get_signatures-ConsensusPartition-method {cola} | R Documentation |
Get signature rows
## 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"), ...)
object |
A |
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 |
anno_col |
A list of colors (color is defined as a named vector) for the annotations. If |
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 |
right_annotation |
Annotation put on the right of the heatmap. Same format as |
col |
Colors. |
... |
Other arguments. |
Basically the function applies statistical test for the difference in subgroups for every row. There are following methods which test significance of the difference:
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.
use SAM (from samr package)/PAM (from pamr package) method to find significantly different rows between subgroups.
use F-test to find significantly different rows between subgroups.
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.
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.
the mean expression (depending rows are scaled or not) in each subgroup.
Zuguang Gu <z.gu@dkfz.de>
# There is no example NULL