n_cells {CATALYST} | R Documentation

## Extraction and replacement methods for objects of class `daFrame`

### Description

Methods for accessing slots in a `daFrame`.

### Usage

    n_cells(x)

    marker_classes(x)

    type_markers(x)

    state_markers(x)

    sample_ids(x)

    cluster_codes(x)

    cluster_ids(x)

    ## S4 method for signature 'daFrame'
    exprs(object)

    ## S4 method for signature 'daFrame'
    n_cells(x)

    ## S4 method for signature 'daFrame'
    marker_classes(x)

    ## S4 method for signature 'daFrame'
    type_markers(x)

    ## S4 method for signature 'daFrame'
    state_markers(x)

    ## S4 method for signature 'daFrame'
    sample_ids(x)

    ## S4 method for signature 'daFrame'
    cluster_codes(x)

    ## S4 method for signature 'daFrame'
    cluster_ids(x)

### Arguments

`x, object` | a `daFrame`.

### Value

`exprs`

  extracts the arcsinh-transformed expressions.

`n_cells`

  extracts the number of events measured per sample.

`type_markers`

  extracts the antigens used for clustering.

`state_markers`

  extracts antigens that were not used for clustering.

`sample_ids`

  extracts the sample IDs as specified in the metadata-table.

`cluster_codes`

  extracts a `data.frame` containing cluster codes for the `FlowSOM` clustering, the `ConsensusClusterPlus` metaclustering, and all mergings done through `mergeClusters`.

`cluster_ids`

  extracts the numeric vector of cluster IDs as inferred by `FlowSOM`.

### Author(s)

Helena Lucia Crowell crowellh@student.ethz.ch

### Examples

    # construct daFrame
    data(PBMC_fs, PBMC_panel, PBMC_md)
    re <- daFrame(PBMC_fs, PBMC_panel, PBMC_md)

    # run clustering
    lineage <- c("CD3", "CD45", "CD4", "CD20", "CD33",
        "CD123", "CD14", "IgM", "HLA_DR", "CD7")
    re <- cluster(re, cols_to_use=lineage)

    # view data summary
    library(SummarizedExperiment)
    cbind(metadata(re)$experiment_info, cells=n_cells(re))

    # access row / cell data
    head(rowData(re))
    plot(table(cluster_ids(re)))

    # access marker information
    type_markers(re)
    state_markers(re)

    # get cluster ID correspondece between 2 clusterings
    old_ids <- seq_len(20)
    m <- match(old_ids, cluster_codes(re)$`20`)
    new_ids <- cluster_codes(re)$`12`[m]
    data.frame(old_ids, new_ids)

    # plot relative change in area under CDF curve vs. k
    metadata(re)$delta_area

[Package CATALYST version 1.4.2 Index]