Remove duplicates of colData in TreeSummarizedExperiment
2
0
Entering edit mode
@3d25553d
Last seen 4 months ago
Norway

Hello,

I need to remove duplicates in the animal_id column. I tried using tidySummarizedExperiment make the code more reproducible. However, distinct returns only a tibble with one column animal_id. I then cannot reassign it back to colData because the number of rows is obviously different.

library(TreeSummarizedExperiment)
library(tidySummarizedExperiment)
library(dplyr)

# Generate assay data
set.seed(42)
assay_data <- matrix(rpois(200, lambda = 10), nrow = 20, ncol = 10)

# Generate sample data
sample_data <- data.frame(
  animal_id = factor(rep(1:5, each = 2)),
  treated = factor(rep(c("yes", "no"), times = 5)),
  disease = factor(rep(c("disease1", "disease2"), times = 5))
)

# Create TSE
tse <- TreeSummarizedExperiment(
  assays = list(counts = assay_data),
  colData = sample_data
)

print(colData(tse))
# Need to remove animal_id duplicates
#> DataFrame with 10 rows and 3 columns
#>    animal_id  treated  disease
#>     <factor> <factor> <factor>
#> 1          1      yes disease1
#> 2          1      no  disease2
#> 3          2      yes disease1
#> 4          2      no  disease2
#> 5          3      yes disease1
#> 6          3      no  disease2
#> 7          4      yes disease1
#> 8          4      no  disease2
#> 9          5      yes disease1
#> 10         5      no  disease2

distinct_tse |> distinct(animal_id, .keep_all = TRUE)
#> tidySummarizedExperiment says: Key columns are missing. A data frame is returned for independent data analysis.

#> # A tibble: 5 × 1
#> animal_id
#> <fct>    
#> 1 1        
#> 2 2        
#> 3 3        
#> 4 4        
#> 5 5
sessionInfo( )
#> R version 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Linux Mint 21.2
#> 
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Europe/Helsinki
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.33     fastmap_1.1.1     xfun_0.40         glue_1.6.2       
#>  [5] knitr_1.44        htmltools_0.5.6   rmarkdown_2.25    lifecycle_1.0.3  
#>  [9] cli_3.6.1         reprex_2.0.2      withr_2.5.0       compiler_4.3.1   
#> [13] rstudioapi_0.15.0 tools_4.3.1       evaluate_0.21     yaml_2.3.7       
#> [17] rlang_1.1.1       fs_1.6.3
dplyr TreeSummarizedExperiment • 345 views
ADD COMMENT
2
Entering edit mode
@james-w-macdonald-5106
Last seen 2 days ago
United States

I avoid the tidyverse as a general rule, so cannot give help regarding the use of distinct. It does appear to be a re-implementation of the existing function unique.data.frame, in which case it may not be what you are looking for, given your original statement (it shouldn't affect the tse object at all).

> unique(colData(tse))
DataFrame with 10 rows and 3 columns
   animal_id  treated  disease
    <factor> <factor> <factor>
1          1      yes disease1
2          1      no  disease2
3          2      yes disease1
4          2      no  disease2
5          3      yes disease1
6          3      no  disease2
7          4      yes disease1
8          4      no  disease2
9          5      yes disease1
10         5      no  disease2

In this context, unique is not removing any rows, because there are no duplicate rows! The 'duplicated' animal_ids aren't duplicated because the animals have different treatments and different diseases. You could however remove any but the first instance of an animal_id.

> distinct_tse <- tse[,!duplicated(tse$animal_id)]
> colData(distinct_tse)
DataFrame with 5 rows and 3 columns
  animal_id  treated  disease
   <factor> <factor> <factor>
1         1      yes disease1
2         2      yes disease1
3         3      yes disease1
4         4      yes disease1
5         5      yes disease1

Which may or may not be what you want to do.

0
Entering edit mode
@mikelove
Last seen 2 days ago
United States

Copying my code from Slack for posterity. James' code is more streamlined for this step I think.

# make a dataset that has duplicate sample data (columns repeated) in sample2 column
se <- tidySummarizedExperiment::pasilla
se <- se |> mutate(sample2 = ifelse(.sample == "untrt4", "untrt3", .sample))

# first distinct() is to reduce duplication in the tidySE representation, essentially tibble of colData
# second distinct() operates on the colData tibble, then we pull out the main ID '.sample'
ids_to_keep <- se |> 
  distinct(.sample, sample2) |> 
  distinct(sample2, .keep_all=TRUE) |> 
  pull(.sample)

se |> filter(.sample %in% ids_to_keep)
ADD COMMENT

Login before adding your answer.

Traffic: 634 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6