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When do I get a WGCNA error code?
I get a warning message when completing the following step: Does anyone no what this error code means as I cannot plot the scale free analysis, with soft thresholding.
Can a WGCNA function take the argument corfnc?
Many WGCNA functions take the argument corFnc that allows one to specify an alternative correlation function to the standard cor and bicor is one option.
Which is the example of deseq2 in WGCNA?
We then recommend a variance-stabilizing transformation. For example, package DESeq2 implements the function varianceStabilizingTransformation which we have found useful, but one could also start with normalized counts (or RPKM/FPKM data) and log-transform them using log2 (x+1).
Can you use WGCNA with RNA Seq data?
Yes. As far as WGCNA is concerned, working with (properly normalized) RNA-seq data isn’t really any different from working with (properly normalized) microarray data.
Why is the WGCNA package not available on Mac?
Note for Mac users: CRAN occasionally fails to compile the WGCNA package for Mac OS X. This leads to the error message “Package WGCNA is not available…” when calling BiocManager::install ().
Which is the latest version of WGCNA for Cran?
For example, if your R version is 3.2.1 and the current R version on CRAN is 3.5.0, the automatic installation and update will not use the newest version of WGCNA. Please update your R to the newest version or use the manual download below.
What kind of packages are needed for WGCNA?
The WGCNA package requires the following packages to be installed: stats, grDevices, utils, matrixStats (0.8.1 or higher), Hmisc, splines, foreach, doParallel, fastcluster, dynamicTreeCut, survival, parallel, preprocessCore, GO.db, impute, and AnnotationDbi.
How are probesets and genes filtered in WGCNA?
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.
Do you use RPKM or FPKM in WGCNA?
Whether one uses RPKM, FPKM, or simply normalized counts doesn’t make a whole lot of difference for WGCNA analysis as long as all samples were processed the same way.
How many samples do you need for WGCNA analysis?
In a typical high-throughput setting, correlations on fewer than 15 samples will simply be too noisy for the network to be biologically meaningful. If at all possible, one should have at least 20 samples; as with any analysis methods, more samples usually lead to more robust and refined results.
Which is the WGCNA source for blockwisemodulesc?
Documented in blockwiseConsensusModulesblockwiseIndividualTOMsblockwiseModuleslowerTri2matrixrecutBlockwiseTreesrecutConsensusTreessizeRestrictedClusterMergeTOMdistTOMsimilarityTOMsimilarityFromExpr
Which is WGCNA source for weighted correlation network analysis?
WGCNA R/blockwiseModulesC.R R/blockwiseModulesC.R In WGCNA: Weighted Correlation Network Analysis