1. technology and computing
2. software
3. databases

Plant and Cell Physiology Advance Access published November 26, 2010
Running Title: Sample-wise network for microarray experiments
Corresponding Author: Yukihisa Shimada
Plant Science Center, RIKEN, Suehirocho 1-7-22, Tsurumi, Yokohama 230-0045, Japan
Tel: +81-45-503-9494;
Fax: +81-45-503-9492;
Subject Areas: (10) genomics, systems biology and evolution, (11) new methodology
Number of figure: Black and white figure (0), Color figure (4), table (1)
© The Author 2010. Published by Oxford University Press on behalf of Japanese
Society of Plant Physiologists. All rights reserved. For Permissions, please e-mail:
journals.permissions@oxfordjournals.org
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E-mail: shimada@postman.riken.go.jp
Title: AtCAST, a Tool for Exploring Gene Expression Similarities among DNA
Microarray Experiments Using Networks
Authors: Eriko Sasaki1,2, Chitose Takahashi1, Tadao Asami2, Yukihisa Shimada1,2,3
1
RIKEN Plant Science Center, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
2
Department of Applied Biological Chemistry, Graduate School of Agricultural and Life
3
Yokohama City University Kihara Institute for Biological Research, Totsuka,
Yokohama, Kanagawa 244-0813, Japan
Abbreviations: ABA, abscisic acid; ACC, 1-aminocycropropane-1-carboxylic acid;
ACN, all-gene-based correlation network; BL, brassinolide; CK, cytokinin; GA,
gibberellin; GEO, Gene Expression Omnibus; GSEA, Gene set enrichment analysis; IAA,
indole-3-acetic acid; IPA, indole-3-pyruvic acid; MAQC, Microarray Quality Control;
MJ, methyl jasmonate; MCN, module-based correlation network; OPDA,
12-oxo-phytodienoic acid; SA, salicylic acid; SCCs, Spearman’s rank-order correlation
coefficients; SR, log2 signal ratio; SH, shade; WL, white light
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Sciences, The University of Tokyo, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
Abstract
The comparison of gene expression profiles among DNA microarray
experiments enables the identification of unknown relations among experiments to
uncover the underlying biological relationships. Despite the ongoing accumulation of
data in public databases, detecting biological correlations among gene expression profiles
from multiple laboratories on a large scale remains difficult. Here, we applied a module
(sets of genes working in the same biological action)-based correlation analysis in
combination with a network analysis to Arabidopsis data and developed “module-based
experiments on a large scale. We developed a Web-based data analysis tool, “AtCAST”
(Arabidopsis thaliana: DNA Microarray Correlation Analysis Tool), which enables
browsing of a MCN or mining of user’s microarray data by mapping the data into a MCN.
AtCAST can help researchers to find novel connections among DNA microarray
experiments, which in turn will help to build new hypotheses to uncover physiological
mechanisms or gene functions in Arabidopsis.
Keywords: gene expression profile; Arabidopsis thaliana; experimental conditions; data
mining; DNA microarray; plant hormone
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correlation network,”(MCN) which represents relationships among DNA microarray
Introduction
In the post-genome era, genome-wide transcript analyses have been conducted
using DNA-microarray technologies to characterize various aspects of the life of model
species. For the model plant Arabidopsis, AtGenExpress consortium has provided
large-scale transcriptome data sets describing developmental processes (Schmid et al.
2005), stress responses (Kilian et al. 2007), and hormone responses (Goda et al. 2008)
along with other phenomena. In addition, a large amount of microarray data are available
http://www.ncbi.nlm.nih.gov/geo/) (Barrett et al. 2007, Edgar et al. 2002) and
NASCArrays (http://affymetrix.arabidopsis.info/narrays/experimentbrowse.pl) (Craigon
et al. 2004).
These microarray data sets have been widely applied to bioinformatics analyses.
For example, large-scale co-expressed gene analyses can be used to predict gene function
by identifying genes showing similar expression patterns with a gene of interest (Usadel
et al. 2009). Gene expression databases are useful for identifying patterns of gene
expression under various conditions and in specific tissues (Craigon et al. 2004, Toufighi
et al. 2005, Zimmermann et al. 2004). In addition to these applications, microarray data
can be used as high-throughput prediction tools for determining drug effects,
physiological status of cells, and gene functions. Moreover, comparisons of gene
expression profiles have enabled the identification of biological relationships among
DNA microarray experiments. When the gene expression profiles of two independent
experiments are highly correlated, we can infer that these experiments are affected in
similar functions. Thus, the integration of these comprehensive transcriptome resources
and comparisons of gene expression patterns will enable us to uncover relationships
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from data repositories such as Gene Expression Omnibus (GEO;
among experiments.
However, the integration and comparison of gene expression profiles collected
from public repositories is challenging. The reproducibility of microarray data between
laboratories is less than that within a laboratory because gene expression profiles are
significantly affected by artificial noise, such as the protocols used for RNA purification,
probe labeling, microarray hybridization, and image acquisition, as well as the conditions
used for growth, or the age and tissue origin of the samples (Bammler et al. 2005, Irizarry
et al. 2005, Shi et al. 2006). These differences can greatly impact analyses of public
Many statistical principles have been applied to extract biological meaning from
the relationships among DNA microarray data. One classic approach is to use clustering
methods such as hierarchical cluster analysis (Eisen et al. 1998), k-means (Jain and
Dubes 1988), and self-organized maps (Tamayo et al. 1999). Such approaches have been
used to find similarities between gene expression profiles from the whole gene lists.
However, these methods are most useful for small-scale analysis of in-house samples
because these approaches are fragile to large amounts of noise above described (Kutalik
et al. 2008, Nielsen HB 2007). To solve this problem, “module” approach was developed.
A module (Kutalik et al. 2008, Segal et al. 2004) or query signature (Lamb et al. 2006)
refers to sets of genes working in the same biological action. In each experiment, the gene
expression responses of modules reflect the status of specific biological actions.
Therefore, one can assume that samples sharing a common response in a module also
share a related biological action or response. This approach is less susceptible to
experimental noise because the comparison between gene expression profiles is focused
on modules extracted from a whole gene list. This approach has been shown to be
efficient for the analysis of large microarray data sets (Kutalik et al. 2008). Furthermore,
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microarray data sets across laboratories.
the module approach can also detect multiple responses to a given treatment (Lamb et al.
2006). For example, Pearson’s correlation coefficient for the gene expression of modules
has been used to detect the effects of various chemicals (Goda et al. 2002, Marton et al.
1998, Soeno et al. 2010) and the physiological status of samples (Goda et al. 2008,
Volodarsky et al. 2009). Gene set enrichment analysis (GSEA) (Lamb et al. 2006,
Subramanian et al. 2005) has provided useful information on drug–gene associations in
drug discovery studies. Meanwhile, Nemhauser et al (2006) demonstrated hormone
crosstalk by comparing the overlap of hormone-responsive gene lists. “FARO” (Nielsen
similarities among microarray data through functional associations based on response
overlaps.
Regarding the software used for the module-based analysis of microarray data,
Connectivity Map (Lamb et al. 2006) is a pattern-matching online tool based on GSEA
with a reference collection of gene expression profiles from cultured human cells treated
with small bioactive molecules. Users can obtain information for an input gene list based
on correlations with reference gene expression profiles. For those working in plants,
Sample Angler at the BAR (http://www.bar.utoronto.ca) and HORMONOMETER
(Volodarsky et al. 2009) can also create gene expression profiles that are correlated with
user-selected or input expression data. MASTA (Reina-Pinto et al. 2010) can be used for
similarity analyses between microarray experiments using differentially expressed gene
lists. Finally, Genevestigator (Zimmermann et al. 2004) provides biclustering (Madeira
and Oliveira 2004) tools for paid members. The choice of applications available for the
module approach is limited despite a wide variety of public microarray data and the
number of proposed analyses. The development of additional analyses tools for
transcriptome data will provide new opportunities for uncovering the biological
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2007) and “MASTA” (Reina-Pinto et al. 2010) have also been used to characterize
relationships among large microarray data sets.
Here we present the novel Web-based tool Arabidopsis thaliana: DNA
microarray Correlation Analysis Tool “AtCAST”
(http://pfg.psc.riken.jp/AtGenExpress/index.html). We conducted a large-scale analysis
of Arabidopsis microarray data collected from public databases and developed a
module-based system, which we designated the “module-based correlation
network”(MCN). The MCN was validated based on the well-known activities of
phytohormones in addition to statistical methods. AtCAST will enable users to browse
module-based correlation analysis. The system also provides user-data analysis tool to
conduct basic statistical analyses and to draw a MCN in combination with pre-computed
server data.
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the network of relationships among DNA microarray experiments based on
Results and discussion
Drawing MCN of Arabidopsis DNA microarray experiments
The strategy that we used to draw MCN of Arabidopsis microarray experiments
is illustrated in Fig. 1. In the first step, we collected Affymetrix ATH1 microarray data
from the AtGenExpress project. We added microarray experiments of chemical
treatments, known mutants, and nutrient deficiency responses from GEO and
omitted because they did not show enough difference in gene expression patterns to be
distinguished from close time points, making the local density of experiments too
crowded in MCN. Eventually, we analyzed 195 experiments consisting of 692 microarray
data (Table S1). Signal intensities were compared in log2-transformed values relative to
the control sample (e.g., the wild-type or mock treatment) in each experiment. If the
experiment had no control sample (e.g., tissue-specific expression data from
AtGenExpress), then one was created using the median values of signal intensities
throughout all samples in the experiment. The quality of repeated samples was controlled
by r2 using a linear regression analysis. All pairs in replicate samples were calculated.
Samples showing low r2 (<0.7) values with the other samples were considered as outliers
and were removed. If no reliable biological replicates were attained, the experiment was
removed from our analysis.
In the second step, we determined modules by choosing genes. In previous studies,
sets of genes obtained using gene ontology terms or pre-reported marker genes have been
used as modules (Lamb et al. 2006, Subramanian et al. 2005). However, these methods
cannot be applied to gene expression profiles from uncharacterized mutants or plants
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NASCArrays. In some cases, time points in detailed time-course experiments were
treated with chemical compounds having unknown functions. For a module, we selected
genes that were induced or repressed significantly in each experiment by statistical
methods based on the P value of Student’s t-test (p < 0.01) and the fold-change in
induction levels between treatment and control samples (top and bottom 10%) (see
method and supplementary result). These genes represent the biological status of the
samples analyzed in each experiment.
The third step involved the calculation of Spearman’s rank-order correlation
coefficients (SCCs) to estimate relationships between experiments based on modules.
module. SCC A was calculated using genes contained in module A, and SCC B was
calculated using genes contained in module B. All SCCs were calculated using the log2
signal ratio (SR) of the treatment to control for all pairs of experiments.
Finally, we combined experiments having a relation based on SCCs as a network.
Each node (circle) represents the gene expression profile of an experiment and the edges
(black arrows) represent relationships between experiments in terms of significance
levels to fulfill SCC values above the following thresholds. The edges are directional and
SCC A was used to draw the relationship from experiment A to B. Two kinds of
thresholds were defined for SCCs to detect more relationships between experiments
without increasing false-positive errors: mild relation (non-stringent threshold: |SCC| ≥
0.5, n = 50 P < 4 x 10-4) and strong relation (stringent threshold: SCC ≥ 0.7, n = 50 P <
10-4, SCC ≤ –0.65, n = 50 P < 10-4). We adopted asymmetric cutoff values in positive and
negative correlations, because negative correlations tend to be more difficult to detect
than positive connections in biological experiments (Lee et al. 2004). The statistical
significance of the correlations was estimated by the bootstrap method (see method, Fig.
S1). When both SCCs between two experiments satisfied the non-stringent threshold, but
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Each pair of experiments has two SCC values because each experiment has a specific
did not satisfy the stringent threshold, these relations were represented as a mild relation
in both directions (see Step 4 in Fig. 1). Alternatively, when only one of the two SCCs
satisfied the stringent threshold, the relation was represented as a strong relation in one
direction. If both SCCs satisfied the stringent threshold, the relation was a strong relation
in both directions. When two experiments had a mild relation (in both directions) and a
strong relation (one-way), the mild relation was superimposed on the strong relation (Fig.
1, Strong relation + mild relation).
In order to extend MCN, we developed AtCAST, a Web-based tool for
microarray data analysis based on MCN. AtCAST provides a viewer for pre-computed
MCNs of microarray experiments. It also provides a data analysis tool, in which users can
analyze their own data sets using modules and then draw a MCN with online data. Two
menus are available to get MCNs by (1) selecting a query experiment from the list of
pre-computed experiments in the database (Fig. 2A) and (2) uploading their data files as a
query experiment (Fig. 2B). On the tool of user data analysis, AtCAST will send e-mail
to the user containing the URL of their results after calculation. The “Result page” (Fig.
2C) contains a MCN (Fig. 2D) and information of experiments strongly correlated with
the query experiment (Fig. 2E). Experiments in a MCN have significant correlations
(|SCC| ≥ 0.5) with the query experiment. The “Basic Statistics page” (Fig. 2F) provides
statistical information of each microarray data set. When researchers analyze public
microarray data, they should be very careful because public microarray data sets
sometimes contain poor quality data or the wrong annotation. To avoid problems caused
by the data quality, AtCAST provides scatterplots to check the reproducibility of replicate
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Web-based similarity analysis tool: AtCAST
samples. Statistics for each gene were summarized in a table (Fig. 2F). The “Correlation
Information page” (Fig. 2G) provides detailed information about the correlation between
the query experiment and the correlated experiment. The gene list shows information
about genes used in the module of the experiment. Scatterplots (Fig. 2H) effectively
visualize the correlation of SR between two experiments and help users to identify what
kinds of genes are affected in both experiments. All pre-computed results of AtCAST can
be downloaded as a tab-delimited text file. This data can be imported by application
software to visualize molecular interactions, such as Cytoscape (Shannon et al. 2003).
Overview of MCN
We introduce the global MCN using all of the collected experiments in AtCAST
to obtain an overview of relationships among DNA microarray experiments. For
comparison, another network was constructed based on correlations calculated using all
gene sets as an all-gene-based correlation network (ACN) to validate MCN (Fig. 3). For
the ACN, SCCs were calculated using all genes: 21,180 annotated as actual Arabidopsis
genes in all experiment pairs. Each experiment pair has one SCC value representing the
strength of relation between the experiment pair. When |SCC| was ≥ 0.3, the nodes were
connected with the edges.
In the ACN, several small clusters were detected. These small clusters tended to
consist of experiments from the same series, such as experiments of flowering mutants
(shoot apex) (Schmid et al. 2005), chemical treatments (Goda et al. 2008), treatments
with different phytohormones (Goda et al. 2008) of the same sampling time points, auxin
mutants and phy mutants (Tepperman et al. 2006) (Fig. 3A). Through all experiments, the
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Users can change the layout and threshold of interaction based on their own choice.
SCCs among the same experiment series tended to be stronger than those among other
experiments (Fig. S2A). This suggested that biological correlations were below the
background levels and could not be detected in the ACN. In MCN, experiments were
connected with each other more closely, especially beyond the same experiment groups.
Most experiments were contained in the largest cluster; exceptions included the flowering
mutants (shoot apex) (Schmid et al. 2005), Imbibed seed (Goda et al. 2008) and GA
treatment (Goda et al. 2008)(Fig. 3B). Strong correlations (between experiments) were
detected over all of the collected experiments (Fig. S2B).
Phytohormones have diverse roles and are involved in processes such as development,
growth regulation, and stress responses. Because the experiments of the “phytohormone
treatment” were expected to become hubs to connect diverse experiments, we inspected
the status of the nodes of “phytohormone treatment” series. In this data set, the following
five compounds were applied to wild-type seedlings and then compared with
mock-treated controls: indole-3-acetic acid (IAA) as auxin, t-zeatin as cytokinin (CK),
1-aminocycropropane-1-carboxylic acid (ACC) in place of ethylene, abscisic acid (ABA),
and methyl jasmonate (MJ). For the other two hormones, brassinolide (BL) was applied
to BL-deficient det2-1 seedlings and gibberellin A3 (GA3) was applied to gibberellin
(GA)-deficient ga1-5 seedlings. Samples were collected at 0.5, 1, and 3 h after these
treatments.
In the ACN, the “phytohormone treatment” series were clustered in high density
and these connections tended to be found in samples collected at the same time points of
different hormone treatments. For example, IAA, ACC, MJ and t-zeatin 3 h were
connected with each other; IAA, ACC, ABA, MJ and t-zeatin 0.5 h, 1 h were also
connected. These clusters showed few relations with other experiment series. On the
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We then focused on the “phytohormone treatment” series (Goda et al. 2008).
other hand, in MCN, experiments involving treatments with the same hormones showed
positive correlations and were highly connected. Most phytohormone treatments were
connected with experiments that are known to be related to the hormone action as
described below.
Close-up view of hormone experiments (MJ, ABA)
We then focused on individual hormone treatment experiments. A positive
negative connection indicated that hormone responses were repressed in experiments
connected with hormone experiments. For example, MJ 0.5, 1, 3 h (treatments with MJ
for the respective time periods) displayed a strong negative relation with “mutant coi1”
(Buchanan-Wollaston et al. 2005), which has a defective jasmonate receptor (Chini et al.
2007), and a positive connection with “OPDA 4 h” (treatment with 12-oxo-phytodienoic
acid for 4 h) (Mueller et al. 2008), which is an intermediate in the jasmonate biosynthesis
pathway. “OPDA 4 h” also displayed a negative connection with “mutant coi1.” Thus,
biological connections of these three experiments formed a minimum cluster of a
negative or positive motif (Fig. S3). Jasmonate mediates the signaling of abiotic and
biotic stresses. We confirmed that MJ also showed a positive relation with pathogen
response such as “B. cinerea 18 h infection.” MJ treatment became a hub of
hormone-deficient mutants, treatments with chemical compounds (having similar
bioactivity), pathogen infections, and stress treatments (Fig. S4).
ABA showed a strong positive relation with “Osmotic stress,” “Salt stress,” and
“Cold stress.” ABA also plays a key role in responses to environmental stresses such as
drought and high salinity (Shinozaki and YamaguchiShinozaki 1997). “ABA 1 h” showed
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connection of experiments indicated that hormone responses were induced, while a
a one-way strong negative relation with “Norflurazon,” in which cotyledons were treated
with this ABA biosynthesis inhibitor. This compound is not a specific inhibitor of ABA
because it inhibits carotenoid biosynthesis at the step of phytoene desaturase (Chamovitz
et al. 1991, Kleudgen 1979). Various metabolites can be synthesized from carotenoids,
including a recently reported novel plant hormone, strigolactone (Gomez-Roldan et al.
2008, Umehara et al. 2008). Thus, the expression of genes related to various metabolic
pathways can be effected by norflurazon treatment, as can genes that respond to ABA
treatment (Fig. S5). An edge of a one-way correlation connected “ABA 1 h” with
module of ABA-responsive genes, whereas “Norflurazon” consists of a module that may
be classified into several smaller modules, one of which is a module of ABA-responsive
genes. For other plant hormones, IAA, BL, ACC, CK, and GA, known relations were
observed among experiments in MCN (see Supplementary Results and Figs. S6–S10).
Above observations showed MCN represents biological connections among
experiments. It will help researchers to understand relationships among DNA microarray
experiments. Because relationships of experiments can be interpreted from the clusters of
experiments, this approach is useful to efficiently interpret the results and construct novel
biological hypotheses.
Case study of the sav3-2 mutant using AtCAST
Here we present a pilot analysis of a gene function using MCN. Sav3-2 is an
auxin biosynthesis-deficient mutant (Tao et al. 2008). The TAA1/SAV3 gene encodes an
aminotransferase that catalyzes the formation of indole-3-pyruvic acid (IPA) from
L-tryptophan. The
sav3 null mutants are defective in multiple shade avoidance responses
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“Norflurazon.” This result was consistent with our prediction that “ABA1 h” consists of a
such as hypocotyl elongation and increasing leaf angle. These mutants also fail to show
rapid shade-induced changes in auxin levels (Tao et al. 2008). We focused on “mutant
sav3-2 (SH)” (the sav3 mutant exposed to simulated shade, i.e., a reduced red to far-red
light ratio and compared it to the wild type) to characterize the gene function. Three auxin
treatment experiments (0.5, 1, and 3 h) constituted a positive motif (Fig. S3), confirming
similarity in gene expression profiles among auxin treatment experiments. “Mutant
sav3-2 (SH)” showed negative motifs with the IAA treatment experiments “IAA 0.5 h,”
“IAA 1 h,” and “IAA 3 h” (seedlings were treated with IAA and compared to the mock
In contrast, “mutant sav3-2 (WL)” (the sav3-2 mutant grown under white light and
compared to the wild type) did not show a negative relation with the IAA treatment
experiments (Fig. 4). These results suggest that the auxin action of the sav3-2 mutant is
weaker than that of the wild type under shade conditions, but not under light conditions.
This result is consistent with the report of Tao et al (2008) demonstrating that the free IAA
level and IAA synthesis rate are reduced in the sav3 mutant, especially under shade
conditions.
This MCN consisted of 37 experiments (Table 1). Nineteen of these experiments
were included in at least one of MCNs centered on one of three IAA treatments (0.5, 1, 3
h), suggesting that the sav3 is an auxin-related mutant. We also noticed that 17 of the
experiments were included in MCN of “Salicylic acid 3 h” (seedlings were treated with
salicylic acid (SA) and compared to the mock treatment) (Goda et al. 2008). The node
“mutant sav3-2 (SH)” showed a positive relation in both directions with SA treatment and
a negative relation in both directions with “mutant NahG leaves” (transgenic NahG)
(Buchanan-Wollaston et al. 2005) which is defective in SA accumulation (van Wees and
Glazebrook 2003). That is, these three experiments constituted a negative motif (Fig. S3).
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treatment), indicating that IAA is inactivated in the sav3 mutant under shade conditions.
In both the “mutant sav3-2 (SH)” and “salicylic acid 3 h” treatment, the SA-responsive
genes WRKY54 (AT2g40750), WRKY70 (AT3g56400), ACD6 (AT4g14400), and PCC1
(AT4g38550) were upregulated, and the early auxin-responsive genes SAUR
(AT3g03830), IAA5 (AT1g15580), IAA29 (AT4g32280), and GH3.3 (AT2g23170) were
downregulated. In contrast, SA-responsive genes were downregulated and early
auxin-responsive genes were upregulated in “mutant NahG leaves” (Fig. S11).
In the sav3-2 mutant grown under white light, the expression levels of most
auxin-responsive genes did not differ from those in the wild type, except for Aux/IAA19
were upregulated (p < 0.001) regardless of the light conditions (Fig. S11). These
observations suggest that shade treatment stimulates auxin-responsive genes controlled
by TAA1, but does not stimulate SA-responsive genes directly. WRKY70 influences
SA-mediated plant senescence and defense signaling pathways independently from
jasmonate and ethylene (Ulker et al. 2007). ACD6 and PCC1 also act as positive
regulators in SA-mediated defense-signaling pathways (Rate et al. 1999, Sauerbrunn and
Schlaich 2004). Zhang et al (2007) reported that GH3.5 is a bifunctional gene that acts in
both SA- and auxin-signaling pathways. Our results suggest that cross talk may occur
between IAA and SA, and that TAA1 may function as a cross talk point of the SA- and
auxin-signaling pathways. Therefore, the module of mutant sav3 appears to consist of
smaller modules, one involved in the auxin pathway and the other involved in the SA
pathway.
Summary and future prospects
AtCAST is a module-based analysis tool for visualizing similarities among gene
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(AT3g15540). However, the SA-responsive genes WRKY54, WRKY70, ACD6, and PCC1
expression profiles from a large-scale DNA microarray experiments. It allows for the
integration of transcriptome resources collected from public databases and the
comprehensive analyses of relationships among those experiments through
module-based comparisons of gene expression patterns. Relationships among DNA
microarray experiments are represented in a network view, termed the MCN. Our results
indicate that MCN is resistant to the artificial noise that is inherent in microarray
experiments due to differences in experimental protocols between laboratories. Using the
MCN, connections between biologically related experiments can be detected more
the correlations between chemically treated samples and mutants will be useful to
efficiently elucidate the relationships between bioactive substances and metabolic
enzymes, receptors, or signaling factors. Further analysis will reveal novel actions of
chemical compounds or the functions of mutant genes.
We found that some of the experiments were solitary islands having no edges
connected to other experiments in the global MCN. This result suggests that there is a
lack of biological experiments implying biological events, and that many biological
questions remain unaddressed. Individual microarray data sets produce snapshots of the
transcriptome status of a sample. With MCN, one can combine these snapshots and
potentially gain insights into the physiological processes in plants. As the volume of
microarray experimental data in databases is increasing, the MCN will serve as a bridge
to solitary islands from known clusters.
In this report, the integration of public DNA microarray data was carried out
using the Affymetrix ATH1 GeneChip because the fundamental microarray experiments
of AtGenExpress were produced using this platform in Arabidopsis. However, the
number of important experiments using diverse platforms has been increasing (Krinke et
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clearly than connections between batch-treated samples. As demonstrated in this study,
al. 2007, Sato et al. 2007). The availability of multiplatform transcriptome data will allow
us to explore new similarities between gene expression profiles induced under various
experimental conditions. Furthermore, module-based correlation analysis can be used to
find common gene expression profiles across species using ortholog information.
McCarroll et al (2004) reported that the highly divergent animals, Caenorhabditis
elegans and Drosophila melanogaster, showed quite similar expression patterns for
orthologous genes involved in critical processes such as DNA repair, cellular transport
and mitochondrial metabolism. However, few comparative studies on gene expression
in gene expression profiles between species will bring about a better understanding of the
fundamental function and mechanisms of life. Future efforts will be aimed at improving
the compatibility of AtCAST.
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patterns across species have been conducted. Identifying the similarities and differences
Materials and methods
Analysis of Affymetrix expression profiles
All signal intensities were obtained and normalized using the MAS5
(Affymetrix) method. Of the 22,810 probe sets present on Affymetrix ATH1, the 21,180
annotated as actual Arabidopsis genes (according to TAIR8) were used for the
module-based correlation analysis. Genes expressed significantly (detection-p value <
Gene expression values were calculated as the ratio of signal intensities to the chip
median, and transformed to a log2 scale. These values were used for all analyses as gene
expression intensities. The actual calculations were performed using R
(http://www.r-project.org).
Module-based correlation analysis
Genes selected using Student’s t-test (p < 0.01) between control and treatment
samples were ranked according to their fold-changes. The top (upregulated) and bottom
(downregulated) 10% of the list were selected as genes for modules (see Supplementary
Result and Fig. S12). As the numbers of genes selected by this threshold did not fulfill the
conditions of p < 0.01 (at SCC = |0.4|) in some of the experiments, the minimum number
was set to n = 50 by adjusting the threshold for the fold-change rank in the following
analysis. When the number of genes could not satisfy this threshold (p < 0.01) (i.e., genes
were fewer than 50), the experiments were excluded from the subsequent analysis. SCC
of SR (treatment / control) was used to estimate the strength of the relation between
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0.01) in at least one point in each of the experiment sets were used for subsequent analysis.
experiments. Control experiments for each data set were assigned according to
information from the original data sets (Table S1). Since control experiments were not
applicable to the series of developmental process (Schmid et al. 2005), the median value
of the signals throughout the developmental series was chosen for each gene and these
values were used as a control.
Permutation experiment
method. For permutations of genes, SR (treatment / control) of 21,180 genes were
collected, and then randomly assigned as SR for genes. In one experiment, 50 randomly
selected genes were permutated and 10,000 pairs of experiments were randomly selected
to calculate the SCC.
Basic statistics
The r2 values were calculated using normalized signal intensities of genes with
satisfied detection-p value (≤ 0.01) for at least one sample among replicate samples. For
the Student’s t-test, normalized signal intensities were transformed to log2 scale. The
q-values were calculated using the R q-value package (Storey et al. 2004). SR was
calculated as the log2 scale of the ratio of the average normalized signal intensities of the
treatment to the control.
Visualization of MCN
20
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The statistical significance of the correlation was estimated by the bootstrap
MCNs were visualized using the software package Graphviz
(http://www.graphviz.org/) as neato-style layouts, unless otherwise noted.
Downloaded from http://pcp.oxfordjournals.org/ by guest on October 6, 2014
21
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29
Figure legends
Fig. 1 Approach used for drawing MCN of Arabidopsis based on modules.
Fig. 2 Operation of AtCAST.
Step-by-step operations are shown with screenshots of the database. (A) “Search page,”
to select a query experiment; “pre-computed experiments” can be searched by keywords,
experimental category, genotype, and type of treatment. (B) “Analysis page,” users can
consists of two sections: (D) a MCN centered on the query experiment and (E) a list of
experiments strongly correlated with the query experiment. Experiments in a MCN have
significant correlations (|SCC| ≥ 0.5) with the query experiment. Each node in a MCN is
clickable, and links to another MCN centered on the clicked node. A summary of
experiments is described in a table that includes the genotype, treatment, tissue, and
control information. Correlation indicates two SCCs: from the query experiment to the
correlated experiments (upper values) and from the correlated experiments to the query
experiment (lower values). “Treatment/tissue” provides links to the original data
deposition site, providing information on the microarray experiments. (F) “Basic
statistics page” provides statistical information of each microarray data set. (G)
“Correlation information page” provides information on the correlations between
experiments as scatterplots, and a table of the expression value and annotation of genes is
included in modules. (H) A larger scatterplot with the AGI code or gene name can be
shown by clicking the scatterplots.
30
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submit their own data files as a query experiment. (C) “Result page,” search result
Fig. 3 Global MCN of Arabidopsis microarray experiments.
(A) ACN of 195 Arabidopsis microarray experiments based on SCCs calculated using all
gene expressions. Pink arrows, mild positive relation (SCCs ≥ 0.3); light blue arrows,
mild negative relation (SCCs ≤ –0.3). (B) MCN based on the module approach. Red
arrows, strong positive relation (SCCs ≥ 0.7); blue arrows, strong negative relation (SCCs
≤ –0.65); pink arrows, mild positive (both directions) relation (SCCs ≥ 0.5); light blue
arrows, mild negative relation (SCCs ≤ –0.5). The network was visualized using the
Fig. 4 Analysis of an auxin-related mutant sav3-2.
MCN centered on “mutant sav3-2 (SH).” Thirty-seven experiments showing a significant
correlation (|SCC| ≥ 0.5) with “mutant sav3-2 (SH)” (indicated by a pink oval) were
collected and drawn on this MCN. Nodes correspond to experiments representing gene
expression profiles in each experiment. Square nodes indicate stress or stimulation
treatments, pentagon-shaped nodes indicate gene expression of tissue-specific profiles,
octagon-shaped nodes indicate chemical treatments, and elliptical-shaped nodes indicate
mutants. Edges indicate correlations between experiments at significant levels. Red
arrows, strong positive (one-way or both directions) relation (SCCs ≥ 0.7); blue arrows,
strong negative (one-way or both directions) relation (SCCs ≤ –0.65); pink arrows, mild
positive (both directions) relation (SCCs ≥ 0.5); light blue arrows, mild negative (both
directions) relation (SCCs ≤ –0.5).
31
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Cytoscape software package (http://www.cytoscape.org) (Shannon et al. 2003).
Table 1. List of experiments included in MCN of “Mutant sav3-2 (SH)”
No.
1
Experiment name
Mutant sav3 -2 (SH)
Genotype
sav3 -2
Tissue
seedling
Control
Col-0 (SH)
SCC
1.0 (1.0)
IAA
1h
IAA
3h
SA
3h
_
_
_
_
_
2
Mutant sav3 -2 (WL)
sav3 -2
seedling
Col-0 (WL)
0.83 (0.92)
3
IAA 1 h
Col-0
seedling
Mock 1 h
-0.76 (-0.73)
_
_
4
IAA 3 h
Col-0
seedling
Mock 3 h
-0.75 (-0.68)
_
_
5
Mutant nph4 -1_arf19 -1 (IAA)
nph4 -1 arf19 -1
seedling
Col (IAA)
0.72 (0.41)
_
_
6
Mutant hy5
hy5
seedling
Col-0
-0.72 (-0.38)
_
7
Senescing leaf
Col-0
senescing leaves
Control for tissue data
0.68 (0.08)
_
8
Mutant siz1 -3
siz1 -3
not identified
Col-0
0.68 (0.37)
_
9
Mutant NahG (leaf)
NahG
leaves
Col-0 (leaf)
-0.67 (-0.54)
10
IAA 0.5 h
Col-0
seedling
Mock 0.5 h
-0.64 (-0.56)
_
11
Salicylic Acid 3 h
Col-0
seedlings
Mock 3 h
0.64 (0.55)
12
Mutant cry1
cry1
seedling
Col-0
-0.63 (-0.3)
13
Mutant cry1 (High Light)
cry1
seedling
Col-0 (High Light)
-0.62 (-0.21)
14
UV Stress 24 h (shoot)
Col-0
shoots
Stress mock 24 h (shoot)
0.61 (0.40)
15
Mutant nph4 -1 (IAA)
nph4 -1
seedling
Col (IAA)
0.61 (0.40)
16
Prohexadione 12 h
Col-0
seedling
Mock 12 h
0.60 (0.13)
17
FR Light 4 h
Col-0
seedlings
WhiteLight 4 h
0.60 (0.61)
_
18
Brassinolide 1 h (det2)
det2 -1
seedling
Mock 1 h (det2)
-0.59 (-0.39)
_
19
Rosette leaf No12
gl1 -T
rosette leaf #12
Control for tissue data
0.59 (0.33)
20
Mutant arf2 -6 (IAA)
arf2 -6
seedling
Col (IAA)
-0.59 (0.04)
21
Brz220 12 h
Col-0
seedling
Mock 12 h
0.57 (0.29)
_
22
AgNO3 3 h
Col-0
seedling
Mock 3 h
0.56 (0.38)
_
23
Sepals stage15
Col-0
flowers stage 15, epals
Control for tissue data
0.56 (0.04)
24
Mutant iaa17 -6
iaa17 -6
seedling
Col
0.56 (0.24)
_
_
_
_
_
_
_
_
_
25
FR Light 45min
Col-0
seedlings
WhiteLight 45min
-0.55 (-0.71)
_
_
RedLight 45min
Col-0
seedlings
WhiteLight 45min
-0.55 (-0.61)
_
_
27
Mutant clv3 -7 (flower)
clv3 -7
flower stage 12
Flowers stage12
-0.55 (-0.24)
Propiconazole 12 h
Col-0
seedling
Mock 12 h
0.53 (0.11)
29
E.orontii Infection 6 h
Col-0
leaves
Uninfection 6 h
-0.53 (0.02)
_
_
30
Mutant axr3 -1
axr3 -1
seedling
Col
0.53 (0.30)
_
31
Osmotic Stress 24 h (shoot)
Col-0
shoots
Stress mock 24 h (shoot)
0.52 (-0.05)
_
32
Heat Recovery 24 h (shoot)
Col-0
shoots
Stress mock 24 h (shoot)
0.51 (0.5)
33
Mutant iaa5_6_19
iaa5 iaa6 iaa19
seedling
Col
0.50 (0.15)
34
Mutant siz1 -3 (Drought)
siz1 -3
not identified
Col-0 (Drought)
0.50 (0.17)
35
Blue Light 4 h
Col-0
seedlings
WhiteLight 4 h
0.40 (0.50)
_
_
_
_
36
Ibuprofen 3 h
Col-0
seedlings
Mock 3 h
0.32 (0.71)
_
37
PNO8 3 h
Col-0
seedlings
Mock 3 h
0.21 (0.55)
_
32
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_
_
26
28
_
Tables
Table 1
List of experiments included in MCN of “Mutant sav3-2 (SH)”
Supplemental Table S1
Supplemental Table S2
Experiments used in the model study to define modules
33
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List of experiments included in the database
Step1: Data collection & preparation
Data collection: Affymetrix Arabidopsis DNA-GeneChip
Normalization: MAS5, per Chip by median
Exclude non-reproducible samples (r2<0.7)
Step2: Selection of genes for module
Experiment A
Control
Treatment
Experiment B
Control
Treatment
Filtering
Module B
Gene set I’
Gene set II’
….
Gene set I
Gene set II
….
Module A
Filtering
Step3: Estimate the relationship between experiments
Data transfer to Log2 Signal Ratio (SR):
Calculation Spearman Correlation coefficient (SCC) between experiment A and experiment B
using genes contained in modules.
Experiment A
SR of Module A
In experiment A
Experiment B
VS.
SR of module A
in experiment B
Experiment A
SR of module B
in experiment A
SCC A
Experiment B
VS.
SR of Module B
In experiment B
SCC B
Step4: Drawing MCN
Combining results based on SCC.
Experiment A
Experiment B
Strong relation: one-way direction
Experiment A
Experiment B
Strong relation: both directions
Experiment A
Experiment B
No Correlation
Fig. 1
Experiment A
Experiment B
Mild relation: both directions
Experiment A
Experiment B
Strong relation + mild relation
One-way direction + Both directions
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Threshold: The t-test(p<0.01), Fold Change (Top and bottom 10%)
Site menu
(A) Search page
(C) Result Page
Select
a pre-computed
experiment
(B) Analysis page
AtCAST sends E-mail
including URL: user's own
web pages
Result contents
(D) Sample network
(F) Basic statistics page
(G) Correlation information page
(E) Information of microarray experiment
Fig. 2
(H) Close up
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Input
user data
A
Chemical
treatment 3 h
Cycloheximide 3 h
Propiconazole 3 h AVG 3 h
TIBA 3 h
Prohexadione 3 h
Paclobutrazol 3 h
Naphthyl- phthalamic acid 3 h
Salicylic Acid 3 h
Brz220 3 h
PNO8 3 h
Uniconazole 3 h
MG132 3 h
P-chlorophenoxy- isobutyric acid 3 h
AgNO3 3 h
phy mutants
Mutant phyA (Red Light 1h)
Mutant phyB (Red Light 1h)
Mutant phyAphyB (Red Light 1h)
Wild type (Red Light 1h)
Chemical
treatment 12 h
Mutant phyB
Mutant phyAphyB
PNO8 12 h Paclobutrazol 12 h
Propiconazole 12 h
Uniconazole 12 h
Prohexadione 12 h
Brz220 12 h
Mutant phyA
ABA 3 h
Mutant iaa17-6 (IAA)
Auxin mutants
Mutant iaa5_6_19 (IAA)
Mutant arf2-6 (IAA)
Mutant axr3-1 (IAA)
Mutant arf19-1 (IAA)
Mutant arf2-6
Mutant axr3-1 Mutant nph4-1_arf19-1
Mutant nph4-1
Mutant nph4-1_arf19-1 (IAA)
Mutant arf19-1
Mutant iaa5_6_19
Mutant iaa17-6
Mutant nph4-1 (IAA)
Flowering mutants (shoot apex)
ACC 0.5 h
Phytohormone
treatment 0.5, 1 h
t-zeatin 0.5 h
IAA 0.5 h
ABA 0.5 h
t-zeatin 3 h
ACC 3 h
Methyl Jasmonate 0.5 h
IAA 3 h
Methyl Jasmonate 3 h
ACC 1 h
GA3 1 h (ga1)
GA3 0.5 h (ga1) GA3 3 h (ga1)
Methyl Jasmonate 1 h
Brassinolide 1 h (det2)
Brassinolide 3 h (det2)
Brassinolide 0.5 h (det2)
B
Phytohormone
treatment 3 h
Imbibed seed
Mutant clv3-7 (shoot apex)
Mutant ap1-15 (shoot apex)
Flowering mutants
(shoot apex)
Imbibed 24 h ABA 3uM (seed) Imbibed 24 h ABA 30uM (seed)
ABA 1 h
IAA 1 h
t-zeatin 1 h
Mutant ap2-6 (shoot apex)
GA4 9 h (ga1 seed)
GA4 6 h (ga1 seed)
Mutant ap3-6 (shoot apex)
Mutant ufo-1 (shoot apex)
GA4 3 h (ga1 seed)
Mutant ag-12 (shoot apex)
Mutant lfy-12 (shoot apex)
GA3 1 h (ga1)
GA3 3 h (ga1)
GA treatment
ABA 3 h
ABA 1 h
t-zeatin 0.5 h
t-zeatin 1 h
t-zeatin 3 h
ABA 0.5 h
Methyl Jasmonate 0.5 h
Methyl Jasmonate 1 h
ACC 0.5 h
Methyl Jasmonate 3 h
ACC 3 h
ACC 1 h
IAA 1 h
IAA 3 h
Brassinolide 3 h (det2)
IAA 0.5 h
Brassinolide 1 h (det2)
Brassinolide 0.5 h (det2)
GA3 0.5 h (ga1)
Fig. 3
Strong positive relation
Strong negative relation
Mild positive relation
Mild negative relation
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Mutant lfy-12 (shoot apex)
Mutant ag-12 (shoot apex)
Mutant ufo-1 (shoot apex)
Mutant ap1-15 (shoot apex)
Mutant ap3-6 (shoot apex)
Mutant clv3-7 (shoot apex)
Mutant ap2-6 (shoot apex)
Propiconazole 12 h
Brassinolide
1 h (det2)
Brz220 12 h
Blue Light 4 h
IAA 0.5 h
FarRed Light
4h
Mutant hy5
E.orontii
Infection 6 h
Prohexadione 12 h
IAA 1 h
Mutant
nph4-1_arf19-1 (IAA)
Red Light
45min
IAA 3 h
Mutant arf2-6
(IAA)
Mutant iaa5_6_19
Mutant sav3-2
(SH)
Heat Recovery
24 h (shoot)
Mutant cry1
(High Light)
Mutant sav3-2
(WL)
Mutant clv3-7
(flower)
FarRed Light
45min
Mutant NahG
(leaf)
Rosette leaf
No12
Salicylic Acid
3h
UV Stress
24 h (shoot)
Senescing
leaf
Sepals
stage15
Ibuprofen 3 h
AgNO3 3 h
Mutant siz1-3
(Drought)
Mutant iaa17-6
Mutant axr3-1
PNO8 3 h
Mutant siz1-3
Osmotic Stress
24 h (shoot)
Strong positive relation
Strong negative relation
Mild positive relation
Mild negative relation
Fig. 4
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Mutant cry1
Mutant nph4-1
(IAA)