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title: Using Bioconductor with High Throughput Assays
toc_depth: 3
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Bioconductor includes packages for analysis of diverse areas of
high-throughput assays such as flow cytometry, quantitative real-time PCR,
mass spectrometry, proteomics and other cell-based data.
# Sample Workflow
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
```{r echo=FALSE, results="hide", warning=FALSE}
## Load packages
library(flowViz) # for flow data visualization
## Load data
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)
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
## Identify T-cells population
lg <- lymphGate(Data(wf[["asinh"]]), channels=c("SSC", "CD3"),
preselection="CD4", filterId="TCells", eval=FALSE,
add(wf, lg$n2gate, parent="asinh")
print(xyplot(SSC ~ CD3| PatientID, wf[["TCells+"]],
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](flowWorkFlow.pdf) or the vignettes of the
[flow cytometry packages](#diverse-assays-resources).
# Installation and Use
Follow [installation instructions](/install/) to start using these
packages. To install the `flowCore` package and all of its
dependencies, evaluate the commands
```{r eval=FALSE}
## try http:// if https:// URLs are not supported
Package installation is required only once per R installation. View a
full list of
[available packages](/packages/release/bioc/).
To use the `flowCore` package, evaluate the command
```{r eval=FALSE}
This instruction is required once in each R session.
# Exploring Package Content
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
to obtain an overview of help on the `flowCore` package, and the
`read.FCS` function, and
```{r eval=FALSE}
to view vignettes (providing a more comprehensive introduction to
package functionality) in the `flowCore` package. Use
```{r eval=FALSE}
to open a web page containing comprehensive help resources.
# Diverse Assays Resources
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
and in Bioconductor [publications](/help/publications/).
## Flow Cytometry ##
These packages use standard FCS files, including infrastructure,
utilities, visualization and semi-autogating methods for the
analysis of flow cytometry data.
Algorithms for clustering flow cytometry data are found in these packages:
A typical workflow using the packages `flowCore`, `flowViz`, `flowQ` and
`flowStats` is described in detail in [flowWorkFlow.pdf](flowWorkFlow.pdf).
The data files used in the workflow can be downloaded from
## Cell-based Assays ##
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).
## High-throughput qPCR Assays ##
These package provide algorithm for the analysis of cycle threshold
(Ct) from quantitative real-time PCR data.
## Mass Spectrometry and Proteomics data ##
These packages provide framework for processing, visualization, and
statistical analysis of mass spectral and proteomics data.
## Imaging Based Assays ##
These packages provide infrastructure for image-based phenotyping and automation of other image-related tasks: