averaging mass spec peak counts by sample column names

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Hopefully this is straightforward, and I'm just thinking too hard. I have a matrix of peak counts from mass spec (MS) where peaks are rows and columns are sample names. The sample locations have several sampling sites and I would like to add the counts between sites within locations.

For example, one sample with three replicates is identified as "S19S_0010_Sed_Field_ICR.D_p2", "S19S_0010_Sed_Field_ICR.M_p2", and "S19S_0010_Sed_Field_ICR.U_p2" where it's the same location but downstream (D), midstream (M), and upstream (U). The first two samples have one count of a specific peak each, so I would like to merge the three samples to just say "S19S_0010_Sed_Field_ICR.all_p2" with two counts of the wavelength. Example dataset:

> dput(data.sed.ex)
structure(list(S19S_0004_Sed_Field_ICR.M_p15 = c(0, 0, 0, 0, 
0, 0, 0, 0, 0, 0), S19S_0006_Sed_Field_ICR.D_p2 = c(0, 0, 0, 
0, 0, 0, 1, 1, 0, 0), S19S_0006_Sed_Field_ICR.M_p2 = c(0, 0, 
0, 0, 0, 0, 1, 0, 0, 0), S19S_0006_Sed_Field_ICR.U_p2 = c(0, 
0, 0, 0, 0, 0, 1, 1, 0, 0), S19S_0008_Sed_Field_ICR.M_p15 = c(0, 
0, 0, 0, 0, 0, 0, 1, 0, 0), S19S_0009_Sed_Field_ICR.M_p2 = c(0, 
0, 1, 0, 0, 0, 1, 0, 0, 0), S19S_0009_Sed_Field_ICR.U_p2 = c(0, 
0, 0, 0, 0, 0, 1, 0, 0, 0), S19S_0010_Sed_Field_ICR.D_p15 = c(0, 
0, 0, 0, 0, 0, 1, 0, 0, 0), S19S_0010_Sed_Field_ICR.M_p15 = c(0, 
0, 0, 0, 0, 0, 1, 0, 0, 0), S19S_0010_Sed_Field_ICR.U_p15 = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c("200.002276", "200.015107", 
"200.0564158", "200.0565393", "200.0578394", "200.0677581", "200.092796", 
"200.1291723", "200.1292836", "200.9238455"), class = "data.frame")

TIA

dataframe r
1 Answers

maybe wrangling to a long format can help. In this format, you can summarize by groups e.g. sample, or sample, and location, using sum, mean, sd among others.

hope this helps,

Convert to long format

## dd is the `data.sed.ex` object above

library(tidyverse)                                                                                                                                                           
ddLong <- dd %>%
  rownames_to_column(var = "peak") %>%
  pivot_longer(cols = matches("^S")) %>%
  mutate(sample = gsub("(.*)\\.(.*)", "\\1", name),           ## pull sample info                                                                                          
         location = gsub("(.*)\\.([DMU])_(.*)", "\\2", name), ## pull D M U                                                                                                
         p = gsub("(.*)\\.([DMU])_(p.*)", "\\3", name),       ## get p2, p15                                                                                               
         peak = as.numeric(peak))             ## coerce peak to numeric                                                                                                    

ddLong
#> # A tibble: 100 × 6
#>     peak name                          value sample               location p    
#>    <dbl> <chr>                         <dbl> <chr>                <chr>    <chr>
#>  1  200. S19S_0004_Sed_Field_ICR.M_p15     0 S19S_0004_Sed_Field… M        p15  
#>  2  200. S19S_0006_Sed_Field_ICR.D_p2      0 S19S_0006_Sed_Field… D        p2   
#>  3  200. S19S_0006_Sed_Field_ICR.M_p2      0 S19S_0006_Sed_Field… M        p2   
#>  4  200. S19S_0006_Sed_Field_ICR.U_p2      0 S19S_0006_Sed_Field… U        p2   
#>  5  200. S19S_0008_Sed_Field_ICR.M_p15     0 S19S_0008_Sed_Field… M        p15  
#>  6  200. S19S_0009_Sed_Field_ICR.M_p2      0 S19S_0009_Sed_Field… M        p2   
#>  7  200. S19S_0009_Sed_Field_ICR.U_p2      0 S19S_0009_Sed_Field… U        p2   
#>  8  200. S19S_0010_Sed_Field_ICR.D_p15     0 S19S_0010_Sed_Field… D        p15  
#>  9  200. S19S_0010_Sed_Field_ICR.M_p15     0 S19S_0010_Sed_Field… M        p15  
#> 10  200. S19S_0010_Sed_Field_ICR.U_p15     0 S19S_0010_Sed_Field… U        p15  
#> # … with 90 more rows

Summarize by one or more groups

## summarise using group_by + verbs                                                                                                                                          
ddLong %>%                                                                                                                                                                   
  group_by(sample, location) %>%                                                                                                                                           
  summarise(n = n(),                                                                                                                                                       
            sum.value = sum(value),                                                                                                                                        
            mean.peak = mean(peak))                                                                                                                                        
#> `summarise()` has grouped output by 'sample'. You can override using the
#> `.groups` argument.
#> # A tibble: 10 × 5
#> # Groups:   sample [5]
#>    sample                  location     n sum.value mean.peak
#>    <chr>                   <chr>    <int>     <dbl>     <dbl>
#>  1 S19S_0004_Sed_Field_ICR M           10         0      200.
#>  2 S19S_0006_Sed_Field_ICR D           10         2      200.
#>  3 S19S_0006_Sed_Field_ICR M           10         1      200.
#>  4 S19S_0006_Sed_Field_ICR U           10         2      200.
#>  5 S19S_0008_Sed_Field_ICR M           10         1      200.
#>  6 S19S_0009_Sed_Field_ICR M           10         2      200.
#>  7 S19S_0009_Sed_Field_ICR U           10         1      200.
#>  8 S19S_0010_Sed_Field_ICR D           10         1      200.
#>  9 S19S_0010_Sed_Field_ICR M           10         1      200.
#> 10 S19S_0010_Sed_Field_ICR U           10         0      200.

                                                                                                    
ddLong %>%                                                                                                                                                                   
    group_by(sample, p) %>%                                             
    summarise(n = n(),                                                                                                                                                       
              sum.value = sum(value),                                                                                                                                        
              mean.peak = mean(peak))                                                                                                                                        
#> `summarise()` has grouped output by 'sample'. You can override using the
#> `.groups` argument.
#> # A tibble: 5 × 5
#> # Groups:   sample [5]
#>   sample                  p         n sum.value mean.peak
#>   <chr>                   <chr> <int>     <dbl>     <dbl>
#> 1 S19S_0004_Sed_Field_ICR p15      10         0      200.
#> 2 S19S_0006_Sed_Field_ICR p2       30         5      200.
#> 3 S19S_0008_Sed_Field_ICR p15      10         1      200.
#> 4 S19S_0009_Sed_Field_ICR p2       20         3      200.
#> 5 S19S_0010_Sed_Field_ICR p15      30         2      200.
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