| Type: | Package |
| Title: | Genotyping Triploids (or Diploids) from Luminescence Data |
| Version: | 1.1.3 |
| Description: | Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids. Two main functions: Run_Clustering() that regroups individuals with a same genotype based on proximity and Run_Genotyping() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny(). |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Imports: | cowplot, doParallel, dplyr, DT, foreach, ggplot2, htmltools, parallel, processx, rlang, Rmixmod, shiny, shinythemes, tidyr |
| Depends: | R (≥ 3.5.0), shinyBS |
| NeedsCompilation: | no |
| Packaged: | 2025-03-25 10:14:48 UTC; Proprietaire |
| Author: | Julien Roche [aut, cre], Florence Phocas [aut], Mathieu Besson [aut], Pierre Patrice [aut], Marc Vandeputte [aut], François Allal [aut], Pierrick Haffray [aut] |
| Maintainer: | Julien Roche <julien.roche@inrae.fr> |
| Repository: | CRAN |
| Date/Publication: | 2025-03-25 16:40:02 UTC |
Clustering function
Description
Clustering function to run clustering with no parallelization process nor auto save
Usage
Clustering(
dataset,
nb_clust_possible,
n_iter = 5,
Dmin = 0.28,
SampleName = NULL
)
Arguments
dataset |
dataset with Contrast and SigStren for each individuals (as SampleName) and each markers (as MarkerName) |
nb_clust_possible |
number of cluster possible (ploidy+1) |
n_iter |
number of iterations to perform for clustering |
Dmin |
minimal distance between two clusters |
SampleName |
vector with all SampleName (important when missing genotype) |
Value
list of results of clustering
Examples
data(GenoTriplo_to_clust)
ploidy=3
res = Clustering(dataset=GenoTriplo_to_clust,
nb_clust_possible=ploidy+1,n_iter=5)
Create dataset in appropriate format
Description
Create SigStren and Contrast variables from luminescence values of probeset A and B of each markers and return a dataframe to be used for clustering or save the result if a saving name is given
Usage
Create_Dataset(data, save_name = NULL)
Arguments
data |
dataframe with probeset_id as first variable (markername finishing by -A or -B depending on the probeset) and individuals as variable with luminescence values for each probeset (dataset created by bash code by shiny app) |
save_name |
saving name |
Value
number of individuals and markers (automatically save the dataset)
Example of dataset for clustering
Description
Example of dataset for clustering
Usage
GenoTriplo_to_clust
Format
A dataframe with 500 rows (corresponding to an individual for a given marker) and 4 columns (SigStren,Contrast,SampleName,MarkerName)
Example of dataset for genotyping
Description
Example of dataset for genotyping
Usage
GenoTriplo_to_geno
Format
A list of 10 each element being the result of clustering for a given marker
Launch parallel clustering
Description
Launch the clustering phase in parallel from the dataset with SampleName, Contrast and SigStren for each markers (MarkerName).
Usage
Run_Clustering(
data_clustering,
ploidy,
save_n = "",
n_iter = 5,
D_min = 0.28,
n_core = 1,
path_log = ""
)
Arguments
data_clustering |
dataframe result from create dataset phase |
ploidy |
ploidy of offspring |
save_n |
name of the saving file |
n_iter |
number of iterations of clustering |
D_min |
threshold distance between two clusters |
n_core |
number of cores used for parallelization |
path_log |
path for log file when run by the shiny app |
Value
the result of clustering or automatically save a list of objects if a saving name has been provided
Examples
data(GenoTriplo_to_clust)
res = Run_Clustering(data_clustering=GenoTriplo_to_clust,
ploidy=3,n_iter=5,n_core=1)
# or if you want to automatically save the result
# This will automatically create a folder and save the result in it
# Run_Clustering(data_clustering=GenoTriplo_to_clust,
# ploidy=3,n_iter=5,n_core=1,save_n='exemple')
Launch genotyping phase in parallel
Description
Function that launch the genotyping phase from the dataset with SampleName, Contrast and SigStren for each markers and the result of the 'Run_clustering' function.
Usage
Run_Genotyping(
data_clustering,
res_clust,
ploidy,
SeuilNoCall = 0.85,
SeuilNbSD = 2.8,
SeuilSD = 0.28,
n_core = 1,
corres_ATCG = NULL,
pop = "Yes",
cr_marker = 0.97,
fld_marker = 3.4,
hetso_marker = -0.3,
save_n = "",
batch = "",
ALL = TRUE,
path_log = ""
)
Arguments
data_clustering |
dataframe result from create dataset phase |
res_clust |
object from clustering phase |
ploidy |
ploidy of offspring |
SeuilNoCall |
threshold of the probability of belonging to a cluster |
SeuilNbSD |
threshold for the distance between an individuals and his cluster (x=Contrast) |
SeuilSD |
threshold for the standard deviation of a cluster (SeuilSD*(1+0.5*abs(mean_contrast_cluster))) |
n_core |
number of cores used for parallelization |
corres_ATCG |
dataframe with the correspondence between A/B of AXAS and A/T/C/G (three columns : probeset_id, Allele_A, Allele_B) |
pop |
Yes or No : are individuals from a same population |
cr_marker |
call rate threshold |
fld_marker |
FLD threshold |
hetso_marker |
HetSO threshold |
save_n |
name of the saving file. If ” no auto save and return value is changed |
batch |
batch number in case of parallelization else ignore |
ALL |
TRUE/FALSE whether the dataset has been cut or not (from the shiny app) |
path_log |
path for log file when run by the shiny app |
Value
if save_n != ” : 3 objects list : dataframe with call rate by individuals, dataframe with call rate and other metrics of markers and another dataframe – Automatically save results. Else : return list with genotype
Examples
data(GenoTriplo_to_clust)
data(GenoTriplo_to_geno)
res = Run_Genotyping(data_clustering=GenoTriplo_to_clust,
res_clust=GenoTriplo_to_geno,
ploidy=3)
Shiny App for genotyping
Description
Launch a shiny interface to use GenoTriplo. Really easy to use and user friendly, this will help you gain time !
Usage
launch_GenoShiny()
Value
void : most results are automatically saved