Comments (7)
from minfi.
@kasperdanielhansen Thanks for fast reply. If I instead run preprocessFunnorm(rgset, sex = "F")
I get this error:
> mset <- preprocessFunnorm(rgset, sex = "F")
[preprocessFunnorm] Background and dye bias correction with noob
[preprocessFunnorm] Mapping to genome
[preprocessFunnorm] Quantile extraction
[preprocessFunnorm] Normalization
Error in oobG[2, ] : subscript out of bounds
> traceback()
3: .buildControlMatrix450k(extractedData)
2: .normalizeFunnorm450k(object = gmSet, extractedData = extractedData,
sex = sex, nPCs = nPCs, verbose = subverbose)
1: preprocessFunnorm(rgset, sex = "F")
No, I only need this one sample, not mixed with others.
I get similar if I try instead with quantile normalisation:
> mset <- preprocessQuantile(rgset)
[preprocessQuantile] Mapping to genome.
Error in kmeans(dd, centers = c(min(dd), max(dd))) :
initial centers are not distinct
> mset <- preprocessQuantile(rgset, sex = "F")
[preprocessQuantile] Mapping to genome.
[preprocessQuantile] Fixing outliers.
[preprocessQuantile] Quantile normalizing.
Error in if (ncol(mat) == 1) return(mat) : argument is of length zero
from minfi.
@kasperdanielhansen Do you have any ideas of what can cause this? Again, I can send you the idat-files (via a private channel), if that can help?
from minfi.
In most preprocessing methods we need to do something special for the sex chromosome (for example due to X inactivation). To do this well, we need to know the sex of the samples. We have a standard way of estimating the sex of the samples using kmeans, and this step fails. It could fail for a number of reasons, the top contenders are (a) you only have 1 sex (the code assumes there are both males and females) (b) you have cancer samples with big CN changes on the sex chromosomes.
In case (a) or (b) you can override the prediction by directly supplying a vector of sex.
from minfi.
In case (a) or (b) you can override the prediction by directly supplying a vector of sex.
@kasperdanielhansen I already tried with the sex = "F"
argument (cf. above), and it still fails. Is there another way to supply the sex?
from minfi.
Ok, I am sorry, I see I basically wrote the same thing twice.
I also see you're trying to run 1 sample through functional normalization, right? That won't work. We essentially remove between-sample variation by regressing out certain confounders and that approach won't work for 1 sample processing.
Are you working in a prediction setting? If so, I would look into using Noob (in its single-sample mode, which is the default). Noob is "true" single sample normalization which means normalizing 1 sample is the same as normalizing many samples together.
from minfi.
I also see you're trying to run 1 sample through functional normalization, right? That won't work. We essentially remove between-sample variation by regressing out certain confounders and that approach won't work for 1 sample processing.
Yes, I started with that. But I also tried preprocessQuantile
- that should work with one sample, right?
Are you working in a prediction setting? If so, I would look into using Noob (in its single-sample mode, which is the default). Noob is "true" single sample normalization which means normalizing 1 sample is the same as normalizing many samples together.
Thanks, I will try that, too.
from minfi.
Related Issues (20)
- How to analyze A/B compartment after using liftOver function to annotate hg38
- Upcoming change to DelayedArray::realize() HOT 7
- getUnMeth() didn't work for EPIC v2 HOT 2
- Problem with preprocessIllumina HOT 5
- Problem in qcReport HOT 3
- how to combine EPIC and EPIC2 array data? HOT 4
- preprocessSWAN fails to normalize RGChannelSet
- Custom minfi manifest and annotation packages
- preprocessNoob using incorrect row indices to Meth and Unmeth matrices HOT 10
- EstimateCellCounts with EPIC V2 data HOT 1
- Error encounter during EPIC methylation analysis HOT 4
- EPIC v2.0 - preprocessIllumina returning error HOT 2
- Construct RGChannelSet object from raw methylated and raw unmethylated csv files HOT 2
- Loading minfi on MacOS HOT 7
- EPIC V2 to EPIC V1 HOT 7
- detectionP broken as of latest matrixStats update
- EPIC v2 preprocessQuantile()
- Error while reading the data using (read.metharray.exp)
- Galaxy Wrapper code repository
- Problem with qcPlot axes HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from minfi.