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fix links #16

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5d429ea
remove ! since not an image
raynamharris Aug 21, 2019
77dffce
remove bad path
raynamharris Aug 21, 2019
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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -17,11 +17,11 @@ In the meantime, you can access the tutorials in the package.

# Available tutorials

* ![Tutorial for creation of CoExp networks](inst/tutorials/Tutorial_1.md)
* [Tutorial for creation of CoExp networks](inst/tutorials/Tutorial_1.md)

* ![Tutorial for using CoExp networks suite](inst/tutorials/Tutorial_2.md)
* [Tutorial for using CoExp networks suite](inst/tutorials/Tutorial_2.md)

* ![Tutorial for preparing expression data before creating the networks](inst/tutorials/Tutorial_3.md)
* [Tutorial for preparing expression data before creating the networks](inst/tutorials/Tutorial_3.md)


# Credits
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12 changes: 6 additions & 6 deletions inst/tutorials/Tutorial_1.md
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Expand Up @@ -43,25 +43,25 @@ Yes, this is a lot of debug information to keep you on track of what happend.
Let us see which files do we have now available.
First of all, we have the beta study for the ROSMAP residuals we generated, stored in the `results` folder under the name `betastudyMyRosMap.signed.pdf` and it is represented by figure below.

![Smoothing parameter study](inst/tutorials/betastudyMyRosMap.signed.pdf)
![Smoothing parameter study](betastudyMyRosMap.signed.pdf)

In that figure you see the beta study generated by WGCNA. The plot on the left shows how the different beta values (x-axis) approximate the linear regression model to a scale free topology (SFT) network with R-squared values. The horizontal red line marks the values of beta over which the network satisfies SFT property. We choose beta=4. The plot on the right shows the average connectivity of genes. You see as the beta increases, the genes are less connected which is good.

Then we have `MyRosMap.9_mod_size.pdf` which is the size of each module in number of genes before applying k-means and appears depicted in figure.

![Figure for module size before applying k-means](inst/tutorials/MyRosMap.4_mod_size.pdf)
![Figure for module size before applying k-means](MyRosMap.4_mod_size.pdf)

you can see that turquoise has a remarkably high size in comparison with the other which is not good at all. Also you can see how the module eigengenes relate to each other, again before applying k-means, at `MyRosMap.4_Eigengenes_clustering.pdf` in figure below.

![Figure for eigengenes clustering before applying k-means](inst/tutorials/MyRosMap.4_Eigengenes_clustering.pdf)
![Figure for eigengenes clustering before applying k-means](MyRosMap.4_Eigengenes_clustering.pdf)

You see that turquoise is far apart from the rest, possibly because of its size but also that, for example, tan and blue are similar and also black and midnightblue, and so on. We can again see, after applying k-means, what is the module size in terms of genes at the file `netMyRosMap.4.it.20.rds_mod_size.pdf` and figure below.

![Figure for module size after applying k-means](inst/tutorials/netMyRosMap.4.it.20.rds.mod_size.pdf)
![Figure for module size after applying k-means](netMyRosMap.4.it.20.rds.mod_size.pdf)

Now we see that the relative size of the turquoise module seems much more reasonable than before. In consequence, the dendrogram for the module eigengenes has changed too, in file `netFCortex.9.it.20.rds_eigengenes_clustering` and figure below.

![Figure for eigengenes clustering after applying k-means](inst/tutorials/netMyRosMap.4.it.20.rds.Eigengenes_clustering.pdf)
![Figure for eigengenes clustering after applying k-means](netMyRosMap.4.it.20.rds.Eigengenes_clustering.pdf)


## Step 2, annotating the network
Expand Down Expand Up @@ -122,7 +122,7 @@ write.csv(CoExpNets::cellTypeByModule(return.processed=F,

This generates three different files. The file `resuts/netMyRosMap.4.it.20.rds.USER_terms.csv` is a list of enrichments generated by WGCNA alone which is useful for assigning cell types and brain areas to each module. Then the file `results/netMyRosMap.4.it.20.rds.celltype.csv` is the proper CTDB enrichment we generate. Its heat-map based pdf representation we find at the file `results/netMyRosMap.4.it.20.rds.celltype.csv.pdf`. And it looks like this heat-map above

![Cell type signals for our ROS/MAP Cortex network](inst/tutorials/netMyRosMap.4.it.20.rds.celltype.pdf)
![Cell type signals for our ROS/MAP Cortex network](netMyRosMap.4.it.20.rds.celltype.pdf)

In which we clearly see the following interesting things

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