pipeline: Run the SeaFlow Pipeline
In flowPhyto: Methods for Continuous Flow Cytometry

run the pipeline

pipeline(cruise.name='', repo=REPO.PATH, range=NULL, steps=1:4, pct=.97,  
	clust.concat.ct=3, resample.size=300, resamp.concat.max=10,
	 filter.width=1.5, filter.notch=1, filter.edge=1, 
	 classify.func =2, classify.varnames=CHANNEL.CLMNS.SM, classify.numc=0, classify.noise=0,
	 map.margin=2, 
	concat.sds=!is.na(match(1,steps)), load.to.db=FALSE,  preplot=FALSE, cleanup=TRUE,
	input.path=paste(repo, '/', cruise.name, sep=''),
	output.path=input.path,  log.dir=output.path,
	def.path=paste(input.path,'/', 'pop.def.tab',sep=''), parallel=TRUE, submit.cmd='qsub')

`cruise.name`	Simplified cruise name (same name as the subdirectory in the seaflow data dir).
`steps`	Which steps of the pipeline to run. step 1 is filter, step 2 is classify, step 3 is census and consensus, step 4 is summarize. 1:2 will do step 1 to 2, etc.
`pct`	percentage completion (number of indicator files created vs input files) each job step should go to.
`clust.concat.ct`	Number of event file to concatenate at a time during the clustering/classification step.
`map.margin`	Margin in latitude/longitude around the map plots.
`resample.size`	Minimum number of events in a population.
`resamp.concat.max`	Maximum number of allowable event files to concatenate to generate statistics from.
`filter.notch`	the location of the x=y (by default) point to create the notch in the gated filter
`filter.width`	the margin of error for particle alignment determination in the filter step.
`filter.edge`	location of the boundary layer between water/air. Particles located at the boundary layer scatter light that can be detected by the position detectors for the filter step.
`classify.func`	Choose the clustering method, either flowClust (func = 1) or flowMeans (func = 2, by default) function
`classify.varnames`	A character vector specifying the variables (columns) to be included in clustering when choosing flowMeans.
`classify.numc`	Number of clusters when choosing flowMeans. If set to 0 (default) the value matches the number of populations defined in pop.def table . If set to NA, the optimal number of clusters will be estimated automatically.
`classify.noise`	Set up the noise threshold for phytoplankton cells. Only cells with chlorophyll value higher than the noise will be clustered
`concat.sds`	Determines if the sds files in the individual julian day directories should be concatenated together into sds.tab
`load.to.db`	Load the sds and stat files to the database.
`preplot`	Preplot the level 2 analysis plots to 'output.path'.
`cleanup`	Cleanup the submission and (non error reporting) R CMD BATCH log files.
`input.path`	Path to the directory with input data (raw evt or opp files.
`output.path`	Path to the directory where you wish to output data.
`log.dir`	Path to the directory where log file will be written.
`def.path`	Path to the file that defines how to gate & cluster the events into populations.
`parallel`	Boolean indicating if the job should be run in parallel using qsub (vs in serial)
`repo`	Full path to your SeaFlow repository
`range`	A named, two-integer vector specifying the start and end (inclusive) range for subsetting the input files used in each analysis step (with the exception of summarize). Values should be a (evt/opp) file numbers and names should be strings corresponding to the year_julianday directory names. The nv() function is useful for creating this vector.
`submit.cmd`	the command used to deploy an R CMD BATCH system call to a cluster. Must be used in conjunction with parallel=TRUE.

example.cruise.name <- 'seaflow_cruise'
temp.out.dir <- '.' #path.expand('~')
output.path <- paste(temp.out.dir,'/',example.cruise.name,sep='')
seaflow.path <- system.file("extdata", example.cruise.name, package="flowPhyto")

file.copy(from=seaflow.path, to=temp.out.dir, recursive=TRUE)

pipeline(repo= temp.out.dir, cruise.name='seaflow_cruise', steps=4, parallel=FALSE) 
unlink(example.cruise.name, recursive=TRUE)