plot {coseq} | R Documentation

## Visualize results from coseq clustering

### Description

Plot a coseqResults object.

### Usage

    plot(x, ...)

    ## S4 method for signature 'coseqResults'
    plot(
      x,
      y_profiles = NULL,
      K = NULL,
      threshold = 0.8,
      conds = NULL,
      average_over_conds = FALSE,
      collapse_reps = "none",
      graphs = c("logLike", "ICL", "profiles", "boxplots", "probapost_boxplots",
        "probapost_barplots", "probapost_histogram"),
      order = FALSE,
      profiles_order = NULL,
      n_row = NULL,
      n_col = NULL,
      add_lines = TRUE,
      ...
    )

    coseqGlobalPlots(object, graphs = c("logLike", "ICL"), ...)

    coseqModelPlots(
      probaPost,
      y_profiles,
      K = NULL,
      threshold = 0.8,
      conds = NULL,
      collapse_reps = "none",
      graphs = c("profiles", "boxplots", "probapost_boxplots", "probapost_barplots",
        "probapost_histogram"),
      order = FALSE,
      profiles_order = NULL,
      n_row = NULL,
      n_col = NULL,
      add_lines = TRUE,
      ...
    )

### Arguments

`x` | An object of class `"coseqResults"`
`...` | Additional optional plotting arguments (e.g., xlab, ylab, use_sample_names, facet_labels)
`y_profiles` | y (n x q) matrix of observed profiles for n observations and q variables to be used for graphing results (optional for `logLike`, `ICL`, `probapost_boxplots`, and `probapost_barplots`, and by default takes value `x$tcounts` if `NULL`)
`K` | If desired, the specific model to use for plotting (or the specific cluster number(s) to use for plotting in the case of `coseqModelPlots`). If `NULL`, all clusters will be visualized, and the model chosen by ICL will be plotted
`threshold` | Threshold used for maximum conditional probability; only observations with maximum conditional probability greater than this threshold are visualized
`conds` | Condition labels, if desired
`average_over_conds` | If `TRUE`, average values of `y_profiles` within each condition identified by `conds` for the `profiles` and `boxplots` plots. This argument is redundant to `collapse_reps = "sum"`, and `collapse_reps` should be used instead.
`collapse_reps` | If `"none"`, display all replicates. If `"sum"`, collapse replicates within each condition by summing their profiles If `"average"`, collapse replicates within each condition by averaging their profiles. For highly unbalanced experimental designs, using `"average"` will likely provide more easily interpretable plots.
`graphs` | Graphs to be produced, one (or more) of the following: `"logLike"` (log-likelihood plotted versus number of clusters), `"ICL"` (ICL plotted versus number of clusters), `"profiles"` (line plots of profiles in each cluster), `"boxplots"` (boxplots of profiles in each cluster), `"probapost_boxplots"` (boxplots of maximum conditional probabilities per cluster), `"probapost_barplots"` (number of observations with a maximum conditional probability greater than `threshold` per cluster), `"probapost_histogram"` (histogram of maximum conditional probabilities over all clusters)
`order` | If `TRUE`, order clusters in `probapost_boxplot` by median and `probapost_barplot` by number of observations with maximum conditional probability greater than `threshold`
`profiles_order` | If `NULL` or `FALSE`, line plots and boxplots of profiles are plotted sequentially by cluster number (K=1, K=2, ...). If `TRUE`, line plots and boxplots of profiles are plotted in an automatically calculated order (according to the Euclidean distance between cluster means) to plot clusters with similar mean profiles next to one another. Otherwise, the user may provide a vector (of length equal to the number of clusters in the given model) providing the desired order of plots.
`n_row` | Number of rows for plotting layout of line plots and boxplots of profiles.
`n_col` | Number of columns for plotting layout of line plots and boxplots of profiles.
`add_lines` | If `TRUE`, add red lines representing means to boxplots; if `FALSE`, these will be suppressed.
`object` | An object of class `"RangedSummarizedExperiment"` arising from a call to `NormMixClus`
`probaPost` | Matrix or data.frame of dimension (n x K) containing the conditional probilities of cluster membership for n genes in K clusters arising from a mixture model

### Value

Named list of plots of the `coseqResults` object.

### Author(s)

Andrea Rau, Cathy Maugis-Rabusseau

### Examples

    ## Simulate toy data, n = 300 observations
    set.seed(12345)
    countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4)
    countmat <- countmat[which(rowSums(countmat) > 0),]
    conds <- rep(c("A","B","C","D"), each=2)

    ## Run the Normal mixture model for K = 2,3,4
    run_arcsin <- coseq(object=countmat, K=2:4, iter=5, transformation="arcsin",
                        model="Normal", seed=12345)
    run_arcsin

    ## Plot and summarize results
    plot(run_arcsin)
    summary(run_arcsin)

    ## Compare ARI values for all models (no plot generated here)
    ARI <- compareARI(run_arcsin, plot=FALSE)

    ## Compare ICL values for models with arcsin and logit transformations
    run_logit <- coseq(object=countmat, K=2:4, iter=5, transformation="logit",
                       model="Normal")
    compareICL(list(run_arcsin, run_logit))

    ## Use accessor functions to explore results
    clusters(run_arcsin)
    likelihood(run_arcsin)
    nbCluster(run_arcsin)
    ICL(run_arcsin)

    ## Examine transformed counts and profiles used for graphing
    tcounts(run_arcsin)
    profiles(run_arcsin)

    ## Run the K-means algorithm for logclr profiles for K = 2,..., 20
    run_kmeans <- coseq(object=countmat, K=2:20, transformation="logclr",
                        model="kmeans")
    run_kmeans

[Package coseq version 1.18.0 Index]