Lecture 19: Analyzing transcriptome datasets. Spring 2018 May 3, 2018

Transcription

1 Lecture 19: Aalyzig trascriptome datasets Sprig 2018 May 3, 2018

2 Measurig the Trascriptome trascriptome: the mrnas expressed by a geome at ay give time (Abbott, 1999) Icludes protei codig trascripts ad various o-codig RNAs (trnas, rrnas, micrornas etc.)

3 Microarrays: before RNA-seq Microarrays cosist of thousads of DNA sequeces immobilized o a support i a grid-like patter. Based o DNA-DNA hybridizatio of complemetary bases: 5 A G C T C G T C G A G C 5

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5 Affymetrix GeeChip

6 Trascriptome ad Proteome Do they agree? mrna abudace is a imperfect proxy for protei abudace Biological Issues: Post-traslatioal modificatios Protei stability ad foldig Techical Issues: Difficult to quatify protei abudace Global aalysis of protei expressio i yeast

7 The data:

8 Classic Huma Trascriptome Studies Golub et al. Molecular classificatio of cacer: class discovery ad class predictio by gee expressio moitorig. Sciece ALL acute lymphoblastic leukemia AML acute myeloid leukemia Alizadeh et al. Discovered a ew subclass of B-cell lymphoma. Nature Scherf et al. A gee expressio database for the molecular pharmacology of cacer. Nature Geetics huma cacer cell lies

9 Aalysis Essetial problem: Fid: Glea biological isight give a large dataset with techical ad biological oise. Patters: Similarities or differeces betwee samples o global/multigee level

10 Aalysis Methods m Statistical tests for testig over-expressed gees (gees that are over-expressed i a treatmet vs. cotrol) m Dimesioality reductio (e.g. usig Pricipal Compoets Aalysis) m Clusterig m Dowstream aalysis for makig sese of the patters foud usig clusterig. m Itegratio with other sources of geomic data

11 Differetially expressed gees Say you ra a kockout experimet or some perturbatio or stress. You wat to kow which gees were affected. Look for gees that are over/uder expressed i compariso to a cotrol experimet. Use a statistical test that esures your results are statistically sigificat.

12 Trascriptome Data Properties: m High dimesioality m Small umber of samples m Complex patters: visualisatio ad extractio

13 PCA High dimesioal data ca ofte be well represeted by projectio to a few saliet directios. What are the mai profiles/patters/modes of expressio Gives you a set of base profiles usig which you ca recostruct each gee profile You the look at these base profiles to see: q How may are there? q What do they look like? Referece: Holter NS, Mitra M, Marita A, Cieplak M, Baavar JR, Fedoroff NV. Fudametal patters uderlyig gee expressio profiles: simplicity from complexity. Proc Natl Acad Sci U S A Jul 18;97(15): PMID:

14 Clusterig A exploratory aalysis method q Iterested i atural groups i the data. q q What are the commo patters of gee expressio i my dataset? Ca be doe o the basis of a otio of similarity (of expressio), e.g. correlatio betwee expressio vectors of two gees. May algorithms available. Most commo: K-meas ad hierarchical clusterig. Referece: Eise MB, Spellma PT, Brow PO, Botstei D. Cluster aalysis ad display of geome-wide expressio patters. Proc Natl Acad Sci U S A Dec 8;95(25): PMID:

15 Hierarchical Clusterig g1 g2 g3 g4 g5 g6 g7 g8 g1 is most like g8 g1 g8 g2 g3 g4 g5 g6 g7 g4 is most like {g1, g8} g1 g8 g4 g2 g3 g5 g6 g7

16 Hierarchical Tree g1 g8 g4 g5 g7 g2 g3 g6

17 Clusterig: Case Study Sorlie et al., 2001

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19 Clusterig Time-Course Data Clusterig: Groups gees/samples ito clusters based o their similarities Number of groups is user defied

20 Uderstadig the results Oce we have a list of iterestig or differetially expressed gees we wat to see whether they make sese. We ca look at features of the group of gees: q What fuctios they perform q Look for commo trascriptio factor bidig sites i their promoter regios (co-expressio as a result of coregulatio)

21 Promoter aalysis Test the hypothesis that co-expressio is a result of coregulatio by lookig for patters i the promoters of the co-expressed gees. Search for over represetatio of kow trascriptio factor bidig sites. Search for ovel bidig sites. Search for combiatio of bidig sites (regulatory modules)

22 The Gee Otology A otology is a specificatio of the cocepts & relatioships that ca exist i a domai. The Gee Otology (GO) project is a effort to provide cosistet descriptios of gee products.

23 Usig GO Fuctioal category aalysis usig GO: q GO allows us to determie whether the gees we foud are eriched for particular fuctios May tools available

24 GO erichmet aalysis Suppose you fid that your list of gees has a distributio of the followig GO terms: 17.5% Others 7.9% Sigal trasducer Clock cell eriched mrnas 12.7% Protei dimerizatio 19.0% Hydrolase activity 30.2% Nucleic acid bidig 12.7% Trascriptio factor activity LNv-eriched mrnas tim + pdf cell eriched mrnas 36.4% Others 9.1% Trasporter activity 22.7% Trascriptio factor activity 31.0% Others 8.0% 22.7% Receptor activity Nucleic acid bidig 9.1% Sigal trasducer 10.0% Sigal trasducer 51.0% Catalytic activity Figure 3 Fuctioal classificatios of the mrnas eriched i the clock euros. The gees were assiged to fuctioal classes o the basis of the gee otology aotatio.

25 Example Results usig the David tool o a list of gees from Arabidopsis

26 Gettig those p-values What is the probability of fidig a list of gees, k out of which are associated with a give GO category? (Assume overall there are N gees ad K gees that are aotated with that category). P (k) = K k N N K k This is called the hyper-geometric distributio

27 Gettig those p-values What is the probability of fidig a list of gees, k out of which are associated with a give GO category? (Assume overall there are N gees ad K gees that are aotated with that category). P (k) = The associated p-value: K k N N K k p =1 kx 1 i=1 K i N N K i