remove_redundancy {tidybulk} | R Documentation |
remove_redundancy() takes as input a 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | for correlation method or | <DIMENSION 1> | <DIMENSION 2> | <...> | for reduced_dimensions method, and returns a 'tbl' with dropped elements (e.g., samples).
remove_redundancy( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, correlation_threshold = 0.9, top = Inf, log_transform = FALSE, Dim_a_column, Dim_b_column ) ## S4 method for signature 'spec_tbl_df' remove_redundancy( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, correlation_threshold = 0.9, top = Inf, log_transform = FALSE, Dim_a_column = NULL, Dim_b_column = NULL ) ## S4 method for signature 'tbl_df' remove_redundancy( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, correlation_threshold = 0.9, top = Inf, log_transform = FALSE, Dim_a_column = NULL, Dim_b_column = NULL ) ## S4 method for signature 'tidybulk' remove_redundancy( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, correlation_threshold = 0.9, top = Inf, log_transform = FALSE, Dim_a_column = NULL, Dim_b_column = NULL ) ## S4 method for signature 'SummarizedExperiment' remove_redundancy( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, correlation_threshold = 0.9, top = Inf, log_transform = FALSE, Dim_a_column = NULL, Dim_b_column = NULL ) ## S4 method for signature 'RangedSummarizedExperiment' remove_redundancy( .data, .element = NULL, .feature = NULL, .abundance = NULL, method, of_samples = TRUE, correlation_threshold = 0.9, top = Inf, log_transform = FALSE, Dim_a_column = NULL, Dim_b_column = NULL )
.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 |
correlation_threshold |
A real number between 0 and 1. For correlation based calculation. |
top |
An integer. How many top genes to select for correlation based method |
log_transform |
A boolean, whether the value should be log-transformed (e.g., TRUE for RNA sequencing data) |
Dim_a_column |
A character string. For reduced_dimension based calculation. The column of one principal component |
Dim_b_column |
A character string. For reduced_dimension based calculation. The column of another principal component |
A tbl object with with dropped redundant elements (e.g., samples).
A tbl object with with dropped redundant elements (e.g., samples).
A tbl object with with dropped redundant elements (e.g., samples).
A tbl object with with dropped redundant elements (e.g., samples).
A 'SummarizedExperiment' object
A 'SummarizedExperiment' object
tidybulk::counts_mini %>% tidybulk(sample, transcript, count) %>% identify_abundant() %>% remove_redundancy( .element = sample, .feature = transcript, .abundance = count, method = "correlation" ) counts.MDS = tidybulk::counts_mini %>% tidybulk(sample, transcript, count) %>% identify_abundant() %>% reduce_dimensions( method="MDS", .dims = 3) remove_redundancy( counts.MDS, Dim_a_column = `Dim1`, Dim_b_column = `Dim2`, .element = sample, method = "reduced_dimensions" )