Description Usage Arguments Value Author(s) Examples
A tool for identifying interesting genes in large pooled CRISPRi and CRISPRa screen using hierarchical mixture models
a hierarchical mixture model for analysing large-scale CRISPRi/a pooled screen
1 2 3 |
x |
log2 fold changes of guides targeting genes (required) |
geneIds |
gene ids corresponding to x (required) |
negCtrl |
log2 fold changes of negative control guides |
max_iter |
maximum number of iterations for EM algorithm, default = 100 |
tol |
tolerance for convergence of EM algorithm, default = 1e-10 |
pq |
initial value of p*q, default = 0.1 |
mu |
initial value of mu for the interesting genes, default = -4 |
sigma |
initial value of sigma for the interesting genes, default = 1 |
nMesh |
the number of points to use in numerical integration of posterior probabilities, default = 100 |
BIMODAL |
boolean variable to fit a bimodal alternative distribution for the case when both directions are of interest |
VERBOSE |
boolean variable for VERBOSE mode, default = FALSE |
PLOT |
boolean variable to produce plots, default = FALSE |
screenType |
acceptable options are "GOF" for gain of function screens (mu > 0) or "LOF" for loss of function screens, default = "LOF" |
a list containing genes, the corresponding posterior probabilities of being non-null, and the mixture fit
vector of gene names
posterior null probabilites of genes
posterior non-null probabilities of genes
estimated global FDR of genes
a list containing the estimated mixture fit
Timothy Daley, tdaley@stanford.edu
1 2 3 | Rosenbluh2017CRISPRi.sim.DESeq.log2fc.CRISPhieRmix =
CRISPhieRmix(x = Rosenbluh2017CRISPRiSim$x, geneIds = Rosenbluh2017CRISPRiSim$geneIds,
negCtrl = Rosenbluh2017CRISPRiSim$negCtrl, mu = -2, sigma = 0.5, nMesh = 200)
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