carlosproca / cdnormbio Goto Github PK
View Code? Open in Web Editor NEWR package: Condition-Decomposition Normalization for Biological Applications
License: MIT License
R package: Condition-Decomposition Normalization for Biological Applications
License: MIT License
Hi,
would you have any recommendations on how to handle non-converging data sets?
I tried to use normalize.svcd on a 3 data sets x 4 conditions matrix. It didn't converge. My naive wish were to increase the number of iterations. This isn't the way to do it though...
Are there some guidelines to be found?
I do not understand these two commands i.e. concept of adding offset.
Could you explain? Please..
offset.added <- rnorm( sample.n )
expr.data <- sweep( expr.data, 2, offset.added, "+" )
I have expression data of 3 sample. When i ran this command, i got following error.
Is replicates of sample is compulsory to run this command?
normalize.result <- normalize.svcd(expr.data, expr.condition, stdvec.graph = "stdvec", p.value.graph = "p_value", verbose = TRUE)
Error in normalize.svcd(expr.data, expr.condition, stdvec.graph = "stdvec", :
normalize.svcd: There must be 2 or more samples for each condition
Dear Dr. Roca,
Following your answers in the issues #3 and #4, indeed, I have 3 conditions with 3 replicates and 1 condition with 2 replicates (one was removed because of a bad reproducibility). So the size factors generated by SVCD have random variations.
In order to have more accurate data, I ran 50 times the normalization and I put the means into DESeq2, but it is not clear for me how to take the uncertainties into account. What could be the best way forward?
Thanks a lot for your answers.
Dear Dr Roca
I have expression data of multiple samples and conditions. When I ran this command:
normalize.svcd(expr.data.log2, expr.condition, verbose = T, convergence.threshold = c(0.01, 0.1, 0.01, 1)) # Default
I got following error.
between.condition.search.h0.feature
Error in rowMeans(edata[stdvec.feature, ]) :
'x' must be an array of at least two dimensions
I traced it back to normalize_svcd_aux.R, but I do not understand what is causing this error in there.
I pasted here an overview of the conditions just in case it is relevant.
Conditions:
table( expr.condition )
expr.condition
Ad_W0_CD Ad_W1_CD Ad_W1_HFD Ad_W12_CD Ad_W12_HFD Ad_W2_CD Ad_W2_HFD
6 6 6 6 6 6 6
Ad_W6_CD Ad_W6_HFD SVF_D3_CD SVF_D3_HFD SVF_W1_CD SVF_W1_HFD SVF_W12_CD
6 6 6 6 6 6 6
SVF_W12_HFD SVF_W2_CD SVF_W2_HFD SVF_W6_CD SVF_W6_HFD Ti_D3_HFD Ti_W1_HFD
6 6 6 6 6 2 2
Ti_W12_CD Ti_W12_HFD Ti_W2_HFD Ti_W6_CD Ti_W6_HFD Ti_WE_CD
2 2 2 2 2 3
Best regards
Dear Dr. Roca,
After reading your paper, I'm testing your normalization method in a RNA-seq experiment. I would like to have your opinion on my workflow:
There are around 20000 transcripts from a microalgae in 4 different conditions to compare, with 3 replicates per condition.
Best regards
*https://www.rdocumentation.org/packages/DESeq/versions/1.24.0/topics/estimateDispersions
**https://www.rdocumentation.org/packages/DESeq/versions/1.24.0/topics/nbinomTest
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