RNA-Seq data issues and pitfalls

Using publicly available RNA-seq data
Issues and pitfalls
RNA-Seq Europe, Basel
4 December, 2013
Mikael Huss
WABI, SciLifeLab / Stockholm University

www.scilifelab.se
10 (+3) senior bioinformaticians
19 (+4) projects going on

Motivation
“Do our new sequencing data make sense?”
Put data into context of existing information
Array vs seq.
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0 5 10 15
0246810
Gene−level correlation, Spearman rho 0.65
array gene RMA expression value
log(1+geneFPKM)

Motivation
“Do our new sequencing data make sense?”
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480− 460− 440− 420− 400−
−50050100150200250
PCA with BodyMap samples
PC 1, explained variance=0.88
PC2,explainedvariance=0.046
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heart_pe
heart_se
kidney_pe
kidney_se
leukocyte_pe
leukocyte_se
liver_pe
liver_se
lung_pe
lung_se
lymphnode_pe
lymphnode_se
prostate_pe
prostate_se
skeletal_pe
skeletal_se
Classify unknown samples?
Skeletal muscle
biopsies

Human tissue RNA-seq data sets
Project name GEO / AE
accessio
n
Representati
ve
publication
FAST
Q
BAM Counts R/FPKM
BodyMap2.0 GSE30611 - Yes (Yes) (Yes) (Yes)
RNA-Seq Atlas E-MTAB-305 PMID: 22345621 Yes No No Yes
Evolution of Gene
Expression
GSE30352 PMID: 22012392 Yes No No No
Wang GSE12946 PMID: 18978772 Yes No No Yes
GTex - - No No Yes Yes
Human Protein Atlas
(SciLifeLab/KTH)
- - No No No No
FANTOM5 (RIKEN) - - No No No No
Comingsoon!

Comparing RNA-seq expression profiles
How reproducible are RNA-seq
expression profiles?
What is the relative importance of
different factors?
-Cross-study biases, batch effects
-Bioinformatics pipelines
RNA extraction
Sequencing library
preparation
Sequencing
Mapping
Quantification
bioinformatics
lab
DE or other
downstream
analysis

Nature Biotechnology 31, 1015–1022 (2013) doi:10.1038/nbt.2702

Comparing RNA-seq expression profiles
RNA extraction
Sequencing library
preparation
Sequencing
Mapping
Quantification
bioinformatics
lab
DE or other
downstream
analysis
“In this paper, we have demonstrated that
technical variation in RNA-seq experiments is
small and that results from RNA-seq
experiments performed in different
laboratories are consistent. This conclusion is
valid as long as all participating laboratories
use the exact same protocols […] and versions
of sample preparation and sequencing kits.”

What about different samples and pipelines?
We wanted to know how consistent expression profiles are between studies

Also, how much the processing pipeline (mapping, quantification etc.) affects results

Decided to look at publicly available human tissue RNA-seq data

Decided to look at publicly available human tissue RNA-seq data
We want the samples from different tissues and studies to cluster by tissue rather than
study – and to find genes we identify as tissue-specific in independent catalogues of
tissue specific genes

First try
Let’s cluster reported R/FPKM values from some processed public data sets!
Actually hypothalamus
A hassle to combine IDs!
WangHeart
WangBrain
GTexRandomBrain2
GTexRandomHeart2
GTexRandomBrain1
GTexRandomHeart1
AtlasHeart
AtlasBrain
WangHeart
WangBrain
GTexRandomBrain2
GTexRandomHeart2
GTexRandomBrain1
GTexRandomHeart1
AtlasHeart
AtlasBrain
0.2
0.4
0.6
0.8
1

Human tissue RNA-seq data sets
Project name GEO / AE
accessio
n
Representati
ve
publication
FAST
Q
BAM Counts R/FPKM
BodyMap2.0 GSE30611 - Yes (Yes) (Yes) (Yes)
RNA-Seq Atlas E-MTAB-305 Krupp et al. (2012)
PMID: 22345621
Yes No No Yes
Evolution of Gene
Expression Levels in
Mammalian Organs
GSE30352 Brawand et al.
(2011)
PMID: 22012392
Yes No No No
Wang GSE12946 Wang et al. (2008)
PMID: 18978772
Yes No No Yes
GTex - Lonsdale et al.
(2013)
PMID: 23715323
No No Yes Yes
Human Protein Atlas
(SciLifeLab/KTH)
- - No No No No
FANTOM5 (RIKEN) - - No No No No
Comingsoon!

PCA PCA / OPS
Blue – brain
Red – heart
Maybe PCA will remove some noise?
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Reported RPKM
PC1
PC2
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Squares – Wang
Triangles – Atlas
Circles - GTex
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PC1
PC2
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Public portals can contain “bioinformatics artifacts”
??

Cufflinks was used to calculate FPKMs

By default, Cufflinks skips loci with >1,000,000 alignments and puts FPKM=0 and a HIDATA flag

By default, Cufflinks skips loci with >1,000,000 alignments and puts FPKM=0 and a HIDATA flag
Fix: Look for HIDATA and rerun Cufflinks with a higher --max-bundle-frags option if you find any

Re-process from FASTQ
Benchmark to find out if we can obtain a sensible clustering of samples from different sources.
Get brain, kidney, heart data in RNA-Seq Atlas, BodyMap, Evolution of Gene Expression
Tips:
SRAdb package in BioConductor can be very handy for obtaining FASTQ files if you like R
InSilicoDb also has a decent interface against GEO/ArrayExpress

Map to genome/transcriptome using TopHat and STAR

Quantify with HTSeq (counts) or Cufflinks (FPKM)

Quantify with HTSeq (counts) or Cufflinks (FPKM)
DE analysis with edgeR/limma/CuffDiff and compare with external set of tissue specific
genes

Samples used (initially)
Study Tissue Read length Prep method # mapped reads
Evolution of
Gene Expression
Brain 1x76 PolyA
17.3M
Evolution of
Gene Expression
Heart 1x76 PolyA 17.0M
Evolution of
Gene Expression
Kidney 1x76 PolyA 15.8M
RNA-Seq Atlas Hypothalamus 1x35 rRNA depletion 25.1M
RNA-Seq Atlas Heart 1x50 rRNA depletion 23.3M
RNA-Seq Atlas Kidney 1x50 rRNA depletion 23.7M
BodyMap Brain 2x76 PolyA 71.9M
BodyMap Heart 2x76 PolyA 81.4M
BodyMap Kidney 2x76 PolyA 77.6M

How much of the RNA
pool is “used up” by
the 1-100 most
expressed genes?
0 20 40 60 80 100
0.00.40.8
EoGE_brain_TH
Top # of ENSEMBL genes
Fractionoftotal#seqs
0 20 40 60 80 100
0.00.40.8
EoGE_heart_TH
0 20 40 60 80 100
0.00.40.8
EoGE_kidney_TH
0 20 40 60 80 100
0.00.40.8
BodyMap_brain_TH
0 20 40 60 80 100
0.00.40.8
BodyMap_heart_TH
0 20 40 60 80 100
0.00.40.8
BodyMap_kidney_TH
0 20 40 60 80 100
0.00.40.8
Atlas_brain_TH
0 20 40 60 80 100
0.00.40.8
Atlas_heart_TH
0 20 40 60 80 100
0.00.40.8
Atlas_kidney_TH
Simple “fingerprint”
of tissue complexity

Red – brain
Blue– heart
Black - kidney ●
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Raw counts (TopHat/HTSeq)
PC1
PC2 ●
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Triangles – Atlas
Circles – BodyMap
Squares - EoGE

RNA-seq normalization: different goals
- R/FPKM: (Mortazavi et al. 2008)
- Correct for: differences in sequencing depth and transcript length
- Aiming to: compare a gene across samples and diff genes within sample
- TMM: (Robinson and Oshlack 2010)
- Correct for: differences in transcript pool composition; extreme outliers
- Aiming to: provide better across-sample comparability
- TPM: (Li et al 2010, Wagner et al 2012)
- Correct for: transcript length distribution in RNA pool
- Aiming to: provide better across-sample comparability
- Limma voom (logCPM): (Lawet al 2013)
- Aiming to: stabilize variance; remove dependence of variance on the mean

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Cufflinks FPKM
PC1
PC2
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(edgeR) TMM
PC1
PC2
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(limma) logCPM
PC1
PC2
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logCPM−TMM
PC1
PC2
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Red – brain
Blue – heart
Black - kidney
Triangles – Atlas
Circles – BodyMap
Squares - EoGE

• If we are right in that gene expression profiles
from tissues are comparable across studies
when normalized in this way, it should work
for a new data set that we have not used up
until this point
“Cross-validation”

• If we are right in that gene expression profiles
from tissues are comparable across studies
when normalized in this way, it should work
for a new data set that we have not used up
until this point
• Introduce Wang/Sandberg data set and
repeat (only brain and heart available, poly-A
enriched, 1x32 bp on earliest Solexa system,
~15 Mreads mapped)

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logCPM−TMM
PC1
PC2
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brain
kidney
heart

Effect of aligner?
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Two aligners
PC1
PC2
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● Darker: TopHat
Lighter: STAR

MYL2
MB
MYH7
MYH6
CKM
MYBPC3
FBXL16
SYT1
SNAP25
CPLX2
NEFM
NEFL
Five most important genes in each direction
0.04−
0.03−
0.02−
0.01−
0.00
0.01
0.02
0.03
heart brain
PC2loading

Concordance of differentially expressed genes with “gold
standard” (TiGER)
Use samples from different studies as pseudo-replicates (no biol reps within the studies)
Look at top 100 genes (in terms of FDR) for each tool
Tissue Method # genes with
FDR < 1%
Overlap with
TiGER
Brain CuffDiff2 208 22/100
Brain limma 2649 26/100
Brain edgeR 4249 37/100
Heart CuffDiff2 42 18/100
Heart limma 576 46/100
Heart edgeR 2438 32/100
Kidney CuffDiff2 82 24/100
Kidney limma 1517 60/100
Kidney edgeR 2688 59/100

brain heart kidney
Top 100 genes' overlap with TiGER
02060100
●
●
●
limma/TiGER
edgeR/TiGER
CuffDiff/TiGER
brain heart kidney
Top 100 genes' overlap with SpeCond
02060100
●
●
●
limma/SpeCond
edgeR/SpeCond
CuffDiff/SpeCond
limma and edgeR can
include additional
explanatory factors (such
as study)

Atlas_heart_TH
EoGE_heart_TH
BodyMap_heart_TH
Atlas_brain_TH
EoGE_brain_TH
BodyMap_brain_TH
EoGE_kidney_TH
Atlas_kidney_TH
BodyMap_kidney_TH
0
5
10
15
Expression of top 100 specific genes (from limma) for each tissue

Some observations
Human tissue RNA-seq data sets from different sources seem to be fairly comparable
at a global level, but proper normalization is important.
It seems easier to compare results of count-based workflows rather than e g Cufflinks
FPKMs, although the latter is more theoretically correct.
The alignment program used, however, does not seem to make a crucial difference.
The ability to include additional explanatory factors in differential gene expression
analysis (edgeR, DESeq, limma) is important for sensitivity.

Recommendations
1. Reprocess the data from FASTQ in a consistent way.
(Corollary; Beware of values reported in portals for processed expression data.)
2. If using Cufflinks, be aware of the HIDATA flag and use the --max-bundle-frags
option to avoid the issue.
3. To compare your samples on a global scale, you may want to start from counts
and try both a scaling normalization like TMM and a log-like transform (e.g. log-
CPM).
4. To compare FPKM values from e g Cufflinks, try including only protein-coding
genes, filter to genes with a mean FPKM>1 or high variance, and take logs.

RNA-Seq data issues and pitfalls

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