R/ctwas_sumstats_noLD.R
ctwas_sumstats_noLD.RdcTWAS analysis using summary statistics with "no LD" version
ctwas_sumstats_noLD(
z_snp,
weights,
region_info,
snp_map,
z_gene = NULL,
thin = 1,
niter_prefit = 3,
niter = 50,
init_group_prior = NULL,
init_group_prior_var = NULL,
group_prior_var_structure = c("shared_all", "shared_type", "shared_context",
"shared_nonSNP", "independent"),
maxSNP = Inf,
min_var = 2,
min_gene = 1,
min_group_size = 100,
min_nonSNP_PIP = 0.5,
min_snp_pval = 5e-08,
min_gene_pval = min_snp_pval,
min_p_single_effect = 0.8,
null_method = c("ctwas", "susie", "none"),
EM_tol = 1e-04,
coverage = 0.95,
include_prior = FALSE,
include_susie_result = FALSE,
outputdir = NULL,
outname = "ctwas_noLD",
ncore = 1,
seed = 99,
logfile = NULL,
verbose = FALSE,
...
)A data frame with four columns: "id", "A1", "A2", "z". giving the z scores for SNPs. "A1" is effect allele. "A2" is the other allele.
a list of pre-processed prediction weights.
a data frame of region definitions.
a list of data frames with SNP-to-region map for the reference.
A data frame with columns: "id", "z", giving the z-scores for genes.
The proportion of SNPs to be used for estimating parameters and screening regions.
the number of iterations of the E-M algorithm to perform during the initial parameter estimation step.
the maximum number of iterations of the E-M algorithm to perform during the complete parameter estimation step.
a vector of initial values of prior inclusion probabilities for different groups.
a vector of initial values of prior variances for different groups.
a string indicating the structure to put on the prior variance parameters.
"shared_all" allows all groups to share the same variance parameter.
"shared_type" allows all groups in one molecular QTL type to share the same variance parameter.
"shared_context" allows all groups in one context (tissue, cell type, condition) to share the same variance parameter.
"shared_nonSNP" allows all non-SNP groups to share the same variance parameter.
"independent" allows all groups to have their own separate variance parameters.
"fixed" sets prior variance parameters to values in init_group_prior_var.
Inf or integer. Maximum number of SNPs in a region. Default is Inf, no limit. This can be useful if there are many SNPs in a region and you don't have enough memory to run the program.
minimum number of variables (SNPs and genes) in a region when estimating paramters and screening regions.
minimum number of genes in a region when estimating paramters and screening regions.
Minimum number of genes in a group.
Groups with number of genes < min_group_size will be removed for the analysis.
Regions with non-SNP PIP >= min_nonSNP_PIP
will be selected to run finemapping using full SNPs.
Select regions with minimum SNP p-values < min_snp_pval.
Select regions with minimum gene p-values < min_gene_pval.
By default, it is set to the same value as min_snp_pval.
Regions with probability greater than min_p_single_effect of
having 1 or fewer effects will be used for parameter estimation.
Method to compute null model, options: "ctwas", "susie" or "none".
A small, non-negative number specifying the convergence tolerance of log-likelihood for the EM iterations.
A number between 0 and 1 specifying the “coverage” of the estimated confidence sets.
If TRUE, include priors in finemapping results.
If TRUE, include the "susie" result object in finemapping results.
The directory to store output. If specified, save outputs to the directory.
The output name.
The number of cores used to parallelize susie over regions.
seed for random sampling when thinning the SNPs in region data.
The log filename. If NULL, print log info on screen.
If TRUE, print detailed messages.
Additional arguments of susie_rss.
a list, including z_gene, estimated parameters, region_data, cross-boundary genes, screening region results, and fine-mapping results.