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[important]updated README.md
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@brianpenghe
brianpenghe authored Oct 6, 2018
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Expand Up @@ -52,7 +52,7 @@ Dispersion=var(PMBC_perc_filtered,0,2)./mean(PMBC_perc_filtered,2);
[P I]=sort(Dispersion,'descend');
```

### Supervised Clustering and visualization
### Supervised manual hierarchical clustering and visualization

PMBCClg=clustergram(PMBC_perc_filtered(I(1:2000),:),'Standardize',0,'DisplayRange',7.5,'Colormap',colormap(jet),'RowPDist','correlation','ColumnPDist','spearman','Symmetric',false,'linkage','complete', 'OptimalLeafOrder',false)

Expand All @@ -75,11 +75,12 @@ And then import this list to an array
```
GenesOfInterest=table2array(readtable('test.dat','ReadVariableNames',false,'Delimiter','\t'))
```
Then plot into a HeatMap matching the cell order of your clustergram
Then plot a HeatMap matching the cell order of your clustergram
```
HeatMap(PMBC_perc(GenesOfInterest,get(PMBCClg,'ColumnLabels')),'Standardize',2,'Colormap',colormap(jet),'Symmetric',true,'DisplayRange',2.5)
```
<img width="675" alt="screen shot 2018-09-28 at 4 58 52 pm" src="https://user-images.githubusercontent.com/4110443/46238332-e78e0380-c33f-11e8-9811-6623f8a46035.png">
<img width="771" alt="screen shot 2018-10-05 at 6 12 10 pm" src="https://user-images.githubusercontent.com/4110443/46565895-37ce0e00-c8ca-11e8-97c1-dcb391241ee4.png">


*If you only know the gene name (e.g. HOPX) but not the gene ID, you can look it up using this:*
```
Expand All @@ -93,32 +94,37 @@ get(PMBCdm(strcontain('HOPX',get(PMBCdm,'RowNames')),:),'RowNames')
Before we do this, let's first drop down our manually assigned cell clusters from clustering analysis.
Based on the coloring in the example image, the clusters are:
```
ClusterID=[repmat([1],1,451) repmat([0],1,44) repmat([2],1,137) repmat([3],1,23) repmat([0],1,664) repmat([4],1,119) repmat([0],1,20) repmat([5],1,313) repmat([0],1,195) repmat([6],1,54) repmat([7],1,202) repmat([8],1,244) repmat([0],1,15)];
ClusterID=repmat([0],size(get(PMBCClg,'ColumnLabels')));
ClusterID(GetOrder(get(Cluster1,'ColumnLabels'),get(PMBCClg,'ColumnLabels')))=1;
ClusterID(GetOrder(get(Cluster2,'ColumnLabels'),get(PMBCClg,'ColumnLabels')))=2;
ClusterID(GetOrder(get(Cluster3,'ColumnLabels'),get(PMBCClg,'ColumnLabels')))=3;
ClusterID(GetOrder(get(Cluster4,'ColumnLabels'),get(PMBCClg,'ColumnLabels')))=4;
ClusterID(GetOrder(get(Cluster5,'ColumnLabels'),get(PMBCClg,'ColumnLabels')))=5;
ClusterID(GetOrder(get(Cluster6,'ColumnLabels'),get(PMBCClg,'ColumnLabels')))=6;
ClusterID(GetOrder(get(Cluster7,'ColumnLabels'),get(PMBCClg,'ColumnLabels')))=7;
ClusterID(GetOrder(get(Cluster8,'ColumnLabels'),get(PMBCClg,'ColumnLabels')))=8;
```
Now inspect top 30 PCs for the normalized dataset (you can use absolute values, if you remove the function zscore())
Now inspect top 30 PCs for the normalized dataset (you can use normalized values, if you use the function [zscore()](https://www.mathworks.com/help/stats/zscore.html?searchHighlight=zscore&s_tid=doc_srchtitle)).
```
PCAPlot(zscore(double(PMBC_perc_filtered(I(1:500),get(PMBCClg,'ColumnLabels')))')',get(PMBCClg,'ColumnLabels'),get(PMBC_perc_filtered(I(1:500),:),'RowNames'),ClusterID,100,30);
[COEFF,SCORE,latent]=PCAPlot(double(PMBC_perc_filtered(I(1:2000),get(PMBCClg,'ColumnLabels'))),get(PMBCClg,'ColumnLabels'),get(PMBC_perc_filtered(I(1:2000),:),'RowNames'),ClusterID',100,30);
```
Among the outputs you get an image of PC scores, where each row represents each PC's scores across individual cells, ordered in the same way as the clustergram you generated.
<img width="512" alt="screen shot 2018-10-02 at 10 57 15 pm" src="https://user-images.githubusercontent.com/4110443/46392557-9ad75f00-c696-11e8-9ab9-d598981db1d5.png">
<img width="515" alt="screen shot 2018-10-05 at 6 41 52 pm" src="https://user-images.githubusercontent.com/4110443/46566430-29382480-c8d3-11e8-8796-3a69eb74c33c.png">

Based on this diagram, we see that only the top 9 PCs show cell-type specificity. So we only use these 9 PCs to plot t-SNE.
Based on this diagram, we see that only the top 10 PCs show cell-type specificity. So we only use these 10 PCs to plot t-SNE.

Now calculate t-SNE coordinates and plot
```
mappedX=tsne(zscore(double(PMBC_perc_filtered(I(1:500),get(PMBCClg,'ColumnLabels')))'),'Algorithm','barneshut','NumPCAComponents',9,'Distance','spearman','Perplexity',30);
mappedX=tsne(double(PMBC_perc_filtered(I(1:2000),get(PMBCClg,'ColumnLabels')))','Algorithm','barneshut','NumPCAComponents',10,'Distance','spearman','Perplexity',30);
scatter(mappedX(:,1),mappedX(:,2),50,[0.8 0.8 0.8],'.')
```
![untitled](https://user-images.githubusercontent.com/4110443/46392760-5b5d4280-c697-11e8-9754-b8db194e5e2a.jpg)
![untitled](https://user-images.githubusercontent.com/4110443/46567228-7375d180-c8e4-11e8-9364-a1ac24ae106e.jpg)

*You should right click mappedX in Workspace window to save it to a file. So that next time you can use mappedX=importdata(); to import it*

Now paint the t-SNE plot with cluster colors.
```
hold on
scatter(mappedX(ClusterID==1,1),mappedX(ClusterID==1,2),10,[0 0 1],'.')
scatter(mappedX(ClusterID==1,1),mappedX(ClusterID==1,2),10,[0 0 1],'.')
scatter(mappedX(ClusterID==1,1),mappedX(ClusterID==1,2),50,[0 0 1],'.')
scatter(mappedX(ClusterID==1,1),mappedX(ClusterID==1,2),50,[0 0.45 0.74],'.')
scatter(mappedX(ClusterID==2,1),mappedX(ClusterID==2,2),50,[0.85 0.33 0.1],'.')
scatter(mappedX(ClusterID==3,1),mappedX(ClusterID==3,2),50,[0.93 0.69 0.13],'.')
Expand All @@ -128,15 +134,23 @@ scatter(mappedX(ClusterID==6,1),mappedX(ClusterID==6,2),50,[0.3 0.75 0.93],'.')
scatter(mappedX(ClusterID==7,1),mappedX(ClusterID==7,2),50,[0.64 0.08 0.18],'.')
scatter(mappedX(ClusterID==8,1),mappedX(ClusterID==8,2),50,[1 0 0],'.')
```
![untitled](https://user-images.githubusercontent.com/4110443/46392851-aa0adc80-c697-11e8-9582-e92dd10096c0.jpg)

![untitled](https://user-images.githubusercontent.com/4110443/46567238-b89a0380-c8e4-11e8-931d-a4ba22b8c7d7.jpg)

#### Unsupervised k-means clustering
Of course, you can do an unsupervised clustering using the top 10 PCs I mentioned above.
kmeans_colors=distinguishable_colors(10);
scatter(mappedX(:,1),mappedX(:,2),50,kmeans_colors(kmeans(SCORE(:,1:10),10),:),'.');

![untitled](https://user-images.githubusercontent.com/4110443/46567688-c30bcb80-c8eb-11e8-94bf-9cbfe379af40.jpg)

#### Check expression of a specific gene
```
figure
scatter(mappedX(:,1),mappedX(:,2),50,PMBC_perc('ENSG00000105369_CD79A',get(PMBCClg,'ColumnLabels')),'.')
colormap(hot)
```
![untitled](https://user-images.githubusercontent.com/4110443/46392894-d1fa4000-c697-11e8-865b-c94143d9ba9b.jpg)
![untitled](https://user-images.githubusercontent.com/4110443/46567725-acb23f80-c8ec-11e8-8858-c5ff76de3a3f.jpg)

## Discussions
### Parameter optimizations
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