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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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			Action	DRS
	SRR13012386.fastq.gz	Fastqsanger.gz
	SRR13012387.fastq.gz	Fastqsanger.gz
	SRR13012388.fastq.gz	Fastqsanger.gz
	SRR13012389.fastq.gz	Fastqsanger.gz

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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.

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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.

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

Project description:Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3 and LIGER. Furthermore, SMNN retains more cell-type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841.0%.

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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.

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Solo: doublet identification via semi-supervised deep learning

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