Lecture 1 - Omics data and Network Model Analyses
heat map: 縱軸為不同的樣本，橫軸為不同的實驗條件。進行聚類，顏色表示相似程度，可以觀察同種實驗條件下那些樣本表達量相近或者同一實驗樣本在哪些實驗條件下表達量相近。
volcano plots:橫軸為log2 fold change;縱軸為-log10 p-value
Gene ontology:www.geneontology.org
ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments.
PPI工具：Berger et al. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics 8:372 (2007)
http://actin.pharm.mssm.edu/genes2networks
Lachmann et al. KEA: kinase enrichment analysis. Bioinformatics 25(5):684-6 (2009)
http://amp.pharm.mssm.edu/lib/kea.jsp
Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool
Edward Y. Chen et al. BMC Bioinformatics (2013）
http://amp.pharm.mssm.edu/Enrichr/index.html
Lecture 2 - Single Cell Time Course Data and Dynamical Model Analyses
第一講是通過基因測序找到了差異表達基因列表，但是這種方法不能解決兩種相反作用于同一個分子時由誰決定的問題，而解決這種問題就用這種方法。舉的例子是使用動力學(xué)模型解釋流式細胞的圖像。
Lecture 3 - Dynamical Model Case Study
Case Study: Interpreting Flow Cytometry Data with Stochastic Dynamical Models隨機動力學(xué)模型
