The new version of ASAP is called PANE, you can find it here: PANE
PANE ('Pa-Neh' - bread in Italian) works much like ASAP, but has a new function to evaluate different models
ASAP leverages PCA and NNLS (Non-Negative Least Squares) to assess the ancestral composition of admixed individuals with high accuracy and reliability. If you use ASAP, please cite BioRxiv preprint.
install.packages("devtools") # if you have not installed "devtools" package
devtools::install_github("lm-ut/ASAP")
library("ASAP")
ASAP requires the following R packages:
install.packages("dplyr")
install.packages("nnls")
install.packages("ggplot2")
ASAP estimations are based on a PCA where the target and source groups are available. Along with those, we suggest to use additional groups to better define the PC space. Once that the PC space is defined, a set of NNLS is then applied on the PC coordinates, effectively summarizing the genetic ancestry. A full tutorial is available here.
For its most basic usage ASAP needs:
- A PCA matrix (a dataframe with N PCs)
- A list of target and source groups (or samples)
$ pca = read_eigen(pca_input = 'data/TOY.pca.evec')
$ ASAP_result = asap(pca_input = pca, sources = c('GST1','GST2'), admixed=c('70GST1.30GST2'))
ASAP package has two functions to read the PCA matrix, read_eigen() and read_flash().
read_eigen()will read a PCA that has been created with smartpca from the EIGENSOFT software.read_flash()instead, will read a PCA that has been created with flashpca software.
The goal of both functions is to set the PCA file as follows:
| POP | IND | PCN |
|---|---|---|
| SRC1 | SRC1_1 | 0.01 |
| SRC1 | SCR1_2 | 0.02 |
| ADM1 | ADM1_1 | 0.08 |
If neither read_eigen() nor read_flash() is for you, you might want to simply use read.table(), and set the file so that it has the aforementioned look.
The funtion asap() requires also a list of the target and reference groups: you can provide the list in two ways.
- You can provide an 'AS_file': a file with the list of the Admixed groups (A) and the Source groups (S). If you want to use ASAP sample-wise rather than group-wise, simply adjust the PCA file so that in the 'POP' column is identical to the 'IND' column, and set the AS_file with the samples list, rather than the group list.
The AS_file is a two-columns file with the population list on the first column, and the 'A/S' information on the second column. The 'A/S' information stands for Admixed (A) or Source (S). For each population/group we will indicate whether ASAP should consider it as a Source (S) or as an admixed target (A), the file looks like this:
| POP | A/S |
|---|---|
| SRC1 | S |
| SRC2 | S |
| ADM1 | A |
To read the AS_file, a simple read_table(file, header=T) will be sufficient.
$ AS_file = read.table('data/Example_AS', header=TRUE)
With the PCA and AS_file loaded, we are finally ready to run ASAP as follows:
$ ASAP_result = asap(pca_input = pca, as_file = AS_file)
- You can avoid relying on the AS_file if you wish, using a vector of the target and source groups directly in asap() function, as follows:
$ ASAP_result = asap(pca_input = pca, sources = c('GST1','GST2'), admixed=c('70GST1.30GST2'))
Finally, if you want to save ASAP results on a table-like format, you can use write_asap():
$ ASAP_result <- asap(pca_input = pca, sources = c('GST1','GST2'), admixed=c('70GST1.30GST2'))
$ write_asap(ASAP_input = ASAP_result, output_name = 'my_dir/my_asap_results.txt')
If you use ASAP, please cite BioRxiv preprint
For questions and bug reports please contact LM.