README.md
# ncdfFlow: A package that provides HDF5 based storage for cytometry data.This package extends the `flowCore` infrastructure by storing the large volume of event-level data on disk as `HDF` format and only keeps the file handler and meta data in memory. Thus the memory consumption is significantly reduced.### INSTALLATION```r# First, install it from bionconductor so that it will pull all the dependent packages automaticallylibrary(BiocInstalller)bicLite(ncdfFlow) # or install the latest version from github using devtools package install.packages("devtools") library(devtools) #load itinstall_github("RGLab/ncdfFlow", ref="trunk")```### Unix/Linux/Mac usersTo build the ncdfFlow package from source, make sure that HDF5 Library is present on your system:If HDF5 is installed to some non-standard location, you may pass the environment variable --with-hdf5 to point to the correct location of HDF5, for example,```bash#install from githubinstall_github('RGLab/ncdfFlow', ref='trunk', args='--configure-args="--with-hdf5=<path-to-hdf>"') #or install from locally downloaded tar ballR CMD INSTALL ncdfFlow_x.y.z.tar.gz --configure-args="--with-hdf5='<path-to-hdf>'"```under '/path/to', you should find "include" and "lib" sub-folders that contain HDF5 headers and shared libraries. Also, make sure add the path of `libhdf5.so` (should be `lib` subfolder of `<path-to-hdf>`) to your environment variable `LD_LIBRARY_PATH` so that it can be found at runtime.```bashexport LD_LIBRARY_PATH=<path-to-hdf>/lib:LD_LIBRARY_PATH```### Create `ncdfFlowSet` object```rlibrary(ncdfFlow)#read from FCS filespath <- system.file("extdata","compdata","data",package="flowCore")files <- list.files(path,full.names=TRUE)[1:3]fs <- read.ncdfFlowSet(files=files) #equivalent to flowCore::read.flowSet#or convert the existing flowSet into ncdfFlowSetdata(GvHD)fs <- GvHD[1:4]fs <- ncdfFlowSet(fs)fs``````## An ncdfFlowSet with 4 samples.## NCDF file : /tmp/RtmpENFQmY/ncfs85d52869372e.nc ## An object of class 'AnnotatedDataFrame'## rowNames: s5a01 s5a02 s5a03 s5a04## varLabels: Patient Visit ... name (5 total)## varMetadata: labelDescription## ## column names:## FSC-H, SSC-H, FL1-H, FL2-H, FL3-H, FL2-A, FL4-H, Time```### Use it as the same way as `flowSet` (except it is memory efficient and fast)```rpData(fs)``````## Patient Visit Days Grade name## s5a01 5 1 -6 3 s5a01## s5a02 5 2 0 3 s5a02## s5a03 5 3 6 3 s5a03## s5a04 5 4 12 3 s5a04``````rsampleNames(fs)``````## [1] "s5a01" "s5a02" "s5a03" "s5a04"``````rkeyword(fs,"FILENAME")``````## FILENAME## s5a01 "s5a01" ## s5a02 "s5a02" ## s5a03 "s5a03" ## s5a04 "s5a04"``````rcolnames(fs)``````## [1] "FSC-H" "SSC-H" "FL1-H" "FL2-H" "FL3-H" "FL2-A" "FL4-H" "Time"``````rlength(fs)``````## [1] 4``````rfs[[1]]``````## flowFrame object 's5a01'## with 3420 cells and 8 observables:## name desc range minRange maxRange## $P1 FSC-H FSC-Height 1024 0 1023## $P2 SSC-H SSC-Height 1024 0 1023## $P3 FL1-H CD15 FITC 1024 1 10000## $P4 FL2-H CD45 PE 1024 1 10000## $P5 FL3-H CD14 PerCP 1024 1 10000## $P6 FL2-A <NA> 1024 0 1023## $P7 FL4-H CD33 APC 1024 1 10000## $P8 Time Time (51.20 sec.) 1024 0 1023## 153 keywords are stored in the 'description' slot``````rfs[2:3]``````## An ncdfFlowSet with 2 samples.## NCDF file : /tmp/RtmpENFQmY/ncfs85d52869372e.nc ## An object of class 'AnnotatedDataFrame'## rowNames: s5a02 s5a03## varLabels: Patient Visit ... name (5 total)## varMetadata: labelDescription## ## column names:## FSC-H, SSC-H, FL1-H, FL2-H, FL3-H, FL2-A, FL4-H, Time```
