R Language with statistics

Search algorithms, post-searching processing, quantitation software, etc. Share and discuss software here.
Biomarker
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R Language with statistics

Postby Biomarker » Tue Oct 30, 2012 7:15 am

Hi All

Can anybody help me in learning R language? You can consider my knowledge in this area is zero. So i am looking for simplest form of interface and where do i can learn as well as solve some tutorials.

Biomarker

:)

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Doug
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Postby Doug » Tue Oct 30, 2012 9:19 am

Hi Biomarker,

R is very powerful and useful but also can be very frustrating. I dont have advice on where to start learning. But I highly recommend you download Rstudio. Once you have downloaded it, open it and go to File -> New -> Rscript. Enter any commands you want to run in there. And if you want to execute them simply highlight what you want to execute and and hit command + return (on Mac, probably something similar on Windows). You can also save these scripts and open them later. This will save you a lot of frustration when starting out.

Hopefully that helps get you started. If you have more specific questions please post them and I will try to answer if I can.

Doug

Biomarker
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Hi

Postby Biomarker » Tue Oct 30, 2012 12:03 pm

Hi Doug,

Thanks a lot for your swift reply. Surely I will ask you questions, since I am going to start using it.


Cheers,
Biomarker
:)

achimt
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Postby achimt » Wed Oct 31, 2012 1:24 am

Hi Biomarker,

a nice page with a list of R-based tools to look at mass spectrometry and proteomics data is here:
http://strimmerlab.org/notes/mass-spectrometry.html

Best wishes,
Achim

aky
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Postby aky » Wed Oct 31, 2012 5:44 am

Hi,

Quick-R blog is a very good place to start. The Author has also written a book on R which is also very easy to follow.

Biomarker
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Postby Biomarker » Wed Oct 31, 2012 7:09 am

Hi Achim and Aky,

Thanks !!

woa
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Postby woa » Tue Dec 11, 2012 11:27 am

I strongly recommend the book by Norman Matloff, "The Art of R Programming" if you want to learn about the structure of the R language, syntax etc. and not just the statistical recipes.

http://www.amazon.com/The-Art-Programming-Statistical-Software/dp/1593273843/

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BioscopeGroup
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Postby BioscopeGroup » Sun Jan 20, 2013 7:16 am

Dear coleagues, we are organizing a course called Course on R software appplied to Mass Spectrometry-based biosciences: Data Preprocessing and Analysis (from clustering to classification). You can see al the information here: http://www.bioscopegroup.org/massspectrometry2013/

For more information do not hesitate contacting hlfernandez@sing.ei.uvigo.es or jlcapelom@gmail.com

Please, find bellow the course contents:

Hands on Course

I. Use of nano particles in proteomics
II. Profiling of Human urine using MALDI-MASS spectrometry.

Course Theory

III. Preprocessing and analysis of mass-spectrometry data.
  • Open Source Tools for MS aata Analysis.
  • Loading and managing MS data with R.
  • MS data preprocessing (intensity transformation; baseline correction; smoothing; peak detection; peak alignment)
  • MS data analysis (classification; clustering)


IV. Practical application with real datasets.

With best regards,

The [color="#0000CD"]Bioscope Group[/color].

ChiLee
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Postby ChiLee » Mon Apr 01, 2013 2:20 pm

Hey Biomarker,

Just a tip but occasionally, I've had issues downloading packages (which contain functions made by other people). So a fail-safe way to get around this to download the packages manually e.g. from a public repository like CRAN or Bioconductor or in some cases from website of the authors and then run the following command:

R CMD INSTALL NameOfPackage.tar.gz

And make sure you download dependancies (a package might rely on another package to work but these are clearly stated so not to worry).

Biomarker
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Postby Biomarker » Fri Apr 12, 2013 3:57 am

Hi,

I am new to R. I would like to do quantile normalization of raw abundance. So in text file, i have list of protein names in one column and control, case average raw abundance values in other columns. Could anybody give me any suggestions?

Thanks in advance...

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Doug
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Postby Doug » Fri Apr 12, 2013 7:39 am

Hi Biomarker,

I have never done quantile normalization but it looks like this can be done using the preprocessCore library from bioconductor. Try the code below. It installs the necessary package and then applies the normalization to two vectors of simulated data. It also plots the data before and after normalization. I can't promise that this is doing the right thing as I just found the package and wrote this code about 5 minutes ago. But it looks promising.


# install the preprocessCore library from bioconductor
source("http://bioconductor.org/biocLite.R")
biocLite()
biocLite('preprocessCore')

# load the preprocessCore library
library('preprocessCore')

# make to fake data set
a = rnorm(100)
b = rnorm(100)*10

# look at the data
plot(density(b),col="dodgerblue2")
lines(density(a),col="firebrick2",lty=2)

# bind the two vectors together
ab = cbind(a,b)

# quantile normalize
c = normalize.quantiles(ab)

# plot the normalized data
plot(density(c[,1]),col="dodgerblue2")
lines(density(c[,2]),col="firebrick2",lty=2)
"If we knew what we were doing it wouldn't be research." -AE

Biomarker
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Quantile normalization

Postby Biomarker » Fri Apr 12, 2013 12:42 pm

Hi Doug,

Thanks for your prompt reply. I will try the mentioned code and will update about the same...

Biomarker....
:)

Doug wrote:Hi Biomarker,

I have never done quantile normalization but it looks like this can be done using the preprocessCore library from bioconductor. Try the code below. It installs the necessary package and then applies the normalization to two vectors of simulated data. It also plots the data before and after normalization. I can't promise that this is doing the right thing as I just found the package and wrote this code about 5 minutes ago. But it looks promising.


# install the preprocessCore library from bioconductor
source("http://bioconductor.org/biocLite.R")
biocLite()
biocLite('preprocessCore')

# load the preprocessCore library
library('preprocessCore')

# make to fake data set
a = rnorm(100)
b = rnorm(100)*10

# look at the data
plot(density(b),col="dodgerblue2")
lines(density(a),col="firebrick2",lty=2)

# bind the two vectors together
ab = cbind(a,b)

# quantile normalize
c = normalize.quantiles(ab

# plot the normalized data
plot(density(c[,1]),col="dodgerblue2")
lines(density(c[,2]),col="firebrick2",lty=2)

ChiLee
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Postby ChiLee » Sun Apr 14, 2013 6:36 am

# Minor edit to Doug's code

c = normalize.quantiles(ab

# Missing bracket at the end
c = normalize.quantiles(ab)

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Doug
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Postby Doug » Sun Apr 14, 2013 10:08 am

ChiLee wrote:# Minor edit to Doug's code

c = normalize.quantiles(ab

# Missing bracket at the end
c = normalize.quantiles(ab)


Good catch. I corrected it in my original post.
"If we knew what we were doing it wouldn't be research." -AE


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