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<!-- class: center, middle -->

class: center, middle
name:opening

## A Data-Driven Nosology:
## Progress, Challenges & Opportunities

<!-- <img src="images/psych.jpg" STYLE="position:absolute; TOP:200px; LEFT:110px; WIDTH:700px;"/> -->


---

### Steps

<br><br>

1. Acquire  data from multiple disparate sites
2. Build pipelines to automatically & robustly process data
3. Develop methods for discovering clusters in the data

---

## Progress: Datasets Acquired!

<img src="images/m2gtab.png" STYLE="position:absolute; TOP:200px; LEFT:00px; WIDTH:900px;"/>

---

## Progress: Pipeline Built!

<img src="images/ndmg-pipeline.png" STYLE="position:absolute; TOP:200px; LEFT:0px; WIDTH:900px;"/>


---
class: center, middle

## Clustering Methods: In Progress

---
### Challenge #1: Spurious Clusters


<img src="images/spikes.png" STYLE="position:absolute; TOP:150px; LEFT:150px; HEIGHT:500px;"/>


---
### Challenge #1: Spurious Clusters

<img src="images/spikes_kde.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic1.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic2.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic3.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic4.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic5.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic1000.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px;"/>

<!-- ---
### Challenge #1: Spurious Clusters

real data goes here -->

---
class:center, middle

### Opportunity #1: Spurious Clusters

<br>

Standard tools find spurious clusters

<br>

Fancy tools require too much data to be practical

<br>

.w[**Gap**: Clustering tools with appropriate statistical guarantees]


---

### Challenge #2: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:150px; HEIGHT:500px"/>

---

### Challenge #2: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels0.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #2: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels1.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #2: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels2.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #2: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels3.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #2: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels4.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #2: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels5.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>


---
class: center, middle

### Opportunity #2: Overlapping Clusters
<br>

Any tree with thresholds will put arbitrarily close individuals arbitrarily far away

<br>

Trees that put individuals near boundary in multiple groups could mitigate this issue

<br>

.w[**Gap**: Tree learning tools with appropriate statistical guarantees]


---
## Challenge #3: Batch Effects

<img src="images/batch.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:400px"/>

---
## Challenge #3: Batch Effects

<img src="images/batch_sex.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:400px"/>

---
class: center, middle

## Opportunity #3: Batch Effects

<br>


Thus far, harmonzing processing does not remove batch effect

<br>

Can further harmonizing processing mitigate batch effect?

<br>

If not, can harmonizing acquisition mitigate batch effect?

<br>

.w[**Gap**: Pipelines that harmonize across datasets,
and data to evaluate pipelines]

---

### Future Outlook Steps


- Micro-arrays never mitigated batch effects sufficiently
- fMRI has another degree of freedom: stimulus

--

### Much More To Do!


<img src="images/lion2.JPG" STYLE="position:absolute; TOP:300px; LEFT:0px; WIDTH:400px;"/>
<img src="images/world.gif" STYLE="position:absolute; TOP:300px; LEFT:400px; WIDTH:500px;"/>



<DIV style="position: absolute; top:510px; left:120px; ">(i love this guy)</DIV>

<DIV style="position: absolute; top:580px; left:340px; "><a href="mailto:<EMAIL>"><EMAIL></a></DIV>








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<commit_msg>updated to have only 2 challenges<commit_after><!DOCTYPE html>
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<!-- class: center, middle -->

class: center, middle
name:opening

## A Data-Driven Nosology:
## Progress, Challenges & Opportunities

<!-- <img src="images/psych.jpg" STYLE="position:absolute; TOP:200px; LEFT:110px; WIDTH:700px;"/> -->


---

### Steps

<br><br>

1. Acquire  data from multiple disparate sites
2. Build pipelines to automatically & robustly process data
3. Develop methods for discovering clusters in the data

---

## Progress: Datasets Acquired

<img src="images/m2gtab.png" STYLE="position:absolute; TOP:200px; LEFT:00px; WIDTH:900px;"/>

---

## Progress: Pipeline Built!

<img src="images/ndmg-pipeline.png" STYLE="position:absolute; TOP:200px; LEFT:0px; WIDTH:900px;"/>


---
class: center, middle

## Clustering Methods: In Progress

--

1) Don't find spurious clusters
<br>
2) Do find legitimate clusters

---
### Challenge #1: Spurious Clusters


<img src="images/spikes.png" STYLE="position:absolute; TOP:150px; LEFT:150px; HEIGHT:500px;"/>


---
### Challenge #1: Spurious Clusters

<img src="images/spikes_kde.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic1.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic2.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic3.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic4.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic5.png" STYLE="position:absolute; TOP:150px;  LEFT:150px; HEIGHT:500px;"/>

---
### Challenge #1: Spurious Clusters

<img src="images/spikes_bic1000.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px;"/>

<!-- ---
### Challenge #1: Spurious Clusters

real data goes here -->

---
class:center, middle

### Opportunity #1: Spurious Clusters

<br>

Standard tools find spurious clusters

<br>

Fancy tools require too much data to be practical

<br>

.w[**Gap**: Clustering tools with appropriate statistical guarantees]



---
## Challenge #2: Legitimate Effects

<img src="images/batch.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:400px"/>

---
## Challenge #2: Legitimate Effects

<img src="images/batch_sex.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:400px"/>

---
class: center, middle

## Opportunity #2: Legitimate Effects

<br>


Thus far, harmonzing processing does not remove batch effect

<br>

Can further harmonizing processing mitigate batch effect?

<br>

If not, can harmonizing acquisition mitigate batch effect?

<br>

.w[**Gap**: Pipelines that harmonize across datasets,
and data to evaluate pipelines]

---

### Future Outlook Steps


- Microarrays never mitigated batch effects sufficiently
- Neither microarrays nor MRI "count"
- fMRI has another degree of freedom: stimulus

--

### Much More To Do!


<img src="images/lion2.JPG" STYLE="position:absolute; TOP:300px; LEFT:0px; WIDTH:400px;"/>
<img src="images/world.gif" STYLE="position:absolute; TOP:300px; LEFT:400px; WIDTH:500px;"/>



<DIV style="position: absolute; top:510px; left:120px; ">(i love this guy)</DIV>

<DIV style="position: absolute; top:580px; left:340px; "><a href="mailto:<EMAIL>"><EMAIL></a></DIV>



---

### Challenge #3: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:150px; HEIGHT:500px"/>

---

### Challenge #3: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels0.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #3: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels1.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #3: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels2.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #3: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels3.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #3: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels4.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>

---

### Challenge #3: Overlapping Clusters

<img src="images/overlap.png" STYLE="position:absolute; TOP:150px; LEFT:50px; WIDTH:800px; HEIGHT:50px"/>
<img src="images/levels5.png" STYLE="position:absolute; TOP:200px; LEFT:50px; WIDTH:800px;"/>


---
class: center, middle

### Opportunity #2: Overlapping Clusters
<br>

Any tree with thresholds will put arbitrarily close individuals arbitrarily far away

<br>

Trees that put individuals near boundary in multiple groups could mitigate this issue

<br>

.w[**Gap**: Tree learning tools with appropriate statistical guarantees]






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