Hi, thank you for the (very quick!) answer. The official tutorial (first heatmap shown here) adopted labeledHeatmap. The second plot was created through ggplot.
Do you have a pheatmap usage example in a WGCNA context?
Hello everyone, I'm new to WGCNA analysis... I'm trying to carry out one by using WGCNA Bioconductor Package. Being a newbie, I'm sticking to the official tutorial. In the Section 4 of the Tutorial it's explained how to create a heatmap correlating the previously obtained modules with sample quantitative traits (such as, for instance, phenotypical, measurable traits), as shown below:

However, I'd need a heatmap showing the correlations between the previously obtained modules and sample conditions instead. This will significantly help me to pick the most significant modules and further analyze them in Cytoscape, highlighting how they correlate with my experimental conditions immediately (for instance, the red module has a positive correlation with samples injected with the pathogen, the purple one with samples collected from root... and so on). The heatmap would look similar to this one (however, here the correlation is between modules and samples, not between modules and samples clustered by condition...from this other tutorial.

Any suggestions about how to create this kind of heatmap?
Thanks in advance for your help, have a nice day.
Sure,
I think a different program was used to make the above HM (because the colors appear different than I am used to seeing from pheatmap), but you shouldn't have any trouble at all making a diagram that is identical except for stylistically.
Hi, thank you for the (very quick!) answer. The official tutorial (first heatmap shown here) adopted labeledHeatmap. The second plot was created through ggplot.
Do you have a pheatmap usage example in a WGCNA context?
Hi again, no, I typically just create what I need.
In one of the WGCNA output files, you should have an object containing a list of all the sample IDs, then the cluster score for each. However this is formatted at present, you want to reformat it such that it is a matrix with row names being the gene module names and column names being the sample IDs. It's pretty likely that whatever output file you have is pretty close to that already. If not, paste the format of the output you do have as well as the format for the metadata you need in your original post and wee can go from there.
Hi LauferVA
gene_id colors
ENSG00000121410.12 turquoise
ENSG00000148584.16 turquoise
ENSG00000128274.17 turquoise
ENSG00000094914.14 turquoise
ENSG00000081760.17 black
ENSG00000103591.13 blue
ENSG00000115977.21 red
ENSG00000087884.16 turquoise
ENSG00000127837.10 blue
ENSG00000131043.13 yellow
ENSG00000205002.4 turquoise
ENSG00000090861.17 turquoise
ENSG00000124608.5 turquoise
ENSG00000266967.7 black
ENSG00000157426.14 turquoise
this is what i got as one ouput
another output is where i got (i guess) the correlation / cluster score of these in each sample
samples B1 B10 B11 B12 B13 B14 B15 B16
black -0.029917627 0.008215218 0.00298303 -0.058609333 -0.0108937 0.032304968 -0.00712045 -0.043307876
blue -0.19572086 0.034153279 0.049654506 0.030429713 0.05364303 0.049925743 -0.02128837 -0.014778403
brown -0.059783447 0.065474055 0.084536969 -0.087345215 0.123531196 0.059711078 -0.159681571 -0.162748366
cyan -0.198371587 -0.021083951 -0.050028852 0.094992144 -0.039469236 0.008094565 0.159868263 0.192145868
green -0.134498917 0.047227554 0.084184572 -0.019087064 0.091375625 0.072386592 -0.171160482 -0.154354049
greenyellow -0.062892188 0.009824482 0.114381523 -0.140938531 -0.244207578 0.135514557 0.068088079 0.111446206
grey 0.1098187 0.072746354 -0.020492397 -0.091551334 0.02884783 0.069656351 0.135998064 0.06916196
magenta -0.251199011 -0.046513272 0.040336461 0.121265223 0.024140415 -0.024805459 -0.081755285 -0.072516821
pink -0.189959424 0.048842012 -0.015831729 -0.016891322 -0.010951634 0.079484222 0.137488993 0.124954037
Also i have a sheet where the categorization is listed of my samples
IGIB1130567172 mild M1
IGIB113002393843 mild M2
IGIB113002298284 mild M3
IGIB1130924841 mild M4
IGIB113002224539 mild M5
IGIB1130143348 mild M6
IGIB1130482394 moderate B1
IGIB1130491776 moderate B2
IGIB113002311561 moderate B3
IGIB1130926534 moderate B4
IGIB113002365734 moderate B5
IGIB1130395724 moderate B6
IGIB1130483166 moderate B7
IGIB113002317760 moderate B8
IGIB113060368 moderate B9
IGIB1130493597 moderate B10
IGIB1130922137 moderate B11
IGIB1130478750 moderate B12
IGIB1130922387 moderate B13
IGIB1130259880 severe S1
IGIB113002313519 severe S2
IGIB1130271063 severe S3
IGIB113002307585 severe S4
I tried this pheatmap:
Why don't i see a single gene cluster (module) to be correlated positively or negatively for at least one group?? (ALSO I HAVE 45 IN MILD, 46 IN MODERATE AND 21 IN SEVERE, SO ISN'T IT SENSIBLE TO SEE CORRELATION OF MODULES IN ALL SAMPLES OF EITHER GROUP)
Hi
Also if anyone can comment on how can we have a heatmap of genes vs sample_conditions like in above query (gene names vs groups: mild, moderate and severe)
Thanks
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