> For the complete documentation index, see [llms.txt](https://help.bioinsight.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.bioinsight.illumina.com/dragen-protein-quantification/after-counting-and-normalization/using-the-adat.md).

# Using the ADAT

ADATs can be analyzed in R or Python using parsers created by SomaLogic.

DRAGEN Protein Quantification v3.0.0 is compatible with SomaData v1.2.4 (Python - formerly called Canopy) and SomaDataIO v6.4.0 (R).

> <mark style="color:orange;">**NOTE**</mark>: The Python package SomaData was formerly called Canopy. The GitHub repository may still use the Canopy name.

* Python: <https://github.com/SomaLogic/Canopy>
* R: <https://github.com/SomaLogic/SomaDataIO>

SomaData creates an ADAT object, which is an extension of a Pandas DataFrame.

* rows correspond to samples
* columns correspond to SOMAmers
* values are normalized counts

Below are examples on parsing an ADAT in Python and R.

### Parsing ADAT in Python

```python
import somadata

# read the adat
my_adat = somadata.read_adat('/path/to/my/file1.adat')

# retrieve sample metadata
sample_meta = my_adat.index.to_frame(index=False)

# retrieve SOMAmer metadata
soma_meta = my_adat.columns.to_frame(index=False)

# retrieve the scale factors for all plates
plate_scale_factors_dict = my_adat.header_metadata['PlatformSpecificPlateScale_ScaleFactor']

### additional optional manipulation 

# concatenate multiple adats into a single adat
my_adat2 = somadata.read_adat('/path/to/my/file2.adat')
my_merged_adat = soma.smart_adat_concatenation([my_adat, my_adat2])

# write it to a file 
my_merged_adat = my_merged_adat.to_adat('/path/to/merged/adat')
```

### Parsing ADAT in R

```r
library(SomaDataIO)

# check all package functions
ls("package:SomaDataIO")

#read the adat
my_adat <- read_adat('/path/to/my/file1.adat')

# retrieve sample metadata
sample_meta <- my_adat[getMeta(my_adat)]

# retrieve SOMAmer metadata
soma_meta <- my_adat[getAnalytes(my_adat)]

# create a function that parses the header
parse_adat_header <- function(adat_file, max_lines = 500) {
  header_lines <- readLines(adat_file, n = max_lines)
  table_row <- grep("TABLE_BEGIN", header_lines)
  header_lines[1:(table_row-1)] %>% 
    strsplit("\t") %>% 
    {
      values <- map(., 2) # Get values from key value pairs
      keys <- map_chr(., 1) # Get keys...
      setNames(values, keys)
    }
}

my_adat_header <- parse_adat_header('/path/to/my/file1.adat')

# retrieve the scale factors for all plates
plate_scale_factors_dict = my_adat_header$PlatformSpecificPlateScale_ScaleFactor

### additional optional manipulation 
my_adat2 = read_adat('/path/to/my/file2.adat')
my_merged_adat = rbind(my_adat, my_adat2)

# write it to a file 
my_merged_adat = write_adat(my_merged_adat, '/path/to/merged/adat')

```


---

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