Bayesian Correlation is a robust similarity measure for single cell RNA-seq data
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ABSTRACT:
PROVIDER: PRJNA554015 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Single-cell analysis of the transcriptome deepens our understanding of an individual cell's contribution to its microenvironment. Using single-cell analysis to study complex biological processes requires state-of-the-art computational tools. Assessing similarity is highly important for bioinformatics algorithms in order to determine correlations between biological information. Similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single cell RNA-seq (scRNA-seq) because the read counts obtained are lower compared to bulk RNA-sequencing and therefore classic bioinformatics tools are insufficient to obtain reproducible results. Recently, a Bayesian correlation scheme, that assigns low correlation values to correlations coming from low expressed genes, has been proposed to assess similarity for bulk RNA-seq and miRNA. This Bayesian method uses a prior distribution before using empirical evidence. Our goal was to extend the properties of this Bayesian correlation scheme to scRNA-seq data. We assessed 3 ways to compute similarity. First, we computed the similarity of each pair of genes over all cells. Second, we identified specific cell populations and computed the correlation in those specific cells. Third, we computed the similarity of each pair of genes over all clusters, by including the total mRNA expression in those cells. To study the effect of the number of cells on the method, we did not rely on simulated data, we generated 4 scRNA-seq mouse liver cell libraries with a varying number of input cells. Results: We show that Bayesian correlations are more reproducible than Pearson correlations in all the scenarios studied. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We demonstrate that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population. Significance: Our results demonstrate that Bayesian correlation is a robust similarity measure for scRNA-seq datasets. The Bayesian method allows researchers to study similarity between pairs of genes without discarding low expressed entities and to minimize biasing the results by fake correlations. Taken together, using our method of Bayesian correlation the reproducibility of scRNA-seq experiments is increased significantly.
2019-12-31 | GSE134134 | GEO
Project description:Researchers may be interested in finding proteomics runs, which have been deposited into online repositories, that are similar to their own data. However, it is difficult to measure the similarity of a pair of proteomics runs. Here, we present a new method, MS1Connect, that only uses intact peptide scans to calculate the similarity between a pair of runs. We show evidence that the MS1Connect score accurately measures the similarity between two proteomics runs. Specifically, we show that MS1Connect outperforms baseline methods for predicting the species a sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities based o peptide fragment scans by observing a high correlation between MS1Connect scores and the Jaccard index between the sets of confidently detected peptides for a pair of runs.
2023-01-05 | PXD027791 | Pride
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Project description:Histone modifications are a key epigenetic mechanism to activate or repress the expression of genes. Data sets of matched microarray expression data and histone modification data measured by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatic approach to detect genes that are differentially expressed between two conditions putatively caused by alterations in histone modification. We introduce a correlation measure for integrative analysis of ChIP-seq and gene expression data and demonstrate that a proper normalization of the ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene expression and histone modification. The method is applied to different data sets and its superiority to a naive separate analysis of both data types is demonstrated. This GEO series contains the expression data of the Cebpa example data set.
2014-02-05 | GSE49975 | GEO
Project description:Histone modifications are a key epigenetic mechanism to activate or repress the expression of genes. Data sets of matched microarray expression data and histone modification data measured by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatic approach to detect genes that are differentially expressed between two conditions putatively caused by alterations in histone modification. We introduce a correlation measure for integrative analysis of ChIP-seq and gene expression data and demonstrate that a proper normalization of the ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene expression and histone modification. The method is applied to different data sets and its superiority to a naive separate analysis of both data types is demonstrated. This GEO series contains the expression data of the Cebpa example data set. This data set was derived from sorted Cebpafl/fl and Cebpafl/fl;Mx1Cre murine hematopoietic LSKCD150- 18 post pIpC injections (conditional deletion of Cebpa). The specimens from three Cebpafl/fl and three Cebpafl/fl;Mx1Cre mice were hybridized separately on six Affymetrix Mouse Gene 1.0 ST arrays. Associated histone modification ChIP-seq data is provided by series GSE43007.
2014-02-05 | E-GEOD-49975 | biostudies-arrayexpress
Project description:Bayesian inference of distinct DNA methylation patterns (epialleles) in RRBS bisulfite sequencing data.
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Project description:RNA-seq as a global measure of the similarity between human pluripotent stem cell and fetal liver derived B cell hierarchies
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Project description:Droplet-based single cell transcriptome sequencing (scRNA-seq) technology is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data, which will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries.
2020-05-18 | GSE148665 | GEO
Project description: Not available
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