Nothing
#==================================================
#==================================================
#==================================================
# Main Function
COUSCOus <- function( fasta.file,
verbose = TRUE )
{
#==================================================
#==================================================
# Step 1: Load alignment and set required variables
start <- proc.time()
if( verbose )
{
print( paste('Step 1: Loading fasta alignment file:', fasta.file ) )
}
data.aln <- read.fasta( fasta.file )
aln.seqs <- data.aln$ali
n.aa <- dim( aln.seqs )[ 2 ]
n <- dim( aln.seqs )[ 1 ]
p <- n.aa
#==================================================
#==================================================
# Step 2: Preprocess data
start <- proc.time()
if( verbose )
{
print( 'Step 2: Preprocess data' )
}
list.preprocessing <- preprocessing( aln.seqs )
S <- list.preprocessing$S
wtsum <- list.preprocessing$wtsum
pa.vec <- list.preprocessing$pa
#==================================================
#==================================================
# Step 3: Shrink sample covariance matrix S
start <- proc.time()
if( verbose )
{
print( 'Step 3: Shrink sample covariance matrix S' )
}
### WHY ARE WE MULTIPLYING n and p ALSO BY 21 FOR SHRINKAGE! ###
### TRY BOTH: (i) REGULAR n AND p FROM THE DATA ###
### (ii) n AND p TIMES 21 ###
S.shrinked <- shrink.S( S,
n,
p )
#==================================================
#==================================================
# Step 4: Generate (non-negative) regularisation matrix rho (similar to PSICOV)
start <- proc.time()
if( verbose )
{
print( 'Step 4: Generate regularisation matrix rho' )
}
rho <- generate.rho( wtsum,
pa.vec,
n.aa )
#==================================================
#==================================================
# Step 5: Calculate precision matrix
start <- proc.time()
if( verbose )
{
print( 'Step 5: Calculate precision matrix' )
}
P <- precision( S.shrinked,
rho )
#==================================================
# Step 6: Generate prediction data frame
start <- proc.time()
if( verbose )
{
print( 'Step 6: Generate prediction data frame' )
}
predictions <- prediction( P,
n.aa )
#==================================================
# Return predictions
return( predictions )
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.