search for: metabolit

Dear friends - We have 25 rats, 14 of these subjected to partial removal of kidney tissue, 11 to sham operation, and then followed for 6 weeks. So far we have data on 26 urine metabolites measured by NMR 7 times during the observation. I have smoothed the measurements by b.splines in fda including a roughness penalty, and inspecting the mean curves for nephrectomized and sham animals indicate differences for several of the metabolites. Now the real idea is to use the NMR measu...

...details of which aren't really important) to structure a vector of rate equation which will be passed into an ODE function and solved with associated functions from 'deSolve.' Roughly what this entails is scanning through a very large stoichiometric matrix containing integers which map metabolites to reactions and then assembling a series of monomials that describe the rates of the reactions. I'm running into trouble because R does not like doing numerical operations on characters, but I need the rate to be in symbolic form. Below I've described an example. The input is a matrix...

Dear R users,   I am new user for elastic net. I am trying to use elasticnet library. I have marker data with 359 markers and 168 samples, and response is metabolites. I am trying to do regression between a metabolite and markers.  But i am getting the following error:   > en<-enet(marker,as.numeric(vio),lambda=0.5,normalize=FALSE,intercept=TRUE) Error in one %*% x : requires numeric matrix/vector arguments Then, I convert marker into numeric by using t...

...patients (5 in GroupA, 7 in GroupB) 3 short acting drugs, (I'm not concerned with residual effects from the previous test effecting the current test) Each patient is given all 8 possible combinations of the 3 drugs (8 measurements from each subject = repeated measures) The dependent effects are metabolite levels in the blood, a continuous variable but often skewed towards 0.1 which is the minimum detectable level. I'm look for individual and combined effects of Group and Drug. There are ~400 metabolites which I intend to test independently. (I know that will leave me with a multiple testing iss...

Hello, I have this data: Time AMP 0 0.2000000 10 0.1958350 20 0.2914560 40 0.6763628 60 0.8494534 90 0.9874526 120 1.0477692 where AMP is the concentration of this metabolite with time. If you plot the data, you can see that it could be fitted using a logistic regression. For this purpose, I used this code: AMP.nls <- nls(AMP~SSlogis(Time,Asym, xmid, scal), data = concentrations,model=T) When plotting the fitted function, it seems that it fits quite well at the en...

...crates.com). Best regards, David ################## Biostatistician (m/f) Job Description BIOCRATES Life Sciences AG is a leading metabolomics company using a mass-spectrometry based technology platform. Its products and services extract, deliver, and present rich information residing in metabolite networks with unprecedented speed, low cost, and high reliability. Biocrates? quantitative approach enables immediate identification of metabolites, measurement of their absolute concentrations, and mapping to their respective pathways. We are looking for a PhD holder in Bioinformatics with a min...

...ary team conducting studies to discover and developdiagnostic biomarkers as well as biomarkers for the characterization ofdisease models and drug effects ? including side effects. Essential functions * Analyzing omics data and integration of Metabolomics with other omics data-selection of likely metabolite, protein and RNA targets for quantification. * Providing software support for team members. * Contributing to presentations, patent applications, scientific publications and reporting on project funding. Requirements * Candidate can work independently and is highly motivated, creative and result...

Hi all, I'm new to PCA in R, so this might be a basical thing, but I cannot find anything on the net about it. I need to make a PCA plot with two response variables (df$resp1 and df$resp2) against eight metabolites (df$met1, df$met2, ...) and I don't have a clue how to do... and I've only used the simplest PCAs before, like this: pcaObj=prcomp(t(df[idx, c(40:47)])) biplot(pcaObj) Anyone who knows how to do? Best rageds, Joel _____________________________________________...

...uals 80 0.083 0.0010 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 So far so good. (Although I do wonder whether I should be using a within subjects predictor variable (e.g. via car::Anova).) However, I also want to test whether genotype affects the concentrations of metabolites, e.g. glycerol, in the growth medium at a given population density. I envisage testing this by one of 2 methods, neither of which I am confident is correct, despite digging aound in various fora and statistics texts. Method 1. Loess splines I could fit a loess curve to glycerol~od, (the date...

Dear All This is more of a statistics question than a question about help for R, so forgive me. I am using lda from the MASS package to perform linear discriminant function analysis. I have 14 cases belonging to two groups and have measured each of 37 variables. I want to find those variables that best discriminate between the two groups, and I want to visualise that and create a

Dear R-list! I am using the e1071 package in R to solve a binary classification problem in a dataset of round 180 predictor variables (blood metabolites) of two groups of subjects (patients and healthy controls). I am confused regarding the correct way to assess the classification accuracy of the trained svm. (A) The svm command allows to specificy via the 'cross=k' parameter to specify a k-fold crossvalidation. This results in k values f...

...now an intricate part of the drug development process as it will allow for more accurate identification of patients who will benefit from those therapies. The blood tissue has generated great interest as a source of new biomarkers because it is easily accessible and also it caries proteins and metabolites that reflect the physiological status of the whole body. However, blood is a deceivingly complex tissue, composed of many different cell types; all interacting with their environment. System biology is a new methodological approach to understand blood-drug interactions. However, the capture of...

Dear Rich, It depends how the data is generated. Although I am not an expert in ecology, I can explain it based on a biomedical example. Certain variables are generated geometrically (exponentially), e.g. MIC or Titer. MIC = Minimum Inhibitory Concentration for bacterial resistance Titer = dilution which still has an effect, e.g. serially diluting blood samples; Obviously, diluting the

Hi Petr and Richard; Thanks for your responses and supports. I just faced a different problem. I have the following R codes and work well. p <- ggplot(a, aes(x=Phenotypes, y=Metabolites, size=abs(Beta), colour=factor(sign(Beta)))) + theme(axis.text=element_text(size = 5)) p1<-p+geom_point() p2<-p1+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(), axis.ticks = element_blank()) p3<-...

...heza.cz>; r-help mailing list <r-help at r-project.org> Subject: Re: [R] plotting the regression coefficients Hi Petr and Richard; Thanks for your responses and supports. I just faced a different problem. I have the following R codes and work well. p <- ggplot(a, aes(x=Phenotypes, y=Metabolites, size=abs(Beta), colour=factor(sign(Beta)))) + theme(axis.text=element_text(size = 5)) p1<-p+geom_point() p2<-p1+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(), axis.ticks = element_blank()) p3<-...

Petr, there was a thinko in your response. tmp <- data.frame(m=factor(letters[1:4]), n=1:4) tmp tmp$m <- factor(tmp$m, levels=c("c","b","a","d")) ## right tmp[order(tmp$m),] tmp <- data.frame(m=factor(letters[1:4]), n=1:4) levels(tmp$m) <- c("c","b","a","d") ## wrong tmp[order(tmp$m),] changing levels

Hi After melt you can change levels of your factor variable. Again with the toy example. > levels(temp$variable) [1] "y1" "y2" "y3" "y4" > levels(temp$variable) <- levels(temp$variable)[c(2,4,1,3)] > levels(temp$variable) [1] "y2" "y4" "y1" "y3" > And you will get graphs with this new levels ordering.