pipeline: Run the SeaFlow Pipeline
In armbrustlab/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.origin=NA,
	 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.
`origin`	correction factor for the stream alignment. When stream is not properly aligned, aligned particles do not scatter light equally on D1 and D2 and must be corrected. By default (NA), the value of the origin is calculated as the difference between the median value of D2 with respect to the median value of D1
`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)