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Supplementary Information to
Dissecting spatio-temporal protein networks driving human
heart development and related disorders
Kasper Lage1-5*, Kjeld Møllgård6, Steven Greenway7, Hiroko Wakimoto7, Joshua M.
Gorham7, Christopher T. Workman4, Eske Bendsen8, Niclas T. Hansen4, Olga Rigina4,
Francisco S. Roque4,5, Cornelia Weise9, Vincent M. Cristoffels9, Amy E Roberts10,11,
Leslie B. Smoot11, William T. Pu11,12, Patricia K. Donahoe1,2,3, Niels Tommerup13, Søren
Brunak4,5, Christine E Seidman7, Jonathan G Seidman7, Lars A. Larsen13*
1
Pediatric Surgical Research Laboratories, MassGeneral Hospital for Children, Massachusetts General Hospital, Boston,
Massachusetts, USA.
2
Harvard Medical School, Boston, Massachusetts, USA.
3
Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
4
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark.
5
Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
6
Developmental Biology Unit, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark.
7
Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
8
Fertility Clinic, Department of Obstetrics and Gynaecology, University Hospital of Odense, Odense, Denmark
9
Center for Heart Failure Research, Academic Medical Centre, Amsterdam, The Netherlands.
10
Partners HealthCare Center for Genetics and Genomics, Boston, Massachusetts, USA
11
Department of Cardiology, Children's Hospital, Boston, Massachusetts, USA
12
Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, USA.
13
Wilhelm Johannsen Centre for Functional Genome Research, Department of Cellular and Molecular Medicine, University of
Copenhagen, Copenhagen, Denmark.
*Corresponding authors. K Lage, Pediatric Surgical Research Laboratory, Massachusetts General Hospital, 185
Cambridge Street, Boston, MA 02114, USA, Tel.: +1 617 840 1799; Fax +1 617 726 5075; E-mail:
[email protected] or LA Larsen , Wilhelm Johannsen Centre for Functional Genome Research,
Department of Cellular and Molecular Medicine, University of Copenhagen, Blegdamsvej 3, 2200 Copenhagen
N, Denmark; Tel: +45 35327827; Fax +45 4535327845; E-mail: [email protected]
1
TABLE OF CONTENTS
Supplementary text
Supplementary experimental procedures
References
Table S1-S7
Figure S1-S15
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SUPPLEMENTARY TEXT
Generating a set of candidates for IH and expression profiling.
Developing a score for identifying novel heart developmental candidates
To systematically identify a set of proteins for validation of proteins and clusters in the
networks, we made a set of raw candidates by determining a set of proteins that
significantly interact with the 255 CD proteins using hyper geometric statistics.
We first scored all 8,800 proteins in our protein interaction database based on their
interaction pattern to the 255 CD proteins. Candidates were scored using the enrichment
of CD proteins in their first order interaction network. The score assigned to a candidate
was the hypergeometric p value of observing the amount of interactions to the CD set out
of all interaction partners of the candidate (Figure S5A). For example: NKX2-5 has a
total of seven interaction partners in our network of which five are other CD proteins.
The hypergeometric probabillity that five of seven interaction partners are from the CD
set (which constitutes less than 2% of the proteins with interactions in our interaction
data) is p = 2.4e-06, which is the score of NKX2-5. Similarly, GATA4 has six
interaction partners in our network of which three are other CD proteins, yielding a score
p = 4.7e-4; and JAG1 has 13 interaction partners of which five are CD proteins yielding a
score p = 1.5e-6. As expected, the first order network of JAG1 includes most of the
known NOTCH pathway components (Ilagan and Kopan, 2007), thus supporting the
biological signal of the interaction data in our network.
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To see how well the hypergeometric pvalues correlate with the likelihood of being
involved in cardiac development we calculated unadjusted p values for all CD proteins
that have interactions in our network, and for a set of randomly chosen negative proteins
not known to be involved in heart development. We then correlated the p values with the
likelihood that a given protein is involved in heart development and see that these values
are clearly correlated, indicating that the lower the p value of interactions to the CD set,
the more likely a given protein is to be involved in heart developmental processes itself
(Figure S5B). To be sure that this analysis was not positively biased in relation to the CD
proteins and heart developmental processes we analyzed the interaction dataset for
positive bias.
Analyzing the interaction data for positive bias on relation to heart developmental
processes
First, due to the incorporation of hundreds of systematic large-scale interaction screens in
our protein network the CD proteins should in principle not be better studied than the
randomly chosen proteins and thus bias the significance assessment. However, to be sure
that this was actually the case we confirmed that the random set of proteins had a similar
distribution of interactions when compared to the 255 heart development proteins,
suggesting that the amount of interaction data for the CD proteins and other proteins in
the network does not differ (data not shown). Second, by text-mining PubMed we
identified a small set of publications that described proteomic analyses of proteins
involved in congenital heart defects and heart development. We then cross referenced this
set of publications with the publications reporting data used in our protein interaction
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network and found that there was no overlap, meaning that if they for some reason had
not been included in the protein interaction databases we draw our data from, and that
none of the thousands of interaction screens incorporated in our network were produced
in an attempt to study congenital heart defects or heart development. This shows that our
predictions should not be positively biased towards known CD proteins.
Scoring all proteins in our database to identify novel heart developmental candidate
proteins
To identify likely novel candidates we then made a proteome-scale rank of all proteins
with interaction data in our network, and identified a list of 70 proteins that had a
significant pvalues after correction for multiple testing. The pvalue threshold for
proteome-wide significance is indicated by the red dashed line in Figure S5B. Twenty
one of 70 proteins are from the CD set yielding a set of 49 candidates (Table S2). We
then analyzed how many of these candidates had been described in the context of relevant
processes in the literature in PubMed and only considered them novel in relation to heart
development, if they were undescribed in this regard
(http://www.ncbi.nlm.nih.gov/pubmed/). Of the 49 candidates nineteen result in a cardiac
or vascular phenotype when mutated in mice (Table S2). There are several explanations
to why these 19 proteins were not in the input data; i) vascular phenotypes were not
among the phenotypes we choose for our input data-set, ii) some of the data has been
added to a later version of the MGI database and iii) database curator errors.
Nevertheless, the large proportion of candidate genes associated with vascular or cardiac
phenotypes in mice support that this group of genes are involved in heart development.
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Using the morphological sub-groups to predict spatio-temporal function of the 49
candidates and to select 12 specific candidates for IH analysis.
To validate further the identified 49 candidates and systematically identify the most likely
developmental stages and processes in which each were involved, we used the
morphological subgroups associated with the known CD proteins (Table S1) to infer
developmental functions to the interacting candidates.
We used a hypergeometric based score as described above, but for this analysis each
candidate was assigned a score to the specific morphological subgroups depending on
their interaction pattern to proteins represented in these specific groups. For example, the
candidate SNX9 interacts with ADAM9 and ADAM15 from the morphological
subgroups ‘abnormal atrioventricular valve morphology’ (AAVM) (Figure S2C), and
ADAM9, ADAM15, and EGFR from the subgroup ‘abnormal semilunar valve
morphology’ (ASVM) (Figure S6A). This interaction pattern yields scores to these two
groups with adjusted (using a Bonferroni correction) p values 6.6e-3 and 3.1e-4
respectively and SNX9 is coloured in Figure S2C and Figure S3A according to these p
values. SNX9 also interacts with TMOD1, from ‘abnormal heart tube morphology’
(AHTM), ‘abnormal looping morphogenesis’ (ALM), the subgroups ‘abnormal
myocardial fiber morphology’ (AMFM) and ‘abnormal myocardial trabeculae
morphology’ (AMTM). However, TMOD1 is the only interaction partner of SNX9 in
these groups, and the interactions do not yield significant association of SNX9 to AHTM,
ALM, AMFM and AMTM. Hereby, the analysis suggests that SNX9 has a role
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specifically in valve formation. Similarly, JAG2 (Figure S5B) significantly interacts with
proteins in the groups ‘abnormal sinus venosus’ (ASV) (Figure S1C), ‘atrial septal
defect’ (ASD) (Figure S2A), ‘small heart’ (SH) (Figure S4F), ‘ventricular septal defect’
(VSD) (Figure S3D), and AMTM (Figure S5D), suggesting a role of JAG2 in septation
and trabeculation (Figure S6B).
We carried out this stratification for all 49 candidates and the 21 previously known CD
proteins, the results can be seen in (Figure S6). Twelve of the 49 candidates were chosen
for IH analysis based on the overlap between morphological subgroups and the
developmental stages present in our panel of embryonic hearts available for validation
experiments.
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Validating the module maps by IH analyses of 12 candidates and two known CD
proteins.
For validation of the module maps we analyzed the spatial and temporal expression
pattern of 14 proteins in the network (Table S4, Figure S7-S13). A total of 382 tissue
sections from 19 human hearts were used, and the expression pattern in each tissues
section was determined in a completely standardized fashion by the same person. Using
this table we estimated the precision of our functional predictions by seeing how often the
expression in a specific tissues and at a specific time point, correlated with our functional
predictions. To be conservative we only considered a prediction to be true if the tissue
and developmental stage corresponding to the function, showed the highest degree of
expression for the relevant protein (Table S5). Precision was then calculated in a
standardized manner as the amount of true positive predictions (TP) divided by the sum
of TP and false positive (FP) predictions, which yielded a precision of 0.72, showing the
strong biological signal in our networks.
As positive controls we used the proteins EGFR and BMP4 which are known heart
developmental proteins in mice. In our network, the proteins represent clusters of ERBB
signalling and TGFbeta/BMP signalling, respectively. The network analysis suggest
diverse roles of TGFbeta/BMP and ERBB signalling modules in human heart
development, which is supported by the IH analyses (Table S4). In particular, the
involvment of ERBB signalling in development of the semilunar (SL) and
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atrioventricular (AV) valves and the involvment of TGFbeta/BMP signalling in
development of the SL valves are supported by the IH analyses.
A module of unknown function containing SNX9 is involved in development of the cardiac
valves
We identified a distinct PPI subcluster associated with development of the cardiac valves
(Figure S2C and S3A). We were not able to assign a specific function to this subcluster,
but a function in regulation of ERBB signalling and endothelial-mesenchymal
transformation (EMT) is likely. SNX9 has been shown to regulate the endocytosis of
EGFR (Lin et al., 2002). The cluster includes several members of the family of
metalloproteases known as “a disintegrin and metalloprotease (ADAM)”. Proteins of the
ADAM family have been implicated in ectodomain shedding of epidermal growthfactor
receptor (EGFR) ligands (Blobel, 2005). The protein SH3PXD2A (SH3MD1, Tks5) is
also included in this subcluster. SH3PXD2A is involved in degradation of the
extracellular matrix and cell invasion (Seals et al., 2005). The specific expression of
SNX9 within the endothelial cells lining the surface of endocardial cushions in the aortic
trunk supports a role of this subcluster in EMT. During development of the EC
endothelial cells receive signals from the underlying myocardium and transform into
mesenchymal cells which invade the underlying cardiac jelly (Armstrong and Bischoff,
2004).
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Notch Signalling is Involved in Several Aspects of Cardiac Morphology
Hierachical clustering of 70 CD candidate proteins show that several proteins involved in
Notch signalling is associated with a specific subset of morphological phenotypes (Figure
S6C). These include abnormal sinus venosus, trabeculation, septal defects and double
outlet right ventricle (DORV) - a condition in which both great arteries arise from the
right ventricle.
By IH analysis of a 6th week human embryonic heart, we were able to investigate the
relationship between DLL1, JAG2,NOTCH3 and NOTCH4 and some of the
morphological phenotypes these CD candidates were predicted to be involved in.
We observed strong DLL1 expression in the endothelial cells lining the developing atrial
septum (AS) with the strongest expression at the leading edge of the AS (Figure S9C-E).
Furthermore we observed strong expression of NOTCH3 and JAG2 in endothelial cells
aligning the left atrial side of the endocardial cushion (EC) (Figure S8C, D and S9A) and
strong expression of DLL1 in cells within the EC (Figure S8A).
These expression patterns support a role of Notch signaling in atrial septation since
fusion of the EC with the mesenchymal cap of the leading edge of the primary AS is an
important event in atrial septation (Anderson et al., 2003; Kirby, 2007; Mommersteeg et
al., 2006).
The ventricular septum (VS) is composed of a muscular part and a membranous part.
Development of the muscular part of the VS is, at least in part, achieved by the
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compaction of trabeculae (Contreras-Ramos et al., 2008; Kirby, 2007; Stadtfeld et al.,
2007). The membranous part of the VS is formed from cushion tissue in the outflow tract
(Kirby, 2007).
The IH analysis of DLL1, JAG2 and NOTCH3 support involvement of Notch signaling
in ventricular septation as we observed expression of all three proteins in endothelial cells
lining the developing trabeculae of the ventricle (Figure S8A-C, E and S9A) and strong
expression of NOTCH3 and JAG2 in endothelial cells of the outflow tract and in the
cardiac cushions of the outflow tract (Figure S8C and S9A,B).
The predicted involvement of Notch signaling in cardiac septation and DORV (Figure
S2A, S3C,D) implies that mutations in the Notch signaling pathway should be found in a
subset of patients with atrial septal defects (ASD), ventricular septal defect (VSD),
DORV and tetralogy of Fallot (ToF). This hypothesis is in part supported by the clinical
phenotype of patients with mutations in JAG1, NOTCH1 and NOTCH2 (Eldadah et al.,
2001; Garg et al., 2005; McElhinney et al., 2002). JAG1 and NOTCH2 mutations are
found in patients with Alagille syndrome (McDaniell et al., 2006; Warthen et al., 2006).
The most prevalent cardiac defect in Alagille syndrome is pulmonary stenosis, but 1/5 of
the patients have ASD, VSD or ToF (McElhinney et al., 2002). ToF is caused by
malalignment of the conal part of the ventricular septum, which results in VSD,
pulmonary stenosis, bi-ventricular connection of the aorta and right ventricular
hypertrophy (Anderson and Weinberg, 2005). From a developmental point of view
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DORV and ToF are very similar, but DORV represent a more extreme septal
malalignment.
Furthermore, JAG1 mutations have been reported in familial ToF (Eldadah et al., 2001)
and in relatives to AS patients with isolated ASD and ToF (Krantz et al., 1999). ASD,
VSD, DORV and ToF were observed in a subset of the affected family members in two
families segregating NOTCH1 mutations (Garg et al., 2005). And interestingly, two out
of ten de novo CNVs identified in patients with ToF contains NOTCH1 and JAG1,
respectively (Greenway et al., 2009).
The functional prediction of CD candidates also suggests an involvement of Notch
signaling in trabeculation (Figure S6C, S2D). Our IH analysis showed that DLL1, JAG2
and NOTCH3 were expressed in the developing trabeculae (Figure S8A-C, E and S9A) in
line with recent data (Grego-Bessa et al., 2007).
However, our analysis does not predict involvement of Notch signalling in development
of the cardiac valves (Figure S6C) although such a connection is well established (High
and Epstein, 2008). This is due to the fact that valve defects were not among the
phenotypes reported for mice with targeted mutations in Notch signalling proteins (Table
S1) thus this is an example of the shortcommings of our strategy.
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In summary, our analysis associates most of the proteins known to be involved in Notch
signaling with specific events in cardiac development and we hypothesize that mutation
in any of these proteins could lead to ASD, VSD, ToF or DORV in humans.
PPI modules and candidates associated with myocardial growth and organisation
Several PPI subclusters and a large proportion of the CD candidate proteins are involved
in myocardial growth and organisation (Figures S6C and S4). IH analysis of seven of the
proteins associated with myocardial development show that all proteins are expressed in
the cardiomyocytes (Table S4, Figures S10, S11 and S13). Furthermore, the staining
patterns suggested a spatial regulation of PTGS2 and CAV3 and a temporal regulation of
BMX during cardiac development. Thus, the IH analysis supports a function in
myocardial development of these proteins.
The candidates belonging to the group of proteins associated with myocardial
development can be divided further into three subgroups, which mainly differ by the
proteins involvement in signalling transduction (Figure S6C, D). The candidate proteins
in two of these subgroups are involved in a large number of signalling pathways.
Examples of proteins within these groups are members of the RAS, SOS, MAPK and
AKT families. Thus, these subgroups probably represent proteins involved in cross-talk
between signalling pathways influencing the growth and organisation of the myocardium.
The protein-protein interactions plotted in Figure S4 also suggest the existence of
extensive signalling crosstalk during myocardial development.
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Independent validation of the network data and module maps by expression
profiling across 12 developmental time points
To validate the network data and module maps we carried out expression profiling of all
49 novel candidates determined by their interaction profile to set of 255 CD proteins in
the previous section. This analysis was carried out using RNA extracted from 14
embryonic human hearts. We chose quantitative real-time quantitative RT-PCR for this
analysis because it is considered to be the most accurate and sensitive method for
detecting RNA differences also at very small amounts (Lang et al, 2009).
First we analyzed the expression of 44 candidates, across two developmental time points
and compared the differential expression of candidates to a set of 29 negative controls.
For this analysis RNA was extracted from two human hearts at 46 days and 67 days post
fertilization. The relative difference in the level of gene expression between day 46 and
67 were significantly larger in the group of candidates (median=0.44) compared to the
group of negative controls (median=0.14) when compared using a Mann-Whitney
(Wilcoxon) W test (p<0.006, Figure 3R), showing that the set of candidates are
significantly more differentially expressed than the control set, indicating that a
significant proportion of the candidates are temporally regulated across the two
developmental time points.
To investigate this trend in more detail, we analyzed the relative expression levels of a
subset of the candidates in 12 additional hearts at 12 different time points between 40 and
67 days post fertilization. It was not possible to analyze all the candidates due to
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limitations in the human heart material, but a large proportion (18 of 49), was tested. This
analysis showed that half of the candidates were significantly differentially expressed
across the two time points further supporting their role in heart development (Figure 3SW).
To further validate the candidates and exclude that polymorphic gene expression between
the individual developing hearts used in the previous analysis could account for the
observed differential and temporal expression trends, we also used Polony Multiplex
Analysis of Gene Expression (PMAGE) (Kim et al., 2007) to measure the expression of
the 49 candidates in right ventricular outflow tract (RVOT) from TOF patients at the time
of primary surgical repair and left ventricle (LV) collected from patients with either heart
failure or diabetic cardiomyopathy. The expression levels of the candidates were
compared to the expression levels of a different set of 49 randomly chosen controls after
normalizing both gene sets against gene expression in glioblastoma tissue. Here, the heart
developmental candidates were significantly higher expressed in heart tissue than the
controls (p = 0.016, Figure S13 and below).
In conclusion, the expression profiling experiments show that novel heart developmental
genes can be identified by systematically analyzing the interaction patterns of the 255
known CD proteins. These results serve as an independent validation of the network data
and module maps.
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Focal adhesion signalling in cardiac development
An interesting observation, which can be derived from the map of functional modules, is
the implication of focal adhesion signalling in several aspects of cardiac development
(Figure 2, Figures S1A, S2D, S3A, S4A, B, D, and E). Focal adhesion signalling is
important for regulation of cell migration, cell proliferation and growth factor signalling
(Mitra et al., 2005). Although such events are regarded as key elements in heart
development, the focus on focal adhesion signalling in heart development has so far been
limited. However, very recent investigations have shown that conditional targeted
deletion of the Focal adhesion kinase (Fak) gene in mice results in CHD (Hakim et al.,
2007; Peng et al., 2008). FAK (PTK2) was not included in our list of input proteins due
to the recent publication of the knockout studies, and our network data precedes
knowledge of FAKs involvement in heart development (see below). Thus, the phenotypes
associated with defective focal adhesion signalling in mouse models are another
functional verification of the predictive power of the networks.
The publications reporting the involvement of focal adhesion signalling by FAK
mutations in heart development are from May 2007 (Hakim et al., 2007) and April 2008
(Peng et al., 2008) respectively. The networks reported here were all generated in January
2008, using data downloaded two months before. Thus, the only possibility of bias is the
article by Hakim et al. To investigate the putative bias we investigated if the Hakim et al.
publication was represented in our PPI data and used to construct our networks, which
was not the case. We then tracked down all publications citing the Hakim et al.,
publication. None of these articles were published before our networks were generated
16
and none of these articles affect the data represented in our networks making it highly
unlikely that our findings are biased by the Hakim et al., publications and supporting the
predictive power of our networks.
Modules of cardiac progenitor cell growth and differentiation
Several of the functional modules identified in this study are known to be involved in
growth and differentiation of cardiac progenitor cells. These include markers for
progenitor cells in the first heart field (TBX5) and second heart field (ISL1) (Domian et
al, 2009; Moretti et al, 2006) and functional modules involved in differentiation of
cardiac progenitors (e.g. transcription modules; Notch signaling modules; WNT signaling
modules and BMP/TGFbeta signaling modules (Dyer & Kirby, 2009; Kwon et al, 2009;
Moretti et al, 2006). Thus, the map of functional modules presented here may aid in the
discovery of new factors involved in growth and differentiation of cardiac progenitors
and in the illumination of molecular events involved in cardiomyogenesis.
17
SUPPLEMENTARY EXPERIMENTAL PROCEDURES
PPI Data
We have previously generated and thoroughly validated an experimentally derived
human protein interaction network which is described in detail in (Lage et al., 2007; Lage
et al., 2008), which integrates and refines experimental proteomic data from more than 13
million articles in Medline. Interested readers can consult these publications and the
online information harbored at:
http://www.cbs.dtu.dk/suppl/dgf/phenome_interactome/index.php, click on the ‘Methods’
link, for the details on generating this interaction resource. The resulting network
includes many unbiased large-scale proteomics datasets, meaning we are not confined to
exploring only existing well-known pathways. An updated version of this network,
containing 12,507 human proteins connected by a total of 350,029 unique interactions,
was used in this work. The underlying data in this network stems from the databases
BIND (Bader et al., 2003), MINT (Zanzoni et al., 2002), IntAct (Hermjakob et al., 2004),
KEGG (Kanehisa et al., 2006), Reactome (Franke et al., 2006), HPRD (Peri et al., 2003),
DIP (Salwinski et al., 2004) and GRID (Breitkreutz et al., 2003). Data is transferred
between organisms using the Inparanoid orthology database (O'Brien et al., 2005) as
previously described in the aforementioned publications.
18
Differential expression in human heart tissues measured by PMAGE
Expression libraries were created from RNA isolated from right ventricular outflow tract
tissue collected from four TOF patients at the time of primary surgical repair (mean
patient age 2.6 ± 2 months), left ventricle collected from patients with either heart failure
or diabetic cardiomyopathy. Tissue was snap-frozen in liquid nitrogen and maintained at
–80˚C prior to processing. RNA extraction and library construction and amplification
was carried out as previously described (Kim et al., 2007). Amplified library was
sequenced on a Illumina Genome Analyzer (Illumina, San Diego, CA). Each library
generated more than 2,000,000 reads with a Chastity score (Illumina, San Diego, CA) >
2. Sense tags were assigned to cognate gene identities and unique tags assigned to the
same UniGene cluster or gene symbol were combined. For candidates and controls we
measured the fold difference by dividing the tag count in RVOT or LV by the tag count
in glioblastoma (collected and analyzed by the same method) and treated all data points
independently. We pooled the RVOT and LV data points to make the final distributions,
meaning that candidate or control gene is represented by 6 data points in the distribution
(4 from RVOT and 2 from LV), ensuring robustness. The fold expression difference
distributions were compared using a Mann-Whitney u-test.
19
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