base-resolution rna-seq - jeff leek

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base-resolution rna-seq

Jeff Leek Johns Hopkins Bloomberg School of Public Health

@simplystats

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1.  types of statistical methods

2.  derfinder

3.  Unexpected expression

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data generation

Genome

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data generation

Genome

Transcripts

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data generation

Genome

Transcripts

Reads

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“simplest” thing – annotate-identify

Genome

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exon model

Genome

Count by Exon

Bullard et al. BMC Bioinformatics 2010

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union model

Genome

Union of all exons

Bullard et al. BMC Bioinformatics 2010

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union-intersection model

Genome

Union/Intersection

Bullard et al. BMC Bioinformatics 2010

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sources of variation in annotate-identify

1.  annotation 2.  gene models 3.  fragment-level biases 4.  technical variation 5.  biological variability

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annotation variation

Frazee et al. Biostatistics under review

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gc-variation

Hansen et al. 2011 Biostatistics

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biological variation

Choy  et  al.    (2008)    vs.    

Pickrell  et  al.    (2010)    

Stranger  et  al.    (2007)    vs.    

Montgomery  et  al.    (2010)    

Hansen et al. 2010 Nat. Biotech

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some data

hCp://bowGe-­‐bio.sourceforge.net/recount/  

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assemble-identify

Genome

Reads

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assemble-identify (align)

Genome

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assemble-identify (assemble)

Genome

Fragments

Transcripts

Trapnell et al. 2010 Nat. Biotech

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assemble-identify (abundance)

Genome

Transcripts

Trapnell et al. 2010 Nat. Biotech

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inherent ambiguity (boundaries)

Genome

Fragments

Transcripts

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inherent ambiguity (assembly)

Genome

Alternative Assemblies

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assembly variation

Frazee et al. in prep

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result of assembly variation

Frazee et al. in prep

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result of assembly variation (bio reps)

Frazee et al. in prep

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result of assembly variation

Frazee et al. in prep

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result of assembly variation

Cufflinks p values

p values

Density

0.0 0.2 0.4 0.6 0.8 1.0

01

23

45

6

Cufflinks p-values

p-value

density

0.0 0.2 0.4 0.6 0.8 1.0

010

2030

4050

Cufflinks v1 Cufflinks v2

Frazee et al. 2012 in prep Frazee et al. in prep

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methods

annotate-identify 1.  align 2.  gene-model 3.  abundances 4.  analyze

Pros: •  analogous to microarray, •  processed data easy to handle Cons: •  incorrect/variable annotation •  gene model choices have a big

impact

assemble-identify 1.  align 2.  assemble 3.  abundances 4.  analyze

Pros: •  alternative transcription •  (potentially) less annotation

dependent Cons: •  ambiguity/variation in

assembly

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differentially expressed region finder

1.  Calculate base pair-resolution coverage 2.  Perform test at each base 3.  Identify regions of differential expression (segment) 4.  Annotate regions (optional) Pros: •  processed data still easier to handle •  less dependent on annotation •  no assembly variability Cons: •  still no transcript-level abundances (but…)

Frazee et al. 2012b in prep

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derfinder notes

•  Ignores annotation •  Coverage data at base resolution, designed

for “differential” expression analysis –  Lose paired end information –  Lose junction information –  Lose potential mapping quality information – …

•  Annotate the resulting differentially expressed regions (DERs)

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Solution ir

5 10 15 202 2 3 6 11 12 14 15 15 16 15 17 16 14 9 8 6 5 5 4 3 1 1

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result n  samples  à  

3  billion  nt    

Frazee et al. Biostatistics in review

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base-pair model (case/control)

g() = Transform (Box-Cox, log(+32) etc.) Yi,j = coverage on sample i at base j lj = genomic location j α() = baseline coverage β() = change in coverage between groups γk() = adjustment’s for confounders Wik = value of kth confounder on ith sample

Frazee et al. Biostatistics in review

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batch-variation

Blue:  3  sds  below  the  mean  Orange:  3  sds  above  the  mean  

Human  chromosome  16  

Horizontal  lines  delimit  process  dates  

Leek et al. 2010 Nat. Rev. Genet.

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finding the statistics for d.e. bases

t ~ π0f0 + π1f1 + π2f2 + π3f3

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empirical bayes

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estimating parameters

t ~ π0f0 + π1f1 + π2f2 + π3f3

Assumed known – the distribution of zeros Alternatively – Gottardo and Raftery 2008 JCGS

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estimating parameters

t ~ π0f0 + π1f1 + π2f2 + π3f3

Estimated null distribution from e.g. Efron 2002

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estimating parameters

Estimated from 2-groups model, assumed symmetric

t ~ π0f0 + π1f1 + π2f2 + π3f3

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hmm

DE   DE   DE   not  DE   not  DE  

t1   t2   t3   t4   t5  

hidden states

emissions are statistics

Frazee et al. Biostatistics in review

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statistic

Observed  

Frazee et al. Biostatistics in review

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monte-carlo p-value

Observed   Null    

Frazee et al. Biostatistics in review Lagnmead et al. in prep

Jaffe et al. Biostatistics 2011

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ma-plots

Frazee et al. Biostatistics in review

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statistical significance

p values

Frequency

0.0 0.2 0.4 0.6 0.8 1.0

0200

400

600

800 DER Finder - sex

p values

Frequency

0.0 0.2 0.4 0.6 0.8 1.0

0200

400

600

800

1000

DER Finder - males

p values

Frequency

0.0 0.2 0.4 0.6 0.8 1.0

050

100

200

300

Cufflinks - sex

p values

Frequency

0.2 0.4 0.6 0.8 1.0

050

100150200250300 Cufflinks - males

p value

Frequency

0.0 0.2 0.4 0.6 0.8 1.0

050

100

150 EdgeR - sex

p value

Frequency

0.0 0.2 0.4 0.6 0.8 1.0

05

1015

2025 EdgeR - males

p value

Frequency

0.0 0.2 0.4 0.6 0.8 1.0

020

4060

80100

140

DESeq - sex

p value

Frequency

0.0 0.2 0.4 0.6 0.8 1.0

010

2030

40 DESeq - malesFrazee et al. Biostatistics in review

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percent “correct hits” by ranking

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caveat

Genome

Bullard et al. BMC Bioinformatics 2010

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caveat

Genome

Bullard et al. BMC Bioinformatics 2010

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annotation incorrect

4.5

5.0

5.5

6.0

6.5

7.0

log2(count+32)

chrY: 15016699 - 15017219

femalemale

12

34

56

7t s

tatis

tic

xaxinds

exons

states

15016742 15016842 15016942 15017119 15017219genomic position

Frazee et al. Biostatistics in review

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annotation missing

4.5

5.0

5.5

6.0

6.5

7.0

log2(count+32)

chrY: 2715932-2716691

femalemale

2.0

2.5

3.0

3.5

4.0

4.5

t sta

tistic

xaxinds

exons

states

2715882 2716082 2716282 2716482 2716682genomic position

Frazee et al. Biostatistics in review

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missed by cufflinks

Frazee et al. Biostatistics in review

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computational goals

•  Aligned reads (say from TopHat) to DERs in < 24 hours, all within R statistical software – Table of DERs and matrix of mean coverage per

sample per region for post-hoc analysis – Annotated using data from UCSC and Ensembl:

counts of features and annotation lists – Visualized DERs, including annotation to identify

novel transcriptional activity –  Easy methods for counting exons from coverage

objects (~2-4 hours from aligned reads for all samples)

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derfinder - fast

1.  Test for differential expression at each base, record statistic (linear modeling)

2.  Identify contiguous/adjacent bases that are differentially expressed above some cutoff (thresholding/ “bumphunter”)

3.  Summarize each DER (area) 4.  Perform significance testing on region-

level (permutations, empirical p-values)

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time and memory needed: derSnyder

•  Load & filter data: 10 cores with mclapply 1hr 15min, 177 GB

•  Make models: 20 min, 52 GB •  Analysis: 10 permutations, 4 cores each chr,

total 59 mins –  chr1 41 min, 46 GB

•  Merging: 30 min, 22 GB •  Report: 27 min, 17 GB •  Total wallclock time: 3 hr 46 min

20 samples

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Counts: derSnyder

•  Load & filter data: 10 cores with mclapply 1hr 15min, 177 GB

•  Create count table: 26 min, 24 GB •  Total wallclock time: 1 hr 41 min

20 samples

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lieber brain samples

•  DLPFC Paired-end RNAseq Data •  36 samples across 6 age ranges, n=6/

group: Fetal (age < 0) ; Infant (0 -1) ; Child (1 - 10) ; Teen (10 - 20) ; Adult (20 -50) ; 50+

•  4 M and 2 F per group; mostly AA, but some Caucasians

•  RINs are evenly distributed across age

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lieber brain samples

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test for base-level de

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thresholding on statistic

F-­‐staGsGc  corresponding  to  p-­‐value  <  10-­‐8    (F5,30)  

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derfinder results

•  alt model: age group + median coverage •  null model: median coverage •  threshold: p-value < 1e-8 •  5,565 DERs with FWER ~ 0 (conservative) – Median length: 148bp [IQR: 112-235]

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annotating

•  Devised “light-weight” R annotation files for UCSC hg19 knownGene and Ensembl GRCh37.p11

•  “Genomic State” objects: each base pair in the genome gets assigned to exactly one “state”, annotations merged across overlapping features

•  Two different configurations: –  “Full” (introns, exons, un-annotated/intragenic) –  “Coding” (introns, coding exons, UTRs, promoters,

un-annotated/intragenic) •  Very fast, 1000s of regions in seconds

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derfinder results

•  2,655 regions (47.7%) show expression of 1+ annotated intron (UCSC: 2,505; 45%)

•  577 regions (10.4%) show expression of an “intragenic” region (UCSC: 800, 14%)

Ensembl   UCSC  

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derfinder results

•  261 regions (4.7%) crossed a known lincRNA – 51 overlapping 535 “intragenic” regions

(9.6%; e.g. no exons)

•  Only one region crossed known miRNA, but same region had annotated exon on other strand

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derfinder results

•  Verifying the 5,565 DERs: – 95% of regions had mappability of 100bp

reads greater than 99% – Only 16 regions were in tracks excluded by

Duke site of Encode (all “BSR/Beta” for satellite repeats) and 0 by Data Analysis Center of Encode

– Only 90 regions (1.5%) mapped to known pseudogenes

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derfinder results

•  Fetal samples had the highest expression in the majority of the regions (84%; 18 [1.7-Inf] fold increase); second highest was 50+ group (7%; 1.4 [1-4.3] fold increase)

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derfinder results

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derfinder subgroup

•  Identified DERs within each 6-sample age group based on mean expression – Represents set of expressed sequences for

each group at a given coverage threshold – Varied mean coverage cutoff

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% of genome expressed

Percen

t  of  G

enom

e  Expressed  

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scaled % of genome expressed

Fetal  is  highest  at  EVERY  cutoff  

Teen  is  lowest  thru  114  reads  

Infant  is  lowest    a<er  114  reads  

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higher cutoffs create longer DERs

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% of genome expressed (L ≥ 12)

Percen

t  of  G

enom

e  Expressed  

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Scaled % of genome expressed (L ≥ 12)

Fetal  is  s=ll  highest  at  EVERY  cutoff  

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Higher cutoffs still create longer DERs

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try that stuff, yo!

https://github.com/lcolladotor/derfinder https://github.com/lcolladotor/derfinderReport https://github.com/lcolladotor/derfinderExample

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acknowledgements Leek Group Alyssa Frazee Prasad Patil Leo Collado Torres Abhi Nellore University of Maryland Héctor Corrada Bravo Harvard Rafael Irizarry Lieber Institute Andrew Jaffe Danny Weinberger Thomas Hyde

Hopkins Kasper Hansen Roger Peng Ben Langmead Sarven Sabunicyan Luigi Marchionni Donald Geman Funding Amazon Web Services Digital Science NIH CCNE Hopkins inHealth