cluster_elements {tidybulk} | R Documentation |
cluster_elements() takes as imput a 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | and identify clusters in the data.
cluster_elements( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'spec_tbl_df' cluster_elements( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'tbl_df' cluster_elements( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'tidybulk' cluster_elements( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'SummarizedExperiment' cluster_elements( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, log_transform = TRUE, action = "add", ... ) ## S4 method for signature 'RangedSummarizedExperiment' cluster_elements( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, log_transform = TRUE, action = "add", ... )
.data |
A 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | |
.element |
The name of the element column (normally samples). |
.feature |
The name of the feature column (normally transcripts/genes) |
.abundance |
The name of the column including the numerical value the clustering is based on (normally transcript abundance) |
method |
A character string. The cluster algorithm to use, ay the moment k-means is the only algorithm included. |
of_samples |
A boolean. In case the input is a tidybulk object, it indicates Whether the element column will be sample or transcript column |
log_transform |
A boolean, whether the value should be log-transformed (e.g., TRUE for RNA sequencing data) |
action |
A character string. Whether to join the new information to the input tbl (add), or just get the non-redundant tbl with the new information (get). |
... |
Further parameters passed to the function kmeans |
identifies clusters in the data, normally of samples. This function returns a tibble with additional columns for the cluster annotation. At the moment only k-means clustering is supported, the plan is to introduce more clustering methods.
A tbl object with additional columns with cluster labels
A tbl object with additional columns with cluster labels
A tbl object with additional columns with cluster labels
A tbl object with additional columns with cluster labels
A 'SummarizedExperiment' object
A 'SummarizedExperiment' object
cluster_elements(tidybulk::counts_mini, sample, transcript, count, centers = 2, method="kmeans")