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18 results for “trajectory inference”
Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior
<p>The datasets used in the paper "Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior". A detailed description of these datasets is available at https://github.com/jaydu1/VITAE/tree/master/data.</p>
1D cell trajectories as studied in "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference"
<p>Trajectories of motile cells represent a rich source of data that provide insights into the mechanisms of cell migration via mathematical modeling and statistical analysis. Here, we present trajectories of MDA-MB-231 breast cancer cells and MCF-10A breast epithelial cells. Cells were confined to 1D using fibronectin lanes and exposed to three different treatments, namely the actin polymerisation inhibitor Latrunculin A (LatA), the ROCK inhibitor Y-27632 (Y27) and a control. Each csv file contains a number of 24h long trajectories of cells corresponding to the name of the file. The column names are:</p> <p>`traject_id`: The trajectories are numbered, starting from 0 in each file.</p> <p>`time (h)`: Time in h, starting at 0h for each trajctory and ending at 24h with a temporal resolution of 2min.</p> <p>`x_front`: Position of the cell's front.</p> <p>`x_nucleus`: Position of the cell's nucleus, where x_nucleus=0 for the first time point of the trajectory</p> <p>`x_rear`: Position of the cell's rear.</p> <p>The data was analysed in our study "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference". Further information can be found there. </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis</p> <p>2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis</p>
Migration trajectories of the diamondback moth Plutella xylostella in China inferred from population genomic variation
<p><span class="fontstyle01"><span><b>BACKGROUND</b></span></span><span class="fontstyle01"><span><b>:</b></span></span></p> <p><span class="fontstyle01"><span>The diamondback moth (DBM),</span></span><span class="fontstyle01"><span><i> Plutella xylostella</i></span></span><span class="fontstyle01"><span> (Lepidoptera: Plutellidae),</span></span><span class="fontstyle01"><span><i> </i></span></span><span class="fontstyle01"><span>is a notorious pest of cruciferous plants. In temperate areas, annual populations of DBM originate from adult migrants. However, the source populations and migration trajectories of immigrants remain unclear. Here, we investigated migration trajectories of DBM in China with genome-wide single nucleotide polymorphisms (SNPs) genotyped using double-digest RAD (ddRAD) sequencing. We first analyzed patterns of spatial and temporal genetic structure among southern source and northern recipient populations, then inferred migration trajectories into northern regions using discriminant analysis of principal components (DAPC), assignment tests and spatial kinship patterns.</span></span></p> <p><span class="fontstyle01"><span><b>RESULTS:</b></span></span></p> <p><span class="fontstyle01"><span>Temporal</span></span><span class="fontstyle01"><span><b> </b></span></span><span class="fontstyle01"><span>genetic differentiation among populations was low, indicating sources of </span></span><span class="fontstyle01"><span>recipient </span></span><span class="fontstyle01"><span>populations and migration trajectories are stable.</span></span><span class="fontstyle01"><span> Spatial genetic structure indicated three genetic clusters in the southern source populations. Assignment tests linked northern populations to the Sichuan cluster, and central-eastern populations to the South and Yunnan clusters, indicating that Sichuan populations are sources of northern immigrants and South and Yunnan populations are sources of central-eastern populations. First-order (full-sib) and second-order (half-sib) kin pairs were always found within populations, but about 35-40% of third-order (cousin) pairs were found in different populations. Closely related individuals in different populations were in about 35-40% of cases found at distances of 900 to 1500 km, while some were separated by over 2000 km.</span></span></p> <p><span class="fontstyle01"><span><b>CONCLUSION:</b></span></span></p> <p><span class="fontstyle01"><span>This study unravels seasonal migration patterns in the DBM. We demonstrate how careful sampling and population genomic analyses can be combined to help understand cryptic migration patterns in insects.</span></span></p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data : </strong></p> <p>1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis<br> 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation<br> 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s</p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. Immgen_cell_types.cls : Microarray Phase 1 expression</p> <p>2. Immgen_norm_exp_data.gct : Microarray Phase 1 class</p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis</p> <p><br> </p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary:</strong> Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>MDAlab_cDC1_maturation.tar : Docker image used for the analysis</p>
Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference [Datasets]
<p>This repository contains processed single-cell dataset files for DELAY.</p>
Migration trajectories of the diamondback moth Plutella xylostella in China inferred from population genomic variation
Open the record for dataset details and reuse information.
Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees: Source Data
<p>Source Data file for the article "Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees"</p>
Visually Inferring Elasticity from the Motion Trajectory of Bouncing Cubes
<p>Dataset relative to the following publication:</p> <p>Paulun, V.C., & Fleming, R. W. (2020). Visually Inferring Elasticity from the Motion Trajectory of Bouncing Cubes. <em>Journal of Vision, </em>20(6):6, 1–14, https://doi.org/10.1167/jov.20.6.6</p> <p>One folder contains the stimuli, another folder contains data from the main and control experiment.</p>
Chromatin module inference on cellular trajectories identifies key transition points and poised epigenetic states in diverse developmental processes
GEO Series GSE97222. Mus musculus. 18 samples. Type: Genome binding/occupancy profiling by array.
Heterogeneity of circulating CD4+CD8+ double-positive T cells characterized by scRNA-seq analysis and trajectory inference
GEO Series GSE199564. Macaca mulatta. 2 samples. Type: Expression profiling by high throughput sequencing.
scEGOT: single-cell trajectory inference framework by entropic Gaussian mixture optimal transport
GEO Series GSE241287. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Plant species and water chemistry data for "Inference of future bog succession trajectory from spatial chronosequence of changing aapa mires"
<p>These files consist of whole plant species and water chemistry data examined in our paper "Inference of future bog succession trajectory from spatial chronosequence of changing aapa mires" (Ecology and Evolution). Species data sets for phytosociological relevés and nested subplots (size of 0.25 m<sup>2</sup>) consist of abundances of all vascular plant, bryophyte, and lichen species in the studied fen, transition, and bog zones of boreal aapa mires. Subplot data includes groupings of species into aerenchymatous and non-aerenchymatous species, and into shallow- and deep-rooted aerenchymatous species. Water chemistry data consist of pH and concentrations of dissolved organic carbon (DOC), Ca, Mg, Fe, Al, Si, and Mn, as well as water-table depth (WTD) for each sampling point.</p>
Pseudocell Tracer - a method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination
GEO Series GSE171867. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Master's thesis: Multimodal, integrative single-cell analysis of embryonic progenitor fates for the refinement of epithelial clustering and trajectory inference
<p>Integrative analysis of 10 scRNA-seq data sets from the embryonic epidermis of X. laevis.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.