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RNA-seq Analysis
- 1. RNA-‐seq analysis Mikael Huss Bioinforma7cs scien7st at WABI (Wallenberg Advanced Infrastructure for Bioinforma7cs), Science for Life Laboratory / DBB, Stockholm university February 13, 2013
- 2. Omics, biology and diseases + + + + Protein “parts Protein Genomics RNA profiles Interactomics list” profiles Systems biology Pathways, molecular targets, diagnos5cs
- 3. Approximate contents oftalk - Gene expression analysis in general; differences between RNA-seq and microarrays - Typical workflow(s) for RNA-seq analysis - Normalization issues - Visualization - Differential expression analysis I have tried to include many references so you can go back to these slides for reference afterwards
- 4. How DNA get transcribed to RNA (and then translated to proteins) varies between e. g. -‐Tissues -‐ Cell types -‐ Cell states -‐Individuals
- 5. What can gene expression tell us? Basic research -‐ How do gene expression paUerns determine cellular iden7ty? (7ssues, cell types …) -‐ How does gene expression control early development in an embryo? -‐ What kinds of genes are expressed in response to specific s7muli (infec7ons, smoking, environmental pollu7on, gym exercise …)? -‐ What kinds of genes do bacteria or other microorganisms express in the human gut / in soil / in oceans under different condi7ons? … and much, much more …
- 6. What can gene expression tell us? Diseases -‐ Which genes are over-‐ (or under-‐)expressed in pa7ents vs. healthy controls? -‐ Which genes are correlated to disease progression? -‐ Can markers of hidden disease be found by sequencing blood plasma?
- 7. Gene expression signatures for disease? Hypothesis: Cell types are stable states in a “space” of gene expression paUerns. Diseases (e g cancers) distort the gene expression so that the cell ends up in the wrong stable state. Furusawa and Kaneko, Biology Direct 2009 4:17
- 8. Can the research community find such paUerns? On-‐line predic7on compe77ons, objec7vely scored by the organizers Diagnosing MS (mul/ple sclerosis), lung cancer, psoriasis, COPD (KOL) Prognos/ca/ng breast cancer outcome
- 9. Human 7ssue RNA-‐seq data sets Genotype-Tissue Expression project http://commonfund.nih.gov/GTEx/ Illumina Human Body Map accessed via ReCount database, bowtie-bio.sourceforge.net/recount/ Wang 2008 data set of ~15 human tissues accessed via ReCount RNA-seq Atlas http://medicalgenomics.org/rna_seq_atlas Human Protein Atlas http://www.proteinatlas.org (tissue RNA-seq data not yet publicly released)
- 10. Tools for genome-‐scale gene expression measurements Microarrays (c:a 1995) Some7mes called “gene chips” Based on hybridiza7on RNA sequencing (c:a 2008 in current form) Based on sampling
- 11. Typical (m)RNA-‐seq experiment “library” -‐> <-‐ reads hUp://cmb.molgen.mpg.de
- 12. Alterna7ve: rRNA deple7on There are various kits for depleting rRNA instead Pluses: - Can use for microorganisms that don’t have poly-A tails - Thus, can use for simultaneous host/pathogen expression profiling - Can find non-coding RNA Minuses: -Usually leaves in quite a lot of rRNA -In practice, often variable efficiency between samples -> hard to compare results
- 13. Sequencing plagorms ABI 3730xl 454 Life Sciences SOLiD + Pacific Biosciences, Sanger Sequencing pyrosequencing Illumina Oxford Nanopore etc Single-‐molecule sequencing Length/read 800 bp 400 bp 100 bp 20 000+ bp Reads/run 96 1 million 2 billion 5 million Bases/run 60 kbp 400 Mbp 500 Gbp 100 Gbp Speed 10 years/HG 1 month/HG 1 day/HG 10 min/HG “old school” “2nd gen” “3rd gen”
- 14. Microarray: Hybridiza7on Source: Wikipedia The design of the microarray determines what you can detect in a sample
- 15. RNA sequencing: Sampling It is possible to detect transcripts that are not known a priori (in advance)
- 16. RNA-‐seq advantages The non-‐dependence on reference makes possible: -‐ meta-‐transcriptomics -‐ detec7ng novel splice variants -‐ detec7ng novel transcripts -‐ Fusion transcripts -‐ Non-‐coding transcripts
- 18. Some examples RNA-seqAtlas Wang 2008
- 19. Some examples RNA-seqAtlas <- Skeletal Wang 2008 muscle -> <-Adipose tissue-> HPA
- 20. What does one do with RNA-‐seq reads? • Mapping (also called alignment) • (de novo) Assembly
- 21. Mapping (alignment) vs. assembly Imagine a book being ripped to pieces with word or sentence fragments ending up on each piece of paper. If you have a copy of the book that you can compare the pieces to, you have a mapping (alignment) problem. If you have no copy of the book, you have a de novo assembly problem.
- 22. Mapping to a reference genome Reads from the sequencer Sequencing error Gene7c varia7on CAATCAGA G TCCCACTGTGG AGACG TCCCACTGTGGGGTG GTGAAGTGTCCGTAGATGTGTG GCAAATGCAATCAGACG TCCC Gene(or transcript) sequence
- 23. Mapping to a reference genome AGACG TCCCACTGTGGGGTG GTGAAGTGTCCGTAGATGTGTG GCAAATGCAATCAGACG TCCC
- 24. Mapping to a reference genome GTGAAGTGTCCGTAGATGTGTG GCAAATGCAATCAGACG TCCC
- 25. Mapping to a reference genome GCAAATGCAATCAGACG TCCC
- 26. Mapping to a reference genome
- 27. Mapping to the genome vs. the transcriptome Vs. the genome: -Can (in principle) detect new transcripts, splice variants - Less sensitive, need a lot of coverage to discover new things - Need a “splice-aware” aligned such as TopHat, MapSplice, RUM etc. Vs. the transcriptome: -Not unbiased anymore, tied to existing annotation -Faster, more sensitive, need less coverage The best of both worlds? - Tools like TopHat (v1.4 and up) now do both
- 28. If it had been de novo assembly CAATCAGA G TCCCACTGTGG AGACG TCCCACTGTGGGGTG GTGAAGTGTCCGTAGATGTGTG GCAAATGCAATCAGACG TCCC Assembly CAATCAGA G TCCCACTGTGG AGACG TCCCACTGTGGGGTG GCAAATGCAATCAGACG TCCC “singleton” GTGAAGTGTCCGTAGATGTGTG Consensus sequence(s)
- 29. Assembly of RNA-‐seq reads Will not be discussed much further here. Most popular de novo assemblers build de Bruijn graphs where overlapping k-mers are connected to each other. The programs then try to find paths through the graph Typically needs a LOT of RAM. Can try to pre-process using “digital normalization” Tools: - Trinity - Velvet/Oases - CLC Bio (commercial)
- 30. Assembly of RNA-‐seq reads Typical workflow could be: - Clean the reads properly (remove adapters, low-quality reads) - Useful tools: FastQC, PRINSEQ, FASTX toolkit etc. - Run assembly tool of choice, resulting in a set of contigs - BLAST the contigs against nt database, check for % overlap by transcript in related organisms - Map your original reads back to the contigs and count the reads overlapping each <- comparison of assembly & mapping
- 31. Quan7fying expression with RNA-‐seq Microarrays give a continuous (floating-point) expression value for each gene RNA-‐seq gives an integer value for each gene (“digital expression”): read counts
- 32. Example (SciLifeLab) mapping workflow FASTQ file(s) TopHat 2.0 BAM file Picard tools (SortSam, MarkDuplicates) Sorted BAM file with duplicate reads removed HTSeq 0.5 Cufflinks 2.0 Gene-level count files Gene- and isoform-level expression (for DE analysis) estimates (FPKM, for reporting)
- 33. RNA-‐seq mapping: different isoforms Isoform 1 Exon 1 Exon 2 Exon 3 Isoform 2 Exon 1 Exon 2
- 34. (what it would look like mapped to the genome) Exon 1 Exon 2 Exon 3 Need a special mapping algorithm which allows large gaps, a “split-‐read aligner”
- 35. (what we would actually observe – of course we don’t know which reads come from which isoform) Sta7s7cal algorithms needed to es7mate what propor7on of reads comes from which isoform. (For example, maximum likelihood / expecta7on maximiza7on)
- 36. Name Free/Commercial/ Type of approach Descrip5on only Xing et al. 2006 D Maximum likelihood Partek C “ Li et al. 2010 D “ Avadis C “ IsoEM F “ MISO F “ (MCMC) Cufflinks F “ rQuant F Least squares (quadra7c programming) Rpkmforgenes.py F Least squares Howard and Heber 2010 D Least squares FluxCapacitor F Linear programming CLC Bio C ? NSMAP F Nonnega7ve Sparse Maximum A Posteriori ALEXA-‐SEQ F Use only reads that are compa7ble with a single isoform NEUMA D Normaliza7on by Expected Uniquely Mappable Area
- 37. Some remarks onisoform quantification - It is necessary for correct gene-level quantification as well because straight read counting methods can never be fully correct (from 2012 CuffDiff2 paper) - Xing et al. (2006) gave the basic idea for EM- based isoform quantification which other programs (Cufflinks, MISO, IsoEM, …) have added various “bells and whistles” to - It is actually pretty hard to do isoform quantification well because there can be a lot of possible isoforms not enough sequence coverage to estimate
- 38. Basic idea ofthe EM approach We have a set of reads mapping to some locus - Some fit one specific isoform - Some fit several isoforms If we knew the isoforms’ expression levels, we could distribute the reads proportionally to those. But we don’t! On the other hand, if we knew the probability of each read to match each isoform, we could estimate the isoforms’ expression pretty well. But we don’t know that either. So … start with a guess and iterate! - Assign reads to isoforms according to some initial guess - Re-estimate isoform expression levels - Repeat until convergence!
- 39. Gene fusion detec7on with RNA-‐seq Beyond isoforms: Detect pieces of different genes that have been fused Look for reads that map in “wrong” ways Wang et al. Briefings in Bioinforma7cs doi:10.1093/ bib/bbs044
- 40. Some further comments on microarrays and RNA-‐seq -‐ Microarrays are s7ll cheaper and faster. -‐ You may be able to run more replicates, which is important for sta7s7cal power. -‐ RNA-‐seq has a wider measurement range. -‐ Low expressed transcripts: -‐ Microarrays have high background signal -‐> poor measurement -‐ RNA-‐seq can measure well if you sequence very deeply -‐ Medium expressed transcripts: -‐ Microarrays measure well -‐ RNA-‐seq measures well if sequenced rela7vely deeply -‐ High expressed transcripts: -‐ Microarrays measure poorly because of satura7on -‐ RNA-‐seq measures well -‐ Less is understood about how to pre-‐process and normalize RNA-‐seq data. -‐ One interes7ng aspect of RNA-‐seq: You can con7nue to sequence a sample more to obtain beUer gene expression es7mates.
- 41. Analysis -‐ Pre-‐processing and normaliza7on -‐ Visualiza7on -‐ Differen7al gene expression analysis -‐ ( Gene set analysis, pathway analysis, gene expression signatures … -‐> try to find the biological significance)
- 42. Pre-‐processing Why do we do pre-‐processing and normaliza7on of RNA-‐seq (or microarray) data?
- 43. Pre-‐processing Why do we do pre-‐processing and normaliza7on of RNA-‐seq (or microarray) data? -‐ To correct for batch effects -‐ Different labs -‐ Different prepara7on 7mes -‐ Etc.
- 44. Pre-‐processing Why do we do pre-‐processing and normaliza7on of RNA-‐seq (or microarray) data? -‐ To correct for batch effects -‐ Different labs -‐ Different prepara7on 7mes -‐ Etc. -‐ To correct for intrinsic technical biases in the technologies
- 45. Pre-‐processing Why do we do pre-‐processing and normaliza7on of RNA-‐ seq (or microarray) data? -‐ To correct for batch effects -‐ Different labs -‐ Different prepara7on 7mes -‐ Etc. -‐ To correct for intrinsic technical biases in the technologies -‐ To make the expression value distribu7ons conform to some assump7ons in order to perform sta7s7cal tests
- 46. RNA-‐seq pre-‐processing For RNA-‐seq data, it is s7ll less understood than for microarrays how one should pre-‐process and normalize the data. Let’s look at some aspects (that some7mes apply to both RNA-‐seq and microarray data)
- 47. R and Bioconductor Very helpful for (e.g.) microarray and RNA-seq differential expression analysis Microarray: RNA-seq: affy, lumi (read raw microarray signal files DESeq, edgeR, baySeq, & preprocess) (differential expression analysis limma (differential expression analysis based on count data) with complex designs) SAMSeq (nonparametric differential expression analysis)
- 48. Variance stabiliza5on Rawdata (could be microarray signal or RNA-seq counts) Higher value -> higher variability (noise) Log transform Lower value -> higher variability. Too aggressive Variance stabilizing transform e.g. voom() in limma package http://bridgecrest.blogspot.se/2011_09_01_archive.html
- 49. Quan5fying expression with RNA-‐seq If you want to compare RNA-‐seq counts between different genes and/or samples, consider: -‐ Longer genes/transcripts are expected to generate more reads -‐ The more you sequence, the more reads you get from each gene Therefore, the standard measure has been RPKM ( ), which corrects for transcript length and sequencing depth: ⎛ X t ⎞ ⎜ l ⎟ 10 9 ⋅ X t (Xt: no of reads mapped to transcript/gene/… t ⎜ eff ,t ⎟ Nlib: no of mapped reads in library RPKM = ⎜ 10 3 ⎟ ⎜ ⎟ = N lib ⋅ leff ,t Leff, t: effec/ve length of transcript/gene/… t) ⎝ ⎠ ⎛ N lib ⎞ ⎜ 6 ⎟ ⎝ 10 ⎠ € € FPKM is a paired-end version of this
- 50. Alterna5ves TPM –“transcripts per million” A slightly modified RPKM measure that accounts for differences in gene length distribution in the transcript population
- 51. Alterna5ves TMM – “trimmed mean of M values” Attempts to correct for differences in RNA composition between samples E g if certain genes are very highly expressed in one tissue but not another, there will be less “sequencing real estate” left for the less expressed genes in that tissue and RPKM normalization (or similar) will give biased expression values for them compared to the other sample RNA population 1 RNA population 2 Equal sequencing depth -> orange and red will get lower RPKM in RNA population 1 although the expression levels are actually the same in populations 1 and 2 Robinson and Oshlack Genome Biology 2010, 11:R25, http://genomebiology.com/2010/11/3/R25
- 52. Across-‐sample comparability Dillieset al., Briefings in Bioinformatics, doi:10.1093/bib/bbs046
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- 55. Prac5cal issues with normaliza5on methods Limma / voom can give negative values TMM cannot be done on a single sample
- 56. RNA-‐seq pre-‐processing In RNA-‐seq, normaliza7on of counts is oven interwoven with differen7al expression analysis and done implicitly in DE packages such as DESeq, edgeR etc. Normalized values like RPKM are usually only used for repor7ng expression values, not tes7ng for differen7al expression. Why?
- 57. Count nature of RNA-‐seq data These methods want to use the added sta7s7cal power provided by the count nature of RNA-‐seq data. Simplified toy example: Scenario 1: A 30000-bp transcript has 1000 counts in sample A and 700 counts in sample B. Scenario 2: A 300-bp transcript has 10 counts in sample A and 7 counts in sample B. Assume that the sequencing depths are the same in both samples and both scenarios. Then the RPKM is the same in sample A in both scenarios, and in sample B and both scenarios. In scenario A, we can be more confident that there is a true difference in the expression level than in scenario B (although we would want more replicates of course!) by analogy to a coin flip – 700 heads out of 1000 trials gives much more confidence that a coin is biased than 7 heads out of 10 trials
- 58. Visualiza5on Can be useful for “sanity checking”, outlier detec7on and exploratory analysis in general Examples of useful visualiza7ons -‐ Heat maps -‐ PCA/MDS/NMF -‐ Box plots, violin plots etc.
- 59. Box plots Usefulfor comparing groups Adding the actual data points is optional but can be interesting
- 60. Sample correla5on heat maps Heat maps are ubiquitous in transcriptomics Correlations between samples, hierarchical clustering Used for “sanity checks”, outlier detection Two tissues Batch effects
- 61. Gene / sample heat maps With a smaller collection of genes, one sometimes looks at gene/sample heat maps
- 62. PCA plots Anotherway to see how samples cluster
- 63. PCA plots Nicething with PCA: you can also see how much each gene contributes to each principal component -> a kind of feature selection
- 64. Alterna5ves to PCA NMF: non-negative matrix factorization. Also a matrix decomposition technique (like PCA) “A bioinformatic assay for pluripotency in human cells”, Nature Methods: doi.10.1038/nmeth.1580
- 65. PCA plot of human 5ssue RNA-‐seq Red – GTex Green – Body Map Black – Human Protein Atlas
- 66. # of genes taking up X% of sequences GTex RPKM HBA1 HBB HBA2
- 67. # of genes taking up X% of sequences GTex
- 68. # of genes taking up X% of sequences Wang/Sandberg
- 69. Differen5al expression analysis Many tools available! Easily the most common type of analysis, even though it is understood that gene expression levels are not independent of each other, and should in principle be considered together. However, since the number of samples is typically << the number of measured genes, a full model is usually not feasible to construct in practice. Some sort of feature selection is needed.
- 70. Differen5al expression analysis One would simply like to do a t-test or something like that for each gene, but …
- 71. Differen5al expression analysis One would simply like to do a t-test or something like that for each gene, but … - Assumes normal distribution & no mean-variance dependence
- 72. Differen5al expression analysis One would simply like to do a t-test or something like that for each gene, but … - Assumes normal distribution & no mean-variance dependence - Hard to estimate variance from few samples
- 73. Differen5al expression analysis One would simply like to do a t-test or something like that for each gene, but … - Assumes normal distribution & no mean-variance dependence - Hard to estimate variance from few samples - Multiple testing issue
- 74. Parametric vs. non-‐parametric methods It would be nice to not have to assume anything about the expression value distributions but only use rank-order statistics. -> methods like SAM (Significance Analysis of Microarrays) or SAM-seq (equivalent for RNA-seq data) However, it is (typically) harder to show statistical significance with non- parametric methods with few replicates. My rule of thumb: - Many replicates (~ >10) in each group -> use SAM(Seq) - Otherwise use DESeq or other parametric method Note that according to Simon Anders (creator of DESeq) says that non- parametric methods are definitely better with 12 replicates and maybe already at five http://seqanswers.com/forums/showpost.php?p=74264&postcount=3
- 75. Standard DE methods Limma (microarrays, RNA-seq) edgeR, DESeq (RNA-seq)
- 76. Standard DE methods Limma (microarrays, RNA-seq) edgeR, DESeq (RNA-seq) Distributional issue: Solved by variance stabilizing transform in limma edgeR and DESeq model the count data using a negative binomial distribution and use their own modified statistical tests based on that.
- 77. Standard DE methods Limma (microarrays, RNA-seq) edgeR, DESeq (RNA-seq) Distributional issue: Solved by variance stabilizing transform in limma edgeR and DESeq model the count data using a negative binomial distribution and use their own modified statistical tests based on that. Multiple testing issue: All of these packages report false discovery rate (corrected p values).
- 78. Standard DE methods Limma (microarrays, RNA-seq) edgeR, DESeq (RNA-seq) Distributional issue: Solved by variance stabilizing transform in limma edgeR and DESeq model the count data using a negative binomial distribution and use their own modified statistical tests based on that. Multiple testing issue: All of these packages report false discovery rate (corrected p values). Variance estimation issue: These packages (in slightly different ways) “borrow” information across genes to get a better variance estimate. One says that the estimates “shrink” from gene-specific estimates towards a common mean value.
- 79. Standard DE methods Limma (microarrays, RNA-seq) edgeR, DESeq (RNA-seq) Distributional issue: Solved by variance stabilizing transform in limma edgeR and DESeq model the count data using a negative binomial distribution and use their own modified statistical tests based on that. Multiple testing issue: All of these packages report false discovery rate (corrected p values). Variance estimation issue: These packages (in slightly different ways) “borrow” information across genes to get a better variance estimate. One says that the estimates “shrink” from gene-specific estimates towards a common mean value.
- 80. CuffDiff2 Integrates isoformquantification + differential expression analysis
- 81. Complex designs Thesimplest case is when you just want to compare two groups against each other. But what if you have several factors that you want to control for? E.g. you have taken tumor samples at two different time points from six patients, cultured the samples and treated them with two different anticancer drugs and a mock control treatment. -> 2x6x3 = 36 samples. Now you want to assess the differential expression in response to one of the anticancer drugs, drug X. You could just compare all “drug X” samples to all control samples but the inter-subject variability might be larger than the specific drug effect. Enter limma / DESeq / edgeR which can work with factorial designs (SAMSeq cannot, which is another reason one might not want to use it)
- 82. Limma and factorial designs limma stands for “linear models for microarray analysis” Essentially, the expression of each gene is modeled with a linear relation http://www.math.ku.dk/~richard/courses/bioconductor2009/handout/19_08_Wednesday/KU-August2009-LIMMA/PPT-PDF/Robinson-limma-linear-models-ku-2009.6up.pdf The design matrix describes all the conditions, e g treatment, patient, time etc y = a + b*treatment + c*time + d*patient + e Baseline/average Error term/noise
- 83. Recent DE so[ware comparison
- 84. Take-‐away messages from DE tool comparison - CuffDiff2, which should theoretically be better, seems to work worse, probably due to the increased “statistical burden” from isoform expression estimation - The HTSeq quantification which is theoretically “wrong” seems to give good results with downstream software - It is practically always better to sequence more biological replicates than to sequence the same samples deeper Omitted from this comparison - gains from ability to do complex designs - non-parametric methods
- 85. The end Contact me at mikael.huss@scilifelab.se if you have any questions