mixture experiment that contain cytotoxic lymphocytes (as a whole for NK cells and through their CD8+ subset for CD3+ T cells). The model is thus set up to assess whether the observed cytotoxic lymphocyte scores can be explained by the proportions of NK and T cells in our mixture samples. Let C, NK, and T designate the mRNA amounts corresponding to cytotoxic lymphocytes, NK cells, and CD3+ T cells, respectively. Let C. MCP designate the cytotoxic lymphocyte MCP-counter score.
We have the following linear relationship at the mRNA level: C ¼ a' NK :NK þ a' T :T þ b'; where a' NK , a' T and b' are scalar constants. C is not controlled for in our dataset. However, under the hypothesis that C correlates with the cytotoxic lymphocyte MCP-counter score, and since the mRNA data are in a log 2 scale, we have: C ¼ a:2 C:MCP þ b" Combining these two equations, we have: 2 C:MCP ¼ a NK :NK þ a T :T þ b with b = (b" -b')/a, a NK = a' NK /a, and a T = a' T /a, which is also a linear relationship. We thus tested it using a bivariate linear model on our dataset.
R implementation of the MCP-counter method We implemented MCP-counter as an R package called "MCPcounter". Users should call the "MCPcounter. estimate" function, which takes a normalized gene expression matrix as its first argument and the type of features that should be mapped to selected TM (probe sets, HUGO symbols, ENTREZ ID) as its second argument. We used mapped TM to "probe sets" to compute MCP-counter scores for Affymetrix Human Genome U133 Plus 2. 0 and Affymetrix 133A samples, and HUGO symbol identifiers for samples obtained on Affymetrix HuGene 1. 0 ST, Illumina Hiseq, and other gene expression platforms. This package is available Zenodo (https://doi. org/ 10. 5281/zenodo. 61372). Simulation of mRNA mixtures (Fig. 4a ) Let's call d a linear scale (genes × samples) GEP matrix, with samples related to six possible phenotypes, five corresponding to immune cell populations ("T cells", "NK cells", "B cells", "monocytic lineage", "neutrophils") and one to non-hematopoietic tumor cell lines.
Representative MCP-counter scores across cancers In each of the three tumor series, samples were split according to their tumor type as represented in Fig. 4b. The median MCP-counter score was computed for each cell population in each tumor type, yielding three "median matrices" (one per technological platform). Since MCP-counter scores are expressed in arbitrary units which depend on the gene expression platform used, we Z-transformed each column of these three matrices (the mean of each column was subtracted on each platform and the resulting values were multiplied by the average of the three standard deviations observed across the three platforms for this column; Additional file 2: Figure  S8 ). The resulting three matrices were averaged (omitting missing values in case of a cancer missing from one or two of the datasets; Fig.