Table of Contents
Sample Workflow
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R version: R version 3.5.0 Patched (2018-05-03 r74699)
Bioconductor version: 3.8
Package version: 1.3.0
Table of Contents
Installation and Use
Version Info
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The following psuedo-code illustrates a typical R / Bioconductor session. It makes use of the flow cytometry packages to load, transform and visualize the flow data and gate certain populations in the dataset.
The workflow loads the flowCore
, flowStats
and flowViz
packages and its
dependencies. It loads the ITN data with 15 samples, each of which includes,
in addition to FSC and SSC, 5 fluorescence channels: CD3, CD4, CD8, CD69 and
HLADR.
## Load packages
library(flowCore)
library(flowStats)
library(flowViz) # for flow data visualization
## Load data
data(ITN)
ITN
## A flowSet with 15 experiments.
##
## An object of class 'AnnotatedDataFrame'
## rowNames: sample01 sample02 ... sample15 (15 total)
## varLabels: GroupID SiteCode ... name (7 total)
## varMetadata: labelDescription
##
## column names:
## FSC SSC CD8 CD69 CD4 CD3 HLADr Time
First, we need to transform all the fluorescence channels. Using a workFlow
object can help to keep track of our progress.
## Create a workflow instance and transform data using asinh
wf <- workFlow(ITN)
## Warning: 'workFlow' is deprecated.
## Use 'flowWorkspace::GatingSet' instead.
## See help("Deprecated")
asinh <- arcsinhTransform()
tl <- transformList(colnames(ITN)[3:7], asinh,
transformationId = "asinh")
add(wf, tl)
Next we use the lymphGate
function to find the T-cells in the CD3/SSC
projection.
## Identify T-cells population
lg <- lymphGate(Data(wf[["asinh"]]), channels=c("SSC", "CD3"),
preselection="CD4", filterId="TCells", eval=FALSE,
scale=2.5)
## Warning in subsetKeywords(x, j): NAs introduced by coercion
## Warning in subsetKeywords(x, j): NAs introduced by coercion
## Warning in subsetKeywords(x, j): NAs introduced by coercion
add(wf, lg$n2gate, parent="asinh")
print(xyplot(SSC ~ CD3| PatientID, wf[["TCells+"]],
par.settings=list(gate=list(col="red",
fill="red", alpha=0.3))))
A typical workflow for flow cytometry data analysis in Bioconductor flow packages include data transformation, normalization, filtering, manual gating, semi-automatic gating and automatic clustering if desired. Details can be found in flowWorkFlow.pdf or the vignettes of the flow cytometry packages.
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Table of Contents
Exploring Package Content
Sample Workflow
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Follow installation instructions to start using these
packages. To install the flowCore
package and all of its
dependencies, evaluate the commands
## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("flowCore")
Package installation is required only once per R installation. View a full list of available packages.
To use the flowCore
package, evaluate the command
library("flowCore")
This instruction is required once in each R session.
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Table of Contents
Diverse Assays Resources
Installation and Use
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Packages have extensive help pages, and include vignettes highlighting common use cases. The help pages and vignettes are available from within R. After loading a package, use syntax like
help(package="flowCore")
?read.FCS
to obtain an overview of help on the flowCore
package, and the
read.FCS
function, and
browseVignettes(package="flowCore")
to view vignettes (providing a more comprehensive introduction to
package functionality) in the flowCore
package. Use
help.start()
to open a web page containing comprehensive help resources.
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Table of Contents
Exploring Package Content
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The following provide a brief overview of packages useful for analysis of high-throughput assays. More comprehensive workflows can be found in documentation (available from package descriptions) and in Bioconductor publications.
Table of Contents
Cell-based Assays
Diverse Assays Resources
These packages use standard FCS files, including infrastructure, utilities, visualization and semi-autogating methods for the analysis of flow cytometry data.
flowCore, flowViz, flowQ, flowStats, flowUtils, flowFP, flowTrans,
Algorithms for clustering flow cytometry data are found in these packages:
flowClust, flowMeans, flowMerge, SamSPECTRAL
A typical workflow using the packages flowCore
, flowViz
, flowQ
and
flowStats
is described in detail in flowWorkFlow.pdf.
The data files used in the workflow can be downloaded from
here.
Table of Contents
High-throughput qPCR Assays
Flow Cytometry
Diverse Assays Resources
These packages provide data structures and algorithms for cell-based high-throughput screens (HTS).
This package supports the xCELLigence system which contains a series of real-time cell analyzer (RTCA).
Table of Contents
Mass Spectrometry and Proteomics data
Cell-based Assays
Diverse Assays Resources
These package provide algorithm for the analysis of cycle threshold (Ct) from quantitative real-time PCR data.
Table of Contents
High-throughput qPCR Assays
Diverse Assays Resources
These packages provide framework for processing, visualization, and statistical analysis of mass spectral and proteomics data.
Table of Contents
Mass Spectrometry and Proteomics data
Diverse Assays Resources
These packages provide infrastructure for image-based phenotyping and automation of other image-related tasks:
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sessionInfo()
## R version 3.5.0 Patched (2018-05-03 r74699)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] splines stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] highthroughputassays_1.3.0 flowViz_1.45.0
## [3] lattice_0.20-35 flowStats_3.39.0
## [5] flowWorkspace_3.29.1 ncdfFlow_2.27.0
## [7] BH_1.66.0-1 RcppArmadillo_0.8.500.0
## [9] cluster_2.0.7-1 fda_2.4.7
## [11] Matrix_1.2-14 flowCore_1.47.2
## [13] BiocStyle_2.9.1
##
## loaded via a namespace (and not attached):
## [1] mclust_5.4 Rcpp_0.12.16 mvtnorm_1.0-7
## [4] corpcor_1.6.9 assertthat_0.2.0 rprojroot_1.3-2
## [7] digest_0.6.15 R6_2.2.2 plyr_1.8.4
## [10] backports_1.1.2 stats4_3.5.0 pcaPP_1.9-73
## [13] evaluate_0.10.1 BiocInstaller_1.31.1 pillar_1.2.2
## [16] zlibbioc_1.27.0 rlang_0.2.0 data.table_1.11.2
## [19] Rgraphviz_2.25.0 hexbin_1.27.2 rmarkdown_1.9
## [22] stringr_1.3.1 munsell_0.4.3 compiler_3.5.0
## [25] xfun_0.1 pkgconfig_2.0.1 BiocGenerics_0.27.0
## [28] IDPmisc_1.1.17 htmltools_0.3.6 tibble_1.4.2
## [31] gridExtra_2.3 bookdown_0.7 matrixStats_0.53.1
## [34] XML_3.98-1.11 rrcov_1.4-3 dplyr_0.7.4
## [37] MASS_7.3-50 grid_3.5.0 gtable_0.2.0
## [40] magrittr_1.5 scales_0.5.0 graph_1.59.0
## [43] KernSmooth_2.23-15 stringi_1.2.2 bindrcpp_0.2.2
## [46] latticeExtra_0.6-28 robustbase_0.93-0 RColorBrewer_1.1-2
## [49] tools_3.5.0 Biobase_2.41.0 glue_1.2.0
## [52] DEoptimR_1.0-8 ks_1.11.1 parallel_3.5.0
## [55] yaml_2.1.19 colorspace_1.3-2 knitr_1.20
## [58] bindr_0.1.1
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