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Dataset results
19 results for “batch effects”
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets
<div> <div> <div> <div> <p>This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis.</p> <h3>Datasets</h3> <p>We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations:</p> <ol> <li> <p><strong>Cell Line (CL)</strong>:</p> <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> <p><strong>Human Dendritic Cells (HDC)</strong>:</p> <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> <p><strong>Human Pancreas (HP)</strong>:</p> <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> <p><strong>Mouse Brain (MB)</strong>:</p> <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> <p><strong>Mouse Cell Atlas (MCA)</strong>:</p> <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> <p><strong>Mouse Hematopoietic Stem and Progenitor Cells (MHSPC)</strong>:</p> <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> <p><strong>Mouse Retina (MR)</strong>:</p> <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> <p><strong>PBMC (human Peripheral Blood Mononuclear Cell)</strong>:</p> <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> <p><strong>Usage Notes</strong>: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file.</p> <p><strong>Keywords</strong>: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics</p> <p><strong>Contact</strong>: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a></p> <p><strong>License</strong>: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Data for accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy
<p>Simulated and experimental data used in the ReSort manuscript. It is necessary and sufficient to reproduce the results in the paper.</p>
Data folder for the Batch Effect correction part of OBRWR
<p>Data folder for the batch effect correction of phosphoproteomics assay conducted in Dr. Destaing's lab.</p> <p>Batch effect assessment using PCA.</p> <p>Proposing two correction methods : one using PCA, the second (and the one used) using MLM.</p>
Data from: Batch effects in a multi-year sequencing study: false biological trends due to changes in read lengths
High-throughput sequencing is a powerful tool, but suffers biases and errors that must be accounted for to prevent false biological conclusions. Such errors include batch effects, technical errors only present in subsets of data due to procedural changes within a study. If overlooked and multiple batches of data are combined, spurious biological signals can arise, particularly if batches of data are correlated with biological variables. Batch effects can be minimized through randomisation of sample groups across batches. However, in long-term or multi-year studies where data are added incrementally, full randomisation is impossible and batch effects may be a common feature. Here we present a case study where false signals of selection were detected due to a batch effect in a multi-year study of Alpine ibex (Capra ibex). The batch effect arose because sequencing read length changed over the course of the project and populations were added incrementally to the study, resulting in non-random distributions of populations across read lengths. The differences in read length caused small misalignments in a subset of the data, leading to false variant alleles and thus false SNPs. Pronounced allele frequency differences between populations arose at these SNPs because of the correlation between read length and population. This created highly statistically significant, but biologically spurious, signals of selection and false associations between allele frequencies and the environment. We highlight the risk of batch effects and discuss strategies to reduce the impacts of batch effects in multi-year high-throughput sequencing studies.
Data from: Batch effects in a multi-year sequencing study: false biological trends due to changes in read lengths
Open the record for dataset details and reuse information.
High-order correction of persistent batch effects in correlation networks
<p>This upload contains data to reproduce the results and figures in our work High-order correction of persistent batch effects in correlation networks. The code is publicly available on <a href="https://github.com/QuackenbushLab/cobra-experiments">GitHub</a>.</p>
Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
GEO Series GSE146974. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Microarray gene expression analysis: Batch effect removal improves the cross-platform consistency
GEO Series GSE54275. Homo sapiens. 486 samples. Type: Expression profiling by array.
Batch effect during human bone marrow stromal cell propagation prevails donor variation and culture duration impact on phenotype, transcriptome and function [MethylCap-seq]
GEO Series GSE194295. Homo sapiens. 20 samples. Type: Methylation profiling by high throughput sequencing.
Batch effects and the effective design of single-cell gene expression studies
GEO Series GSE77288. Homo sapiens. 873 samples. Type: Expression profiling by high throughput sequencing.
The effect of MEK inhibition on transcription in melanoma [Batch 2]
GEO Series GSE51113. Homo sapiens. 28 samples. Type: Expression profiling by array.
Annotation- and Batch Effect Correction in TCGA IsomiR Expression Data [Third-party re-analysis]
GEO Series GSE164767. Homo sapiens. 0 samples. Type: Other; Third-party reanalysis.
iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement
GEO Series GSE161138. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
The effect of MEK inhibition on transcription in melanoma [Batch 1]
GEO Series GSE51051. Homo sapiens. 24 samples. Type: Expression profiling by array.
Adjusting for batch effects in DNA methylation microarray data, a lesson learned
GEO Series GSE108567. Homo sapiens. 59 samples. Type: Methylation profiling by genome tiling array.
Batch effect during human bone marrow stromal cell propagation prevails donor variation and culture duration impact on phenotype, transcriptome and function [RNA-seq]
GEO Series GSE194298. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Batch effect during human bone marrow stromal cell propagation prevails donor variation and culture duration impact on phenotype, transcriptome and function
GEO Series GSE194303. Homo sapiens. 40 samples. Type: Methylation profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Effect of varying amounts of simulated lunar soil batch #005 upon growth of lettuce explants
The following general conclusions can be made concerning the use of tissue culture for assaying the effects of mineral elements from simulated lunar soil or other mineral samples: The method used here is a very sensitive one for showing that soluble materials are reaching the tissue explant. The effects found (inhibiting) are very probably caused by excess toxic materials or competition of non-essential elements with essential elements rather than pH changes because previously pH has been found- to have relatively little effect over a wide range. Any stimulatory effect of the simulated lunar soil will probably appear at concentrations below those used in these experiments. If this is the case, the method is much less wasteful of mineral materials than methods in which pre-grown callus is treated with mineral materials. Measurements of uptake of minerals will be difficult in this system because of the relatively small growth obtainable from the tissue and also because of the apparently large background of minerals contributed by the purified agar.
Assessing the effects of transcription factor knockouts on growth in high-density fed-batch cultures
GEO Series GSE221706. Escherichia coli str. K-12 substr. MG1655; Escherichia coli BW25113. 122 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.