Find research datasets worth reusing

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: 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. 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. Data : 1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: 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. 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.   Data: 1. Immgen_cell_types.cls : Microarray Phase 1 expression 2. Immgen_norm_exp_data.gct : Microarray Phase 1 class

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: 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. 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. Data: cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: 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. 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. Data:  MDAlab_cDC1_maturation.tar : Docker image used for the analysis

opencc-zeroJan 2022View details →

dryad36/100

Machine learning identifies girls with central precocious puberty based on multi-source data

Objective: The study aimed to develop simplified diagnostic models for identifying girls with central precocious puberty (CPP), without the expensive and cumbersome gonadotropin-releasing hormone (GnRH) stimulation test, which is the gold standard for CPP diagnosis. Materials and Methods: Female patients who had secondary sexual characteristics before 8 years old and had taken a GnRH analog (GnRHa) stimulation test at a medical center in Guangzhou, China were enrolled. Data from clinical visiting, laboratory tests and medical image examinations were collected. We first extracted features from unstructured data such as clinical reports and medical images. Then, models based on each single-source data or multi-source data were developed with Extreme Gradient Boosting (XGBoost) classifier to classify patients as CPP or non-CPP. Results: The best performance achieved an AUC of 0.88 and Youden index of 0.64 in the model based on multi-source data. The performance of single-source models based on data from basal laboratory tests and the feature importance of each variable showed that the basal hormone test had the highest diagnostic value for a CPP diagnosis. Conclusion: We developed three simplified models that use easily accessed clinical data before the GnRH stimulation test to identify girls who are at high risk of CPP. These models are tailored to the needs of patients in different clinical settings. Machine learning technologies and multi-source data fusion can help to make a better diagnosis than traditional methods.

AHTpin results for peptides identified in the filtrate of nanofiltration of Cynara cardunculus swine blood hydrolysate

AHTpin results for peptides identified by peptidomics in the filtrate of nanofiltration of Cynara cardunculus swine blood hydrolysate. Peptides with a SVM> 1.0 were predicted as antihypertensive.

Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci

Trans-ethnic meta-analysis results for eGFR EWAS Description of variables: CpG             CPG name chr             Chromosome      pos_hg38         Position hg38         Beta             Effect   SE             Standard error   P             P value  totalsamplesize     number of participants  direction     direction of effect for studies        hetpval         P value for heterogeneity      hetdf            Degree of freedom    gene         Gene location Upload of this dataset was completed by The EWAS Catalog team. The data can be queried along with hundreds of other EWAS at ewascatalog.org. To upload your EWAS summary statistics and have a zenodo DOI generated for you go to ewascatalog.org/upload.

Coloc summary results for "Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets"

Text files containing coloc results between MS GWAS loci and CD4 T and B cell cis-eQTLs from DICE. These results accompany the paper "Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets"

Genome-wide DNA methylation profiling identifies epigenetic signatures of β-lactams induced fatal anaphylactic shock

Drug hypersensitivity is one of the most frequent causes of anaphylaxis in adults, of which antibiotics are the most common culprits, particularly β-lactams induced anaphylactic shock deaths. We provided the genome wide DNA methylation profiling study of PBMC from 14 individuals peripheral venous blood samples. Illumina Infinium Human Methylation EPIC BeadChip was used. Among the 14 individuals, 8 patients were died from β-lactams induced anaphylactic shock, 6 healthy individuals were controls. Inclusion criteria for the β-lactams induced anaphylactic shock as follows: ①A clear history of β-lactams transfusion or components of β-lactams detected from blood (or skin of suspicious infusion site); ②Shock symptoms occurred after infusion of β-lactams and died within a short period of time; ③The immunohistochemical results of throat, lung and gastrointestinal tissues showed that tryptase or chymase were mostly positive expression; ④ Excluding other causes (disease, poisoning) of death; ⑤ No decay occurred in the corpse.Inclusion criteria for controls as follows: ① Healthy, no previous common underlying diseases and no history of genetic disease; ② No history of allergies to drugs, food, pollen and so on; ③Skin prick test (SPT) is negative, allergen-specific IgE (sIgE)< 100IU/mL; the skin test results of β-lactam drugs were negative.

Single Cell Phenotypic Profiling to Identify a Set of Immune Cell Protein Biomarkers for Relapsed and Refractory Diffuse Large B Cell Lymphoma: A Single-Center Study

Diffuse large B-cell lymphoma (DLBCL) is the most common invasive type of non-Hodgkin lymphoma. Cell-of-origin (COO) classification is related to patients’ prognoses. Primary drug resistance in treatment for DLBCL has been observed. The specific serum biomarkers in these patients who suffer from relapsed and refractory (R/R)-DLBCL remains unclear. In the current study, using single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF), we determined and verified immune cell biomarkers at the mRNA and protein levels in single-cell resolution from 18 diagnostic peripheral blood mononuclear cell (PBMC) specimens collected from patients with R/R DLBCL. As controls, five PBMC specimens from healthy volunteers were obtained. We identified a panel of 35 surface marker genes for the features of R/R DLBCL unique cell cluster by scRNA-seq of eight R/R DLBCL patient samples and validated its efficiency in an external cohort consisting of 10 R/R DLBCL patients by CyTOF. The cell clustering and dimension reduction were compared among R/R DLBCL samples in CyTOF Space with COO as well as the C-MYC expression designation. Immune cells from each patient occupied unique regions in the 32-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Significant heterogeneity observed in subgroups was mainly attributed to individual differences among samples and not to expression differences in a single, homogeneous immune cell subpopulation. The marker panel showed reliability in labeling R/R DLBCL without any influence from COO stratification and C-MYC expression designation. Furthermore, we compared all the markers between R/R DLBCL and normal samples. A total of 12 biomarkers were significantly overexpressed in R/R DLBCL relative to the normal samples. Therefore, we further optimized the diagnostic biomarker panel of R/R DLBCL comprising CD82, CD55, CD36, CD63, CD59, IKZF1, CD69, CD163, CD14, CD226, CD84, and CD31. In summary, we developed a novel set of biomarkers for the diagnoses of patients with R/R DLBCL. Detections procedures at single-cell resolution provide precise biomarkers which may substantially overcome intertumoral and intratumoral heterogeneity among primary samples. The findings confirmed that each case was unique and may comprise multiple, genetically distinct subclones. Here we uploaded the dataset of CyTOF for external validation. For more detailed information, please contact Dr zheng (zenki_zheng@163.com)

The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae classification probabilities with SuperNNova

Classification probabilities obtained for the Dark Energy Survey 5-year photometrically identified SN Ia. Probabilities were obtained with SuperNNova (Möller et al. 2020) using multi-band light-curves and host galaxy redshifts. The classification models were trained to disentangle type Ia vs. non Ia supernovae.

Data Integration to Identify and Prioritize Potential Regulatory Variants for Rheumatoid Arthritis in a Post-GWAS era

The Data set contain data related to the SNPs in linkage disequilibrium with published GWAS rheumatoid arthritis SNPs and the subsequent annotation and pathway analysis files performed to identify putative 'missed' variants of rheumatoid arthritis.

Doodleverse/Segmentation Zoo Res-UNet models for identifying coins in photos of sediment.

Doodleverse/Segmentation Zoo models for identifying coins in photos of sediment. These model data are based on images of sand and coins and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: other, 1: coin}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.   2. '.h5' weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function  `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images   3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model   References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym

Doodleverse/Segmentation Zoo Res-UNet models for identifying water in oblique aerial photos of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for identifying water in oblique aerial photos of coasts. These model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: water, 1: land}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.   2. '.h5' weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function  `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images   3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model   References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym

A framework to identify priority wetland habitats and movement corridors for urban amphibian conservation

Core corridors: derived from Circuitscape modeling results -top 50% of wood frog, boreal chorus frog and tiger salamander results converted to binary value and summed to produce core corridors (probable movement for 2-3 amphibian species).  To derive core corridors, we summed connectivity models for three amphibian species. For each species we derived focal nodes from high habitat value (based on HSI modelling), used three resistance scenarios and summed the three scenarios for each species to represent probable movement pathways between focal nodes. Species connectivity models were then classified to top 50% of the model, and converted to a binary value of 0 to 1. The three species were summed where by values 0-3 represent number of amphibian species the corridor probably supports movement.

opencc-zeroFeb 2022View details →

dryad36/100

Identifying potential cues of species identity in complex animal signals

Communication signals are often key for encoding information on species identity, but determining the features important in conveying those cues is challenging. This is especially the case when attempting to compare across closely related sympatric taxa, where the need for accurate species recognition is critical. We developed an analytical framework to comprehensively quantify the complex movement displays used by male lizards to advertise territory ownership in 11 taxa of Puerto Rican Anolis, many of which were sympatric. Our analyses were able to assign the majority of individuals to the correct population of a given species based on only a handful of displays, showing ample information exists in these displays for species recognition. Instances where lizards were misassigned appeared to have occurred because of similarities in display design resulting from local adaptation to similar environments or recent shared evolutionary history. Our analyses also revealed there was no common ('magic') display characteristic for recognition across the 11 taxa. Instead, lizards likely assess the entire display or a combination of different display cues. Taken together, we illustrate a powerful approach that offers a rigorous statistical and holistic evaluation of complex animal signals for elucidating features likely to be important in species recognition. We provide a guide for implementing this analytical framework in R, with associated code and worked examples. Information obtained from these analyses can then be used to design experiments testing the utility of identifying cues or comparative studies investigating how those cues contribute to reproductive isolation among populations and ultimately speciation.

A framework to identify priority wetland habitats and movement corridors for urban amphibian conservation - Raw Data

Call of the Wetland Program observation data reported by citizen scientists at wetlands in the City of Calgary during three amphibian seasons from 2017 to 2019.  Wetlands were surveyed up to 11 times per season and participants reported observations to a smartphone application – where they documented species, and type of observation (eggs, adult, juvenile, tadpole or call). Participants also recorded when they participated in a survey and there were no observations.  Observations associated without a site ID represent opportunistic observations at non-survey wetlands. This data was used to validate habitat suitability models derived from the occupancy modeling results.    Survey wetland location shapefile (centroid point of wetland) also included.

opencc-zeroMar 2022View details →

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Sarah Tassell, <a href="https://orcid.org/0000-0002-8083-3111">https://orcid.org/0000-0002-8083-3111</a>. Claims were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-by-4.0Mar 2022View details →

Event list for intense geoelectric fields identified from EarthScope sites during geomagnetic storms

This data set contains properties of intense geoelectric field events identified from 24 EarthScope sites during geomagnetic storms with Dst minima of less than - 100 nT from 2006 to 2019. Columns are the name of EarthScope site that detected the event (station), geoelectric field component in which the event was detected (Ecomp), the component value of geoelectric fields (Edata), geoelectric field peak prominence, geoelectric field peak width, dBx/dt at the time of the geoelectric field peak, dBy/dt at the time of geoelectric field peak, date and time when the intense geoelectric field peak was observed, geographical latitude of the EarthScope site (glat), geographical longitude of the EarthScope site (glon), geomagnetic latitude of the EarthScope site (mlat), geomagnetic longitude of the EarthScope site (mlon), and magnetic local time of the EarthScope site when the event was detected (mlt).