Highlights of R-based Flow Cytometry Tools and FlowCAP

New Core R Software Infrastrucutre for Flow Cytometry

• ncdfFlow: NetCDF, high-performace, disk-based access to large flow data sets.
• flowWorkspace: FlowJo workspace support. Import and reproduce FlowJo manual gating from wsp and xml files.
• OpenCyto: Template-based, data-driven, automated hierarchical gating.

FlowCAP III - 2012

• Automated gating of standardized Lyoplate-based flow cytometry data.

R provides a suite of free, open-source tools for flow cyotometry data analysis.

• From storage, preprocessing, transformation, compensation, and gating, to downstream analysis.

Gating and Clustering

OpenCyto
flowClust
flowMerge
flowMeans
SamSpectral
FLAME
flowPhyto
flowFP
flowPeaks
flowKoh
NMF-curvHDR
SPADE
PRAMS

Discovery and Downstream Analysis

flowType
RchyOptimyx
MIMOSA

Infrastructure, Preprocessing and Visualization

flowWorkspace
ncdfFlow
flowCore
flowUtils
flowTrans
flowStats
plateCore
flowViz
flowQ
QUALIFIER

NetCDF-based storage of large flow cytometry data sets.

http://www.github.com/RGLab/ncdfFlow (Bioconductor)

• Data remains on disk (e.g. network drive) - accessed as if in memory - small RAM footprint.
• Handles large studies (1000's of FCS files).
  • e.g. 34 FCS files from one lyoplate panel from nine sites.

f <- list.files(path="./Data/T-cell FCS files/",pattern="fcs",recursive=TRUE,full=TRUE)
dat<-read.ncdfFlowSet(f,ncdfFile="./myncfile")

http://www.github.com/RGLab/flowWorkspace (Bioconductor)
Reproduce FlowJo gating in R from an exported workspace.

ws<-openWorkspace("./Data/Centralized T-cell.xml");
G<-parseWorkspace(ws);
plotGate(G[[1]],"24hi 38hi"); #Plot transitional gate
plot(G[[1]]);           #Plot gating hierarchy

http://www.github.com/RGLab/openCyto

Integrates flowWorkspace infrastructure with automated gating tools (Bayesian flowClust, flowCore, and others)

• Modular framework: plug-in your own gating algorithms
• High-level automated gating
  • User defines hierarchy of cell populations and relevant markers
  • Gating is data-driven. (User doesn't define gates just cell populations)
  • Higher-dimensional gating (e.g. >2D) is available.
    Framework abstracts away most of the R-coding.

Example CSV Gating Template Definition (Lyoplate B-cell Panel)

R Code to Run the Gating

template<-gatingTemplate("bcellTemplate.csv")
fs<-readFlowSet(file="Data/Bcells/")
gs<-GatingSet(fs)
G<-gating(template,gs)

Three-year old series of workshops for benchmarking automated gating methods vs. manual gating

Identify Gating Methods with low variability and bias relative to centralized manual gating

• FlowCAP focused on the T-cell and B-cell panels.
• 9 sites, 4 replicates of cryopreserved cells per site.

In clinical trials, the things we want to measure are well defined a-priori.

• Flow assays are well defined.
• Cell populations of interest are well defined.
• No immediate need to go fishing with high-dimensional gating for "discovery".

Generally large data sets.

• Gating is tedious and subject to human error (this has been shown).
• Automate the repetitive tasks.
  • Robust
  • Reproducible

DENSE ( A. Brandes, Broad Institute )

flowDensity ( J. Taghiyar, BC Cancer Agency )

OpenCyto ( J. Ramey, FHCRC )

emcytom ( K. Wang, University of Queensland )

FLOCK ( R. Stanton, JCVI )

Centralized Gating ( Current best practice )

Assess automated methods relative to central manual gating.

• Variability
  • Coefficients of variation across centers
• Bias: \(RMSD_{gpc} = \sqrt{\frac{\sum(y_{gcpr}-\mu_{mpc})}{R}}\)
• Mixed Effects Model: \(y_{gpcr}=\mu+\phi_{p}+\color{red}{\gamma_{g}}+\color{red}{\phi\gamma_{pg}}+(\phi\chi)_{pc}+\epsilon_{gpcr}\)
  • Fixed gating and cell population effects.
  • Random center \(\times\) cell population effects.
  • Interested in interaction and contrasts of fixed effects. (\(\gamma_g + \phi\gamma_{pg} -\gamma_0 - \phi\gamma_{p0} = 0\))
    An ideal automated gating method will have low bias and low variability for each population.

Cross center variability of automated gating methods is comparable to centralized gating.

At least one method per panel matches the variability of centralized gating for all populations.

There are automated gating algorithms that are sufficiently robust to be useful for data analysis today.

• DENSE (Broad Institute), flowDensity (BCCA), OpenCyto Framework (FHCRC)

A wealth of FREE open-source flow tools are available for R.

• OpenCyto framework emphasizing ease of use.
• Handling real-world data sets (large studies)
• Access manually gated FlowJo data in R.
  • (support for Mac, Windows, version X and older)

There is now little reason not to start exploring your flow data in R.

Get these slides online: http://www.github.com/gfinak/Talks/RFlowToolsFlowCAP