Codeplot
Introduction
Script
Input
Output
umap
Introduction

embed the neighborhood graph using umap

Script
Input
Task nameAttribute nameTypeDescription
*
      main
    project_nameString
      project name
    
*
      main
    anndataFile
      The annotated data matrix for input, .h5ad-formatted hdf5 file
    
*
      main.umap
    spreadFloat
      The effective scale of embedded points. In combination with min_dist this determines how clustered/clumped the embedded points are
    
*
      main.umap
    negative_sample_rateInt
      The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding
    
*
      main.umap
    n_componentsInt
      The number of dimensions of the embedding
    
*
      main.umap
    min_distFloat
      The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the spread value, which determines the scale at which embedded points will be spread out.
    
*
      main.umap
    memoryString
      Number of memory running tasksnotice:1. The value range is 0.25-32 cores, in addition, 48 cores and 64 cores can be selected, and the CPU must be an integer multiple of 0.25 cores; 2. The memory value range is 1GB-512GB, and the memory must be an integer multiple of 1GB. 3. The CPU / memory ratio must be between 1:2 and 1:8
    
*
      main.umap
    init_posString
      How to initialize the low dimensional embedding,['paga', 'spectral', 'random], ndarray, None] (default: 'spectral')
    
*
      main.umap
    gammaFloat
      Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples.
    
*
      main.umap
    dockerString
      --
    
*
      main.umap
    cpuString
      Number of CPU running tasks.notice:1. The value range is 0.25-32 cores, in addition, 48 cores and 64 cores can be selected, and the CPU must be an integer multiple of 0.25 cores; 2. The memory value range is 1GB-512GB, and the memory must be an integer multiple of 1GB. 3. The CPU / memory ratio must be between 1:2 and 1:8
    
*
      main.umap
    alphaFloat
      The initial learning rate for the embedding optimization
    
Output
Task nameAttribute nameTypeDescription
*
      main
    clustfileFile
      Return the output file to the column name of the corresponding table by this.xxx
    
*
      main
    h5adfileFile
      Return the output file to the column name of the corresponding table by this.xxx