| predict.baymap {BayMAP} | R Documentation |
Predictions if T-to-C substitution positions are PAR-CLIP induced substitutions or not. Results of several PAR-CLIP experiments can be combined for a combined prediction.
## S3 method for class 'baymap'
predict(object, data, chr = "chr", pos = "pos",
count = "count", coverage = "coverage",
mutation = "mutation", mutation.type = "TC",
covariates = NULL,
dist = c("truncated", "binomial"),
ran = FALSE, cluster = NULL,
print.i = 100, thin = NULL, burn.in = 0, ...)
object |
either a |
data |
either a data frame with at least the count for mutations per genomic position, the number of reads/coverage and the mutation type (e.g., T-to-C) or a list of data frames. |
chr |
the name of the variable that contains the chromosome information. |
pos |
the name of the variable that contains the position information. |
count |
the name of the variable that counts the number of mutations. |
coverage |
the name of the variable that contains the number of reads. |
mutation |
the name of the variable that contains the different types of mutations. |
mutation.type |
the name of the mutation type that is induced by the PAR-CLIP method. |
covariates |
a vector containing the names for the covariates for the regression model, e.g., c("tpUTR", "cds", "fpUTR"). Intercept is automatically added as first variable. |
dist |
the distribution for the number of mutations. Possible entries are "truncated" (default) and "binomial. |
ran |
a logical value indicating if neighborhood dependencies should be included via a random effect (default) or not. |
cluster |
the name of the varialbe indicating to which cluster a position belongs. Only necessary if |
print.i |
a positive integer indicating if every ith iteration step should be printed. |
thin |
an additional thinning rate that should be applied on the |
burn.in |
length of burn in, i.e. number of iterations to discard at the beginning. |
... |
further arguments for |
If a single PAR-CLIP data set is analyzed, the returned object is a data frame including the prior odds, the bayes factor and the posterior odds. If several PAR-CLIP data sets are analyzed, the returned object is a list combined predictions as well as separate predictions for each dara set. Posterior odds greater than one means that the probability of having a method-incuced substitution position given the data is larger than 0.5.
Predictions of the separate analyzis are made for all entries of the included data set even for substitution types other than T-to-C.
Eva-Maria Huessler, eva-maria.huessler@uni-duesseldorf.de
Huessler, E., Schaefer, M. Schwender, H., Landgraf, P. (2019): BayMAP: A Bayesian hierarchical model for the analysis of PAR-CLIP data. Bioinformatics, 35(12), 1992-2000.
## Not run:
data(data_test)
res <- baymap(data = data_test,
inits.mu = c(0.05, 0.85, 0.2), n.iter = 4500)
data_new <- predict(res, data_test, burn.in = 3000)
## End(Not run)