dbFrame-class {CATALYST} | R Documentation

## Debarcoding frame class

### Description

This class represents the data returned by and used throughout debarcoding.

### Details

Objects of class `dbFrame` hold all data required for debarcoding:

1. as the initial step of single-cell deconcolution, `assignPrelim` will return a `dbFrame` containing the input measurement data, barcoding scheme, and preliminary assignments.

2. assignments will be made final by `applyCutoffs`. Optionally, population-specific separation cutoffs may be estimated by running `estCutoffs` prior to this.

3. `plotYields`, `plotEvents` and `plotMahal` aim to guide devoncolution parameter selection, and to give a sense of the resulting barcode assignment quality.

`show(dbFrame)` will display

- the dimensionality of the measurement data and number of barcodes

- current assignments in order of decreasing population size

- current separation cutoffs

- the mean & per-population yield that'll be achieved upon debarcoding

### Slots

`exprs`

  a matrix containing raw intensities of the input flowFrame.

`bc_key`

  binary barcoding scheme with numeric masses as column names and samples names as row names OR a numeric vector of barcode masses.

`bc_ids`

  vector of barcode IDs. If a barcoding scheme is supplied, the respective binary code's row name, else, the mass of the respective barcode channel.

`deltas`

  numeric vector of separations between positive and negative barcode populations computed from normalized barcode intensities.

`normed_bcs`

  matrix containing normalized barcode intensities.

`mhl_dists`

  mahalanobis distances.

`sep_cutoffs`

  numeric vector of distance separation cutoffs between positive and negative barcode populations above which events will be unassigned.

`mhl_cutoff`

  non-negative and non-zero numeric value specifying the Mahalanobis distance below which events will be unassigned.

`counts`

  matrix of dimension (# barcodes)x(101) where each row contains the number of events within a barcode for which positive & negative populations are separated by a distance between in [0,0.01), ..., [0.99,1], respectively.

`yields`

  a matrix of dimension (# barcodes)x(101) where each row contains the percentage of events within a barcode that will be obtained after applying a separation cutoff of 0, 0.01, ..., 1, respectively.

### Author(s)

Helena Lucia Crowell helena.crowell@uzh.ch

[Package CATALYST version 1.6.7 Index]