Comments (3)
Hi,
Thank you for your interest :)
quote from our paper:
We recommend prior preprocessing and normalization of input gene or transcript expression data, including any necessary batch correction.
The data in the tutorial is the TPM matrix and within the package, we usually would like to remove any low-expressed genes across all samples, and TPMcutoff
is the threshold to define low-expressed genes however it's up to the user to decide which input would work best so, in summary, you can pass any form of gene expression data as an input (csv, tsv or AnnData) and if you wish to not remove any low expressed genes you can put TPMcutoff to zero
from pywgcna.
Ok, thanks. So beyond TPMcutoff there is no assumptions of data being TPM and I can pass anything so long I set TPMcutoff=0. Did I understand correctly?
from pywgcna.
The only assumption is that you pass an expression data.
TPMcutoff
can also considered as an expression cutoff and you can assign any value that suits your data
from pywgcna.
Related Issues (20)
- how to set figsize HOT 2
- SystemExit: All your genes ended up in one modules! Reconsider your input parameters in order to get more modules. HOT 6
- module_trait_relationships_heatmap HOT 4
- Pandas error HOT 4
- Visualization error HOT 6
- Tutorial data
- gene name question HOT 1
- analyseWGCNA subplot error HOT 11
- Error with Bubble plot when comparing WGCNA objects HOT 3
- Error during "plotting module heatmap eigengene..." step of WGNCA.analyseWGCNA() HOT 1
- Module 'scores' for each sample to each module HOT 2
- p-value
- Error in documentation and in cutreeHybrid HOT 2
- Error at stage of getGeneList
- Error for calculating module eigengenes
- Plot issue HOT 1
- Re-naming modules HOT 1
- In the module Network Plot, Can we label the node as gene_name instead of gene_id ? HOT 2
- Several errors when performing findModules steps individually HOT 8
- 2
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from pywgcna.