1. Steve Horvath University of California, Los Angeles Module preservation statistics

2. Module preservation is often an essential step in a network analysis

3. Construct a network Rationale: make use of interaction patterns between genes Identify modules Rationale: module (pathway) based analysis Relate modules to external information Array Information: Clinical data, SNPs, proteomics Gene Information: gene ontology, EASE, IPA Rationale: find biologically interesting modules Find the key drivers of interesting modules Rationale: experimental validation, therapeutics, biomarkers Study Module Preservation across different data Rationale: Same data: to check robustness of module definition Different data: to find interesting modules

4. Motivational example: Studying the preservation of human brain co-expression modules in chimpanzee brain expression data. Modules defined as clusters (branches of a cluster tree) Data from Oldam et al 2006

5. Preservation of modules between human and chimpanzee brain networks

6. Standard cross-tabulation based statistics have severe disadvantages Disadvantages 1.only applicable for modules defined via a clustering procedure 2.ill suited for making the strong statement that a module is not preserved We argue that network based approaches are superior when it comes to studying module preservation

7. Is my network module preserved and reproducible? Langfelder et al PloS Comp Biol. 7(1): e1001057.

8. Broad definition of a module Abstract definition of module=subset of nodes in a network. Thus, a module forms a sub-network in a larger network Example: module (set of genes or proteins) defined using external knowledge: KEGG pathway, GO ontology category Example: modules defined as clusters resulting from clustering the nodes in a network Module preservation statistics can be used to evaluate whether a given module defined in one data set (reference network) can also be found in another data set (test network)

9. Network of cholesterol biosynthesis genes Message: female liver network (reference) Looks most similar to male liver network

10. Question How to measure relationships between different networks (e.g. how similar is the female liver network to the male network)? Answer: network concepts aka statistics

11. Connectivity (aka degree) Node connectivity = row sum of the adjacency matrix –For unweighted networks=number of direct neighbors –For weighted networks= sum of connection strengths to other nodes

12. Density Density= mean adjacency Highly related to mean connectivity

13. Network concepts to measure relationships between networks Numerous network concepts can be used to measure the preservation of network connectivity patterns between a reference network and a test network E.g. Density in the test set cor.k=cor(k ref,k test ) cor(A ref,A test )

14. One can study module preservation in general networks specified by an adjacency matrix, e.g. protein-protein interaction networks. However, particularly powerful statistics are available for correlation networks weighted correlation networks are particularly useful for detecting subtle changes in connectivity patterns. But the methods are also applicable to unweighted networks (i.e. graphs) Module preservation in different types of networks

15. Input: module assignment in reference data. Adjacency matrices in reference A ref and test data A test Network preservation statistics assess preservation of  1. network density: Does the module remain densely connected in the test network?  2. connectivity: Is hub gene status preserved between reference and test networks?  3. separability of modules: Does the module remain distinct in the test data? Network-based module preservation statistics

16. Several connectivity preservation statistics For general networks, i.e. input adjacency matrices  cor.kIM=cor(kIM ref,kIM test )  correlation of intramodular connectivity across module nodes  cor.ADJ=cor(A ref,A test )  correlation of adjacency across module nodes For correlation networks, i.e. input sets are variable measurements  cor.Cor=cor(cor ref,cor test )  cor.kME=cor(kME ref,kME test ) One can derive relationships among these statistics in case of weighted correlation network

17. Choosing thresholds for preservation statistics based on permutation test For correlation networks, we study 4 density and 4 connectivity preservation statistics that take on values <= 1 Challenge: Thresholds could depend on many factors (number of genes, number of samples, biology, expression platform, etc.) Solution: Permutation test. Repeatedly permute the gene labels in the test network to estimate the mean and standard deviation under the null hypothesis of no preservation. Next we calculate a Z statistic

18. Gene modules in Adipose Permutation test for estimating Z scores For each preservation measure we report the observed value and the permutation Z score to measure significance. Each Z score provides answer to “Is the module significantly better than a random sample of genes?” Summarize the individual Z scores into a composite measure called Z.summary Zsummary 10 strong evidence

19. Overview of Preservation statistics

20. Module preservation statistics are often closely related Red=density statistics Blue: connectivity statistics Green: separability statistics Cross-tabulation based statistics Message: it makes sense to aggregate the statistics into “composite preservation statistics” Clustering module preservation statistics based on correlations across modules

21. Composite statistic in correlation networks based on Z statistics

22. Gene modules in Adipose Analogously define composite statistic: medianRank Based on the ranks of the observed preservation statistics Does not require a permutation test Very fast calculation Typically, it shows no dependence on the module size

23. Summary preservation Network based preservation statistics measure different aspects of module preservation –Density-, connectivity-, separability preservation Two types of composite statistics: Zsummary and medianRank. Composite statistic Zsummary based on a permutation test –Advantages: thresholds can be defined, R function also calculates corresponding permutation test p-values –Example: Zsummary<2 indicates that the module is *not* preserved –Disadvantages: i) Zsummary is computationally intensive since it is based on a permutation test, ii) often depends on module size Composite statistic medianRank –Advantages: i) fast computation (no need for permutations), ii) no dependence on module size. –Disadvantage: only applicable for ranking modules (i.e. relative preservation)

24. Application: Modules defined as KEGG pathways. Comparison of human brain (reference) versus chimp brain (test) gene expression data. Connectivity patterns (adjacency matrix) is defined as signed weighted co-expression network.

25. Preservation of KEGG pathways measured using the composite preservation statistics Zsummary and medianRank Humans versus chimp brain co-expression modules Apoptosis module is least preserved according to both composite preservation statistics

26. Visually inspect connectivity patterns of the apoptosis module in humans and chimpanzees Weighted gene co- expression module. Red lines=positive correlations, Green lines=negative cor Note that the connectivity patterns look very different. Preservation statistics are ideally suited to measure differences in connectivity preservation

27. Literature validation: Neuron apoptosis is known to differ between humans and chimpanzees It has been hypothesized that natural selection for increased cognitive ability in humans led to a reduced level of neuron apoptosis in the human brain: –Arora et al (2009) Did natural selection for increased cognitive ability in humans lead to an elevated risk of cancer? Med Hypotheses 73: 453–456. Chimpanzee tumors are extremely rare and biologically different from human cancers A scan for positively selected genes in the genomes of humans and chimpanzees found that a large number of genes involved in apoptosis show strong evidence for positive selection (Nielsen et al 2005 PloS Biol).

28. Application: Studying the preservation of a female mouse liver module in different tissue/gender combinations. Module: genes of cholesterol biosynthesis pathway Network: signed weighted co-expression network Reference set: female mouse liver Test sets: other tissue/gender combinations Data provided by Jake Lusis

29. Network of cholesterol biosynthesis genes Message: female liver network (reference) Looks most similar to male liver network

30. Note that Zsummary is highest in the male liver network

31. Jeremy Miller, et al Dan Geschwind (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. PNAS 2010

32. Slide from Jeremy Miller

33. Why compare human and mouse brain transcription? 1) Module membership (kME) in conserved modules may be used to identify reliable markers for cell types and cellular components. 2) Studying differences in network organization could provide a basis for better understanding diseases enriched in human populations, such as Alzheimer’s Disease

34. Slide from J. Miller

35. Co-expression modules based on multiple human and mouse gene expression data Human Brain Modules Mouse Brain Modules Human modules M7h and M9h were enriched with AD genes These modules could not be found In mouse brains

36. Preservation of human network modules in mouse brains Zsummary

37. Human specific modules M9h and M7h are related to AD Module preservation analysis identified two highly human- specific module, M9h and M7h –No clear functional annotation Guilt by association approaches show these modules are related to neurodegenerative dementias –M9H showed significant overlap with an Alzheimer’s disease module that was identified using independent data sets run on different brain regions, on different platforms, and in different labs –M7h contained two intramodular hub genes related to AD and frontotemporal dementia (FTD) in humans: GSK3β and tau These two modules provide key targets for furthering our understanding of neurodegenerative dementias

38. Genetic Programs in Human and Mouse Early Embryos Revealed by Single-Cell RNA-Sequencing Zhigang Xue, Kevin Huang, Xiaofei Ye, et al Guoping Fan

39. Background Mammalian preimplantation development is a complex process involving dramatic changes in the transcriptional architecture. Through single-cell RNA-sequencing (RNA-seq), we report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos.

40. PCA of RNA seq data reveals known trajectory

41. WGCNA analysis

42. Module eigengenes vs stages

43. Module preservation analysis

44. General information on weighted correlation networks Google search –“WGCNA” –“weighted gene co-expression network” R function modulePreservation is part of WGCNA package Tutorials: preservation between human and chimp brains www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreser vation Implementation and R software tutorials, WGCNA R library

45. Network Methods for Describing Sample Relationships in Genomic Datasets: Application to Huntington's Disease Michael C Oldham et al BMC Syst Biol. 2012 PMID: 22691535

46. Rich but complex HD data Affymetrix microarray data from “the HD study” –Hodges et al: Regional and cellular gene expression changes in human Huntington’s disease brain. Hum Mol Genet 2006, 15(6):965-977 Brain samples of patients with HD (n = 44 individuals) and unaffected controls (n = 36 individuals, matched for age and sex) caudate nucleus (CN), cerebellum (CB), primary motor cortex (Brodmann’s area 4; BA4), and prefrontal cortex (Brodmann’s area 9; BA9) across five grades using Vonsattel’s neuropath criteria Further, age, sex, the country where the experiment was performed (samples were processed in the United States and New Zealand) and the microarray hybridization batch

47. Defining sample adjacency

48. Why define this sample network adjacency measure? Our proposed sample adjacency measure (based on β = 2) also has several other advantages. –it preserves the sign of the correlation –while any other power β could be used, the choice of β = 2 results in an adjacency measure that is close to the correlation when the correlation is large (e.g. larger than 0.6, which is often the case among samples in microarray data). The adjacency measure allows one to define network concepts.

49. Connectivity Gene connectivity = row sum of the adjacency matrix –For unweighted networks=number of direct neighbors –For weighted networks= sum of connection strengths to other nodes –Scaled connectivity:

51. Clustering Coefficient Measures the cliquishness of a particular node « A node is cliquish if its neighbors know each other » Clustering Coef of the black node = 0 Clustering Coef = 1 This generalizes directly to weighted networks (Zhang and Horvath 2005)

52. C(k) curve is a plot of scaled clustering coefficient versus scaled connectivity

53. Sample network concepts reveal the profound effect of Huntington’s disease in caudate nucleus.

54. Summary sample network Z.k is a very useful measure for finding array outliers. The correlation cor(K,C) between the connectivity and the clustering coefficient (two important network concepts) is a sensitive indicator of homogeneity among biological samples. –It can distinguish biologically meaningful relationships among subgroups of samples. –Advantage: This measure can highlight differences that cannot be found using differential expression –Disadvantage: It requires some work to figure out which genes lead to this effect. –Here: effect is concentrated in specific modules of genes Sample network approach is implemented in an R function and tutorial

55. Acknowledgement Current and former lab members: Peter Langfelder first author on many related articles Jason Aten, Chaochao (Ricky) Cai, Jun Dong, Tova Fuller, Ai Li, Wen Lin, Michael Mason, Jeremy Miller, Mike Oldham, Chris Plaisier, Anja Presson, Lin Song, Kellen Winden, Yafeng Zhang, Andy Yip, Bin Zhang Colleagues/Collaborators Neuroscience: Dan Geschwind, Giovanni Coppola, Jeremy Miller, Mike Oldham, Roel Ophoff Mouse: Jake Lusis, Tom Drake