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  • Integrating scRNA-seq and scATAC-seq data - Satija Lab
    In Stuart*, Butler* et al, 2019, we introduce methods to integrate scRNA-seq and scATAC-seq datasets collected from the same biological system, and demonstrate these methods in this vignette In particular, we demonstrate the following analyses: How to use an annotated scRNA-seq dataset to label cells from an scATAC-seq experiment
  • Integrating scRNA-seq and scATAC-seq with inter-type . . .
    It also shows the highest average iLISI score of 0 810, indicating that it maintains strong alignment performance across various cell types and datasets scMI’s overall results highlight its robust and effective integration of scRNA-seq and scATAC-seq data, particularly in cases where datasets are unmatched
  • scDART: integrating unmatched scRNA-seq and scATAC-seq data . . .
    It is a challenging task to integrate scRNA-seq and scATAC-seq data obtained from different batches Existing methods tend to use a pre-defined gene activity matrix to convert the scATAC-seq data into scRNA-seq data The pre-defined gene activity matrix is often of low quality and does not reflect the dataset-specific relationship between the two data modalities We propose scDART, a deep
  • Benchmarking atlas-level data integration in single-cell genomics
    To test whether performance of scRNA-seq integration methods transfers to scATAC-seq data, we integrated three datasets of chromatin accessibility for mouse brain generated by different technologies
  • Integration of scATAC-Seq with scRNA-Seq Data | SpringerLink
    3 1 Integration of Multiple scRNA-Seq Datasets We used the following workflow to integrate the 15 scRNA-Seq experiments 1 A quality control (QC) step, based on the number of identified counts, number of expressed genes, and the fraction of mitochondrial genes, for each experiment independently
  • A comparison of integration methods for single-cell RNA . . .
    In this section, we evaluate several integration methods designed for paired and unpaired data on some benchmark datasets For paired data, we choose scRNA-seq and scATAC-seq from mouse kidney cells and cell line mixture of SNARE-seq Mouse kidney data is downloaded from the GitHub of Tabula Muris


















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