=Paper=
{{Paper
|id=None
|storemode=property
|title=Gene Co-Expression in Mouse Embryo Tissues
|pdfUrl=https://ceur-ws.org/Vol-753/paper01_cubistws11.pdf
|volume=Vol-753
|dblpUrl=https://dblp.org/rec/conf/cubist/AndrewsM11
}}
==Gene Co-Expression in Mouse Embryo Tissues==
https://ceur-ws.org/Vol-753/paper01_cubistws11.pdf
Gene Co-Expression in Mouse Embryo Tissues
Simon Andrews1 and Kenneth Mc Leod2
1
Conceptual Structures Research Group
Communication and Computing Research Centre
Faculty of Arts, Computing, Engineering and Sciences
Sheffield Hallam University, Sheffield, UK
s.andrews@shu.ac.uk
2
School of Mathematical and Computer Sciences,
Heriot-Watt University, Edinburgh, UK
kenneth.mcleod@hw.ac.uk
Abstract. This paper develops some existing ideas in FCA to provide
an analysis of a large data set of mouse embryo gene expressions. It
develops new techniques for managing complexity and visualisation in
FCA to identify and approximate large groups of co-expressed genes.
This work has been carried out as part the European CUBIST Project:
http://www.cubist-project.eu/
1 Introduction
Formal Concept Analysis (FCA) has already proved useful in the study of gene
co-expression. FCA is attractive in the field because formal concepts are natural
representations of maximal groups of co-expressed genes. In [5] FCA was used to
extract groups of genes with similar expressions profiles from data of the fungus
Laccaria bicolor and in [4] human SAGE data provides the example from which
clusters of concepts with similar properties are visualised. In both approaches
the complexity, in terms of the large number of formal concepts present in the
raw data, is managed by specifying a concept’s minimum size (the well known
idea of minimum support in FCA and frequent itemset mining). In [4], tools
were developed to query the set of extracted concepts according to various cri-
teria (e.g., presence of a keyword in a gene description) and then to cluster
concepts according to similarity, in terms of the attributes (samples) and ob-
jects (genes above a threshold of expression) in them. They called these clusters,
quasi-synexpression-groups (QSGs). By contrast, in [5], ranges of a measure of
gene concentration were used as attributes and the genes as objects. Individual
concepts that satisfied a specified minimum size were then examined by, for ex-
ample, plotting the actual measures of concentration of genes together in a line
plot.
In this paper we develop some of these ideas and use some freely available,
open-source, tools to apply them to a set of mouse-embryo gene expression data.
We employ the idea of minimum support to focus on ‘large’ co-expressions and
use a similar notion to that of QSGs in identifying clusters of similar large co-
expressions, giving rise to larger approximate co-expressions by using a similar
notion to that of FCA ‘fault tolerance’ [7]. We show that the technique of cluster-
ing co-expressions can be straightforward and is a simple way of approximating
and visualising a large amount of gene expression data. We demonstrate that
FCA can act as a tool for knowledge discovery and can be used to identify pos-
sible ‘gaps’ in knowledge; data that may be missing, erroneous or inconsistent
and thus where further investigation or experimentation may be required.
2 The mouse-embryo gene expressions data set: EMAGE
A gene is a unit of instructions that provides directions for one essential task,
i.e., the creation of a protein. Gene expression information describes whether
or not a gene is expressed (active) in a location. Broadly speaking there are
two types of gene expression information: those that focus on where the gene is
expressed, and those whose primary concern is the strength of expression. This
work concentrates on the former category, and in particular a technology called
in situ hybridisation gene expression.
Information on gene expression is often given in relation to a tissue in a
particular model organism. Here the model organism is the mouse. This organ-
ism is studied from conception until adulthood. The time window is split into
28 Theiler Stages (TS). Each stage has its own anatomy, and corresponding
anatomy ontology called EMAP [3].
Gene expression information allows biologists to discover relationships be-
tween genes, in particular when genes are active in the same location. This
co-expression information provides insights into the ways in which relationships
between genes affect the development of a tissue.
The result of an in situ experiment is documented as an image displaying an
area of a mouse (from a particular Theiler Stage) in which some subsections of
the mouse are highly coloured. Areas of colour indicate that the gene is expressed
in that location. Additionally, the image provides some indication of the level
(strength) of expression: the more intense the colour, the stronger the expression.
Results are analysed manually under a microscope. A human expert deter-
mines in which tissues the gene is expressed, and at what level of expression. As
volume information is not the main focus of the experiment, its description uses
vague natural language terms such as strong, moderate, weak or present. For
example, the gene Bmp4 is strongly expressed in the future brain from Theiler
Stage 15.
Completed in situ gene expression experiments are published online. One of
the main resources in this field is EMAGE [8]. EMAGE documents the result of
an experiment using a series of textual annotations. Each annotation is a triple:
gene - tissue - level of expression. The entire collection of annotations is used as
the data set for this work.
For the sake of brevity, both genes and tissues will be referred to by short
names or identifiers rather than their full name. For example, the gene “bone
morphogenetic protein 4” will be referred to as “Bmp4 ”. Likewise, the tissue
“mouse.embryo.skeleton.cranium.viscerocranium.orbito-sphenoid from TS 23”
will be known by its unique EMAP identifier “EMAP:8385”.
3 An approach using freely available FCA tools
The approach was to convert the EMAGE data into a formal context, mine the
context for concepts satisfying a specified minimum size and then approximate
the results using FCA ‘fault tolerance’. To do this, three tools that are open
source and freely available at Sourceforge were used:
– FcaBedrock [1] to convert the EMAGE data into a formal context by con-
verting (tissue, level, gene) triples into (tissue-level, gene) pairs.
– In-Close [2] to mine to context for concepts satisfying a specified size and
produce a corresponding ‘reduced’ context.
– Concept Explorer (ConExp) [10] to visualise the ‘large’ concepts and ap-
ply ‘fault tolerance’ to produce even larger, ‘approximate’ concepts.
In addition to their main tasks, In-Close was used to sort the formal context to
allow easy identification of clusters of similar concepts (a simple way of finding
QSG-type groupings [4]) and ConExp was used as a context editor to extract
these clusters and to provide a simple manual method of producing the larger
approximate concepts.
3.1 Converting and concept-mining the raw EMAGE data
The EMAGE data set was obtained in the form of csv triples. FcaBedrock was
used to automatically convert the data set into a formal context in the standard
Burmeister .cxt format. The context contained 6838 attributes (tissue-levels) and
4627 objects (genes). In-Close was used to mine the context generating 208,377
concepts. By a process of trial and error, a minimum size of concept of 14 tissue-
levels and 18 genes was determined that produced a reduced context that was
possible to visualise in ConExp (Figure 1). Note that the process of visualising
the reduced context shows concepts additional to those satisfying the minimum
size because where two concepts that satisfy the minimum size ‘overlap’ in the
context grid (share relations), smaller concepts will exist.
3.2 Identification of co-expression clusters
There are two large concepts at the bottom of the lattice in Figure 1 giving a
suggestion of two distinct clusters of concepts. A clear visualisation of the two
groups is shown in the reduced context produced by In-Close (Figure 2). Because
In-Close, as part of its processing, sorts context rows to reduce the difference
between them, patterns that would otherwise be difficult to detect become clear.
It is apparent that there are two disjoint clusters of concepts, i.e., two disjoint
clusters of gene co-expression.
Fig. 1. Concept lattice produced from EMAGE gene expression data (for clarity, only
the tissue-levels are displayed)
3.3 Using fault tolerance to produce large approximate concepts
Figures 3 and 4 show the concept clusters as separate context grids. They now
appear as dense grids of crosses with only a few crosses ‘missing’. The notion
of fault tolerance in FCA [7] is that a certain amount of missing information
can be tolerated as being errors of omission, or that at least a sensible ap-
proximation is possible by adding a limited number of relations to ‘complete’
a concept. In Figure 3, for example, there is only one cross missing from the
column of EMAP:8394-strong. A fault tolerance level of one gene would add
that cross and the ones missing for EMAP:7749-strong, EMAP:8389-strong and
EMAP:7847-strong. A fault tolerance level of two would also complete the col-
umn for EMAP:8146-strong. It is perhaps equally legitimate to apply fault tol-
erance to missing attributes, thus a fault tolerance level of three would supply
all the missing crosses in both grids. Such an approximation results in the lattice
in Figure 5.
XX..XXXX.XXX.X.XXXXX........X..X
XX..XXXX.XXX.X.XXXXX........X...
XX..XXXX.XXX.X.XX.XX........X..X
XX..XXXX..XX.X.XX.XX........X...
XX..XXXX.XXX.X.XXXXX........X..X
XX..XXXX.XXX.X.XXXXX........X..X
XX..XXXX.XXX.X.XXXXX........X...
XX..XXXX.XXX.X.XXXXX........X..X
XX..XXXX.XXX.X.XXXXX........X..X
XX..XXXX.XXX.X..XXXX........X..X
XX..XXX..XXX.X.XXXXX........X...
XX..XXXX.XXX.X.XX.XX........X..X
XX..XXXX.XXX.X.XXXXX........X..X
XX..XXXX.XXX.X.XXXXX........X..X
XX..XXXX.XXX.X.XXXXX........X..X
XX..XXXX.XXX.X.XXXXX........X...
XX..XXXX.XXX.X.XX.XX........X..X
XX..XXXX.XXX.X..XXXX........X..X
XX..XXXX.XXX.X..XXXX........X..X
XX..XXX..XXX.X.XXXXX........X...
XX..XXXX.XXX.X.XXXXX........X..X
XX..XXXX.XXX.X.XXXXX........X...
XX..XXXX.XXX.X..XXXX........X...
XX..XXXX.XXX.X..X.XX........X...
XX..XXXX..XX.X.XXXXX........X..X
XX..XXXX..XX.X..X.XX........X..X
XX..XXXX..XX.X..X.XX........X..X
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XX.XXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.X..
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XX.XXXXX.XX.
..XX....X...X.X.....XXX.XXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXX..XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
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..XX....X...X.X.....XXXX.XXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
..XX....X...X.X.....XXXXXXXX.XX.
Fig. 2. Two distinct clusters in the reduced context
EMAP:12758-strong
EMAP:8385-strong
EMAP:8339-strong
EMAP:7371-strong
EMAP:7204-strong
EMAP:8360-strong
EMAP:8359-strong
EMAP:8146-strong
EMAP:7847-strong
EMAP:8389-strong
EMAP:7364-strong
EMAP:7363-strong
EMAP:7749-strong
EMAP:8371-strong
EMAP:8394-strong
Gene Co-Exp 1
Mapk8ip2 ×××××××××××××××
Tgfbi ××××××× ×××××××
Zcchc6 ×××××××××××××××
Brpf3 ×××××××××××××××
Caly ×××××××××××××××
Bcl9l ×××××××××××××××
Zc3h18 ×××××××××××××××
Dnajc18 ××××××××××××××
H2-T22 ×××××××××××××××
Colec12 ××××××× ×××××××
Wwp2 ×××××××× ××××××
Emp3 ×××××××××××××××
Tcam1 ×××××××××××××××
Papss2 ×××××××××××× ××
Ubxn10 ×××××××××××××××
Cytl1 ×××××××××××××××
BC024814 ×××××××××××××××
Plekhb1 ×××××××××××××××
Apitd1 ×××××××××××××××
Cebpz ×××××××××××××××
1110017D15Rik × × × × × × × × × × × × × × ×
Copb1 ××××××××× ×××××
Unc5cl ×××××××××××××××
Haus4 ×××××××××××××××
Fig. 3. Cross-table for cluster 1
EMAP:12758-moderate
EMAP:8385-moderate
EMAP:8339-moderate
EMAP:7371-moderate
EMAP:7204-moderate
EMAP:8146-moderate
EMAP:7847-moderate
EMAP:7364-moderate
EMAP:7363-moderate
EMAP:8389-moderate
EMAP:7749-moderate
EMAP:8394-moderate
EMAP:7843-moderate
EMAP:8360-moderate
EMAP:8359-moderate
EMAP:8371-moderate
EMAP:8373-moderate
Gene Co-Exp 2
Sult1c2 ×××××××××××××××××
Klk7 ××××××××××××××××
Ing4 ×××××××××××× ××××
Mir300 ×××××× ××××× ×××
Plcg2 ×××××××××××××××××
U2af1 ×××××××××××××××××
Fzr1 ××××××××××××××××
Gm22 ×××××××××××××××××
Gm10232 ×××××××××××××××××
Tnfsf4 ×××××××××× ××××××
Acot6 ××××× ××××××××××
Cit ×××××××××××× ××××
Pop4 ×××××××××××××××××
Atp6v0a2 ×××××××××××××××××
Ebi3 ×××××××××××××××××
Fcgrt ××××××××××××××××
Mvk ×××××××××××× ××××
Mdfi ×××××××××× ××××××
Adrm1 ×××××××××× ××××××
Clca1 ××××× ××××××××××
Tnfaip1 ×××××××××××××××××
Arhgap27 ××××××××××××××××
Tmc5 ×××××××××× ×××××
Cops7b ×××××××××× × ×××
Itpr3 ×××××× ××××××××××
Tsga10ip ×××××× ××× × ××××
Upk2 ×××××× ××× × ××××
Fig. 4. Cross-table for cluster 2
Fig. 5. Original lattice with ‘fault tolerance’ applied
4 Analysis of the gene co-expression results
When analysing the output of the FCA process, the first task was to convert
the EMAP identifiers back into tissue names to determine which locations were
flagged. This revealed that with the exception of EMAP:8146 and EMAP:7749,
which are both cartilages, all the tissues are bones.
Intuitively all bones will have a similar expression profile, as they are essen-
tially very similar structures. The list of tissues obtained through FCA demon-
strates this as it covers the majority of the mouse including the limbs, body,
and head. However, interesting gaps remain: for example, in the list there are no
bones from the tail. Why not? Answering this question requires further study.
Looking at the output of the above processes, a reader may ask why this
expression profile is found in only one of the twenty-eight Theiler Stages? The
answer to this question is that TS 23 has the most experimental data; there are
many experiments performed on TS 23 that are not repeated on other stages.
As such, this pattern of expression may, or may not, be realised in other stages.
Until the requisite experiments have been performed it is impossible to tell.
In a similar vein, many of the “missing” crosses from the cross table are
a consequence of EMAGE having no experimental result discussing the gene -
tissue pair. As such, it is unknown at what level the gene is expressed in the
tissue, or if it is expressed at all. Accordingly, the process has revealed future
experiments to perform.
Additionally, observe that EMAGE is only one of a number of resources
that serve the current domain. Some of these resources provide proprietary in
situ gene expression information that is not available to EMAGE, whilst others
publish the results of different types of gene expression experiment. By reviewing
just one extra resource, GXD [9], it is possible to add a cross missing from the
initial lattice: Acot6 - EMAP:8146 - moderately expressed. This leads to the
conclusion that if the data from the other resources were integrated with the
data from EMAGE it may be possible to add further crosses. Doing so may
produce a “better” cross table, and thus a “truer” lattice. Unfortunately, there
are significant difficulties in integrating such data [6], and this has been left as
future work.
Future work may also investigate whether or not FCA can help resolve incon-
sistent information. Unfortunately, due to the nature of biology, a small number
of textual annotations are inconsistent, i.e., they suggest different levels of ex-
pression for the same gene in the same tissue. Perhaps the process documented
in this paper can help identify the most likely level. Furthermore, it might be
possible to suggest the probable level of expression when EMAGE contains no
data.
5 Conclusion
This paper explored FCA within a biological use case. In particular it demon-
strated how FCA can be used to analyse in situ gene expression data for the
developmental mouse.
Analysis was based on large concepts (14 by 18), leaving smaller concepts to
be considered as future work. Additionally, further research will be required to
understand the full significance of the cross tables documented in this paper.
The list of tissues contained within the cross tables is comprised of a wide
selection of bones covering the vast majority of the mouse’s skeleton. Yet certain
anatomical structures are missing, e.g., the tail. Why are the absent structures
not present? What unique features of tail bones prevent them being included in
the cross tables?
A further biological question arises in that all the expression levels in each
group are the same, i.e., there is a group of genes expressed strongly and a group
expressed moderately. There is no reason from an FCA point of view why this
should be the case. There may be a biological explanation, perhaps either to do
with the nature of the experiments or the nature of the mouse embryo.
From an FCA perspective there are a number outstanding questions too.
The appropriateness, and reliability, of fault tolerance needs to be investigated.
Additionally, within the context of CUBIST, there is a requirement to improve
the user friendliness of FCA to the extent that a biologist is able to perform the
analysis independently of an expert.
Manifestly, the work documented here is at an early stage. Nevertheless, this
paper demonstrates there is significant potential that can be exploited for the
benefit of both the biological and FCA communities.
Acknowledgement This work is part of the CUBIST project (“Combining
and Uniting Business Intelligence with Semantic Technologies”), funded by the
European Commission’s 7th Framework Programme of ICT, under topic 4.3:
Intelligent Information Management.
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