dataesr/scientific-paragraphs-categorization huggingface dataset:
). From the optimized contigs sets, a total of 51,821 (35%) contigs built with CLC and 78,929 (40. 5%) contigs built with TRINITY had a hit in the database. There was redundancy in both optimized assemblies since the 51,821 sequences from CLC optimized assembly matched 16,270 unique best blast hit (BBH) and the 78,929 sequences from TRINITY optimized assembly matched 18,024 unique BBH. This result suggested the presence of homologous sequences such as gene families in the assemblies. Thus, From those, 8,951 (89%) contigs that were deciphered with CLC and TRINITY, 739 contigs (7%) were specifically assembled with TRINITY and 370 contigs (4%) were built only with CLC. Selected sequences have length ranging from 202 bp to 15,595 bp and a median value of 1,480. 5 bp (Fig3B).
Acknowledgement We would like to thank  Vegepolys Innovation , more specifically  Fabienne Mathis , for their contribution in raising the  GenoParfum  project. We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) for providing computing and storage resources. We would like to thank all our colleagues from  INRA (Angers, Orleans, Orsay, Rennes, Versailles ) for the enriching and constructive exchanges around this project, especially  Patricia Faivre-Rampant  for reviewing the article. The authors thank the  Ministere of Agriculture  for the funding of the Casdar project GENOPARFUM that allowed to perform this study.
After trimming on quality, length, ambiguous nucleotide and adapters, a total of 144,322,837 reads totaling 19. 7 Gb (77% of initial dataset) remained for de novo transcriptome assembly with a mean PHRED score per read around 40. Depending on the genotype, the number of paired-end DNA reads sequenced ranged from 29,985,684 to 145,227,504(S2 Table). Given that the genome size of Lavandula angustifolia is approximately 1Gb (Iteipmai CASDAR 2011,[45]), this corresponds to a theoretical mean depth of coverage ranging from 5X to 22X. The clone Maillette reaches the highest sequencing depth because it was used for the transcriptome-guided DNA assembly described below. De novo transcriptome assembly 13Results of de novo assemblies are presented on TRANSRATE standards, meaning for which both members of a pair were aligned on the same contig, in the correct orientation and without overlapping either end of a contig.
we took advantage of ). From the optimized contigs sets, a total of 51,821 (35%) contigs built with CLC and 78,929 (40. 5%) contigs built with TRINITY had a hit in the database. There was redundancy in both optimized assemblies since the 51,821 sequences from CLC optimized assembly matched 16,270 unique best blast hit (BBH) and the 78,929 sequences from TRINITY optimized assembly matched 18,024 unique BBH. This result suggested the presence of homologous sequences such as gene families in the assemblies. Thus, From those, 8,951 (89%) contigs that were deciphered with CLC and TRINITY, 739 contigs (7%) were specifically assembled with TRINITY and 370 contigs (4%) were built only with CLC. Selected sequences have length ranging from 202 bp to 15,595 bp and a median value of 1,480. 5 bp (Fig3B).
Table. Out of the 10,060 sequence of the reference Unigene, 9,816 (97. 6%) had a BLASTP hit against swissprot database, 8,928 (88. 7%) had at least one GO annotation, 4,931(49%) had at least one KEGG pathway annotation and 4,647 (46%) had a KEGG and a GO annotation. Search of conserved domain against Pfam database allowed to increase the rate of sequence annotated with a GO term or a KEGG ontology (S3 Table). The identification of various GO terms and KEGG pathways indicated that we assembled sequences related to a relatively diversified panel of protein functions.
A substantial amount of highfrequency noise leads to an excessively elevated threshold value, leading to a massive loss of microsaccades and hence potentially biased statistical analyses. More recently, Bayesian methods were developed for (micro)saccade detection that are not based on velocity thresholds but assume a particular generative model for saccades and Gaussian noise sources  (Daye & Optican, 2014; Mihali, van Opheusden, & Ma, 2017). In this paper we propose a new (micro)saccade detection method, based on an unsupervised clustering approach, that can effectively separate fixational microsaccades from high-frequency recording noise. The accuracy of the method is assessed on a new labeled dataset consisting of experimental recordings from aged subjects. The method is compared with previous methods in terms of performance and practical use, and finally, a procedure for tuning its main free parameter from the experimental data is proposed.
Data collection Eye-position data were recorded monocularly from 14 subjects aged 69-81 years (75. 27 6 4. 25). The participants were recruited as part of the Silversight cohort study (Vision Institute, Paris, France) and they were ascertained to have no visual, audio-vestibular, neuropsychological, or cognitive losses. Subjects with refractive errors larger than þ4. 00d or lower than À4. 00d, astigmatism larger than 1d, visual acuity lower than 8/10 (0. 1 logMAR), or dissociate phoria larger than 6d were excluded. The clinical screening was ethically approved by the ''CPP Ile de France,'' and it was carried out under medical supervision at the Clinical Investigation Center of the Quinze-Vingts Ophtalmologic Hospital, Paris. The EyeLink 1000 (SR Research Ltd. , Ontario, Canada) static tower-mounted video-based eye tracker with forehead and chin rest was used for recordings at 1-kHz frequency and manufacturer-stated spatial resolution ,0.
All traces were labeled independently by three experts (including M. B. and C. W. ), who marked all microsaccade-like events as either ''microsaccade,'' ''ambiguous event,'' or ''artifact. '' These labels were then combined into a final labeled dataset by the majority vote, resulting in 5,049 labeled microsaccades, 480 ambiguous events, and 38 artifacts in total. In addition, the performance of the algorithm was comparatively assessed on an existing labeled dataset provided by M. Rucci (Active Perception Laboratory, University of Rochester). This dataset included twodimensional traces from four young subjects (22-31 years old) performing a fixation task similar to the one used in our experiment (each subject performed 16-90 fixation trials, 12-15 s each). Monocular recordings at 1 kHz were conducted using a high-resolution Dual-Purkinje eye tracker system.
Preprocessing and event detection Raw recording data for each subject consisted of horizontal and vertical coordinates of the eye in degrees of visual angle, along with the corresponding timestamps (in milliseconds). The data from all sessions/ trials were stacked into one matrix (per subject) with three columns ½ t; x; ỹ (this is not a necessary step as the algorithm can work with traces from a single trial, provided that they include a sufficient number of microsaccades; see Discussion). Parts of the trace corresponding to a 200-ms period around blink events, automatically detected by the EyeLink software, were removed as an obvious source of noise. Due to the presence of blinks and concatenation of multiple trials, the combined data traces exhibited artifactual jumps in the x and y traces.
The nearest neighbor distance is computed as (Rodriguez & Laio, 2014): d i ¼ min j:q j. q i d ij : ð5Þ A plot of d i as a function of q i for all data points for one sample subject shows an example of the decision graph, with a clearly separated cluster center (Figure  1B ). The cluster center (i. e. , a prototypical microsaccade) is determined automatically as the data point with the largest product q i d i. In the case of multiple clusters, a corresponding number of points with the largest product qd is determined, and each point in the dataset is assigned to the same cluster as its closest neighbor with a higher local density  (Rodriguez & Laio, 2014). This clustering approach is not iterative; it is thus fast compared to classical algorithms such as k-means, and it does not suffer from related overfitting problems.
The data points with largest indices (corresponding to the lowest k-dist values) are those located near the cluster centers. As the data point index decreases, there is a sharp increase in the associated kdist value, indicating a fast decrease in the density. We set the characteristic distance d c to the k-dist value corresponding to the point of the steepest decrease in density (i. e. , the fastest change in the k-dist curve), which is in turn calculated as the maximum of the derivative of the k-dist function. The derivative is estimated using Savitzky-Golay differentiation filters of fourth order (filter size was set to 10% of the number of data points to smooth out noise). Depending on the size of the dataset, other methods can be used to detect the maximum of the derivative, including semi-automatic approaches.
Receiver operating characteristic analysis Receiver operating characteristic (ROC) analysis was used to compare the performance of our method against methods proposed by  Engbert and Kliegl (2003)  and  Otero-Millan et al. (2014) , referred in the following as EK and OM methods, respectively. For the EK algorithm (Version 2. 1 written in MATLAB), the k parameter varied from 3 to 15, with minimal duration of microsaccade fixed to 6. The OM algorithm (Version 1. 1, downloaded from http://smc. neuralcorrelate. com/sw/microsaccade-detection/) does not have free parameters, since it automatically detects the best separation between the microsaccade cluster and noise cluster in a chosen low-dimensional feature space. Therefore, when showing ROC curves, we indicate the performance of this method by a point corresponding to the optimal performance, instead of a curve.
Detection of microsaccadic FEMs in low-noise regime We next verified whether the difference in performance between the three methods is caused by specific noise properties rather than other possible reasons. We thus compared the performance of the three methods on a dataset from a different study, in which eye tracking was performed using a high-precision double-Purkinje-image (DPI) system with a much lower level of recording noise  (Poletti et al. , 2017 ; see Methods). Under these conditions, the performance of the new method was similar to that of the OM method, and both of these methods outperformed the EK method (Figure  5A  and B ). This result suggests that the method proposed here is more efficient than the two previous methods in the high-noise regime, while its performance is comparable to the OM method for low-noise data.
An advantage of these methods is that they are not datahungry and they work equally well on large and small datasets (given that the generative model and parameters are suitably chosen). Feature-based clustering methods, like the one proposed in the present work, are in contrast conceptually simple and usually faster, but in order to work reliably they need a dataset with a sufficient number of microsaccades to form a cluster. In our analysis this was the case even when working only with labeled trials (160 s total time per subject). The problem of insufficient number of microsaccades can easily be detected from the decision graph (see Figures  1B  and 8C ), where it would correspond to the absence of outliers for microsaccade clusters. While our method is proposed in the context of aging research, it is not limited to the age-related ocular noise.
Acknowledgments We thank  Silvia Marchesotti  for her assistance in testing the algorithm. This research was supported by  ANR  - Essilor  SilverSight Chair  ANR-14-CHIN-0001. Commercial relationships: none. Corresponding author:  Denis Sheynikhovich. Email: denis. sheynikhovich@upmc. fr. Address:  Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.
Main text STREAM (Single-cell Trajectories Reconstruction, Exploration And Mapping) can accurately recover complex developmental trajectories and provide informative and intuitive visualizations to highlight important genes that define subpopulations and cell types. STREAM reliably reconstructs trajectories and pseudotime (the distance from the start of a developmental trajectory) when multiple branching points are present, assumes no prior knowledge about the structure or the number of trajectories, and does not require extensive bioinformatics knowledge thanks to a userfriendly and interactive web interface. Additionally, STREAM has four innovations compared to other existing methods: 1) a novel density-level trajectory visualization useful to study subpopulation composition and cell-fate genes along branching trajectories, 2) a documented end-to-end pipeline to reconstruct trajectories from chromatin-accessibility data, 3) the first interactive database focused on single-cell trajectory visualization for several published studies ,and 4) a trajectory mapping procedure to readily map new cells to precomputed structures without pooling data and recomputing trajectories.
This last innovation allows facile analysis of data from genetic perturbation studies or to assign diseased/stimulated cells to a normal/resting developmental hierarchy. STREAM has been extensively tested using several published datasets from different organisms (zebrafish, mouse, human) and single-cell technologies (qPCR, scRNA-seq, scATAC-seq). It also has been compared to 10 other methods on both synthetic and real datasets. STREAM takes as input a single-cell gene expression or epigenomic profile matrix and approximates the data in three or more dimensions with a structure called the principal graph, a set of curves that naturally describe the cells' pseudotime, trajectories and branching points (Fig. 1a ). STREAM first identifies informative features such as variable genes or top principal components.
ElPiGraph is very robust to background noise, does not require pre-clustering, can work in multidimensional space, and is able to manage large-scale datasets on an ordinary laptop (Online Methods). Figure  1  STREAM pipeline overview on single-cell RNA-seq data from the mouse hematopoietic system. (a) STREAM trajectory inference. Starting with a single-cell gene expression matrix, STREAM performs three main steps: selection of informative genes, dimensionality reduction, and simultaneous tree structure learning and fitting by ElPiGraph. The optimal structure is selected based on the elastic energy minimization among a set of candidate structures that are constructed every time a tree node is added. The final tree is interpreted as a set of connected curves representing different trajectories.
To facilitate the exploration of the inferred structure, STREAM includes a flat tree plot that intuitively represents trajectories as linear segments in a 2D plane. In this representation, the lengths of tree branches are preserved from the MLLE embedding (Fig. 1b ). In addition, cells are projected onto the tree according to their pseudotime locations and the distances from their assigned branches. If the process under study has a natural starting point (for example a known origin in a developmental hierarchy), the user can specify a root node. This allows easy re-organization of the tree using a breadth-first search to obtain a subway map plot that better represents pseudotime progression from a selected starting node (Fig. 1c ). Although these visualizations capture trajectories and branching points, they are not informative on the density and composition of cell types along pseudotime, a common challenge when modeling large datasets.
1e-f , Supplementary Fig. 1 2 , Supplementary Note 1). STREAM is the only trajectory inference method that explicitly implements a mapping procedure, which allows reusing a previously inferred principal graph as reference to map new cells not included in the original fitting procedure. This avoids pooling old and new cells and re-computing trajectories from scratch, a computationally-intensive operation that also perturbs the original structure and complicates the interpretation of the pseudotime. This feature is particularly helpful when studying genetic perturbation data or exploring unlabeled data. To show the utility of the mapping feature, we applied STREAM to scRNA-seq data from Olsson et al  3. This study focused on the mouse hematopoietic system, specifically on the consequences of cell determination within the granulocyte-monocyte progenitors (GMP) population after the transcription factors Gfi1 and/or Irf8 are knocked out.
STREAM recovers the correct trajectories for the wildtype cells and, using the mapping feature, also predicts and effectively visualizes the consequences of the genetic perturbation as validated in the original study (Supplementary Fig. 3 4 , Supplementary Note 2). To test the robustness and scalability of STREAM, we next explored data derived from different platforms and organisms. We used two recently published zebrafish datasets obtained with single-cell qPCR  4  and inDrop  5  (profiling ~10000 cells) assays. These data provided the first comprehensive model of the zebrafish hematopoiesis system without biases introduced by FACS sorting subpopulations. Our analyses successfully recovered developmental trajectories at unprecedented resolution compared to previous analysis (Supplementary Fig.
5 6 , Supplementary Note 3-4). We next systematically compared STREAM with 10 other state of the art methods for pseudotime inference on three different datasets  [6] [7] [8] [9] [10] [11] [12] [13] [14]. First, we assessed the quality of the topology using a previously proposed synthetic dataset  14. Second, we assessed the pseudotime accuracy using known marker genes on scRNA-seq data for myoblast differentiation, a classic dataset to compare trajectory inference methods  15. Finally, we quantitatively compared the number and quality of trajectories in terms of precision and recall for known marker genes that completely diverge during development. In all the comparisons, STREAM consistently outperforms the other methods in inferring the correct topology, provides smooth pseudotime for myoblast differentiation and reconstructs the most balanced branching structure (avoiding under/over branching) in terms of precision and recall (F1-score) among the methods (Supplementary Figs.
7-14, Supplementary Note 5). Figure  2  STREAM on single cell epigenomic data from the human hematopoietic system. (a) Single cell ATAC-seq workflow. FACS sorting is used to isolate populations from CD34+ human bone marrow and single-cell ATAC-seq measurements are performed. After mapping reads to the reference genome, reads within peaks are selected and ChromVAR is used to calculate k-mers' z-scores. Finally, PCA is applied to the z-score matrix and top principal components are selected as features for STREAM analysis. (b) STREAM learns a principal graph from chromatin accessibility data and accurately reconstructs cellular developmental trajectories of the human hematopoiesis. As in Figure  1 , the structure can be easily visualized thanks to the subway map and stream plots. In the first branch, the HSCs segregate through MPP into lymphocyte-committed, erythrocyte-committed and myelocyte-committed branches.
15 , Supplementary Note 6). For example, two of the detected 7-mer sequences match binding models for the transcription factors GATA1 and CEPBA, which regulate differentiation towards erythroid and myeloid lineages, respectively (Fig. 15b-c ). STREAM is available as user-friendly open source software and can be used interactively to explore several precomputed datasets and to compute new trajectories at stream. pinellolab. org (Supplementary Fig 16 ), or as a standalone command-line tool using Docker (github. com/pinellolab/stream) (Supplementary Note 7-8).
Each grammar operation Y p produces a set of s new candidate graph topologies 𝐷 ) , possibly taking into account the dataset X: {{𝐷 ) , ϕ(𝐷 ) )}, 𝑘 = 1 … 𝑠} = Y h ({𝐺 # , ϕ # (𝐺 # )}, 𝑋) Given the pair {Gi, fi(Gi)} characterizing the i th step of the algorithm, a set of r different graph operations {Y 1 ,…, Y r } (which we call a "graph grammar"), and an energy function 𝑈 Z (𝑋, 𝐺), the algorithm applies all the grammar operations selected, fit the newly derived graph topologies to the data, and choose the most energetically favorable embedment as principal graph of the step (i+1) th : {𝐺 #iP , ϕ jiP (𝐺 #iP )} = argmin {k I ,ZAk I C} l𝑈 ZAk I C A𝐷 ) , 𝑋C: n𝐷 ) , ϕA𝐷 ) Co ∈ q Y h ({𝐺 # , ϕ j (𝐺 # )}, 𝑋) hOP…r s where {𝐷 ) , ϕA𝐷 ) C} is supposed to be fit to the data after the application of a graph grammar.
g. , a = 1) hardly penalizes any branching while smaller values (e. g. , a = 0. 01) leads to keeping essential branches. 4) R0 is the trimming radius, allowing robust estimation of node positions. ElPiGraph implements a simple scaling statistics allowing to automatically estimate R0 if needed. In the simplest case, R0 = ¥, a = 0, and it is recommended to keep l » µ/10. For all the single cell datasets in this paper it is desirable to set a = 0. 02 and sometimes use trimming (automatically defined finite value for R0).
Adjusting the final tree structure The resulting principal graph is refined based on the following procedures: 1) Principal tree branches can be extrapolated from the terminal vertices, i. e. a branch can grow, if necessary, to better fit cells that maybe fall far away from a terminal node. This allows a smoother pseudotime mapping and a more reliable characterization of cells close to initial or terminal points 2) Branches not supported by at least nminload data points can be removed or shrunk. 3) A k-star node, (node with connectivity k>2) can be rewired to another graph node if the latter has a higher local density or a larger number of cells projected into it to improve the positioning of candidate branching points. In this paper, the ElPiGraph. R R package has been used, available at https://github.
Discovery of marker genes: Diverging gene detection: For each pair of branches 𝐵 # and 𝐵 * , and for the gene 𝐸, the gene expression values across cells from both branches are scaled to the range [0,1]. For gene expression 𝐸 # from 𝐵 # and gene expression 𝐸 * from 𝐵 * , we first calculate their mean values. Then, we check the difference between mean values to make sure it is above a specified threshold (the default value is 0. 2). Mann-Whitney U test is then used to test whether 𝐸 # is greater than 𝐸 * or 𝐸 # is less than 𝐸 *. Since the statistic 𝑈 could be approximated by a normal distribution for large samples, and 𝑈 depends on specific datasets, we standardize 𝑈 to Z-score to make it comparable between different datasets. For small samples where this test is underpowered (<20 cells per branch), we report only the fold change to qualitatively evaluate the differences between 𝐸 # and 𝐸 *.
STREAM analysis on scATAC-seq data: For the scATAC-seq analysis, a total of 3,072 cells were profiled using FACS to isolate 9 distinct populations from CD34+ human bone marrow, which encompassed progenitors for four well-defined lineages  16. 2,034 high-quality cells passed quality control filtering and were used in the downstream analysis with STREAM. Specifically, cells were filtered so that 1000 unique nuclear fragments were observed for each cell and at least 60% of these reads aligned in open chromatin peaks. After filtering low quality cells, the mean intensity and GC content for each peak that was called for this dataset was computed using the addGCBias function for the hg19 genome using the BSgenome. Hsapiens. UCSC. hg19 package available through chromVAR  17. These two coordinates were used to infer an empirically-defined set of background peaks to compute accessibility deviations, which have been described elsewhere  16, 18.