get_signatures-ConsensusPartition-method {cola} | R Documentation

## Get signature rows

### Description

Get signature rows

### Usage

    ## S4 method for signature 'ConsensusPartition'
    get_signatures(object, k,
        silhouette_cutoff = 0.5,
        fdr_cutoff = cola_opt$fdr_cutoff,
        group_diff = cola_opt$group_diff,
        scale_rows = object@scale_rows,
        row_km = NULL,
        diff_method = c("Ftest", "ttest", "samr", "pamr", "one_vs_others"),
        anno = get_anno(object),
        anno_col = get_anno_col(object),
        internal = FALSE,
        show_row_dend = FALSE,
        show_column_names = FALSE, use_raster = TRUE,
        plot = TRUE, verbose = TRUE, seed = 888,
        left_annotation = NULL, right_annotation = NULL,
        col = if(scale_rows) c("green", "white", "red") else c("blue", "white", "red"),
        ...)

### Arguments

`object` | A `ConsensusPartition-class` object.
`k` | Number of partitions.
`silhouette_cutoff` | Cutoff for silhouette scores. Samples with values less than it are not used for finding signature rows. For selecting a proper silhouette cutoff, please refer to https://www.stat.berkeley.edu/~s133/Cluster2a.html#tth_tAb1.
`fdr_cutoff` | Cutoff for FDR of the difference test between subgroups.
`group_diff` | Cutoff for the maximal difference between group means.
`scale_rows` | Whether apply row scaling when making the heatmap.
`row_km` | Number of groups for performing k-means clustering on rows. By default it is automatically selected.
`diff_method` | Methods to get rows which are significantly different between subgroups, see 'Details' section.
`anno` | A data frame of annotations for the original matrix columns. By default it uses the annotations specified in `consensus_partition` or `run_all_consensus_partition_methods`.
`anno_col` | A list of colors (color is defined as a named vector) for the annotations. If `anno` is a data frame, `anno_col` should be a named list where names correspond to the column names in `anno`.
`internal` | Used internally.
`show_row_dend` | Whether show row dendrogram.
`show_column_names` | Whether show column names in the heatmap.
`use_raster` | Internally used.
`plot` | Whether to make the plot.
`verbose` | Whether to print messages.
`seed` | Random seed.
`left_annotation` | Annotation put on the left of the heatmap. It should be a `HeatmapAnnotation-class` object. The number of items should be the same as the number of the original matrix rows. The subsetting to the significant rows are automatically performed on the annotation object.
`right_annotation` | Annotation put on the right of the heatmap. Same format as `left_annotation`.
`col` | Colors.
`...` | Other arguments.

### Details

Basically the function applies statistical test for the difference in subgroups for every row. There are following methods which test significance of the difference:

ttest

  First it looks for the subgroup with highest mean value, compare to each of the other subgroups with t-test and take the maximum p-value. Second it looks for the subgroup with lowest mean value, compare to each of the other subgroups again with t-test and take the maximum p-values. Later for these two list of p-values take the minimal p-value as the final p-value.

samr/pamr

  use SAM (from samr package)/PAM (from pamr package) method to find significantly different rows between subgroups.

Ftest

  use F-test to find significantly different rows between subgroups.

one_vs_others

  For each subgroup i in each row, it uses t-test to compare samples in current subgroup to all other samples, denoted as p_i. The p-value for current row is selected as min(p_i).

`diff_method` can also be a self-defined function. The function needs two arguments which are the matrix for the analysis and the predicted classes. The function should returns a vector of FDR from the difference test.

### Value

A data frame with more than two columns:

`which_row`:

  row index corresponding to the original matrix.

`fdr`:

  the FDR.

`km`:

  the k-means groups if `row_km` is set.

other_columns:

  the mean expression (depending rows are scaled or not) in each subgroup.

### Author(s)

Zuguang Gu <z.gu@dkfz.de>

### Examples

    # There is no example
    NULL

[Package cola version 1.0.1 Index]