Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

Designing single-cell experiments to harvest fluctuation information while rejecting measurement noise

Numerical simulation outputs and model schematic figures for the Python portion (main text 3.1 and Supporting Information) in the preprint Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise Huy D. Vo, Linda Forero, Luis Aguilera, Brian Munsky bioRxiv doi: https://doi.org/10.1101/2021.05.11.443611

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>

opencc-by-sa-4.0Jun 2023View details →

Imaging Data for: Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis

This dataset contains the microfluidic microscopy time-lapse data for the publication <a href="https://www.frontiersin.org/articles/10.3389/fmicb.2023.1198170/">"Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis"</a>. The imaging data is recorded as raw 16-bit tif-stacks. The sequences 17406 - 17410 and 17411 - 17415 contain the aerobic and anaerobic conditions, respectively. For details about cultivation conditions and image processing please have a look into our <a href="http://www.frontiersin.org/articles/10.3389/fmicb.2023.1198170/">paper</a> or the public code repository <a href="https://github.com/JuBiotech/Supplement-to-Kasahara-et-al.-2023a">Supplement-to-Kasahara-et-al.-2023a</a>.

Single cell analysis of human mesenchymal stem cells

This dataset including the RDS files, including meta data, labeled assays, and reduction map information in order to ensure the reproducibility of the scRNA-seq data of human MSCs. The research "Multi-omics analysis of human mesenchymal stem cells shows cell aging that alters immunomodulatory activity through the downregulation of PD-L1" has been published on Nature Communications (https://doi.org/10.1038/s41467-023-39958-5).

opencc-by-4.0Mar 2023View details →

Preprocessed dataset of the Spatially Resolved Single-cell Translatomics at Molecular Resolution

Here are the pre-processed image datasets of RIBOmap included in "Spatially Resolved Single-cell Translatomics at Molecular Resolution" from Zeng et al. Please refer to the README file for more detailed information.    Abstract The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation of thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed a remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in adult mouse brain tissues yielding a spatial cell atlas of 119,173 cells. The pairwise spatial mapping of single-cell translatome and transcriptome in two adjacent mouse brain slices revealed cell-type and brain-region-dependent translational regulation and suggested a translation remodeling during oligodendrocyte lineage maturation. The spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.

Inferring phenotypes of single cells based on the expression profiles of phenotype-associated marker genes in bulks and single cells

The datasets analysed in the manuscript "Inferring phenotypes of single cells based on the expression profiles of phenotype-associated marker genes in bulks and single cells".

Unbiased single-cell morphology with self-supervised vision transformers -- Cell Painting

The data necessary to reproduce the Cell Painting results in the paper <a href="https://www.biorxiv.org/content/10.1101/2023.06.16.545359v1">Unbiased single-cell morphology with self-supervised vision transformers</a>.

Unbiased single-cell morphology with self-supervised vision transformers -- HPA FOV

The data necessary to reproduce the HPA FOV results in the paper <a href="https://www.biorxiv.org/content/10.1101/2023.06.16.545359v1">Unbiased single-cell morphology with self-supervised vision transformers</a>.

processed single-cell data from "Cancer cell non-autonomous tumor progression from chromosomal instability"

The h5ad files can be used as processed scRNA-seq input to the ContactTracing code (https://zenodo.org/badge/latestdoi/625036312). There is one file for the highCIN/lowCIN comparison, and another for the highCIN/noSTING comparison.

Unbiased single-cell morphology with self-supervised vision transformers -- HPA single cells

The data necessary to reproduce the HPA single cells results in the paper <a href="https://www.biorxiv.org/content/10.1101/2023.06.16.545359v1">Unbiased single-cell morphology with self-supervised vision transformers</a>.

Unbiased single-cell morphology with self-supervised vision transformers -- WTC11

The data necessary to reproduce the WTC11 results in the paper <a href="https://www.biorxiv.org/content/10.1101/2023.06.16.545359v1">Unbiased single-cell morphology with self-supervised vision transformers</a>.

(SNP Array) Single-Cell Multi-Omics Identifies Chronic Inflammation as a Driver of TP53 mutant Leukaemic Evolution

Single nucleotide polymorphism (SNP) array data files related our publication titled "Single-Cell Multi-Omics Identifies Chronic Inflammation as a Driver of TP53 mutant Leukaemic Evolution".

opencc-zeroJun 2023View details →

dryad36/100

Single-cell chromatin accessibility profiling reveals a self-renewing muscle satellite cell state

A balance between self-renewal and differentiation is critical for the regenerative capacity of tissue-resident stem cells. In skeletal muscle, successful regeneration requires the orchestrated activation, proliferation, and differentiation of muscle satellite cells (MuSCs) that are normally quiescent. A subset of MuSCs undergoes self-renewal to replenish the stem cell pool, but the features that identify and define self-renewing MuSCs remain to be elucidated. Here, through single-cell chromatin accessibility analysis, we reveal the self-renewal versus differentiation trajectories of MuSCs over the course of regeneration in vivo. We identify Betaglycan as a unique marker of self-renewing MuSCs that can be purified and efficiently contributes to regeneration after transplantation. We also show that SMAD4 and downstream genes are genetically required for self-renewal in vivo by restricting differentiation. Our study unveils the identity and mechanisms of self-renewing MuSCs while providing a key resource for comprehensive analysis of muscle regeneration.

Single-cell transcriptional atlas of the developing Drosophila visual system (V1.1)

An updated version of a single-cell transcriptional atlas of the developing Drosophila visual system (V1.1): https://doi.org/10.5281/zenodo.8097374 Changes are described in Yoo et al. 2023 (https://doi.org/10.1101/2023.04.03.534791) <pre>  </pre>

Structure preserving adversarial generation of labeled training samples for single cell segmentation

Supplementary package (dataset, generated data and code) for the article: "Structure preserving adversarial generation of labeled training samples for single cell segmentation". (Cell Reports Methods, 2023 - under submission) Content of the package: <ul> <li>datasets.zip: the images and annotations for the fallopian tube and salivary gland dataset.</li> <li>mask_quality_experiment.zip: the generated masks using SIMCEP and the generated microscopy images using pix2pix using the SIMCEP masks.</li> <li>pix2pix_models.zip: the models for fold 0 for both datasets. It can be used to synthesize new images.</li> <li>StyleGAN2-ada_models.zip: the models for synthesizing new masks.</li> <li>code.zip: a github repository (named .git) for executing the pipeline.</li> </ul>

Multi-Nucleic Acid Interaction Mapping in Single Cell (MUSIC) for simultanouse chromatin, RNA-chromatin and transcriptome mapping at single cell resolution

<a href="https://doi.org/10.1101/2023.06.28.546457">MUSIC manuscript:</a> Joint profiling of multiplex chromatin interactions, gene expression, and RNA-chromatin associations in single cells of the human brain. MUSIC-docker is the customized pipeline to process the raw fastq files to bam files: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/intro.html. Each bam file records the final output of our single cell mixed species analysis. Read name recods the cell barcode, complex barcode and I7 index. For each DNA/RNA read, read header contains <code>raw read name</code>| <code>BC3</code>_<code>BC2</code>_<code>BC1</code> - <code>10x barcode</code> # <code>UMI</code>. Details of the read name can be found here: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/step.html#demultiplexing. merge_DNA [RNA]_human [mouse].sort.bam: bam file of DNA [RNA] reads from the mix species library (H1+E14) that can uniquely mapped to the human [mouse] genome. PCR duplicates have been removed.

Characterising neutrophil subtypes in cancer using human and murine single-cell RNA sequencing datasets

Single cell RNA sequencing data generated by 10xGenomics for Neutrophils derived from colorectal cancer (CRC) KPN tumours (CRC_KPN_counts.csv) and normalised counts (CRC_KPN_NormalisedCounts.csv) as well as from other mouse models of CRC carrying AKPT, BPN, BP and KP mutations (CRC_other_counts.csv and CRC_other_NormalisedCounts.csv), together with the relevant metadata (CRC_KPN_metadata.csv and CRC_other_metadata.csv).

Single cell FUCCI tracking data

These datasets contain single cell FUCCI tracking data for the data analysis R scripts in the FUCCI_analysis folder in the heldring-E2S-modeling GitHub repository available via the persistent link https://doi.org/10.5281/zenodo.8143692.

Single-Cell Mapping Reveals Several Immune Subsets Associated with Liver Metastasis of Pancreatic Ductal Adenocarcinoma

Identifying a metastasis-correlated immune cell composition within the tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) will help to develop promising and innovative therapeutic strategies. Twenty-six samples from 11 patients (including 11 primary tumor tissues, 10 blood, and 5 lymph nodes) with different stages were used to develop a multiscale immune profile. High-dimensional single-cell analysis with mass cytometry was performed to search for metastasis-correlated immune changes in the microenvironment. The details about the files uploaded are as follows: 1. panelA_Blood.zip includes 10 .fcs files from blood samples in Panel A; 2. panelA_LN.zip includes 5 .fcs files from lymph node samples in Panel A; 3. panelA_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel A; 4. panelB_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel B; 5. panel_metadata.xlsx describes marker used in Panel A and B; 6. sample_metadata.xlsx describes detailed sample information.