View source: R/pairscan_null.R
pairscan_null | R Documentation |
This script generates a null distribution for the pairscan. For each permutation, it runs a single scan and selects the top N markers. It then uses these markers to perform a permutation of the pairscan. the null distribution generated here uses a fixed number of the TOP ranking markers from the permuted single scan we need to update it to use select_markers_for_pairscan if marker_selection_method is netwas, you need to provide a list of genes from the netWAS analysis
pairscan_null(
data_obj,
geno_obj = NULL,
scan_what = c("eigentraits", "raw_traits"),
pairscan_null_size = NULL,
max_pair_cor = NULL,
min_per_geno = NULL,
model_family = "gaussian",
marker_selection_method = c("top_effects", "uniform", "effects_dist", "by_gene"),
run_parallel = FALSE,
n_cores = 4,
verbose = FALSE
)
data_obj |
a |
geno_obj |
a genotype object |
scan_what |
A character string uniquely identifying whether eigentraits or raw traits should be scanned. Options are "eigentraits", "raw_traits" |
pairscan_null_size |
The total size of the null distribution. This is DIFFERENT than the number of permutations to run. Each permutation generates n choose 2 elements for the pairscan. So for example, a permutation that tests 100 pairs of markers will generate a null distribution of size 4950. This process is repeated until the total null size is reached. If the null size is set to 5000, two permutations of 100 markers would be done to get to a null distribution size of 5000. |
max_pair_cor |
A numeric value between 0 and 1 indicating the maximum Pearson correlation that two markers are allowed. If the correlation between a pair of markers exceeds this threshold, the pair is not tested. If this value is set to NULL, min_per_genotype must have a numeric value. |
min_per_geno |
The minimum number of individuals allowable per genotype. If for a given marker pair, one of the genotypes is underrepresented, the marker pair is not tested. If this value is NULL, max_pair_cor must have a numeric value. |
model_family |
Indicates the model family of the phenotypes. This can be either "gaussian" or "binomial". |
marker_selection_method |
options are "top_effects", "uniform", "effects_dist", "by_gene" |
run_parallel |
Whether to run the analysis on multiple CPUs |
n_cores |
The number of CPUs to use if run_parallel is TRUE |
verbose |
Whether to write progress to the screen |
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