Marot, Guillemette

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HAL Id: hal-03942820https://hal.inria.fr/hal-03942820Submitted on 17 Jan 2023HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Meta-analysis of RNA-Seq dataGuillemette MarotTo cite this version:Guillemette Marot. Meta-analysis of RNA-Seq data. Le RNASeq, de la paillasse à l’analyse in silico,PLBS, bilille and GoaL platforms, Jun 2022, Lille, France. ￿hal-03942820￿Meta-analysis of RNA-Seq dataGuillemette MarotUniv. Lille, CHU Lille METRICS & Inria MODAL28 June 20221 / 33IntroductionOverview1 Introduction2 Statistical analysis of one study3 Meta-analysis4 metaRNASeq and SMAGEXP5 Conclusion2 / 33IntroductionMeta-analysisParticular context :Differential expression analysisSeveral studies available but direct comparison impossibleVery few individuals in each individual study, a lot of genes ortranscripts.Objectives of meta-analysisIncrease of sensitivity, the proportion of truly declared differentiallyexpressed (DE) genesEliminate false positives (genes declared DE but not DE in reality)3 / 33Statistical analysis of one studyOverview1 Introduction2 Statistical analysis of one study3 Meta-analysis4 metaRNASeq and SMAGEXP5 Conclusion4 / 33Statistical analysis of one studyStatistical analysis of one studyResearch of differentially expressed genesNormalisationUse of a test statistic appropriate for differential expression analysisCorrection for multiple testingMicroarrays RNA-SeqInformation Intensities Counts of readsModelling Normal distribution Poisson, Negative binomialTests Moderated t-tests Likelihood ratio testsAdaptation of usual test statistics to find a compromise between a gene-by-gene analysis and a”common parameter” analysis5 / 33Statistical analysis of one studyMean-variance relationship in RNA-Seq dataThe Poisson distribution to model countsDescribes the number of occurences of rare events during a giventime intervalProperty : Mean = VarianceFrom D. Robinson and D. McCarthy6 / 33Statistical analysis of one studyNegative Binomial ModelsA supplementary dispersion parameter φ to model the variancePoisson vs Negative Binomial modelsTechnical variability is the main source of variability in low counts, whereasbiological variability is dominant in high counts7 / 33Statistical analysis of one studyEstimating the dispersion : the key questionProblemEstimate a reliable dispersion from a very small number of replicates(sometimes less than 5)Why using sophisticated approaches ?gene-specific tests ⇒ lack of sensitivity (proportion of true positivesamong positives) due to the lack of informationcommon dispersion parameter for all tests ⇒ many false positivesEmpirical bayesian approaches = compromise between gene-specific andcommon dispersion parameter estimationExemple : edgeR, DESeq28 / 33Statistical analysis of one studyDispersion estimation with DESeq2Hypothesis : genes of similar average expression strength have similardispersion1 Estimate gene-wise dispersion estimates using maximum likelihood(ML) (black dots)2 Fit a smooth curve (red line)3 Shrink the gene-wise dispersion estimates (empirical Bayes approach)toward the values predicted by the curve to obtain final dispersionvalues (blue arrow heads).9 / 33Statistical analysis of one studyp-values histograms for diagnosisExamples of expected overall distribution(a) : the most desirable shape(b) : very low counts genes usually have large p-values(c) : do not expect positive tests after correction10 / 33Statistical analysis of one studyp-values histograms for diagnosisExamples of not expected overall distribution(a) : indicates a batch effect (confounding hidden variables)(b) : the test statistics may be inappropriate (due to strong correlationstructure for instance)(c) : discrete distribution of p-values : unexpected11 / 33Meta-analysisOverview1 Introduction2 Statistical analysis of one study3 Meta-analysis4 metaRNASeq and SMAGEXP5 Conclusion12 / 33Meta-analysisBack to microarray meta-analysis(Marot et al., Bioinformatics, 2009)⇒ p-value combination showed better performance in terms of sensitivityand AUC than effect size combination.p-value combination was suggested with the inverse normal method(Liptak, 1958)Ng =S∑s=1wsΦ−1(1− pgs)ws =√ ∑c Rcs∑`∑c Rc`Ng ∼ N (0, 1)Trick for microarrays : use unilateral p-values to avoid conflicts.13 / 33Meta-analysisBack to microarray meta-analysisGene for which we observe a conflict of expressiont1 < 0 observed value of test statistic for study 1t2 > 0 observed value of test statistic for study 214 / 33Meta-analysisBack to microarray meta-analysisscores from unilateral p-valuesz = Φ−1(1− p1sided) = Φ−1(F (t))15 / 33Meta-analysisBack to microarray meta-analysisscores from unilateral p-valuesz = Φ−1(1− p1sided) = Φ−1(F (t))15 / 33Meta-analysisBack to microarray meta-analysisscores from bilateral p-valuesz = Φ−1(1− p2sided) = Φ−1(1− 2.(1− F (|t|)))16 / 33Meta-analysisBack to microarray meta-analysisscores from bilateral p-valuesz = Φ−1(1− p2sided) = Φ−1(1− 2.(1− F (|t|)))16 / 33Meta-analysisBack to microarray meta-analysisBuilding of individual scores from p-valuesunilateral bilateralthen bilateral test then uni(bi)lateral test17 / 33Meta-analysisRNASeq data meta-analysis18 / 33Meta-analysisRNA-seq data meta-analysisInverse normal combination (Liptak, 1958)Ng =S∑s=1wsΦ−1(1− pgs)Ng ∼ N (0, 1)Fisher’s method (1932)Fg = −2S∑s=1ln(pgs)Fg ∼ χ22SConflicts to be treated a posteriori19 / 33Meta-analysisGlobal differential analysis (DESeq)Gene counts : Ygcrs ∼ NB(ηgcrs , φgs)Full model log(ηgcrs) = βg + λgc + δgs + log(`crs)where βg is an intercept, λgc is a fixed condition effect, δgs a fixed studyeffect, `crs library size normalization factorReduced model log(ηgcrs) = βg + δgs + log(`crs)Likelihood ratio testH0,g : ∀c , λgc = 0 vs H1,g : ∃c | λgc 6= 0.∼ χ2 with degrees of freedom equal to the number of conditions minus 120 / 33Meta-analysisSimulationsData simulated according to a negative binomial distribution,Ygcrs ∼ NB(µgcs , φgs)where µgcs and φgs represent the mean and dispersion, respectivelyMean-variance relationship defined byVar(Ygcrs) = µgcs +µ2gcsφgs.To incorporate inter-study variability :log(µgcs) = θgc + εgcs , and εgcs ∼ N (0, σ2),where σ2 is the size of the inter-study variability.21 / 33Meta-analysisMeta-analysisSensitivity : E ( VPVP+FN )0.30.40.50.60.70.80.91.02 studiesσ= 0.15Sensitivity0.30.40.50.60.70.80.91.0σ= 0.50SensitivityIndiv InverseNormalFisher DESeq(no study)DESeq(study)0.30.40.50.60.70.80.91.03 studies0.30.40.50.60.70.80.91.0Indiv InverseNormalFisher DESeq(no study)DESeq(study)0.30.40.50.60.70.80.91.05 studies0.30.40.50.60.70.80.91.0Indiv InverseNormalFisher DESeq(no study)DESeq(study)22 / 33Meta-analysisMeta-analysis0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.0σ= 0.15Sensitivity2 studies0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.0σ= 0.501 − SpecificitySensitivity0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.03 studies0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.01 − Specificity0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.05 studies0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.01 − SpecificityFisherDESeq (no study)DESeq (study)23 / 33Meta-analysisMeta-analysis% TP among unique discoveries0.00.20.40.60.81.0(27.9) (78)2 studiesσ= 0.15% TP among unique% TP among unique discoveries0.00.20.40.60.81.0(125.8) (80)σ= 0.50% TP among uniqueDESeq(study)Fisher0.00.20.40.60.81.0(42.9) (63)3 studies0.00.20.40.60.81.0(54) (207)DESeq(study)Fisher0.00.20.40.60.81.0(49.3) (36)5 studies0.00.20.40.60.81.0(61.8) (170)DESeq(study)FisherProportion of true positives among unique discoveries.24 / 33metaRNASeq and SMAGEXPOverview1 Introduction2 Statistical analysis of one study3 Meta-analysis4 metaRNASeq and SMAGEXP5 Conclusion25 / 33metaRNASeq and SMAGEXPR packageR package metaRNASeq available on CRANPublication : Rau, Marot and Jaffrézic, BMC Bioinformatics (2014)library(metaRNASeq)vignette("metaRNASeq")data simulation with sim.functionsingle individual analyses with DESeq2use of HTSFilter (if needed) to validate the p-value uniformdistribution assumptionp-value combination with fishercomb and invnormtreatment of conflicts from the extraction of fold changes26 / 33metaRNASeq and SMAGEXPKey figuresDE (differentially expressed) : number of DE genesIDD (integration-driven discoveries) : number of genes that aredeclared DE in the meta-analysis that were not identified in any ofthe single studies aloneLoss : number of genes that are identified DE in single studies but notin meta-analysisIDR (integration-driven discovery rate) : corresponding proportion ofIDDIRR (integration-driven revision) : corresponding proportion of loss27 / 33metaRNASeq and SMAGEXPSMAGEXPSMAGEXP available on Galaxy main tool shed or in a dockerised instancePublication : Blanck and Marot, Gigascience, (2019)28 / 33metaRNASeq and SMAGEXPSMAGEXPSource code, help, and installation instructions available on Github :https ://github.com/sblanck/smagexp29 / 33metaRNASeq and SMAGEXPSMAGEXP30 / 33ConclusionOverview1 Introduction2 Statistical analysis of one study3 Meta-analysis4 metaRNASeq and SMAGEXP5 Conclusion31 / 33ConclusionConclusion - DiscussionMeta-analysis useful when strong inter-study effect and more than 3studiesp-value combinations enable to take advantage of empirical bayesianapproaches - especially appropriate when few replicateswith RNA-Seq data, necessity to treat conflicts a posteriorip-values histograms and PCA graphs enable to decide whether usingor not metaRNASeq.metaRNASeq available on CRANSMAGEXP available on Galaxy tool shed, Docker, Github.32 / 33ConclusionAcknowledgementsAndrea Rau Florence Jaffrézic Samuel BlanckClaus-Dieter Mayer Jean-Louis Foulley33 / 33

Meta-analysis of RNA-Seq data

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Meta-analysis of RNA-Seq data

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