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23 months ago
abhisek061 ▴ 30
What is needed to produce the WGCNA module? Is a series matrix of 15 samples is enough for creating a wgcna module? I failed repeated time to create it please clear it anyone who is experienced. Another question, What type of gene id it's accepts or provide meaningful insight? Thanks!
I tried to create a wgcna module from a series matrix of an RNA-seq experiment which is specifically created for differential gene expression analysis following the wgcna tutorial but I failed initially at the data loading process the datasets were of a 9 samples. I just tried to learn the process instead of getting accurate biological interpretations.
Okay, cool, what are the errors that you are receiving? Please show code, where appropriate.
As a beginner, I am following WGCNA tutorial for better understanding. I loaded my data for analysis and now facing a small issue.
Now I need to remove the auxiliary data and transpose the expression data for further analysis in WGCNA. What code should I follow for further analysis please help me. Starting few lines of my series matrix file is following-
As a piece of additional information, I want to know from you that my series matrix I created which is based on the highest standard deviation of 4000 genes from 30,000 genes of an experiment. Is there will be any problem with this filtering process? Thanks!
These are just standard data operations. Transposing can be done with
t(), while removing 'auxilliary' data can be done via data-frame indexing. If in doubt, please search online for simple things like
subsetting data in Rand
transposing data in R
Regarding filtering in this way, please read through the FAQ:
Also please see this answer by the WGCNA developer:
I am trying to create a WGCNA module but not getting any of these colors in the cluster (it is only black) and heatmap and gene name also, I gave only one series matrix. little code is here. please look into it. Thanks
Your data may be too 'flat'; thus, WGCNA only identifies a single cluster. Please review all diagnostic plots along the way, such as soft threshold, tree cut height, etc.
Thanks, I am facing a problem with data filtering in WGCNA tutorial they advising - " Probesets or genes may be filtered by mean expression or variance (or their robust analogs such as median and median absolute deviation, MAD) since low-expressed or non-varying genes usually represent noise. "
I have a question what should I apply ? Mean-variance is similar to calculate the mean or if I get the mean-variance for each gene which genes I should take for getting better results which are low or which are high?
If I do median absolute deviation for the same which value should I take in this case? Thanks!
Hi, they literally mean mean expression (mean()) or variance (var()). In R, you'd calculate these per row (gene), via:
Then choose a cut-off to use.