detecting and ameliorating systematic variation from large ... · detecting and ameliorating...
TRANSCRIPT
Detecting and Ameliorating Systematic Variation from Large-scale RNA Sequencing Sheng Li1,2,*, Paweł P. Łabaj3,*, Paul Zumbo1,2*, Peter Sykacek3, Wei Shi5, Leming Shi6, John Phan7, Leo Wu7, May Wang7, Charles Wang8, Danielle Thierry-Mieg9, Jean Thierry-Mieg9, David P. Kreil3,4,x, Christopher E. Mason1,2,x 1Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065 USA 2The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065 USA 3Chair of Bioinformatics, Boku University, Vienna, Austria, Europe 4University of Warwick, U.K. 5Department of Bioinformatics, WEHI, Australia 6Center for Pharmacogenomics, School of Pharmacy, Fudan University, Shanghai, China 7Department of Biomedical Engineering, GeorgiaTech and Emory University, Atlanta, GA USA 8Center for Genomics and Division of Microbiology & Molecular Genetics, School of Medicine, Loma Linda University, Loma Linda, CA 92350. 9National Center for Biotechnology Information (NCBI), Bethesda, MD, USA 1. Multi-pipeline agreement of site outliers. 2. Boxplots of the inter-site false positive DEGs. 3. Inter-site 3’UTR genes false positive DEGs count. 4. Intra-site and inter-site correlations. 5. Q-Q plots for gene expression inter-site repeatability (samples A & B). 6. Q-Q plots for gene expression inter-site repeatability (samples C & D). 7. Q-Q plots for DEG inter-site repeatability. 8. MDS plot of normalized gene expression. 9. Intra-site DEGs detected from 6 sites. 10. Evaluation on inter-site DEGs reproducibility. 11. Correlation of RNA-seq normalized gene expression with TaqMan assays. 12. Taqman genes highly expressed in the RNA-seq data. 13. Evaluation of the performance of intra-site DEGs using TaqMan data. 14. Illustration of measures about sample identification. 15. Spearman correlation of the adjusted p-value between inter-site DEGs and intra-site DEGs. 16. Inter-site/intra-site DEG validation. 17. Site-specific base content examination of an independent control library for assessing site-
variance. 18. Intra- and inter-site variations of three additional quality metrics for the Illumina dataset. 19. Duplication rate per library. 20. GC content quality metric and latent variables from PGM data. 21. Examination of quality metrics and DEG detection for PGM data. 22. PGM Inter-site and intra-site DEGs analysis.
Nature Biotechnology: doi:10.1038/nbt.3000
A" B"
C"
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ILM2-ILM3
ILM2-ILM5
ILM3-ILM5
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Supplementary Figure 1. Multi-pipeline analysis of false positive rates across sites. We examined the numbers of false-positives (A vs A) in DEG detection for three different pipelines and consistently observed a higher number of false positive DEGs for one of the sites (ILM3) (a) DEGs found for WHAM with Cufflinks. (b) DEGs found for MapSplice with HTSeq (c) DEGs found for Novoalign with HTSeq. Only RefSeq genes were considered in this analysis. Given higher stringencies (FC=3, FDR=0.005), a reduction in false positives could be obtained, but ILM3 still showed false positives in all cases.
Nature Biotechnology: doi:10.1038/nbt.3000
FC: 1.5FDR: 0.05
FC: 1.5FDR: 0.01
FC: 1.5FDR: 0.001
FC: 2FDR: 0.05
FC: 2FDR: 0.01
FC: 2FDR: 0.001
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comp: A
comp: B
comp: C
comp: D
Analysis
Fals
e po
sitiv
e D
EGs
Without SVA With SVA
Supplementary Figure 2. Boxplots of the number of inter-site false positive DEGs without or with SVA clean up, using 3 different false discovery rate (FDR) cutoffs (0.05, 0.01, 0.001) and 2 different fold-change (FC) cutoffs (1.5, 2). All comparisons are for four replicate libraries of the same sample (A,B,C,D) at six different sites.
Nature Biotechnology: doi:10.1038/nbt.3000
comp: A comp: B comp: C comp: D
01000200030004000
01000200030004000
01000200030004000
01000200030004000
01000200030004000
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FC: 1.5FC: 1.5
FC: 1.5FC: 2
FC: 2FC: 2
FDR: 0.05FDR: 0.01
FDR: 0.001FDR: 0.05
FDR: 0.01FDR: 0.001
AGR−BG
IAG
R−CNL
AGR−CO
HAG
R−M
AYAG
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BGI−CNL
BGI−CO
HBG
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Comparison
Num
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f DEG
stype up down
3' UTR gene read count
Supplementary Figure 3. Inter-site false positive DEG counts for gene expression quantified from 3’ UTR reads. Reads were filtered by match to the 3’ UTR of Aceview genes. See Supp. Figure 8 for definitions. Site #3 still shows a high false positive DEG rate.
Nature Biotechnology: doi:10.1038/nbt.3000
intra−site inter−site0.
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rela
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ficie
nts
Supplementary 4. Intra-site and inter-site correlations (Pearson Correlation, y-axis) of normalized gene expression for samples A-D across all sites (x-axis).
Nature Biotechnology: doi:10.1038/nbt.3000
a
b
Supplementary Figure 5. Q-Q plot for gene expression inter-site repeatability for sample A (a) and B (b). For each sample, we compared the normalized gene expression between two test sites, among all 6 SEQC test sites.
Nature Biotechnology: doi:10.1038/nbt.3000
a
b
Supplementary Figure 6. Q-Q plot for gene expression inter-site repeatability for sample C (a) and D (b). For each sample, we compared the normalized gene expression between two test sites, among all 6 SEQC test sites.
Nature Biotechnology: doi:10.1038/nbt.3000
Supplementary Figure 7. Q-Q plots for DEG inter-site repeatability for sample A vs. B at ILM1 (a) A vs. C at ILM 1 (b), A vs. A between ILM1 and ILM2 (c) and another A vs. A between ILM1 and ILM6 (d). For each sample, we compared the gene expression DEGs between two test sites, among all 6 SEQC test sites, and we can see that even the same sites show a drift in their p-value distribution, indicating false positives.
Nature Biotechnology: doi:10.1038/nbt.3000
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Supplementary Figure 8. MDS plot of normalized gene expression. All replicates clustered by sample type (A-D). Site information is coded by color (see legend).
Nature Biotechnology: doi:10.1038/nbt.3000
FC: 1.5FDR: 0.05
FC: 1.5FDR: 0.01
FC: 1.5FDR: 0.001
FC: 2FDR: 0.05
FC: 2FDR: 0.01
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ComparisonPe
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DEG
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1 2 3 4 5 6site.num
FC: 1.5FDR: 0.05
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FC: 1.5FDR: 0.05
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Comparison
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FDR: 0.05FC: 1.5
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FDR: 0.01FC: 2
FDR: 0.001FC: 1.5
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comp: A−B
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Analysis
Num
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FC: 1.5FDR: 0.05
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comp: A−B
comp: A−C
comp: A−D
comp: B−C
comp: B−D
comp: C
−D
Analysis
Num
ber o
f DEG
s
Without SVA With SVA
a
Supplementary Figure 9. Intra-site DEGs detected from 6 sites without or with SVA analysis, with 3 FDR cutoff and 2 FC cutoff. FDR and FC are labeled on top of each grid.
Nature Biotechnology: doi:10.1038/nbt.3000
60%
70%
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sample
Shar
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EGs
perc
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tes
0.92
0.94
0.96
A−B
A−C
A−D
B−C
B−D
C−D
comparisonSp
earm
an C
orre
latio
n of
Inte
r−si
teco
mm
on D
EG a
djus
ted
p−va
lues
SEQCA/B/C/D
ILM1Norm/DEGs
ILM2Norm/DEGs
ILM3Norm/DEGs
ILM4 Norm/DEGs
ILM5 Norm/DEGs
ILM6 Norm/DEGs
5 10 15 20 25
510
1520
2530
A vs. C (ILM4) adjusted p−value (−log10)
A vs
. C (I
LM6)
adj
uste
d p−
valu
e (−
log1
0)
ILM4 ILM6
A VS C
1159 12034424
0.82
0.84
0.86
0.88
0.90
A −
B
A −
C
A −
D
B −
C
B −
D
C −
D
comparisonM
atth
ews
corre
latio
n co
effic
ient
(Taq
Man
val
idat
ion)
a b
e f
c
d
Supplemental Figure 10. Reproducibility of RNA-seq data DEG analysis across 6 test sites. (a) Schematic plot of RNA-seq data normalization by sample and followed by DEGs analysis, at each site independently. (b) Spearman correlation of adjusted p-values of the common DEGs among 6 test sites. (c) Scatter plot of –log10 adjusted p-values of common DEGs comparing sites ILM4 and ILM6, for A vs. C. (d) The percentage of DEGs shared between two sites. (e) Venn diagram of the number of DEGs shared between site ILM4 and site ILM6. (f) Matthews correlation coefficient (MCC) for RNA-Seq DEG detection performance, as benchmarked by TaqMan. Thresholds for DEG calls: FDR: 0.05, FC: 2.
Nature Biotechnology: doi:10.1038/nbt.3000
Supplementary Figure 11. Taqman genes highly expressed in the RNA-seq data. Most of the TaqMan genes are those expressed around top 87% of genes, but we do also see some lowly expressed genes covered.
Nature Biotechnology: doi:10.1038/nbt.3000
AGR BGI CNL COH MAY NVS
r2 = 0.744
r2 = 0.776
r2 = 0.708
r2 = 0.698
r2 = 0.734
r2 = 0.776
r2 = 0.703
r2 = 0.694
r2 = 0.732
r2 = 0.777
r2 = 0.7
r2 = 0.687
r2 = 0.74
r2 = 0.777
r2 = 0.708
r2 = 0.697
r2 = 0.74
r2 = 0.778
r2 = 0.703
r2 = 0.696
r2 = 0.743
r2 = 0.78
r2 = 0.709
r2 = 0.696
−10
−5
0
5
10
−10
−5
0
5
10
−10
−5
0
5
10
−10
−5
0
5
10
AB
CD
−15 −10 −5 0 5 −15 −10 −5 0 5 −15 −10 −5 0 5 −15 −10 −5 0 5 −15 −10 −5 0 5 −15 −10 −5 0 5TaqMan
RNA−seq
AGR BGI CNL COH MAY NVS
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051015
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AB
CD
−15−10 −5 0 5 −15−10 −5 0 5 −15−10 −5 0 5 −15−10 −5 0 5 −15−10 −5 0 5 −15−10 −5 0 5TaqMan
RNA−seq
FC: 1.5FDR: 0.001
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Comparison
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f DEG
s
up down
a
b
c
ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
Supplementary Figure 12. Correlation of RNA-seq normalized gene expression with TaqMan assays and TaqMan DEGs count. Each column of the grid is a site from all 6 sites. Each row of the grid is a sample from all 4 samples. In each scatter plot, the x-axis is the -1 times the normalized TaqMan Ct values; the y-axis is the RNA-seq log 2 of
Nature Biotechnology: doi:10.1038/nbt.3000
normalized gene expression. The solid blue lines represent a linear regression fit. The linear fit r2 value of each plot is indicated in the text on top left of each plot. (a) RNA-seq annotation using Aceview gene model. (b) RNAseq annotation using Taqman primer sequence. (c) TaqMan genes DEG count. 3 FDR cutoff and 2 FC cutoff were applied for limma DEG detection pipeline, as above.
Nature Biotechnology: doi:10.1038/nbt.3000
FC: 1.5
FDR: 0.001FC: 1.5
FDR: 0.01FC: 1.5
FDR: 0.05FC: 2
FDR: 0.001FC: 2
FDR: 0.01FC: 2
FDR: 0.05
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0.40.50.60.70.80.9
0.40.50.60.70.80.9
0.40.50.60.70.80.9
0.40.50.60.70.80.9
comp: A−B
comp: A−C
comp: A−D
comp: B−C
comp: B−D
comp: C
−D
Site
MC
C
Analysis Without SVA With SVA
FC: 1.5FDR: 0.05
FC: 1.5FDR: 0.01
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0.750.800.850.900.95
0.750.800.850.900.95
0.750.800.850.900.95
0.750.800.850.900.95
comp: A−B
comp: A−C
comp: A−D
comp: B−C
comp: B−D
comp: C
−D
Site
TPR
Analysis Without SVA With SVA
FC: 1.5FDR: 0.001
FC: 1.5FDR: 0.01
FC: 1.5FDR: 0.05
FC: 2FDR: 0.001
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comp: A−B
comp: A−C
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comp: B−D
comp: C
−D
Site
FPR
Analysis Without SVA With SVA
a
c
b
Supplementary Figure 13. Evaluation of the performance of intra-site DEGs from Illumina data using TaqMan data. (a) MCC: Matthews correlation coefficient. (b) TPR: True positive rate. (c) FPR: False positive rate. Each vertical facet stands for 6 different combinations of FC and FDR cutoff. Each horizontal facet stands for a comparison amongst all 4 samples.
Nature Biotechnology: doi:10.1038/nbt.3000
ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
Supplementary Figure 14. Illustration of measures about sample identification. All symbols represent the average value of a particular metric among all genes per each site; magenta stars indicate the numbers according to a non-parametric estimate of the probability of measurements to meet the order constraint which is implied by the titration experiment; red stars indicate the numbers according to the mutual information between measurements and samples A and B; green stars indicates the numbers corresponding to the mutual information of the measurements about samples C and D, while cyan stars indicates the numbers according to the mutual information of the measurements about sample titration.
Nature Biotechnology: doi:10.1038/nbt.3000
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A−C, ILM1 A−C, ILM2 A−C, ILM3 A−C, ILM4 A−C, ILM5 A−C, ILM6
A−D, ILM1 A−D, ILM2 A−D, ILM3 A−D, ILM4 A−D, ILM5 A−D, ILM6
B−C, ILM1 B−C, ILM2 B−C, ILM3 B−C, ILM4 B−C, ILM5 B−C, ILM6
B−D, ILM1 B−D, ILM2 B−D, ILM3 B−D, ILM4 B−D, ILM5 B−D, ILM6
C−D, ILM1 C−D, ILM2 C−D, ILM3 C−D, ILM4 C−D, ILM5 C−D, ILM6
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orig
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nRU
V2sv
aPE
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alED
ASeq
cqn
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sva
PEER
orig
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type ●● original EDASeq cqn RUV2 sva PEER
Nature Biotechnology: doi:10.1038/nbt.3000
Supplementary Figure 15. Spearman correlation of the adjusted p-value between inter-site DEGs and intra-site DEGs.
ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
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●●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
0.5
0.6
0.7
0.8
orig
inal
EDAS
eqcq
nRU
V2sv
aPE
ERor
igin
alED
ASeq
cqn
RUV2
sva
PEER
orig
inal
EDAS
eqcq
nRU
V2sv
aPE
ERor
igin
alED
ASeq
cqn
RUV2
sva
PEER
orig
inal
EDAS
eqcq
nRU
V2sv
aPE
ERor
igin
alED
ASeq
cqn
RUV2
sva
PEER
method
Inte
r−si
te D
EGs
MC
Cva
lidat
ed b
y in
tra−s
ite D
EGs
A−B A−C A−D B−C B−D C−D
●
●
●
●●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●●
●
●●
●●
●
●
●●
●●
●
●
●
●●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
0.80
0.85
0.90
0.95
orig
inal
EDAS
eqcq
nRU
V2sv
aPE
ERor
igin
alED
ASeq
cqn
RUV2
sva
PEER
orig
inal
EDAS
eqcq
nRU
V2sv
aPE
ERor
igin
alED
ASeq
cqn
RUV2
sva
PEER
orig
inal
EDAS
eqcq
nRU
V2sv
aPE
ERor
igin
alED
ASeq
cqn
RUV2
sva
PEER
method
Mat
thew
s co
rrela
tion
coef
ficie
nt:
intra−s
ite D
EGs
valid
ated
by
TaqM
an
a
b
Supplementary Figure 16. Inter-site/intra-site DEG validation. (a) Inter-site DEG validation by TaqMan, evaluated by MCC for all six comparisons (A-B, A-C, A-D, B-C, B-D, C-D). (b) Evaluation of the intra-site DEGs detections using TaqMan data by MCC.
Nature Biotechnology: doi:10.1038/nbt.3000
aa b c
d e f
g h i
AGR-A-1 BGI-A-1 CNL-A-1
COH-A-1 MAY-A-1 NVS-A-1
BGI-A-5 CNL-A-5 MAY-A-5
A G
C T
20
40
60
20
40
60
0 25 50 75 100 0 25 50 75 100Position in read
Nuc
leot
ide
frequ
ency
(%)
site ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
sample 1 5j
ILM1.A.1 ILM2.A.1 ILM3.A.1
ILM4.A.1 ILM5.A.1 ILM6.A.1
ILM2.A.5 ILM3.A.5 ILM5.A.5
Supplementary Figure 17. Site-specific base content examination of an independent control library for assessing site-variance. (a-f) We plotted the percentage of each base
Nature Biotechnology: doi:10.1038/nbt.3000
(y-axis) as a function of the cycle (x-axis) for each of the six sites. We saw that each site (a, b, c, d, e, f) had a slightly different base composition plot. (g-i) We plotted the percentage of each base (y-axis) as a function of the cycle (x-axis) for the three sites that sequenced the vendor-prepared control library (#5). (j) Nucleotide frequency versus position for aligned reads. The percentage of each base was plotted as a function of the read length for each base (A, G, C, T) for two replicates (1, 5) for all sites. Sample 1 was prepared and sequenced independently at each site, whereas sample 5 was prepared at a single site and then sequenced at a subset of all sites. Replicates 1-4 displayed site-dependent base composition frequencies, whereas replicate 5 showed similar base composition frequencies regardless of where it was sequenced, suggesting that base composition frequency is largely a result of library preparation.
Nature Biotechnology: doi:10.1038/nbt.3000
Percent error vs. read length
ILM1.A.1
ILM1.A.2
ILM1.A.3
ILM1.A.4
ILM2.A.1
ILM2.A.2
ILM2.A.3
ILM2.A.4
ILM2.A.5
ILM3.A.1
ILM3.A.2
ILM3.A.3
ILM3.A.4
ILM3.A.5
ILM4.A.1
ILM4.A.2
ILM4.A.3
ILM4.A.4
ILM5.A.1
ILM5.A.2
ILM5.A.3
ILM5.A.4
ILM5.A.5
ILM6.A.1
ILM6.A.2
ILM6.A.3
ILM6.A.4
ILM1.B.1
ILM1.B.2
ILM1.B.3
ILM1.B.4
ILM2.B.1
ILM2.B.2
ILM2.B.3
ILM2.B.4
ILM2.B.5
ILM3.B.1
ILM3.B.2
ILM3.B.3
ILM3.B.4
ILM3.B.5
ILM4.B.1
ILM4.B.2
ILM4.B.3
ILM4.B.4
ILM5.B.1
ILM5.B.2
ILM5.B.3
ILM5.B.4
ILM5.B.5
ILM6.B.1
ILM6.B.2
ILM6.B.3
ILM6.B.4
ILM1.C
.1ILM
1.C.2
ILM1.C
.3ILM
1.C.4
ILM2.C
.1ILM
2.C.2
ILM2.C
.3ILM
2.C.4
ILM2.C
.5ILM
3.C.1
ILM3.C
.2ILM
3.C.3
ILM3.C
.4ILM
3.C.5
ILM4.C
.1ILM
4.C.2
ILM4.C
.3ILM
4.C.4
ILM5.C
.1ILM
5.C.2
ILM5.C
.3ILM
5.C.4
ILM5.C
.5ILM
6.C.1
ILM6.C
.2ILM
6.C.3
ILM6.C
.4ILM
1.D.1
ILM1.D
.2ILM
1.D.3
ILM1.D
.4ILM
2.D.1
ILM2.D
.2ILM
2.D.3
ILM2.D
.4ILM
2.D.5
ILM3.D
.1ILM
3.D.2
ILM3.D
.3ILM
3.D.4
ILM3.D
.5ILM
4.D.1
ILM4.D
.2ILM
4.D.3
ILM4.D
.4ILM
5.D.1
ILM5.D
.2ILM
5.D.3
ILM5.D
.4ILM
5.D.5
ILM6.D
.1ILM
6.D.2
ILM6.D
.3ILM
6.D.4
1
100
2
4
6
8
10
12
GC distribution
ILM1.A.1
ILM1.A.2
ILM1.A.3
ILM1.A.4
ILM2.A.1
ILM2.A.2
ILM2.A.3
ILM2.A.4
ILM2.A.5
ILM3.A.1
ILM3.A.2
ILM3.A.3
ILM3.A.4
ILM3.A.5
ILM4.A.1
ILM4.A.2
ILM4.A.3
ILM4.A.4
ILM5.A.1
ILM5.A.2
ILM5.A.3
ILM5.A.4
ILM5.A.5
ILM6.A.1
ILM6.A.2
ILM6.A.3
ILM6.A.4
ILM1.B.1
ILM1.B.2
ILM1.B.3
ILM1.B.4
ILM2.B.1
ILM2.B.2
ILM2.B.3
ILM2.B.4
ILM2.B.5
ILM3.B.1
ILM3.B.2
ILM3.B.3
ILM3.B.4
ILM3.B.5
ILM4.B.1
ILM4.B.2
ILM4.B.3
ILM4.B.4
ILM5.B.1
ILM5.B.2
ILM5.B.3
ILM5.B.4
ILM5.B.5
ILM6.B.1
ILM6.B.2
ILM6.B.3
ILM6.B.4
ILM1.C
.1ILM
1.C.2
ILM1.C
.3ILM
1.C.4
ILM2.C
.1ILM
2.C.2
ILM2.C
.3ILM
2.C.4
ILM2.C
.5ILM
3.C.1
ILM3.C
.2ILM
3.C.3
ILM3.C
.4ILM
3.C.5
ILM4.C
.1ILM
4.C.2
ILM4.C
.3ILM
4.C.4
ILM5.C
.1ILM
5.C.2
ILM5.C
.3ILM
5.C.4
ILM5.C
.5ILM
6.C.1
ILM6.C
.2ILM
6.C.3
ILM6.C
.4ILM
1.D.1
ILM1.D
.2ILM
1.D.3
ILM1.D
.4ILM
2.D.1
ILM2.D
.2ILM
2.D.3
ILM2.D
.4ILM
2.D.5
ILM3.D
.1ILM
3.D.2
ILM3.D
.3ILM
3.D.4
ILM3.D
.5ILM
4.D.1
ILM4.D
.2ILM
4.D.3
ILM4.D
.4ILM
5.D.1
ILM5.D
.2ILM
5.D.3
ILM5.D
.4ILM
5.D.5
ILM6.D
.1ILM
6.D.2
ILM6.D
.3ILM
6.D.4
(0,2](2,4](4,6](6,8](8,10](10,12](12,14](14,16](16,18](18,20](20,22](22,24](24,26](26,28](28,30](30,32](32,34](34,36](36,38](38,40](40,42](42,44](44,46](46,48](48,50](50,52](52,54](54,56](56,58](58,60](60,62](62,64](64,66](66,68](68,70](70,72](72,74](74,76](76,78](78,80](80,82](82,84](84,86](86,88](88,90](90,92](92,94](94,96](96,98](98,100]
0
2
4
6
8
Coverage across genebody (%)
ILM1.A.1
ILM1.A.2
ILM1.A.3
ILM1.A.4
ILM2.A.1
ILM2.A.2
ILM2.A.3
ILM2.A.4
ILM2.A.5
ILM3.A.1
ILM3.A.2
ILM3.A.3
ILM3.A.4
ILM3.A.5
ILM4.A.1
ILM4.A.2
ILM4.A.3
ILM4.A.4
ILM5.A.1
ILM5.A.2
ILM5.A.3
ILM5.A.4
ILM5.A.5
ILM6.A.1
ILM6.A.2
ILM6.A.3
ILM6.A.4
ILM1.B.1
ILM1.B.2
ILM1.B.3
ILM1.B.4
ILM2.B.1
ILM2.B.2
ILM2.B.3
ILM2.B.4
ILM2.B.5
ILM3.B.1
ILM3.B.2
ILM3.B.3
ILM3.B.4
ILM3.B.5
ILM4.B.1
ILM4.B.2
ILM4.B.3
ILM4.B.4
ILM5.B.1
ILM5.B.2
ILM5.B.3
ILM5.B.4
ILM5.B.5
ILM6.B.1
ILM6.B.2
ILM6.B.3
ILM6.B.4
ILM1.C
.1ILM
1.C.2
ILM1.C
.3ILM
1.C.4
ILM2.C
.1ILM
2.C.2
ILM2.C
.3ILM
2.C.4
ILM2.C
.5ILM
3.C.1
ILM3.C
.2ILM
3.C.3
ILM3.C
.4ILM
3.C.5
ILM4.C
.1ILM
4.C.2
ILM4.C
.3ILM
4.C.4
ILM5.C
.1ILM
5.C.2
ILM5.C
.3ILM
5.C.4
ILM5.C
.5ILM
6.C.1
ILM6.C
.2ILM
6.C.3
ILM6.C
.4ILM
1.D.1
ILM1.D
.2ILM
1.D.3
ILM1.D
.4ILM
2.D.1
ILM2.D
.2ILM
2.D.3
ILM2.D
.4ILM
2.D.5
ILM3.D
.1ILM
3.D.2
ILM3.D
.3ILM
3.D.4
ILM3.D
.5ILM
4.D.1
ILM4.D
.2ILM
4.D.3
ILM4.D
.4ILM
5.D.1
ILM5.D
.2ILM
5.D.3
ILM5.D
.4ILM
5.D.5
ILM6.D
.1ILM
6.D.2
ILM6.D
.3ILM
6.D.4
5'
3'
0.2
0.4
0.6
0.8
1
1.2
a
b
c
GC distribution
Percent error vs. read.length
Coverage across genebody (%)
Supplementary Figure 18. Intra- and inter-site variations of three additional quality metrics for the Illumina dataset. (a) Percentage of GC content over all reads. GC content (%) was binned in 2% increments and plotted on the y-axis. (b) Percent error
Nature Biotechnology: doi:10.1038/nbt.3000
rate (y-axis) per base for all reads. Error rate was calculated relative to the reference sequence from STAR aligner results. (c) Coverage uniformity across gene bodies. All transcript lengths were scaled and reads attributed to 100 bins along the sequence. The coverage (percentage) along each gene body from 5’→ 3’ was plotted by color along the y-axis. Plots were made with pheatmap in R.
Nature Biotechnology: doi:10.1038/nbt.3000
●
●
●
●
●
●
●
●
●
●
●
●
40
50
60
ILM1 ILM2 ILM3 ILM4 ILM5 ILM6site
Dup
licat
ion
rate
per
libr
ary
(%)
samplesABCD
Supplementary Figure 19. Duplication rate per library. On average, there are 45% of duplication rate per library. This quality metrics significantly associated with the latent variables from the PEER and SVA.
Nature Biotechnology: doi:10.1038/nbt.3000
GC distribution
PGM1.A.1
PGM1.A.2
PGM1.A.3
PGM1.A.4
PGM2.A.1
PGM2.A.2
PGM3.A.1
PGM3.A.2
PGM1.B.1
PGM1.B.2
PGM1.B.3
PGM1.B.4
PGM2.B.1
PGM2.B.2
PGM3.B.1
PGM3.B.2
(0,2](2,4](4,6](6,8](8,10](10,12](12,14](14,16](16,18](18,20](20,22](22,24](24,26](26,28](28,30](30,32](32,34](34,36](36,38](38,40](40,42](42,44](44,46](46,48](48,50](50,52](52,54](54,56](56,58](58,60](60,62](62,64](64,66](66,68](68,70](70,72](72,74](74,76](76,78](78,80](80,82](82,84](84,86](86,88](88,90](90,92](92,94](94,96](96,98](98,100]
0
2
4
6
8
10
12A B
−0.6
−0.3
0.0
0.3
0.6
−0.6
−0.3
0.0
0.3
0.6
−0.6
−0.3
0.0
0.3
0.6
V1V2
V3
PGM
1
PGM
2
PGM
3
PGM
1
PGM
2
PGM
3
site
Late
nt v
aria
bles
replicates 1 2 3 4
a b
Supplementary Figure 20. GC content quality metric and latent variables from PGM data. Percentage of GC content over all reads in the Proton dataset. GC (%) was binned into 2% increments and plotted on the y-axis. GC content varies among sites for the same library (A, B).
Nature Biotechnology: doi:10.1038/nbt.3000
−2 −1 0 1
−0.5
0.0
0.5
1.0
1.5
Dimension 1
Dim
ensi
on 2
A.1A.2 A.3
A.4
B.1B.2B.3B.4
A.1
A.2
B.1B.2
A.1A.2
B.1B.2
●
●
●
PGM1PGM2PGM3
A B
4.5
5.0
5.5
6.0PG
M1
PGM
2
PGM
3
PGM
1
PGM
2
PGM
3site
Max
imum
GC
read
%)
● ● ●PGM1 PGM2 PGM3
1 2 3 4
A B
56789
1011
PGM
1
PGM
2
PGM
3
PGM
1
PGM
2
PGM
3
siteAver
age
base
erro
r rat
e (%
)
● ● ●PGM1 PGM2 PGM3
1 2 3 4
A B
26
28
30
PGM
1
PGM
2
PGM
3
PGM
1
PGM
2
PGM
3
site
Coe
ffici
ent o
f var
iatio
n(g
eneb
ody
%)
● ● ●PGM1 PGM2 PGM3
1 2 3 4
comp: A comp: B
0
50
100
150
200PG
M1−
PGM
2
PGM
1−PG
M3
PGM
2−PG
M3
PGM
1−PG
M2
PGM
1−PG
M3
PGM
2−PG
M3
Comparison
Fals
e po
sitiv
e D
EGs
Without SVA With SVA
0
2500
5000
7500
10000
12500
PGM
1
PGM
2
PGM
3
ComparisonN
umbe
r of D
EGs
Without SVA With SVA
0.0
0.1
0.2
0.3
0.4
PGM
1
PGM
2
PGM
3
Site
MC
C
Without SVA With SVA
d e f g
a b c
Supplementary Figure 21. Examination of quality metrics and DEG detection for PGM data. (a) Maximum percentage of reads with the corresponding GC content (0% to 100%) for each sequencing replicate 1..4. Replicates are indicated by circle, triangle, square, and plus-sign. Sites are shown as different colors. (b) Average error rate across all sequencing bases. (c) Coefficient of variation of the percentage of genebody coverage, which is a measure of the evenness of coverage across genebody. (d) MDS plot of three PGM sites for sample A and B. Sample A and B is clearly distinguished by dimension 1. For sample A, replicate 4 from PGM1 site and replicate 1 from PGM2 site were distinguish at dimension 2 from the other A samples.
Nature Biotechnology: doi:10.1038/nbt.3000
a b c PG
M1.
A −
PGM
2.A
PGM
1.A −
PGM
3.A
PGM
1.A −
PGM
1.B
PGM
1.A −
PGM
2.B
PGM
1.A −
PGM
3.B
PGM
2.A −
PGM
3.A
PGM
2.A −
PGM
1.B
PGM
2.A −
PGM
2.B
PGM
2.A −
PGM
3.B
PGM
3.A −
PGM
1.B
PGM
3.A −
PGM
2.B
PGM
3.A −
PGM
3.B
PGM
1.B −
PGM
2.B
PGM
1.B −
PGM
3.B
PGM
2.B −
PGM
3.B
original
Num
ber o
f DEG
s
0
2000
4000
6000
8000
PGM
1.A −
PGM
2.A
PGM
1.A −
PGM
3.A
PGM
1.A −
PGM
1.B
PGM
1.A −
PGM
2.B
PGM
1.A −
PGM
3.B
PGM
2.A −
PGM
3.A
PGM
2.A −
PGM
1.B
PGM
2.A −
PGM
2.B
PGM
2.A −
PGM
3.B
PGM
3.A −
PGM
1.B
PGM
3.A −
PGM
2.B
PGM
3.A −
PGM
3.B
PGM
1.B −
PGM
2.B
PGM
1.B −
PGM
3.B
PGM
2.B −
PGM
3.B
EDASeq
Num
ber o
f DEG
s
0
1000
2000
3000
4000
5000
6000
PGM
1.A −
PGM
2.A
PGM
1.A −
PGM
3.A
PGM
1.A −
PGM
1.B
PGM
1.A −
PGM
2.B
PGM
1.A −
PGM
3.B
PGM
2.A −
PGM
3.A
PGM
2.A −
PGM
1.B
PGM
2.A −
PGM
2.B
PGM
2.A −
PGM
3.B
PGM
3.A −
PGM
1.B
PGM
3.A −
PGM
2.B
PGM
3.A −
PGM
3.B
PGM
1.B −
PGM
2.B
PGM
1.B −
PGM
3.B
PGM
2.B −
PGM
3.B
PEER
Num
ber o
f DEG
s
0
1000
2000
3000
4000
d e f
0.5
0.6
0.7
0.8
orig
inal
EDAS
eq
PEER
type
Spea
rman
cor
rela
tion
coef
ficie
nts
of in
tra−s
ite D
EGs
adju
sted
p−v
alue
0.60
0.65
0.70
0.75
0.80
0.85PG
M1
PGM
2
PGM
3
site
Mat
thew
s co
rrela
tion
coef
ficie
nt:
inte
r−si
te D
EGs
valid
ated
by
intra−s
ite D
EGs
original EDASeq PEER
0.0
0.2
0.4
0.6
PGM
1
PGM
2
PGM
3
site
Mat
thew
s co
rrela
tion
coef
ficie
nt:
intra−s
ite D
EGs
valid
ated
by
TaqM
an
original EDASeq PEER
Supplementary Figure 22. PGM Inter-site and intra-site DEGs analysis. (a-c) PGM Inter-site and intra-site DEGs using original method (a), EDASeq (b), and PEER (c). (d-e) Evaluation of the inter-site DEGs detections using intra-site DEGs. (d) The spearman correlation of adjust p-value between inter-site DEGs with intra-site DEGs. (e) Inter-site DEGs validated by intra-site DEGs, measured by Matthews Correlation Coefficient (MCC). (f) Evaluation of the intra-site DEGs detections using TaqMan data by MCC.
Nature Biotechnology: doi:10.1038/nbt.3000