plot.mnem {mnem} | R Documentation

## Plot mnem result.

### Description

Plot mnem result.

### Usage

    ## S3 method for class 'mnem'
    plot(
      x,
      oma = c(3, 1, 1, 3),
      main = "M&NEM",
      anno = TRUE,
      cexAnno = 1,
      scale = NULL,
      global = TRUE,
      egenes = TRUE,
      sep = FALSE,
      tsne = FALSE,
      affinity = 0,
      logtype = 2,
      cells = TRUE,
      pch = ".",
      legend = FALSE,
      showdata = FALSE,
      bestCell = TRUE,
      showprobs = FALSE,
      shownull = TRUE,
      ratio = TRUE,
      method = "llr",
      marginal = FALSE,
      showweights = TRUE,
      ...
    )

### Arguments

`x` | mnem object
`oma` | outer margin
`main` | main text
`anno` | annotate cells by their perturbed gene
`cexAnno` | text size of the cell annotations
`scale` | scale cells to show relative and not absolute distances
`global` | if TRUE clusters all cells, if FALSE clusters cells within a component
`egenes` | show egene attachments, i.e. number of E-genes assigned to each S-gene
`sep` | seperate clusters and not put them on top of each other for better visualization
`tsne` | if TRUE use tsne instead of pca
`affinity` | use hard clustering if TRUE
`logtype` | logarithm type of the data (e.g. 2 for log2 data or exp(1) for natural)
`cells` | show cell attachments, .i.e how many cells are assigned to each S-gene
`pch` | cell symbol
`legend` | show legend
`showdata` | show data if TRUE
`bestCell` | show probability of best fitting cell for each S-gene
`showprobs` | if TRUE, shows responsibilities for all cells and components
`shownull` | if TRUE, shows the null node
`ratio` | use log ratios (TRUE) or foldchanges (FALSE)
`method` | "llr" for ratios
`marginal` | logical to compute the marginal likelihood (TRUE)
`showweights` | if TRUE, shows mixture weights for all components
`...` | additional parameters

### Value

visualization of mnem result with Rgraphviz

### Author(s)

Martin Pirkl

### Examples

    sim <- simData(Sgenes = 3, Egenes = 2, Nems = 2, mw = c(0.4,0.6))
    data <- (sim$data - 0.5)/0.5
    data <- data + rnorm(length(data), 0, 1)
    result <- mnem(data, k = 2, starts = 1)
    plot(result)

[Package mnem version 1.10.0 Index]