Data: vsn-normalization.xls 'Control' Data
Setting:
murange = [0 .1 .5 1 2 4 8 12 16 32 64 100];
10 seeds for vbssm model training
Procedures:
1. Sort 'top50 & reg' as blocks: pdf / xls
2. In k-th experiment, incoporate blocks[1:k] as prior, take the average of 10 seeds for significance computation
3. Analyze the recovered true arcs, Total # = 10.
They are grouped into 2 catogaries: tdcA-arcs & tdcR-arcs, where:
tdcA-arcs: (color is agree with legend)
tdcA pd tdcB
tdcA pd tdcC
tdcA pd tdcD
tdcA pd tdcE
tdcA pp tdcA
More details about this work
1. Sort the 'top50 & reg' as blocks.
That means, arcs with same 'from-gene' are grouped as one block, ignore the 'to-gene'.
E.g. The group containing all tdcA-arcs are named block-1, the group containing all tdcR-arcs are named block-2.
block-idx from to
>From block-3, block-index is numbered by the alphabet order of 'from-genes'
2. Add prior for vbssm training.
There are 29 experiment, since the block # I sort in 'top50®' is exactly 29. Each experiment also explores mu range.
1st test, add all tdcA-arcs (i.e. block-1) as prior
2nd test, keep the tdcA-arcs (block-1) as prior, meanwhile add tdcR-arcs (block-2) as prior.
....
29th test, all arcs (i.e. block(1:29) ) are added as priors.
3. Organize the 29 experiment data, each murange = [0 .1 .5 1 2 4 8 12 16 32 64 100]
Since mu value play an important role in recovery evaluation, I made the figure to reflect this point.
There is no true network at hand, only 10 known arcs. I named them as 2 groups
tdcA-arcs:(from gene = tdcA, to gene = don't care)
tdcA pd tdcB
tdcA pd tdcC
tdcA pd tdcD
tdcA pd tdcE
tdcA pp tdcA
tdcR-arcs:(from gene = tdcR, to gene = don't care)
tdcR pp tdcA
tdcR pp tdcB
tdcR pp tdcC
tdcR pp tdcE
tdcR pp tdcD
Different colors, blue and pink, to demonstrate 10 true arcs recovery results. Blue = tdcA-arcs, pink = tdcR-arcs.
The bule+pink stack gives the total number of vbssm identified true arcs.
Based on my understanding, figure revealed at least 2 information:
1. mu = 0.5 is optimum mu value, at which # of recovered arcs reaches peak.
2. In global view, pink-arcs only showed with some special mu, whileas, blue-arcs are not significantly affected by mu value.
tdcA pd tdcD
tdcA pd tdcE
tdcA pp tdcA
tdcR-arcs: (color is agree with legend)
tdcR pp tdcA
tdcR pp tdcB
tdcR pp tdcC
tdcR pp tdcE
tdcR pp tdcD
tdcR pp tdcC
tdcR pp tdcE
tdcR pp tdcD
More details about this work
1. Sort the 'top50 & reg' as blocks.
That means, arcs with same 'from-gene' are grouped as one block, ignore the 'to-gene'.
E.g. The group containing all tdcA-arcs are named block-1, the group containing all tdcR-arcs are named block-2.
block-idx from to
| 1 | tdcA | tdcA |
| 1 | tdcA | tdcB |
| 1 | tdcA | tdcD |
| 1 | tdcA | tdcE |
| 1 | tdcA | tdcF |
| 1 | tdcA | tdcG |
| 1 | tdcA | tdcC |
| 2 | tdcR | tdcA |
| 2 | tdcR | tdcB |
| 2 | tdcR | tdcC |
| 2 | tdcR | tdcE |
| 2 | tdcR | tdcF |
| 2 | tdcR | tdcG |
| 2 | tdcR | tdcD |
>From block-3, block-index is numbered by the alphabet order of 'from-genes'
2. Add prior for vbssm training.
There are 29 experiment, since the block # I sort in 'top50®' is exactly 29. Each experiment also explores mu range.
1st test, add all tdcA-arcs (i.e. block-1) as prior
2nd test, keep the tdcA-arcs (block-1) as prior, meanwhile add tdcR-arcs (block-2) as prior.
....
29th test, all arcs (i.e. block(1:29) ) are added as priors.
3. Organize the 29 experiment data, each murange = [0 .1 .5 1 2 4 8 12 16 32 64 100]
Since mu value play an important role in recovery evaluation, I made the figure to reflect this point.
There is no true network at hand, only 10 known arcs. I named them as 2 groups
tdcA-arcs:(from gene = tdcA, to gene = don't care)
tdcA pd tdcB
tdcA pd tdcC
tdcA pd tdcD
tdcA pd tdcE
tdcA pp tdcA
tdcR-arcs:(from gene = tdcR, to gene = don't care)
tdcR pp tdcA
tdcR pp tdcB
tdcR pp tdcC
tdcR pp tdcE
tdcR pp tdcD
Different colors, blue and pink, to demonstrate 10 true arcs recovery results. Blue = tdcA-arcs, pink = tdcR-arcs.
The bule+pink stack gives the total number of vbssm identified true arcs.
Based on my understanding, figure revealed at least 2 information:
1. mu = 0.5 is optimum mu value, at which # of recovered arcs reaches peak.
2. In global view, pink-arcs only showed with some special mu, whileas, blue-arcs are not significantly affected by mu value.
