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. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis 2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis

opencc-by-4.0Jan 2022View details →

CrossDomainTypes4Py: A Python Dataset for Cross-Domain Evaluation of Type Inference Systems

This dataset contains python repositories mined on GitHub on January 20, 2021. It allows a cross-domain evaluation of type inference systems. For this purpose, it consists of two sub-datasets, each containing only projects from the web or scientific calculation domain, respectively. Therefore we searched for projects with dependencies to either <a href="https://numpy.org/">NumPy</a> or <a href="https://flask.palletsprojects.com/en/2.0.x/">Flask</a>. Furthermore, only projects with dependencies to <a href="http://mypy-lang.org/">mypy</a> were considered, because this should ensure that at least parts of the projects have type annotations. These can be used later as ground truth. Further details about the dataset will be described in an upcoming paper, as soon as it is published it will be linked here. The dataset consists of two files for the two sub-datasets. The web domain dataset contains 3129 repositories and the scientific calculation domain dataset contains 4783 repositories. The files have two columns with the URL to the GitHub repository and the used commit hash. Thus, it is possible to download the dataset using shell or python scripts, for example, the pipeline provided by <a href="https://github.com/saltudelft/many-types-4-py-dataset">ManyTypes4Py</a> can be used. If repositories do not exist anymore or are private, you can contact us via the following email address: bernd.gruner@dlr.de. We have a backup of all repositories and will be happy to help you.

opencc-by-4.0Mar 2022View details →

ManyTypes4TypeScript: A Comprehensive TypeScript Dataset for Sequence-Based Type Inference

In this paper, we present ManyTypes4TypeScript, a very large corpus for training and evaluating machine-learning models for sequence-based type inference in TypeScript. The dataset includes over 9 million type annotations, across 13,953 projects and 539,571 files. The dataset is approximately 10x larger than analogous type inference datasets for Python, and is the largest available for TypeScript. We also provide API access to the dataset, which can be integrated into any tokenizer and used with any state-of-the-art sequence-based model. Finally, we provide analysis and performance results for state-of-the-art code-specific models, for baselining. ManyTypes4TypeScript is available on Huggingface and Zenodo. This dataset was collected on January 22, 2022 and deduplicated with Allamanis code deduplication tool.

openapache2.0Mar 2023View details →

Replication Package for "Type Inference in Kotlin: An Exploratory Study of Developer Usage Patterns"

This dataset includes scripts and data files used to generate all analysis and results from the paper. A README.md file is included for details on using the scripts. The dataset is quite large.  It is broken down into three archives.  All scripts are in replication-pkg.zip and the other 2 files only contain data.  So if you want to just inspect the analysis, you only need that single zip. If you grab the data-cache.zip file and extract it, it will need around 1GB of space.  This is the processed dataset stored in Parquet files.  Use this if you want to just recreate the tables/figures from the paper. If you want to make changes to the analyses, you will need the raw data in data.zip.  This will need around 13GB of space once extracted.  If you then generate the CSV files from those TXT files (which you will need to do for any custom analysis), you will need an additional 12GB of space.

opencc-by-4.0Dec 2022View details →

Inferring Type II-P Supernova Progenitor Masses from Plateau Luminosities

Data release for "Inferring Type II-P Supernova Progenitor Masses from Plateau Luminosities," B. Barker, E. O'Connor, and S. Couch. Submitted to ApJL. Data includes luminosities and inferred iron core masses for all observations used in the work, with uncertainties.

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-by-4.0May 2024View details →

Rapid Inference of Antibiotic Susceptibility Phenotype of Uropathogens using Metagenomic Sequencing with Neighbour Typing - RASE Database for EuSCAPE

RASE databases used for the prediction of antibiotic phenotype in the paper titled "Rapid Inference of Antibiotic Susceptibility Phenotype of Uropathogens using Metagenomic Sequencing with Neighbour Typing". A database for Klebsiella pneuomiae constructed from EuSCAPE isolates.

opencc-by-4.0Apr 2023View details →

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

The uploaded files are source datasets for the scMTNI algorithm. scMTNI is a multi-task learning framework that integrates the cell lineage structure, scRNA-seq and scATAC-seq measurements to enable joint inference of cell type-specific GRNs. See more details at Zhang, S., Pyne, S., Pietrzak, S. et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 14, 3064 (2023). <a href="https://doi.org/10.1038/s41467-023-38637-9">https://doi.org/10.1038/s41467-023-38637-9</a> The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/scMTNI_sourcedata.tar.gz?versionId=a19f69c1-5bb2-436e-9a30-a7efbf1ab3da">scMTNI_sourcedata.tar.gz</a> contains the following 3 parts: 1) The cluster-specific scRNA-seq matrices and the prior networks for all three datasets and scMTNI inferred consensus networks. 2) Gold standard human and mouse datasets for evaluation. 3) Source data for scMTNI figures 2-8 and supplementary figures. The key for each figure and its corresponding file path is in SourceData_Key_v2.xlsx. The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/Buenrostro_Hematopoiesis.tar.gz">Buenrostro_Hematopoiesis.tar.gz</a> contains the scRNA-seq data for human hematopoietic differentiation downloaded from Data S2 of Buenrostro et al. The source data <a href="../api/files/5c75876b-c08a-4186-a08d-da9db78f64f3/RawMotifFiles.tar.gz">RawMotifFiles.tar.gz</a> contains the motif instance files and promoter files for human and mouse for generating prior networks using scATAC-seq data for scMTNI. Check <a href="https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md">https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md</a> for examples and scripts. The <a href="../api/records/11876980/draft/files/Buenrostro_priorNetwork_bamfiles.tar.gz/content">Buenrostro_priorNetwork_bamfiles.tar.gz</a> contains the raw bam files of scATAC-seq data for human hematopoietic differentiation downloaded from Buenrostro et al.

opencc-by-4.0Oct 2019View details →

Supplemental Data for: Segmentation-free inference of cell types from in situ transcriptomics data

Supplemental Data for: Segmentation-free inference of cell types from in situ transcriptomics data

opencc-by-4.0May 2023View details →

Additional raw data in `Cell-type-specific co-expression inference from single cell RNA-sequencing data'.

This repository holds the additional raw data used to generate figures in the publication "Cell-type-specific co-expression inference from single cell RNA-sequencing data" (preprint version: <a href="http://source%20code%20repo%20for%20%60cell-type-specific%20co-expression%20inference%20from%20single%20cell%20rna-sequencing%20data%27./">https://www.biorxiv.org/content/10.1101/2022.12.13.520181v1</a>). Table of contents: <ul> <li>Figure_1B.rds:  <ul> <li>raw data of Figure 1B </li> <li>co-expression estimates of 500*499/2 gene pairs across 100 replicates for 7 methods under two settings of sequencing detph variations</li> </ul> </li> <li>Supplementary_Figure_1B.rds:  <ul> <li>raw data of Supplementary Figure 1B </li> <li>co-expression estimates of 500*499/2 gene pairs across 100 replicates for 7 methods under two settings of sequencing detph variations</li> </ul> </li> <li>Supplementary_Figure_2.rds:  <ul> <li>raw data of Supplementary Figure 2 </li> <li>empirical power evaluated for 4999 gene pairs and 6 methods</li> </ul> </li> <li>Figure_3B.rds:  <ul> <li>raw data of Figure 3B</li> <li>co-expression estimates of a network of 500 genes for 9 methods across 100 replicates</li> </ul> </li> <li>Additional_Raw_Data.xlsx <ul> <li>raw data of Figure 3A: (geometric mean expression levels, co-expression estimates) for 4999 gene pairs and 11 methods</li> <li>raw data of Figure 3C: running times for 11 methods</li> <li>raw data of Supplementary Figure 3: (geometric mean expression levels, co-expression estimates) for 4999 gene pairs and 11 methods under two settings of sequencing detph variations</li> </ul> </li> </ul>

dryad32/100

Data from: Relationship type affects the reliability of dispersal distance estimated using pedigree inferences in partially sampled populations: a case study involving invasive American mink in Scotland

Estimating dispersal—a key parameter for population ecology and management—is notoriously difficult. The use of pedigree assignments, aided by likelihood-based software, has become popular to estimate dispersal rate and distance. However, the partial sampling of populations may produce false assignments. Further, it is unknown how the accuracy of assignment is affected by the genealogical relationships of individuals and is reflected by software-derived assignment probabilities. Inspired by a project managing invasive American mink (Neovison vison), we estimated individual dispersal distances using inferred pairwise relationships of culled individuals. Additionally, we simulated scenarios to investigate the accuracy of pairwise inferences. Estimates of dispersal distance varied greatly when derived from different inferred pairwise relationships, with mother–offspring relationship being the shortest (average = 21 km) and the most accurate. Pairs assigned as maternal half-siblings were inaccurate, with 64%–97% falsely assigned, implying that estimates for these relationships in the wild population were unreliable. The false assignment rate was unrelated to the software-derived assignment probabilities at high dispersal rates. Assignments were more accurate when the inferred parents were older and immigrants and when dispersal rates between subpopulations were low (1% and 2%). Using 30 instead of 15 loci increased pairwise reliability, but half-sibling assignments were still inaccurate (>59% falsely assigned). The most reliable approach when using inferred pairwise relationships in polygamous species would be not to use half-sibling relationship types. Our simulation approach provides guidance for the application of pedigree inferences under partial sampling and is applicable to other systems where pedigree assignments are used for ecological inference.

opencc-zeroDec 2016View details →

opennotspecifiedDec 2017View details →

FIGURE 1. Likelihood-based phylogeny inferred from a in Altitudinal distribution and advertisement call of Colostethus latinasus (Amphibia: Dendrobatidae), endemic species from eastern Panama and type species of Colostethus, with a molecular assessment of similar sympatric species

FIGURE 1. Likelihood-based phylogeny inferred from a partitioned analysis of partial DNA sequences of the COI and 16S mitochondrial genes. Scale bar indicates patristic distances estimated from partitioned likelihood analysis. Clade support values indicate Bayesian marginal posterior probabilities followed by likelihood bootstrap support values as percentage. Unscaled photographs of Silverstoneia aff. nubicola (CH 5558, SVL = 20.48 mm), Colostethus aff. panamansis (CH 5546, SVL = 25.40 mm), C. latinasus (CH 6389, SVL = 30.16 mm) and C. aff. pratti (CH 1087, SVL = 18.48 mm).

opennotspecifiedNov 2021View details →

FIGURE. The Bayesian tree of the Adaintum pedatum complex based on chloroplast markers and corresponding rhizome type. Support values (Bayesian inference posterior probability (BIPP) (upper) ≥ 0.5, and maximum likelihood bootstrap support (MLBS) (nether) ≥ 50%) are shown above the main branches, the thickened branches indicate MLBS=100 and BIPP=1. Yellow bar means erect rhizome; blue bar means creeping rhizome; gray bar means decumbent or short-creeping rhizome. in Adiantum japonicum, a new species of the Adiantum pedatum complex (Pteridaceae) from Japan

FIGURE. The Bayesian tree of the Adaintum pedatum complex based on chloroplast markers and corresponding rhizome type. Support values (Bayesian inference posterior probability (BIPP) (upper) ≥ 0.5, and maximum likelihood bootstrap support (MLBS) (nether) ≥ 50%) are shown above the main branches, the thickened branches indicate MLBS=100 and BIPP=1. Yellow bar means erect rhizome; blue bar means creeping rhizome; gray bar means decumbent or short-creeping rhizome.

opennotspecifiedAug 2022View details →

FIGURE. Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, ex-neotype, or reference strain). in Six new species of Cladosporium associated with decayed leaves of native bamboo (Bambusoideae) in a fragment of Brazilian Atlantic Forest

FIGURE. Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, ex-neotype, or reference strain).

opennotspecifiedAug 2022View details →

FIGURE. (Continued) Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, exneotype, or reference strain). in Six new species of Cladosporium associated with decayed leaves of native bamboo (Bambusoideae) in a fragment of Brazilian Atlantic Forest

FIGURE. (Continued) Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, exneotype, or reference strain).