Description Usage Arguments Value Examples
Calculate differential expression scores, subsetting by plate.
1 2 3 4 5 6 7 8 | diffexp(x, treat, control = "auto", method = "cd",
split_by_plate = FALSE, where_clause = list(), gold = TRUE,
inferred = TRUE, verbose = FALSE, ...)
## S4 method for signature 'Slinky'
diffexp(x, treat, control = "auto", method = "cd",
split_by_plate = FALSE, where_clause = list(), gold = TRUE,
inferred = TRUE, verbose = FALSE, ...)
|
x |
An object of class Slinky |
treat |
A SummarizedExperiment containing the treated samples, or the pert_iname of desired perturbagen. See details. |
control |
A SummarizedExperiment containing the control samples, or the pert_iname of desired controls. Default is 'auto'. See details. |
method |
Scoring method to use. Only |
split_by_plate |
Should the analysis be split by plate? This is one way to control for batch effects, but requires at least two treated sample and two control samples on each plate in the dataset. Default is FALSE. Not supported for method = 'ks'. |
where_clause |
If treat is a pert_iname, further query
terms may be specified here (e.g. |
gold |
Restrict analysis to gold instances as defined by LINCS. Ignored if treat and control are SummarizedExperiments. |
inferred |
Should the inferred (non-landmark) genes be included in the analysis? Default is TRUE. |
verbose |
Do you want to know how things are going? Default is FALSE. |
... |
Additional arguments for |
Vectors of scores, one per subset (plate).
This function looks for rna_plate
in
colData(treat)
and colData(control)
to slice the data
into subsets, and then performs differential expression analysis on
the subsets. If a perturbation
identifier is provided instead of an SummarizedExperiment, the
necessary SummarizedExperiment is constructed by calling this
package's toSummarizedExperiment
function (which requires
that you have
initialized this class with appropriate clue.io key and location of
gctx file). Note that the control dataset can be automatically
generated by the default option of control=\"auto\"
. In this
case, appropriate same-plate controls are identified for the samples
in the treat dataset and loaded. For more complex queries, you can
create the requisite SummarizedExperiments yourself with
toSummarizedExperiment
, or
create a SummarizedExperiment by any other methods, ensuring that
treat
and control
contain the
rna_plate
metadata variable for subsetting.
Note that this function assumes that each plate represented in
treat
is also represented in control
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | #'
# 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')$user_key
sl <- Slinky(user_key,
system.file('extdata', 'demo.gctx',
package='slinky'),
system.file('extdata', 'demo_inst_info.txt',
package = 'slinky'))
scores <- diffexp(sl, sl[,1:5], sl[,18:22])
head(scores)
# for build/demo only. You MUST use your own key when using the slinky
# package.
## Not run:
user_key <- httr::content(httr::GET('https://api.clue.io/temp_api_key'),
as='parsed')$user_key
sl <- Slinky(user_key,
system.file('extdata', 'demo.gctx',
package='slinky'),
system.file('extdata', 'demo_inst_info.txt',
package = 'slinky'))
cd_vector <- diffexp(sl,
treat = "amoxicillin",
split_by_plate = FALSE,
verbose = FALSE)
## End(Not run)
|
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