dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 1. 1 is a dedicated visualization and analysis tool for scRNAseq developed for analysing scR-NAseq datasets mostly produced by 10×Genomic platforms. It allows importing datasets and visualizing custom projections of either gene expression or antibody-only datasets, across t-SNE or UMAP computed by the Cell Ranger 3. 1 pipeline. Despite its ease of use however, this tool only displays a single dimensionality reduced map fea-turing the dataset clusters and heatmaps of the graph-based differentially expressed genes or mAbs. Although this tool may export images and selection of cells, it lacks the dual displays of phenotypes and transcriptomes to perform any simultaneous exploration of CITEseq data. iCellR. iCellR is a R package for scRNAseq analysis able to produce interactive graphs for either of transcriptome or immunophenotype data, but not both simultaneously.
To our knowledge iCellR does not reproduce a flow cytometry interface and, similarly to Seurat, is accessible for users skilled in R. CytoBank. CytoBank (3) and some other flow cytometry softwares can import single cell data files after adequate file conversions, and can be used for visualizing single cell phenotype data. CytoBank is also able to produce t-SNE but its limited capacity to process a maximum of 818 parameters is not compatible with current scRNAseq transcriptomic datasets. Furthermore, Cytobank does not display interactively the density plot subpopulations in the t-SNE, nor does it allow users to export the cell tags for further use.
SeqGeq-FlowJo. SeqGeq-FlowJo (https://www. flowjo. com/solutions/seqgeq) is a commercial bioinformatics platform of 506 Mo for desktop scRNAseq analysis with an intuitive interface. Under administrator's authorization for it use, it allows to import, analyze and visualize scRNAseq data with interactive graphs, and to share data with other analytic applications provided as exchange plug-ins, such as Seurat, Monocle and the commercial flow cytometry software FlowJo™, to quote a few. So the SeqGeq™ platform provides access to several sophisticated tools performing various types of analyses successively, but does not display both transcriptomes and epitopes simultaneously in the same integrated layout as does Single-Cell Virtual Cytometer. So currently, while only a few existing open source tools allow to analyze both types of data together, Single-Cell Virtual Cytometer presents the advantage of web-based and user friendly interface for the simultaneous display of both immunophenotype dot plots and transcriptome-based UMAP/t-SNE plots.
The Single-Cell Virtual Cytometer interface Single-Cell Virtual Cytometer was primarily applied to analyze an original CITE-seq dataset of human PBMC. So the PBMC from a healthy individual were primarily labeled with a mix of 12 TotalSeq™-B ADT (Supplementary Table  S1 ) at five concentrations respectively labeled by five HTO (Supplementary Table  S2 ). The stained PBMC were analyzed for CITE-seq using a 10XGenomics 3 chemistry V3 platform, sequenced, pre-processed and dimensionality reduction of the transcriptome datasets was performed with UMAP. As QC of the transcriptomic part of 4 NAR Genomics and Bioinformatics, 2020, Vol. 2, No. 2 the CITE-seq dataset, the single cells with outlier counts of total UMI or number of genes were discarded. For the QC of the phenotypic part of the CITE-seq dataset, cells labeled with over-diluted ADT (Supplementary Figure  S1 ) and cells displaying mutually exclusive phenotypes (e.
g. CD3 + CD19 + CD14 + ) were discarded. This finally yielded a CITE-seq dataset encompassing the epitopes and transcriptomes of n = 5559 PBMC (Figure  1 ). It was deposited as GSE144434 in the GEO database. Importantly, analysing this dataset by either the flow cytometry tool Cytobank or Single-Cell Virtual Cytometer yielded the same plots and rates of CD3 + CD4 + T and CD3 + CD8 + T cells from the PBMC (Figure  1A  and Supplementary Figure  S2 ). However, only the interface of Single-Cell Virtual Cytometer displays both the ADT-based phenotype plot and the corresponding transcriptome-based UMAP/t-SNE map (Figure  1B ).
Simultaneous visualization of single cell phenotypes and transcriptome-based signatures from an healthy donor's PBMC CITEseq dataset Any multigene signature can be robustly scored for each single cell by computing its summed expression ponderated by that cell's total transcriptome. We previously developed Single-Cell Signature Explorer to score likewise multigene signatures and visualize these signature scores across entire scRNAseq datasets  (1). Hence scores for any signature can be imported from Single-Cell Signature Explorer to be used and selected in the (x,y) parameter list of Single-Cell Virtual Cytometer. We reasoned that the above-depicted possibility to explore at the same time both epitopes and single gene expressions opens the additional possibility to visualize simultaneously both immunophenotype data and multigene signatures.
So, the above CITEseq PBMC dataset from one healthy donor was then analyzed in more details through both a multigene signature and cell surface markers. We previously defined a myeloid-specific set of genes most differentially expressed by a myeloid cell cluster relative to all other PBMC clusters (1). The single cell scores of a this myeloid cellspecific signature were computed across the dataset (Methods) and plotted (1) versus ADT staining for the T-cell surface marker CD3. The T lymphocytes and myeloid cells were then visualized across the UMAP of the entire dataset (Figure  2A ). The myeloid cells were discarded, while the T cells were gated and analyzed further for cell surface expression of the CD4 and CD8 cell surface epitopes. This delineated the four subsets of CD4 + T (n = 1796), CD8 + T (n = 885), CD4 + CD8 + (double positive) T cells (n = 52) and the CD4 -CD8 -(double negative) T cells (n = 99) which include the TCR ␥␦ T lymphocytes  (6) (7) (8).
These four T-cell subsets were readily delineated by Single-Cell Virtual Cytometer in the dimension-reduced UMAP of the PBMC dataset (Figure  2B ). Parallel analyses of the cell surface expression of the CD19 and CD16 epitopes in the non-T-cell subsets of PBMC identified the B lymphocytes (CD19 + CD16 -) (n = 157), the NK cells (CD19 -CD16 + ) (n = 902) and the monocytes (CD19 -CD16 -) (n = 1341) (Figure  2 ). To validate these results, a second CITE-seq dataset for 10k PBMC (3' chemistry V3) was sourced from the 10XGenomics website, pre-processed with Seurat and both transcriptomes and epitopes were analyzed with Single-Cell Virtual Cytometer as above. This allowed to directly visualize the CD3 + T lymphocytes including their subsets defined by the CD4 and CD8 epitopes, as well as the non-T-cell subsets of (CD19 + CD16 -) B lymphocytes, (CD19 -CD16 + ) NK cells and (CD19 -CD16 -) monocytes (Supplementary Figure  S3 ).
With this second CITE-seq dataset, the transcriptome-based and the epitope-based cell gatings were compared for each of the B, T, NK and myeloid cell populations, respectively (Supplementary Figures  S4A  and B ). These comparisons indicated that gatings based on cell surface epitopes were more exhaustive than those based on expression of the corresponding single genes, namely CD19 for B lymphocytes, CD3G for T lymphocytes, CD16 for NK cells and CD14 for monocytes. In most cases for which multigene signatures specifying cell types could be defined however, the multigene signature-based gatings were equivalent to those based on cell surface epitopes. Altogether, these results showed that Single-Cell Virtual Cytometer allows the simultaneous analysis and visualization of both transcriptomes and cell surface immunophenotypes at the single cell level.
entiated (Temra) lymphocytes  (10). These respective stages are classically defined through cell surface expression of proteins markers such as CD45RA and CD62L, IL7Ra and CCR7 (Tn), CD45RO, CD62L, IL7Ra and CCR7 (Tcm), CD45RO (Tem), and CD45RA (Temra), but their respective cell surface immunophenotypes and transcriptomes have never been characterized on the same cells so far. In a test experiment with the above CITE-seq dataset and Single-Cell Virtual Cytometer, we now aimed at defining these differentiation signatures for either of the CD4, CD8, DP and DN subsets of T cells. The cell surface expression of CD4 and CD8 markers indicated that T lymphocytes comprised n = 1796 CD4 + T cells, n = 885 CD8 + T cells, n = 52 CD4 + CD8 + T lymphocytes and n = 99 double negative T lymphocytes, embedded in distinct areas of the PBMC dataset UMAP (Figure  2B ).
1 and relative variance >1. These differentiation signatures were then scored across each single cell of CD4 + T lymphocyte, and the same procedure was applied separately for the differentiation signatures of CD8 + T lymphocytes, double positive (DP) T lymphocytes and double negative (DN) T lymphocytes (Supplementary Tables  S3-6 ). For each of these gated T-cell subsets, these differentiation signatures were consistent with the corresponding cell surface expression of the CD45RA and CD62L epitopes (Figure  3 ). In a validation tests with Single-Cell Virtual Cytometer, the T cells from the 10×Genomics' 10k PBMC CITEseq dataset were analyzed as above. The CD4 + T, CD8 + T,  CD4 -CD8 -T and CD4 + CD8 + T lymphocytes were identified by their expression of the CD3, CD4 and CD8 epitopes, and scored for the above differentiation signatures.
Within this second PBMC dataset, all the transcriptome signatures of T-cell differentiation were also consistent with their respective differentiation phenotype, here defined by the cell surface expression of IL7R (CD127) and CD45RA protein markers (Supplementary Figure  S5 ). Furthermore for each T cell population, Single-Cell Virtual Cytometer allowed to visualize simultaneously the four differentiation stages in the transcriptome-based UMAP of the entire dataset (Supplementary Figure  S6 ). Consistent with these results, the marker-driven and the transcriptome signature-driven gatings of CD4 + Tcm cells pinpointed the same cell clusterings in the dataset UMAP (Supplementary Figure  S7 ). Together, these results extended and validated those obtained with our PBMC CITE Seq dataset, illustrating further the advantage of Single-Cell Virtual Cytometer for the simultaneous analysis and visualization of gene expression and cell surface epitopes from CITE-seq datasets.
DISCUSSION The advent of Cellular Indexing of Transcriptome and Epitopes by sequencing (CITE-seq) has brought the major possibility to jointly explore both gene expression and immunophenotypes at the single cell level (2). However, this decisive innovation does not allow biologists to immediately visualize its results from the datasets. Importantly, this task always requires a prior phase of extensive pre-processing of the raw data performed by several bioinformatic tools, such as Seurat or CellRanger™, to quote a few. Such preparation of the data typically involves alignment on genome and quantifications of reads, normalization of gene counts across cells, quality control of cells and genes, principle component analyses, reduction of dimensionality, clustering and mapping of the dataset. Most current softwares performing this stage of the analyses can indeed analyze both transcriptomes and epitopes datasets together, but they do not display both features simultaneously on a user-friendly interface.
Here, we introduce Single-Cell Virtual Cytometer for this very precise aim: the simultaneous visualization of both transcriptomes and immunophenotypes from CITEseq datasets. This novel tool indeed intervenes only after the pre-processing of such data. Hence, it is particularly relevant for fully exploiting the distinct modalities measured within single cells since each readout, e. g. gene, protein or signature score, is directly plotted across the entire dataset plot. Here, by pinpointing the gene signatures of functional differentiation stages in peripheral T lymphocytes from healthy individuals, we showed that it permits straightforward analyses of bimodal data, such as mRNA and cell surface proteins in circulating T lymphocytes from healthy individuals. We forecast that likewise, Single-Cell Virtual Cytometer will prove broadly applicable to the visualization of any kind of readout from any multimodal single cell technology, after adequate integration of the datasets  (11).
Its versatility enables users to analyze likewise any kind of single cell data about chromatin accessibility  (12, 13) , epigenomics  (14) , mutations  (15) , chromosome conformation  (16) , RNA modification  (17) , spatial transcriptomics  (18, 19)  and spatial proteomics (20,21). Hence, Single-Cell Virtual Cytometer represents a unvaluable resource for integrated visualization and analyses of multimodal datasets at the single cell level.
Electronic modularity and autonomy Both prototypes are based on a Raspberry Pi single board computer which enables a centralized method to control the electronics, acquire and process the images, and serve as the user/machine interface(Fig. S1 ). The magnetic coupling of the modular unit enables electronic connectivity through the contact of copper ribbons that connect each module at their interface to form a custom BUS for I 2 C connection and power. The different independent microcontrollers, here Arduinos, receive queries as actuators from the Raspberry Pi and send logs as sensors back to the Raspberry Pi. Instead of utilizing a custom BUS which is time consuming to establish, the flow-through version utilizes Pi HATs (Hardware Attached on Top) for both assembling and deploying code in the Arduinos.
The Pi HATs enable the rapid addition of numerous off-the-shelf specialized boards. Three HATs are utilized here to serve different functions: one for cooling the CPU of the Raspberry Pi, one for controlling the focus stage and the pump, and a final HAT supporting a GPS for geolocalization. Thanks to the massive community built around Raspberry Pi, hundreds of other possibilities exist for new modules and more functions built on top of this platform. Both instruments can be powered either through standard wall AC power or from battery cells for field deployment (Fig. 5A ). For an acquisition frequency of 0. 5 Hz and a standard Li or polymer battery of 20,000mAh, the flow-through version can collect continuously for more than 5 hours.
Stop flow enables high quality imaging Since stop-flow synchronizes the acquisition and the pump, the objects are stationary when imaged, canceling any morphological deformation and allowing quantitative analysis. Moreover, this enables a longer exposure, increasing the resolution and reducing the need for powerful illumination. This lower frame-rate strategy enables the capture of the full camera sensor for a larger field of view than via the continuous mode. Open Software (Fig. S4  software architecture) The entire software architecture (Fig. S4 ) is based on existing programs and python libraries such as Node-RED for the Graphical User Interface and first layer of programming interface, MorphoCut for handling the image processing from the raw images to the online platform, and EcoTaxa for classification and annotation.
User/Machine interface By utilizing the headless configuration for the Raspberry Pi, we removed the need for a dedicated monitor, mouse and keyboard, enabling control of the instrument from any device able to access a browser over a WiFi connection (Fig. 1F ). This strategy enables any user to immediately interact with the device without OS or software compatibility issues. The user can then access a browser based dashboard powered by Node-RED for an interactive collection of the metadata crucial for acquisition as well as rapid state modification of the actuators.
Flow based programming strategy The back-end of the GUI is also based on Node-RED, a flow-based development tool for visual programming which is provided by default on any Raspberry Pi software suite. Node-RED provides a web browser-based flow editor, which can be used to create JavaScript functions. Elements of applications can be saved or shared for re-use. The strategy makes it more accessible to those with limited experience in scripting. This visually modifiable program can easily be shared over a json text file.
Image processing The current workflow has image processing done automatically after the acquisition of a defined batch of images. They are then processed by MorphoCut, a python-based library designed to handle large volumes of imaging data. Several operations (Fig. 3A ) are applied on the raw images acquired in fluidic mode: first an average background from five frames surrounding the current one is calculated for subtraction (1), current raw frame (2), current frame with background removed (3), threshold (4), labeling (5). This way, large data sets can be compressed on-board by storing only relevant regions of interest. MorphoCut extracts different features on each region of interest that are stored, along with contextual metadata generated by the user on the GUI, in a compressed file ready to be uploaded on EcoTaxa (Fig.
Visual data collection There are relatively few continual time series that visually document marine ecosystems. Much of the data on our planet's planktonic biodiversity is composed of bulk counts or bulk sequences from mixed populations without visual representations of the sampled specimens. However, much of organismal behavior and underlying cellular processes are beyond the reach of counts and genomic surveys alone. To convert this data into deeper knowledge of biodiversity we need to observe these cells and how their behavior influences the environment. Symbiosis and predation are good examples of the important interactions missed by sequencing (Colin et al. 2017). There is a growing need to supplement the current oceanographic measurements with visual representation. A frugal high throughput microscope could play roles in many different types of studies.
Continual culture monitoring Cultivation of marine cultures is notoriously challenging and in many cases infeasible for extended periods. However valuable behavioral and morphological data can be collected through continuous visual monitoring for even limited periods of time. Thousands of images, with millions of features, could help identify diel behavior, or characterize cell or life-cycle dependent morphological changes. Our limited data set of diatom and dinoflagellate cultures demonstrates how a visual time series performed in the laboratory could bring insight or depth to behavioral or ecological studies. Several studies have displayed the unique power of visual inspection of planktonic communities throughout time  (Brownlee, Olson, and Sosik 2016). We hope tools such as the Planktonscope will help expand this detailed exploration of our oceans by equipping and uniting more researchers around a platform that evolves with each new question.
Design thinking Because the original modular design is made of different units that can be stacked on top of each other, we decided to utilize a parametric design to optimize a physical interface common to all modules using Autodesk Fusion 360 (v2. 0. 5688). Different parameters define the dedicated areas of the interface such as the electronic connection area, the magnetic linkage, and the optical path. The thickness of the material plays an important role to characterize the interface as well as the outer dimensions of all the electronics used inside the instrument. This shareable online 3D environment provided by Fusion 360 contains the main 3D model as well as other models that form the electronic and optical parts. Most of these models have been generated by measuring the existing objects but some have been downloaded from an online library named GrabCad.
Illumination module The illumination is made of individual white LEDs 5 mm LTW2S -17000 mcd forming five concentric rings. From the center to the external ring, the number of LEDs used is reciprocally 1, 6, 12, 18 and 24. Since the LEDs aren't individually driven, each ring of LEDs are controlled separately by an Arduino Mini Pro with a defined I2C address. The Arduino is capable of applying the value of the analog intensities for each ring by receiving a setting from the Raspberry Pi. To simplify the wiring in this module, a custom PCB has been created by Eagle Version 9. 0. 1. The flow-through version utilizes a single LED for illumination.
Vertical stratification of planktonic communities in Comau Fjord To investigate the diversity and distribution of plankton in the stratified fjord waters we employed a horizontal water sampler from LaMotte (CODE 1087) to sample the vertical distribution of plankton from 0-10 meters below the water's surface. From the 1,200 mL samples collected for every depth, we conserved 15ml for flow-through imaging. The salinity at every depth was measured using a hand refractometer from Atago. For the acquisition, the frame-rate was set at 8 frames/second and 2000 frames were taken for each depth corresponding to 5. 6 mL imaged per depth. The data from the experiment in the Chilean fjord consisted of thousands of images of plankton with different sizes and features changing as a function of depth. Using a collage with actual images of planktonic organisms as data points serves as a tool to display qualitative and quantitative information in one figure.
Acknowledgements We acknowledge all members of PrakashLab for insightful comments and suggestions throughout this work. We thank  Jorge Mardones  and  Lara Zamora  ( IFOP, Chile ) and  Gurudeep Rastogi  ( Wetland Research and Training Center, Chilika Development Authority ) for support during field testing. We acknowledge  Rainer Kiko  for introducing us to MorphoCut (Github https://github. com/morphocut/morphocut). A. G. L. is a Simons Fellow supported by the  Simons Foundation. M. P. acknowledges funding support from  Schmidt Futures, Moore Foundation  and  NSF Center for Cellular Construction NSF   STC award   DBI 1548297 ,  CZ BioHub  and  HHMI-Gates Faculty Fellows Program.
2B ). There is some overlapping of individuals from different language families, except that AN groups are distinct from the others, closer to the AA-speaking Cambodian (#41), Htin Mal (#42), Mlabri (#43), and many ISEA populations, all of whom speak AN languages (fig. 2A  and supplementary table  S1 , Supplementary Material online). When considering additional PCs, the Ede and Giarai are strongly differentiated on PC3 from all other groups, except for the AA-speaking Khomu and Mang (supplementary fig. S3 , Supplementary Material online). Additional PCs tend to highlight the distinctiveness of the Mang, ST-speaking Sila and TK-speaking Colao and Lachi. We next performed an ADMIXTURE analysis and found that the lowest cross-validation error occurs at K ¼ 6 (supplementary fig. S4 , Supplementary Material online).
Admixture Graph Inference Based on the sharing profiles revealed by the f3, IBD, and f4 analyses, we next built admixture graphs for Vietnamese groups from each language family. Admixture graphs, which depict a history of population divergence and admixture events, use either a combination of F-statistics or a covariance matrix of the allele frequencies  (Nielsen 2018). We first applied TreeMix  (Pickrell and Pritchard 2012)  and AdmixtureBayes  (Nielsen 2018)  to systematically survey (i. e. , without supervision) the potential admixture graphs based on the covariance matrix of allele frequencies, and we further tested if these graphs are accepted in qpGraph  (Patterson et al. 2012) , using a combination of F-statistics. Before building the graph for each language family, we first built a tree with all the Vietnamese groups, the representative source populations used in the f4 analyses, the Onge, selected ancient samples, and the Mbuti as an outgroup (supplementary fig.