similar to: Re: [BioC] normalisation for universal reference in 2 channel arrays

Dear All, I would like to ask for help on how to read different files automatically and do analysis using scripts. 1. Description of the data 1.1. there are 5 text files, each of which contains cleaned data for the same 100 SNPs. Observations (e.g., position on gnome, alelle type, ...) for SNPs are rows ordered by the SNP numbers, 1.2. there are 1 text file, containing the expression level of

Hi all, since the migration of our old Win2k domain to Samba4 some years ago I get a lot of errors like the following when doing a "samba-tool dbcheck --cross-ncs --fix" : ERROR: Normalisation error for attribute whenCreated in CN=rpc-Ns-Group,CN=Schema,CN=Configuration,DC=samdom,DC=com value '19700101000000.0Z' should be '16010101000000.0Z' Fix normalisation for

Hi all, Our migration is coming along nicely, everything seems to work like it should... I thought... Only samba-tool dbcheck reports five errors: root at dc1:~# samba-tool dbcheck Checking 1143 objects ERROR: Normalisation error for attribute 'objectClass' in 'CN=phdseminar,CN=Users,DC=my,DC=samba,DC=domain' Values/Order of values do/does not match: ['top',

Hi All, I wonder if there should be one character for quote= in read.table, i.e., > args(read.table) function (file, header = FALSE, sep = "", quote = "\"'", dec = ".", ... I have a file containing the following lines, 08248-GOTERM 3'-phosphoadenosine 5'-phosphosulfate biosynthetic process 08279-GOTERM 3'-phosphoadenosine

Hi, After manipulate my data I have ended up with 5 different data frames with different number of observations but the same number of variables (columns) An example, if I write str(object1), I see this, data.frame': 47 obs. of 3 variables: $ ORF : Factor w/ 245 levels "YAL038W","YAL054C",..: 10 19 38 39 44 45 50 51 59 60 ... $ mRNA : num 0.891 1.148 1.202

Hi there, I wonder if there is a way of efficiently generating a correlation matrix of two expression matrices. I want to correlate miRNA and mRNA expression and used the following code: ##dat.mi miRNA expression matrix, dat.m mRNA expression matrix nc <- nrow(dat.mi) cor.mat <- data.frame(rep(NA,nrow(dat.m))) pval.mat <- data.frame(rep(NA,nrow(dat.m))) for(i in 1:nc) { cr <- vector()

Hello I have installed samba 2.2.6 with winbind and winbind-auth-challenge from ports. I have sucessfully added the machine to the win2k domain (still running in NT compat mode). when I try to run winbind... I get this: winbind -i -d100 winbindd version 2.2.6 started. Copyright The Samba Team 2000-2001 Initialising global parameters params.c:pm_process() - Processing configuration file

On 04.06.2017 11:50, jing hua zhao wrote: > Hi All, > > > I wonder if there should be one character for quote= in read.table, i.e., > > >> args(read.table) > function (file, header = FALSE, sep = "", quote = "\"'", dec = ".", > ... > > I have a file containing the following lines, > > > 08248-GOTERM

Hey all. I was an "early" adopter of mp3 (1994-1995), and I took great pains to use digital audio extractors and avoid jitter and whatnot thru proper hardware - back then, this was no mean feat. Well, I'm starting to switch to Ogg (dragging out all the CD's, starting over) and I want to know if anyone has an informed opinion on the merits or demerits of "normalization"

I think that you misunderstood me. As far as I know, RMA does three things: background correction, quantile normalization, and summary from probes to probesets. I want the probe values after background correction and quantile normalization but before the summary. On Sat, Dec 26, 2009 at 12:07 PM, Benilton Carvalho <bcarvalh at jhsph.edu> wrote: > pm(data) > > b > > On Dec

Hello R-Community, I am a rookie in R and I am fascinated with the power of bio computing by R. I am analysing gene expression data from Real time PCR. I have used absolute gene quantitation to measure gene copy number in all my transcripts. All my data has been normalised them to a housekeeping gene, which is constitutive expressed. My problem is as follows. After normalising some of the genes

For any given pre-specified gene or short list of genes, yes the Cox model works fine. Two important caveats: 1. Remeber the rule of thumb for a Cox model of 20 events per variable (not n=20). Many microarray studies will have very marginal sample size. 2. If you are looking at many genes then a completely different strategy is required. There is a large and growing literature; I like Newton

Dear All, I have two matrices A (40 x 732) and B (40 x 1230) and would like to calculate correlation between them.  I can use: cor(A,B, method="pearson") to calculate correlation between all possible pairs. But the issue is that there is one-many specific mappings between A and B and I just need to calculate correlations for those pairs (not all). Some variables in A (proteins, say p1)

Can anybody explain why RMA has to have a default normalization method: quantile-quantile? Why don't leave the choices to users? If I just want to use RMA to do a background correction without normalization, how should I specify the ? in the normalize.method="?" ? Hairong [[alternative HTML version deleted]]

Hi Guys, I've done some line recordings, and to save space compressed straight to vorbis, and as such have no access to the original. Upon listening, I found the volume to be quite quiet and I am wondering if there exists a native vorbis normaliser, so I don't have to decompress to wave, normalise and recompress. Does anybody know of such a beast? I'm aware of

Dear group, i have 114 files (all have 5 columns) and variable number of rows. I want to read all the files and select the first column and 4th column and fill the values into a big matrix. I have a pre-made matrix that would have all rownames (1st column) and coloumn names (4th column from file to be read). mymat <- matrix(data = 1, nrow = length(gomfs), ncol = length(x))

Dear Sir/Madam, I could succesfully normalise my microarray data using marrayNorm package. However, i have not been able to get normalised red and green channel intensities through R package. Is there a possibility to write a formula to calculate back the red and green channel intensities after normalisation of the data. Do I need to incorporate this formula in my R script? I am biologist

mogene10stprobeset.db is generated with AnnotationDbi for mouse gene array. I don't find a package that seems generated by AnnotationDbi for exon arrays on the webpage you mentioned. Is it correct? On Tue, Oct 27, 2009 at 7:00 PM, Marc Carlson <mcarlson at fhcrc.org> wrote: > Hi Peng, > > I am not completely clear from your post what you want. ?But most of our > annotation

On Wed, Jul 25, 2018 at 04:33:58PM +0200, Kim Walisch wrote: > I am using libxapian in a C++ project (hence I am using Xapian's C++ API) > and some user has requested that search requests should ignore accents. > E.g. when the user searches for "Herr Müller" he expects that "Herr Muller" > is also a search hit. Simply stripping accents can be a reasonable

Thanks for the advice. My question is more on how to do this? Let me use a biology gene analysis example to illustrate: In biology, there are always some house keeping genes which differ little even at pathological conditions. We know that at different batches, there are external factors affect the measurements. For example, overall signal intensity might be different due to lab reagents. A