3. 관심있는 Gene Set에서 시작하는 일반적인 네트워크 분석(sample gene set)
Example set을 만들어 분석한 내용을 토대로 자료를 만든다.
Seeding : Gene set(DEGs, Mutation Gene list, Fusion Gene list)
Expanding : 어떤 관계 데이터를 이용하여 네트워크를 그릴 것인가?
PPI
TF(transcription factor) : TG(target gene)
miRNA : TG
canonical pathway를 이루는 relation
Network Visualization
MONGKIE
Network Clustering
clique percolation method (소개만)
MCODE
MCL
Sub-network annotation
Sub-network를 이루는 유전자들의 개별조사(NCBI EntrezGene)
생물학자의 조언
관련 Canonical pathway 조사 (Enrichment Analysis)
text mining을 이용한 문헌조사,,,,,,
4. Sample Network
NFKB1 RELA Dimer ESC
NFKB1 NFKB1 Dimer ESC
SP1 PDPN TFregulation ESC
SP1 SLC39A1 TFregulation ESC
hsa-miR-34a E2F3 miRregulation ESC
hsa-miR-34a NOTCH1 miRregulation ESC
Node Property
SP1 Gene TF ESC
SMAD3 Gene TF ESC
RELA Gene TF ESC
hsa-miR-34a miR miR ESC
hsa-miR-34a miR miR ESC
hsa-miR-34a miR miR ESC
DLX1 soxTarget ESC
SSR4 soxTarget ESC
6. hESC :1750
ChIP-on-chip Data generation
- Used to investigate interactions
between proteins and DNA in vivo hESC : Young Lab(MIT)
SOX2 Target Prediction
http://en.wikipedia.org/wiki/ChIP-on-chip
7. SOX2 Cofactor Analysis
- TRANSFAC Annotation Data
- TRANSFAC
Match(Prediction)
OR
TSS
SOX2
Gene a mR A
i N
Protein A
ESC Regulation
37. MCODE
Step 1. Vertex Weighting
1-1. Finding neighber 1-2. Get highest k-core graph 1-3. Calculate density of 1-4. Calculate vertex weight
k-core graph
Step 2. Complex Prediction Step 3. Post-Processing
2-1. Seed complex by nodes 3-1. Complex must contain at
with highest weight least a 2-core graph
2.2. Include neighbors if the 3-2. Include neighbors if the
vertex weight is above vertex weight is above the
threshold(VWP) : vertex fluff parameter(Optional)
weight percentage
3-3. Haircut : Remove nodes
2-3. Repeat step 2 until no with a degree less than
more nodes can be included two(Optional)