Description Usage Arguments Value Examples
View source: R/prepareDataforGlm.R
Given two design matrices (one corresponding to a knockdown and one to a control well), create a concatenated design matrix with a column "Response" encoding for original membership. This unified data matrix is then divided into 90 variables containing NA/NaN are dropped.
1 2 | prepareDataforGlm(active, control, drop.feat = NULL, drop.sep = FALSE,
test = 10, verbose = FALSE)
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active |
Data coming from a well where a gene knockdown occurred. |
control |
Data belonging to a control well. |
drop.feat |
A vector of strings of column names that will be dropped. |
drop.sep |
Drop variables that separate data. |
test |
The fraction of rows to be used for testing is 1/test. If NULL is supplied, all data is used for training. |
verbose |
Whether to list all modified/dropped features |
A list with entries "test" and "train" each holding a data frame containing a design matrix and a response vector.
1 2 3 4 5 6 7 8 9 10 11 12 13 | # get gene locations
mtor.loc <- findWells(experiments="brucella-du-k1", contents="MTOR")
scra.loc <- findWells(plates=sapply(mtor.loc, getBarcode),
contents="SCRAMBLED", well.names="G23")
# combine for faster fetching
data <- getSingleCellData(list(mtor.loc[[1]], scra.loc[[1]]))
mtor.dat <- meltData(cleanData(data[[1]]$H6))
scra.dat <- meltData(cleanData(data[[1]]$G23))
# prepare data for glm
data <- prepareDataforGlm(mtor.dat$mat$Cells, scra.dat$mat$Cells)
data <- makeRankFull(data)
# run glm
model <- glm("Response ~ .", binomial, data$train)
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