Kaitlyn’s notebook: adding in undetected proteins and heatmaps

Goals

My current cluster eliminates proteins that were never detected because I combined the data sets from silo 3 and silo 9 that contained only the corresponding silo’s abundant proteins. This means that when my cluster analysis is finished, I have uneven amounts of proteins for each silo. I want to create an even number of proteins per silo at the end of the cluster. This means I will need to edit the original raw data containing all of the silos rather than working off of the separate silo data sets.

After I do this, I will rerun the cluster analysis to get a new list of ‘unique’ proteins (unique proteins are defined as those that were in separate cluster groups based on temperature [ie. silo]). This final unique-proteins dataframe will be used for gene enrichment and to create heatmaps. I think a unique possible heatmap would be of parent terms based on the abundance of the proteins whose genes are annotated to that parent term.

Heatmaps

I got my computer back so I made sure that all my files are up to date on all systems. I heavily modified my cluster code to create a more accurate unique-proteins dataframe since previously it had redundant and incorrect columns mixed in with correct columns. Scales for heatmaps are normalized abundance values.

heatmap-allclus

All proteins from hierarchical clustering analysis with both proteins and time clustered.

heatmap-dayclus
All proteins from hierarchical clustering analysis with time clustered.
heatmap-protclus

All proteins from hierarchical clustering analysis with proteins clustered.

heatmap-silo3

Protein abundance over time with proteins clustered based on a Diocletian distance matrix. Proteins were chosen based on different cluster assignments from Silo 9 when hierarchical clustering was preformed with all proteins from Silo 3 and Silo 9.

heatmap-silo9

Protein abundance over time with proteins clustered based on a euclidean distance matrix. Proteins were chosen based on different cluster assignments from Silo 9 when hierarchical clustering was preformed with all proteins from Silo 3 and Silo 9.

Metboanalyst Heatmap

heatmap2_0-unqprot.png

Note that in this heatmap, the data has been:

  • filtered
    • mSet<-ReplaceMin(mSet)
  • normalized (mean centered)
    • mSet<-Normalization(mSet, "NULL", "NULL", "MeanCenter", ratio=FALSE, ratioNum=20)  
    • mSet<-PlotNormSummary(mSet, "norm_0_", "png", 72, width=NA)
    • mSet<-PlotSampleNormSummary(mSet, "snorm_0_", "png", 72, width=NA)

 

 

If needed later: displaying two heatmaps by each other.