Widen Olink data

widen_data() transforms the data from long to wide format. It should be used to transform Olink data from long to wide format with Assays as columns names and NPX as values. The first column contains the DAids.

Usage

widen_data(olink_data)

Arguments

olink_data

A tibble containing Olink data to be transformed.

Value

A tibble containing the data in wide format.

Examples

# Olink data in long format
example_data
#> # A tibble: 56,142 × 10
#>    DAid    Sample   OlinkID UniProt Assay Panel     NPX Assay_Warning QC_Warning
#>    <chr>   <chr>    <chr>   <chr>   <chr> <chr>   <dbl> <chr>         <chr>     
#>  1 DA00001 AML_syn… OID213… Q9BTE6  AARS… Onco…  3.39   PASS          PASS      
#>  2 DA00001 AML_syn… OID212… P00519  ABL1  Onco…  2.76   PASS          PASS      
#>  3 DA00001 AML_syn… OID212… P09110  ACAA1 Onco…  1.71   PASS          PASS      
#>  4 DA00001 AML_syn… OID201… P16112  ACAN  Card…  0.0333 PASS          PASS      
#>  5 DA00001 AML_syn… OID201… Q9BYF1  ACE2  Card…  1.76   PASS          PASS      
#>  6 DA00001 AML_syn… OID201… Q15067  ACOX1 Card… -0.919  PASS          PASS      
#>  7 DA00001 AML_syn… OID203… P13686  ACP5  Card…  1.54   PASS          PASS      
#>  8 DA00001 AML_syn… OID214… Q9NPH0  ACP6  Onco…  2.15   PASS          PASS      
#>  9 DA00001 AML_syn… OID200… P62736  ACTA2 Card…  2.81   PASS          PASS      
#> 10 DA00001 AML_syn… OID204… O43707  ACTN4 Infl…  0.742  PASS          PASS      
#> # ℹ 56,132 more rows
#> # ℹ 1 more variable: PlateID <chr>

# Transform Olink data in wide format
widen_data(example_data)
#> # A tibble: 586 × 101
#>    DAid    AARSD1   ABL1  ACAA1    ACAN    ACE2  ACOX1   ACP5    ACP6  ACTA2
#>    <chr>    <dbl>  <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>  <dbl>
#>  1 DA00001   3.39  2.76   1.71   0.0333  1.76   -0.919 1.54    2.15    2.81 
#>  2 DA00002   1.42  1.25  -0.816 -0.459   0.826  -0.902 0.647   1.30    0.798
#>  3 DA00003  NA    NA     NA      0.989  NA       0.330 1.37   NA      NA    
#>  4 DA00004   3.41  3.38   1.69  NA       1.52   NA     0.841   0.582   1.70 
#>  5 DA00005   5.01  5.05   0.128  0.401  -0.933  -0.584 0.0265  1.16    2.73 
#>  6 DA00006   6.83  1.18  -1.74  -0.156   1.53   -0.721 0.620   0.527   0.772
#>  7 DA00007  NA    NA      3.96   0.682   3.14    2.62  1.47    2.25    2.01 
#>  8 DA00008   2.78  0.812 -0.552  0.982  -0.101  -0.304 0.376  -0.826   1.52 
#>  9 DA00009   4.39  3.34  -0.452 -0.868   0.395   1.71  1.49   -0.0285  0.200
#> 10 DA00010   1.83  1.21  -0.912 -1.04   -0.0918 -0.304 1.69    0.0920  2.04 
#> # ℹ 576 more rows
#> # ℹ 91 more variables: ACTN4 <dbl>, ACY1 <dbl>, ADA <dbl>, ADA2 <dbl>,
#> #   ADAM15 <dbl>, ADAM23 <dbl>, ADAM8 <dbl>, ADAMTS13 <dbl>, ADAMTS15 <dbl>,
#> #   ADAMTS16 <dbl>, ADAMTS8 <dbl>, ADCYAP1R1 <dbl>, ADGRE2 <dbl>, ADGRE5 <dbl>,
#> #   ADGRG1 <dbl>, ADGRG2 <dbl>, ADH4 <dbl>, ADM <dbl>, AGER <dbl>, AGR2 <dbl>,
#> #   AGR3 <dbl>, AGRN <dbl>, AGRP <dbl>, AGXT <dbl>, AHCY <dbl>, AHSP <dbl>,
#> #   AIF1 <dbl>, AIFM1 <dbl>, AK1 <dbl>, AKR1B1 <dbl>, AKR1C4 <dbl>, …