View source: R/PipelineParts.R
makeTrainingAndPredictionData | R Documentation |
Step 5 of uORFome pipeline
makeTrainingAndPredictionData(
df.rfp,
df.rna,
organism = get("organism", mode = "character", envir = .GlobalEnv),
biomart = get("biomart_dataset", envir = .GlobalEnv),
mode = "uORF",
features = c("countRFP", "disengagementScores", "entropyRFP", "floss", "fpkmRFP",
"ioScore", "ORFScores", "RRS", "RSS", "startCodonCoverage", "startRegionCoverage",
"startRegionRelative"),
max.artificial.length,
requiredActiveCds = 30,
BPPARAM = bpparam()
)
df.rfp |
ORFik experiment of Ribo-seq |
df.rna |
ORFik experiment of RNA-seq, set to NULL if you don't have RNA-seq |
organism |
scientific name of organism, like Homo sapiens, Danio rerio, etc. |
biomart |
character or NULL, default: get("biomart_dataset", envir = .GlobalEnv) |
mode |
character, default: "uORF". alternative "aCDS". Do you want to predict on uORFs or artificial CDS. if "aCDS" will run twice once for whole length CDS and one for truncated CDS to validate model works for short ORFs. "CDS" is option to predict on whole CDS. |
features |
features to train model on, any of the features created
during ORFik::computeFeatures, default:
|
max.artificial.length |
integer, default: 100, only applies if mode = "aCDS", so ignore this for most people, when creating artificial ORFs from CDS, how large should maximum ORFs be, this number is 1/6 of maximum size of ORFs (max size 600 if artificialLength is 100) Will sample random size from 6 to that number, if max.artificial.length is 2, you can get artificial ORFs of size (6, 9 or 12) (6, + 6 + (3x1), 6 + (3x2)) |
requiredActiveCds |
numeric, default 30. How many CDSs are required to be detected active. Size of minimum positive training set. Will abort if not bigger than this number. |
BPPARAM |
An instance of a |
invisible(NULL)
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