controls {slinky} | R Documentation |
controls #' Fetch the same plate control samples applicable for given ids (distil_id). Expects that the specified ids have pert_type of trt_sh or trt_cp.
controls(x, ids, verbose = FALSE, cl = NULL) ## S4 method for signature 'Slinky' controls(x, ids, verbose = FALSE, cl = NULL)
x |
A slinky object |
ids |
The distil_id(s) to lookup. |
verbose |
Do you want to know how things are going? Default is FALSE. |
cl |
Optional cluster object to parallelize this
operation. If verbose is TRUE, use this pattern in order for
progress bar to update:
|
The name of the vehicle control for the queried perturbagen(s). \For a given set of distil_ids, this function finds the distil_ids for the corresponding control samples based on the the pert_type and (for trt_cp) the specified vehicle. The returned dataframe can be used, among other things, to create a control dataset for differential expression or other analysis. See also diffexp.
# for build/demo only. You MUST use your own key when using the slinky # package. user_key <- httr::content(httr::GET('https://api.clue.io/temp_api_key'), as='parsed')$user_key sl <- Slinky(user_key, system.file('extdata', 'demo.gctx', package='slinky'), system.file('extdata', 'demo_inst_info.txt', package = 'slinky')) amox_gold <- clueInstances(sl, where_clause = list("pert_type" = "trt_cp", "pert_iname" = "amoxicillin", "cell_id" = "MCF7", "is_gold" = TRUE), poscon = "omit") colnames(sl[,1:5]) rownames(sl[1:5,1:5]) ids.ctrl <- controls(sl, ids = amox_gold)$distil_id