Extract sample-wise information

Source: R/methods.R, R/methods_SE.R

pivot_sample() 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)) and returns a `tbl` with only sample-related columns

pivot_sample(.data, .sample = NULL)

# S4 method for class 'spec_tbl_df'
pivot_sample(.data, .sample = NULL)

# S4 method for class 'tbl_df'
pivot_sample(.data, .sample = NULL)

# S4 method for class 'tidybulk'
pivot_sample(.data, .sample = NULL)

# S4 method for class 'SummarizedExperiment'
pivot_sample(.data, .sample = NULL)

# S4 method for class 'RangedSummarizedExperiment'
pivot_sample(.data, .sample = 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))

.sample

The name of the sample column

Value

A `tbl` with transcript-related information

A consistent object (to the input)

A consistent object (to the input)

Details

`r lifecycle::badge("maturing")`

This functon extracts only sample-related information for downstream analysis (e.g., visualisation). It is disruptive in the sense that it cannot be passed anymore to tidybulk function.

Examples



  pivot_sample(tidybulk::se_mini )
#> # A tibble: 5 × 6
#>   .sample    Cell.type         time  condition  days  dead
#>   <chr>      <chr>             <chr> <lgl>     <dbl> <dbl>
#> 1 SRR1740034 b_cell            0 d   TRUE          1     1
#> 2 SRR1740035 b_cell            1 d   TRUE         10     1
#> 3 SRR1740043 monocyte          1 d   FALSE       500     1
#> 4 SRR1740058 t_cell            0 d   TRUE       1000     0
#> 5 SRR1740067 dendritic_myeloid 1 d   FALSE      2000     1