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Provides reports for quantitative mass spectrometry data.

Home Page: http://fgcz-bfabric.uzh.ch

R 98.96% Batchfile 0.39% TeX 0.65%

mass-spectrometry maxquant proteomics quantitative-data reproducible-research

srmservice's Introduction

protViz - Visualizing and Analyzing Mass Spectrometry Related Data in Proteomics

Documentation

The package ships with two pdf vignettes.

browseVignettes('protViz')

vignette('protViz')
vignette('PTM_MarkerFinder')

Installation

CRAN

install.packages('protViz')

from github

install the latest development version

install.packages('devtools')
library(devtools)
install_git('https://github.com/cpanse/protViz', build_vignettes = FALSE, quiet = FALSE)
library(protViz)

BiocManager::install('cpanse/protViz')

R CMD build hints

Rcpp::compileAttributes()

tools::package_native_routine_registration_skeleton(".", character_only = FALSE)


RcppExport SEXP _rcpp_module_boot_MyModule();

static const R_CallMethodDef CallEntries[] = {
    {"_rcpp_module_boot_MyModule", (DL_FUNC) &_rcpp_module_boot_MyModule, 0},
    {NULL, NULL, 0}
};

RcppExport void R_init_minModuleEx(DllInfo *dll) {
    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
    R_useDynamicSymbols(dll, FALSE);
}

Docker

docker pull cpanse/protviz \
&& docker run -d -p 8792:8787 cpanse/protviz

connect to http://yourdockerhost:8791 using a web browser

username: rstudio
password: rstudio

Documentation

The package ships with a package vignette (browseVignettes('protViz') and a reference manual (just type ?protViz on the R shell).

Both documents are also available on the package's CRAN page.

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lucaskook cpanse

srmservice's Issues

generic 2grp

Hei Witold,

Wir hatten doch mals die RMD_generic_2grp??

Die sind bei mir aus SRMService verschwunden??

gruess jonas

Compare sample intensities: Plot 5 number summaries to asses sample distribution

Code sample:

Use line plots and group them according explanatory variables.

untransSummaries<-longFormat %>% group_by(Filename, Condition, Donor) %>% 
  summarise(median  = median(FG.Quantity), mean = mean(FG.Quantity), Q3 = quantile(FG.Quantity, 0.75 ) )
untransSummaries <- melt(untransSummaries)

ggplot(untransSummaries, aes(Condition, value, group=variable)) + geom_line(stat = "identity") + facet_grid( ~ Donor  ) + geom_line(aes(color=variable))  + theme(axis.text.x= element_text(angle=-90))

Filtering the matrix (Number of NAs, Peptides per proteins)

Filtering the matrix is not working on my end..
Not with peptides per protein
Not with number of NAs

Experiment is called

The experiment is always called: "First Experiment"

Data processing summaries : How many transitions, proteins, peptides, precursors at each filtering step.

summarizeCounts <- function(longNoDecoy){
  precursor <- unique(select(longNoDecoy,EG.PrecursorId, EG.StrippedSequence, EG.ModifiedSequence, FG.Charge))
  peptide <- unique(select(longNoDecoy, EG.StrippedSequence))
  list(nrprecursor = nrow(precursor), nrpeptide = nrow(peptide) )
}

Export clustering results visualized in heatmap

Export protein clusters based on clustering as visualized in Grp2Analysis.pdf (vignettes/Grp2Analysis.Rmd).

The matrix returned by getResultTableWithPseudo() should contain an additional column with the protein cluster ids.

Preferred solution would be that after initializting a Grp2Analysis object you can call a function
grp2$getProteinClusterIDs(nrOfCluster = 5)
which returns a data.frame with 2 columns first proteinID second clusterID

When rendering the Grp2Analysis.Rmd the exacly same clustering is generated and visualized.

Possible solutions:
a) Cluster and have a heatmap function where you can pass a hclust object for visualization (preffered).
b) parametrize heatmap in such a way that it generates the same clustering as the Grp2Analysis Class.
c) call heatmap which return a hclust object and store it in Grp2Analysis.

Ad a) Look for heatmap functions. But it needs to have a nice color scheme and RowSideColumn and ColSideColumn annotation and ideally also option to annotate clusters.

TODO
Fork and clone
https://github.com/protviz/SRMService
install in R
Fork and clone
https://github.com/protviz/quantable
install in R

implement in quantable a heatmap wrapper.

reference class in : Grp2Analysis.R
/data SRMService::mqQuantMatrixGRP2

Building markdown with references to images and tables:
rmarkdown::render("Grp2Analysis.Rmd", params=list(grp = SRMService::mqQuantMatrixGRP2), bookdown::pdf_document2())

Package fails to build when Grp2AnalysisHeatmap3.Rmd in vignette folder.

Please check why package build works when
Grp2AnalysisHeatmap3.Rmd is renamed to Grp2AnalysisHeatmap3.Rmd_tmp

C:\Users\wolski\prog>R CMD build SRMService
* checking for file 'SRMService/DESCRIPTION' ... OK
* preparing 'SRMService':
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... OK
* checking for LF line-endings in source and make files and shell scripts
* checking for empty or unneeded directories
Removed empty directory 'SRMService/inst/exec'
* looking to see if a 'data/datalist' file should be added
* building 'SRMService_0.1.9.23.tar.gz'

However it fails when Grp2AnalysisHeatmap3.Rmd present in vignette folder:

C:\Users\wolski\prog>R CMD build SRMService
* checking for file 'SRMService/DESCRIPTION' ... OK
* preparing 'SRMService':
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... ERROR
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
No id variables; using all as measure variables
Warning: Removed 821 rows containing non-finite values (stat_ydensity).
Warning: Removed 821 rows containing non-finite values (stat_summary).
No id variables; using all as measure variables
Warning: Removed 821 rows containing non-finite values (stat_ydensity).
Warning: Removed 821 rows containing non-finite values (stat_summary).
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
No id variables; using all as measure variables
Warning: Removed 821 rows containing non-finite values (stat_ydensity).
Warning: Removed 821 rows containing non-finite values (stat_summary).
No id variables; using all as measure variables
Warning: Removed 821 rows containing non-finite values (stat_ydensity).
Warning: Removed 821 rows containing non-finite values (stat_summary).
Quitting from lines 66-70 (./Grp2Analysis.Rmd)
Quitting from lines 3-28 (./Grp2Analysis.Rmd)
Error: processing vignette 'Grp2PullDownExample.Rmd' failed with diagnostics:
attempt to apply non-function
Execution halted

retrieve confidence intervals from limma

See Gordon Smyths comment:
https://support.bioconductor.org/p/37524/

Scatter plot within condition

on all level - Precursor, protein.
on which condition - mayor condition

use quantable::mypairsSmooth

ftable(xtabs( ~ Time + Treatment, data= annotation))
ftable(xtabs( ~ Strain + Time + Treatment + Plant, data= annotation))

examine bookdown for referencing figures and tables

We need referencing of figures and tables in reports. bookdown was released recently and we should use it.

protviz / srmservice Goto Github PK

srmservice's Introduction

protViz - Visualizing and Analyzing Mass Spectrometry Related Data in Proteomics

Documentation

Installation

CRAN

from github

R CMD build hints

Docker

Documentation

Related approaches

srmservice's People

Stargazers

Watchers

Forkers

srmservice's Issues

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