Package 'GenoTriplo'

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)

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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)

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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

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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

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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)

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