Find research datasets worth reusing

opencc-by-4.0Jan 2023View details →

Single-cell RNA sequencing of mouse embryonic cells from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages

STRT-N is a newly optimized single-cell RNA sequencing method for studies of early genome activation in mammalian preimplantation development. Single embryos from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages were sampled for experiments and were sequenced using STRT-N method. Here is the raw data from STRTN-seq. FASTQ files are available in <a href="https://www.ebi.ac.uk/biostudies/studies/S-BSST976">BioStudies database</a>.

Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers

This repository contains the raw IMC data of ZTMA 26 as continuation of dataset 10.5281/zenodo.7494413. The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of those parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files. Additionally, this repository contains the metadata of the patients analyzed in this study, the panel information and the single-cell data that was extracted from the multiplexed images together with the associated metadata in SingleCellExperiment format for analysis in R. The analysis.zip folder contains files that were written out during the analysis according to the scripts in https://github.com/BodenmillerGroup/BC_LN_metastses. The single-cell and other data outputs from CellProfiler can be found in the cpout.zip file. The IF_whole_sections.zip file contains the IF images of the primary breast cancer sections (czi files) and the extracted single-cell data.

Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers

This repository contains the raw IMC data of ZTMA 21 and 25 of the matched primary breast cancer and lymph node metastasis study presented in Fischer and Jackson et al., 2023. The code that was used to process and analyze this data can be found at https://github.com/BodenmillerGroup/BC_LN_metastses. The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of the respective parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.

opencc-by-4.0Feb 2023View details →

Data for Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response

Data from simulations used to generate the figures in the paper Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response. To reproduce analyses, extract colony_data.zip in the data folder after cloning the vivarium-ecoli repository. The extracted folder contains the following items: <ul> <li>sim_dfs: a folder containing the CSV files that represent a subset of the raw simulation data used for downstream analyses.</li> <li>glc_10000_fluxome.csv: Each row represents a reaction in central carbon metabolism (in same order as listed in validation/ecoli/flat/toya_2010_central_carbon_fluxes.tsv). Each column represents a single time point for a single cell in a baseline glucose simulation (seed 10000). Each value is a flux (mmol/L/hr). Provided as input to ecoli/analysis/centralCarbonMetabolism.py script to reproduce fluxome validation plot.</li> <li>glc_10000_proteome_avgs.csv: Each row represents a protein monomer (in same order as sim_data.translation.monomer_data["id"] where sim_data is reconstruction/sim_data/kb/validationData.cPickle). Each column represents a cell in a baseline glucose simulation (seed 10000). Each row represents a protein monomer. Each value represents the average count of a given protein monomer for a given cell. Provided as input to ecoli/analysis/proteinCountsValidation.py script to reproduce proteome validation plot.</li> <li>glc_10000_expressome.csv: Each column represents a gene (with the exception of the final two metadata columns: "Time" and "Agent ID"). Each row represents a specific cell (agent) at a specific time in a baseline glucose simulation (seed 10000). Each value represents the number of new RNA transcripts for a given gene in a given cell at a given time. Provided as input to ecoli/analysis/antibiotics_colony/subgen_gene_plots/count_subgen.py script to calculate number of sub-generational genes among all genes and antibiotic response genes.</li> <li>glc_10000_total_mrna.json: Mapping of agent IDs for all cells in a baseline glucose simulation (seed 10000) to their average total mRNA count. Used by ecoli/analysis/antibiotics_colony/plot.py to generate Fig. 2C,D.</li> <li>jenner_2013.csv: Data extracted from Fig. 2C of <a href="https://doi.org/10.1073/pnas.1216691110">10.1073/pnas.1216691110</a>. Used by ecoli/analysis/antibiotics_colony/plot.py to generate Fig. S6A.</li> <li>olson_2006.csv: Data extracted from Fig. 2D of <a href="https://doi.org/10.1128%2FAAC.01499-05">10.1128/AAC.01499-05</a>. Used by ecoli/analysis/antibiotics_colony/plot.py to generate Fig. S6A.</li> <li>lysis_ratios.csv: Data extracted from Fig. 2 of <a href="https://doi.org/10.1099/00221287-31-3-339">10.1099/00221287-31-3-339</a>. Used by ecoli/analysis/antibiotics_colony/plot.py to generate Fig. 4N.</li> </ul>

opencc-by-4.0Jan 2022View details →

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Datasets associated with https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009797

opencc-by-4.0Sep 2022View details →

Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability

Additional files for "Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability". Additional file 1 - list of benchmarks with consolidate survey answers (CSV) Additional file 2 - review form (CSV)  Additional file 3 - unedited (anonymized) survey responses (TXT)

Single-cell RNA-seq count data used in differential expression benchmark study

Count matrices and meta data tables from simulated and real world immune cell single-cell RNA-seq experiments. All files are in Rds format and can be read by R using "readRDS()".  <ul> <li>10k_*: These files contain a filtered version of the 10k Human PBMCs, 3' v3.1 data <a href="https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high">published</a> by 10x Genomics</li> <li>blueprint_data.Rds: This file contains the bulk RNA-seq data downloaded from <a href="http://dcc.blueprint-epigenome.eu">BLUEPRINT</a></li> <li>blueprint_immune_comparisons.Rds: Results from running three bulk RNA-seq differential expression methods</li> <li>sim_data*: These files contain the count matrices and meta data tables for the simulated data. Every file contains a list of 13 replicates.</li> </ul>

Extended Data for Single-cell Transcriptional Uncertainty Landscape of Cell Differentiation

The dataset includes supporting information for the study titled "Single-cell Transcriptional Uncertainty Landscape of Cell Differentiation".

opencc-by-4.0May 2022View details →

Supporting data for "Morphological profiling by high-throughput single-cell biophysical fractometry"

No specific description. For more information, please contact: iriszqzh@connect.hku.hk.

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-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>.

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.

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.