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ISMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement

ABSTRACT: iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement

PROVIDER: PRJNA675647 | ENA |

REPOSITORIES: ENA

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iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement

Project description:In the current study, we present iSMNN, a supervised batch effect correction method for scRNA-seq data via multiple iterations of mutual nearest neighbor refinement. To validate the performance of iSMNN, we performed single-cell RNA-seq for adult murine heart using 10X Chromium platform.

2021-04-14 | GSE161138 | GEO

Imprecise DNMT1 activity coupled with neighbor-guided correction enables robust yet flexible epigenetic inheritance

Project description:Imprecise DNMT1 activity coupled with neighbor-guided correction enables robust yet flexible epigenetic inheritance

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Imprecise DNMT1 activity coupled with neighbor-guided correction enables robust yet flexible epigenetic inheritance

Project description:The epigenome, including DNA methylation, is stably propagated during mitotic division. However, single-cell clonal expansion produces heterogeneous methylomes, raising the question of how the DNA methylome remains stable despite constant epigenetic drift. Here, we report that a clonal population of DNA (cytosine-5)-methyltransferase 1 (DNMT1)-only cells produces a heterogeneous methylome, which is robustly propagated on cell expansion and differentiation. Our data show that DNMT1 has imprecise maintenance activity and likely possesses weak de novo activity, leading to spontaneous ‘epimutations’. However, these epimutations tend to be corrected through a neighbor-guided mechanism, ikely enabled by environment-sensitive de novo activity (‘tuner’) and maintenance activity (‘stabilizer’) of DNMT1. By generating base-resolution maps of de novo and maintenance activities, we find that H3K9me2/3-marked regions show enhanced de novo activity, and CpG islands have both poor maintenance and de novo activities. The imprecise epigenetic machinery coupled with neighbor-guided correction may be a fundamental mechanism underlying robust yet flexible epigenetic inheritance.

2020-07-01 | GSE116482 | GEO

SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection.

Project description: Not available

| S-EPMC8324985 | biostudies-literature

Visual experience-dependent expression of Fn14 is required for retinogeniculate refinement (development RNA-Seq)

Project description:Sensory experience influences the establishment of neural connectivity through molecular mechanisms that remain unclear. Here, we employ single-nucleus RNA-sequencing to investigate the contribution of sensory-driven gene expression to synaptic refinement in the dorsal lateral geniculate nucleus of the thalamus, a region of the brain that processes visual information. We find that visual experience induces the expression of the cytokine receptor Fn14 in excitatory thalamocortical neurons. By combining electrophysiological and structural techniques, we show that Fn14 is dispensable for early phases of refinement mediated by spontaneous activity, but that Fn14 is essential for refinement during a later, experience-dependent period of development. Refinement deficits in mice lacking Fn14 are associated with functionally weaker and structurally smaller retinogeniculate inputs, indicating that Fn14 mediates both functional and anatomical rearrangements in response to sensory experience. These findings identify Fn14 as a molecular link between sensory-driven gene expression and vision-sensitive refinement in the brain.

2021-07-12 | GSE117022 | GEO

Visual experience-dependent expression of Fn14 is required for retinogeniculate refinement (stimulus RNA-Seq)

2021-07-12 | GSE117021 | GEO

Visual experience-dependent expression of Fn14 is required for retinogeniculate refinement (Fn14 KO RNA-Seq)

2021-07-12 | GSE117023 | GEO

Visual experience-dependent expression of Fn14 is required for retinogeniculate refinement (stimulus single nucleus RNA-Seq)

2021-07-12 | GSE117020 | GEO

Solo: doublet identification via semi-supervised deep learning

Project description:Solo: doublet identification via semi-supervised deep learning

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Supervised Lowess normalization of comparative genome hybridization data: application to lactococcal strain comparisons.

Project description:Background Array-based comparative genome hybridization (aCGH) is commonly used to determine the genomic content of bacterial strains. In aCGH data, systematic errors are comparable to those occurring in transcriptome data. However, especially for microbes, an additional source of variation exists: differences in hybridization due to gene sequence divergence between the strains hybridized. Current normalization methods do not take this source of variation into consideration. Results We present Supervised Lowess, or S-Lowess, an application of the subset Lowess normalization method that does take difference in genomic content into account. The performance of S-Lowess was assessed on aCGH experiments in which differentially labeled genomic DNA fragments of Lactococcus lactis IL1403 and L. lactis MG1363 strains were hybridized to IL1403 microarrays. Since both genome sequences are known (they have only on average 85 % sequence identity), the success rate in detecting deletions in the MG1363 genome of different aCGH normalization methods can be compared. S-Lowess detects 97% of the deletions, whereas other aCGH normalization methods detect up to only 60% of the deletions. Conclusions S-Lowess removes systematic errors from dual-dye aCGH data in two steps: (i) determination of likely homologous genes (LHG); and (ii) estimation of correction factors for systematic errors from spots of LHG and subset Lowess normalization of the remaining spots using these correction factors. It is implemented in a user-friendly web-tool accessible from http://bioinformatics.biol.rug.nl/websoftware/s-lowess. We demonstrate that it outperforms existing normalization methods and maximizes the number of detectable genomic deletions or duplications from microbial aCGH data. Keywords: comparative genome hybridisation

2008-07-20 | GSE9039 | GEO

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