https://github.com/saezlab/bradiplus
BraDiPluS (Braille Display Plugs Data Analysis) is an R package to process and analyse data produced using the microfluidic platform for functional drug screening of patient biopsies developed in collaboration with Merten group (EMBL).
https://github.com/saezlab/bradiplus
Last synced: 11 months ago
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BraDiPluS (Braille Display Plugs Data Analysis) is an R package to process and analyse data produced using the microfluidic platform for functional drug screening of patient biopsies developed in collaboration with Merten group (EMBL).
- Host: GitHub
- URL: https://github.com/saezlab/bradiplus
- Owner: saezlab
- License: gpl-3.0
- Created: 2014-11-11T16:15:08.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2018-07-12T13:00:02.000Z (about 8 years ago)
- Last Synced: 2025-08-10T06:38:32.786Z (12 months ago)
- Language: R
- Homepage:
- Size: 5.02 MB
- Stars: 2
- Watchers: 2
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog.txt
- License: LICENSE
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README
BraDiPluS
=========
BraDiPluS - Braille Display Plugs Data Analysis - is an R package developed to analyse the data of plugs produced using in-house combinatorial microfluidics platform and adquired using an in-house LabVIEW program (written by R. Utharala) allowing the detection of three channels:
* _blue_: sample barcodes
* _green_: Caspase-3 activity assay
* _orange_: marker dye to monitor mixing of reagents
For extended documentation of BraDiPluS please refer to https://saezlab.github.io/BraDiPluS/
## How to install it
Open R and, from the folder with the package, type:
```
install.packages('BraDiPluS',repos=NULL,type='src')
```
## Workflow example
Load the example data provided in the package
```
data("BxPC3_data", package="BraDiPluS")
data("sampleNames", package="BraDiPluS")
```
plot the data
```
plotData(data=MyData, channels=c("blue", "orange", "green"))
```
separate the different samples (separated by barcode)
```
res <- samplesSelection(data=MyData, BCchannel="blue",BCthr=0.01, BCminLength=100, distThr=16, plotMyData=F, barcodePos="before")
samples<-res$samples
names(samples)<-sampleNames
plotData(data=MyData, channels=c("blue", "orange", "green"), samples = samples)
```
select the peaks for each sample
```
samplesPeaks <- selectSamplesPeaks(samples, channel="green", metric="median", baseThr=0.01, minLength=350, discartPeaks="first", discartPeaksPerc=5)
```
remove outliers based on orange channel
```
runs<-list(run1=samplesPeaks)
runs.qa<-qualityAssessment(runs=runs)
```
look at the median sample values
```
allData<-do.call(cbind, lapply(runs.qa, function(myRun){
sapply(sapply(myRun, get, x="green"), median)
}))
```
compute z-score
```
allData_scale<-apply(allData,2,scale)
```
