Drop redundant elements (e.g., samples) for which feature (e.g., transcript/gene) abundances are correlated

remove_redundancy() takes as input A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) for correlation method or | <DIMENSION 1> | <DIMENSION 2> | <...> | for reduced_dimensions method, and returns a consistent object (to the input) 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,
  transform = identity,
  Dim_a_column,
  Dim_b_column,
  log_transform = NULL
)

# S4 method for spec_tbl_df
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

# S4 method for tbl_df
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

# S4 method for tidybulk
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

# S4 method for SummarizedExperiment
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

# S4 method for RangedSummarizedExperiment
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

Arguments

.data

A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment))

.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 method to use, correlation and reduced_dimensions are available. The latter eliminates one of the most proximar pairs of samples in PCA reduced dimensions.

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

transform

A function that will tranform the counts, by default it is log1p for RNA sequencing data, but for avoinding tranformation you can use identity

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

log_transform

DEPRECATED - A boolean, whether the value should be log-transformed (e.g., TRUE for RNA sequencing data)

Value

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

Details

Examples

 tidybulk::se_mini |>
 identify_abundant() |>
   remove_redundancy(
     .element = sample,
     .feature = transcript,
       .abundance =  count,
       method = "correlation"
       )
#> No group or design set. Assuming all samples belong to one group.
#> Getting the 182 most variable genes
#> class: SummarizedExperiment 
#> dim: 527 4 
#> metadata(0):
#> assays(1): count
#> rownames(527): ABCB4 ABCB9 ... ZNF324 ZNF442
#> rowData names(2): entrez .abundant
#> colnames(4): SRR1740035 SRR1740043 SRR1740058 SRR1740067
#> colData names(5): Cell.type time condition days dead

counts.MDS =
 tidybulk::se_mini |>
 identify_abundant() |>
  reduce_dimensions( method="MDS", .dims = 3)
#> No group or design set. Assuming all samples belong to one group.
#> Getting the 182 most variable genes
#> tidybulk says: to access the raw results do `attr(..., "internals")$MDS`

remove_redundancy(
  counts.MDS,
  Dim_a_column = `Dim1`,
  Dim_b_column = `Dim2`,
  .element = sample,
  method = "reduced_dimensions"
)
#> class: SummarizedExperiment 
#> dim: 527 3 
#> metadata(0):
#> assays(1): count
#> rownames(527): ABCB4 ABCB9 ... ZNF324 ZNF442
#> rowData names(2): entrez .abundant
#> colnames(3): SRR1740035 SRR1740058 SRR1740067
#> colData names(8): Cell.type time ... Dim2 Dim3