Description Usage Arguments Value
DMS experiments analysis using PUlasso method
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protein_dat |
input data. A data table containing (sequence, labeled, unlabeled, seqId) |
py1 |
a numeric value representing the prevalence of positives in the unlabeled data |
order |
an integer; 1= main effects, 2= main effects + pairwise effects |
refstate |
a character which will be used for the common reference state; the default is to use the most frequent amino acid as the reference state for each of the position. |
verbose |
a logical value. The default is TRUE |
nobs_thresh |
the number of minimum required mutations per position |
lambda |
l1 penalty |
nlambda |
if lambda= NULL, a sequence of nlambda is created for fitting |
pvalue |
a logial value; if TRUE, p-values based on the asymptotic distribution are obtained |
n_eff_prop |
proportion of an effective sample size |
intercept |
a logical value; if TRUE, an estimated intercept is reported together with other coefficients |
maxit |
maximum number of iterations |
eps |
convergence threshold for the outer loop |
inner_eps |
convergence threshold for the inner loop |
initial_coef |
a vector representing an initial point where we start PUlasso algorithm from. |
p.adjust.method |
method for multiple comparison |
outfile |
NULL or a string; if a string is provided, an output with the name of the string will be exported in a working directory. |
nCores |
the number of threads for computing. |
exclude_gap |
a logical value. The default is TRUE. If TRUE, mutations corresponding to the gap (*) will not be considered for group p-value calculations |
a list containing a fit (from grpPUlasso), result_table (data.frame), and refstate
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