Measuring cell identity in noisy biological systems
Kenneth D. Birnbaum1and Edo Kussell1,2,*
1Center for Genomics and Systems Biology, Department of Biology and2Department of Physics, New York
University, NY 10003, USA
Received April 25, 2011; Revised June 30, 2011; Accepted July 1, 2011
Global gene expression measurements are increas-
ingly obtained as a function of cell type, spatial
position within a tissue and other biologically mean-
ingful coordinates. Such data should enable quanti-
tative analysis of the cell-type specificity of gene
expression, but such analyses can often be con-
founded by the presence of noise. We introduce
a specificity measure Spec that quantifies the
information in a gene’s complete expression profile
regarding any given cell type, and an uncertainty
measure dSpec, which measures the effect of
noise on specificity. Using global gene expression
data from the mouse brain, plant root and human
white blood cells, we show that Spec identifies
genes with variable expression levels that are none-
theless highly specific of particular cell types. When
samples from different individuals are used, dSpec
measures genes’ transcriptional plasticity in each
cell type. Our approach is broadly applicable to
mapped gene expression measurements in stem
cell biology, developmental biology, cancer biology
and biomarker identification. As an example of
such applications, we show that Spec identifies
a new class of biomarkers, which exhibit variable
expression without compromising specificity. The
approach provides a unifying theoretical framework
for quantifying specificity in the presence of noise,
which is widely applicable across diverse biological
Multicellular organisms have evolved a diversity of cell
types, which attain their distinct identity and function
through differential gene activity. An understanding of
the global regulation of genes within specialized cells
addresses fundamental biological questions, such as how
different cell types carry out distinct functions, how new
cell types evolve, and which genes are the best diagnostic
markers for cancer cells (1–3). Recent studies have
characterized genome-wide transcription of cell types
within an organ, such as in mouse brain (4), the
Arabidopsis root (5,6) and other complex tissues (7,8).
A theoretical basis for analyzing such data is needed to
address questions about the global structure of gene
expression within an organism, e.g. which components
of the genome are dedicated to the specialization of
single cell types? How is gene expression at the genome
level partitioned and reused among specialized cells?
While the concept of cell specificity is fundamental in
developmental biology, the field lacks a measure that
quantifies the biological concept of specificity. The need
for a quantitative description of specificity arises from the
inherent variability of gene expression within cells and cell
types (9–12). For example, Figure 1a depicts three
idealized genes whose distributions represent their bio-
logical variance in gene expression within three cell-type
populations. Gene A varies in a narrow range in each cell
type. Gene B’s profile exhibits inherently more variability
among target cells, giving it reduced specificity even
though its mean expression level is the same as gene A.
Gene C has virtually no specificity. How should such
profiles be quantified with respect to cell-type specificity?
Here we develop a quantitative measure, based on the
information content of gene expression, which provides
both a conceptual basis for describing cell type specificity
in general and a quantitative approach that we apply to
obtain a genome-wide view of cell-specific gene expression.
MATERIALS AND METHODS
Expression level binning
To obtain the estimate of the specificity measure (Spec)
based on a few discrete samples from the distribution
P(x|y) obtained from microarray data, we employ a con-
servative binning procedure that is designed to minimize
spurious high specificities that could otherwise be
obtained due to outlier replicates. The number of bins
used is denoted nbin, and we use a value of nbin=3,
except if indicated otherwise. See below for a justification
of bin number. For each gene, we obtain the size of each
*To whom correspondence should be addressed. Tel: +1 212 998 7663; Fax: +1 212 995 3691; Email: firstname.lastname@example.org
Published online 29 July 2011Nucleic Acids Research, 2011, Vol. 39, No. 219093–9107
? The Author(s) 2011. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
bin as follows. In each cell type y, we calculate the
harmonic mean of the expression level of the gene over
all replicates, h(y). We find the maximum value of this
quantity over all cell types, denoted hmax. The bin size is
then given by hmax/nbin, with the first bin spanning expres-
sion levels (0, hmax/nbin), and continuing with equally
spaced bins. Expression levels greater than hmax are
binned into the last bin. In this way, the continuous dis-
tribution P(x|y) is replaced by a discrete distribution in x
over the nbinbins; from this point, the computation of
Spec proceeds as described in Figure 1. We note that
each gene’s bin size is chosen separately.
While the above procedure provides one way to
estimate Spec, and works well on the data analyzed
here, it is by no means the only way to estimate Spec,
and may not necessarily be ideal in every scenario. For
example, the distribution P(x|y) could be modeled as
a Gaussian or other continuous probability distribution,
in which case a parametric estimate of Spec can be made.
However, such an approach is only useful in cases where
the appropriate model of the noise is known for the given
set of experiments, for each gene in each cell type. In the
absence of such knowledge (e.g. for the data used here),
a non-parametric approach such as our binning method
is strongly preferred, since errors in the choice of
model will result in large errors in the estimate of Spec.
The shuffling controls presented in Figure 2 provide
a rigorous test of the reliability of any given estimator,
and should be carried out each time Spec estimation is
performed on a new data set.
Estimation of Spec based on a small number of replicates
To test whether the bin-based estimator of Spec gave
reliable results, given the small number of replicates avail-
able in each cell type (i.e. between two and four replicates
per cell type), we constructed continuous probability
distributions, P(x|y), using the plant microarray data.
We calculated the mean and variance of the logarithm
of expression levels, in each cell type. These were used
to construct lognormal mock distributions of gene
Figure 1. Method overview and examples. (A) Idealized profiles of cell type-specific gene expression for two genes in three different cell types. Gene
A exhibits highly specific expression profiles in each cell type, with no discernible overlap of distribution. Gene B exhibits distinct profiles in each cell
type, with overlapping distributions, reducing the specificity of expression. Gene C exhibits no discernible specificity. (B) Overview of the specificity
value, Spec. The mathematical formulation of Spec is general (right panel), and the quantity conceptually does not depend on any cutoffs, thresholds,
or other details of a binning procedure; Spec depends exclusively on P(x|y), the underlying distribution of gene expression levels in each cell type.
To measure Spec using microarray data, a binning procedure is used (left panel), whereby gene expression measurements in each cell type and
replicate experiment (colored squares) are binned into several discrete levels (three are used here).
9094Nucleic Acids Research, 2011,Vol.39, No. 21
expression levels in each cell type. These distributions are
merely for testing purposes, and do not necessarily reflect
the true shapes of the distributions in each cell type. Using
these mock distributions, we resampled the entire dataset,
obtaining the same number of replicates in each cell type
as existed in the real dataset. The constructed test dataset
was used to obtain the bin-based estimate of Spec.
The estimates were compared with the values obtained
from the mock distributions by numerically integrating
the lognormal distributions, according to the Spec
formula given in Figure 1. The results are plotted in
Supplementary Figure S5, and demonstrate that using
three bins of expression provides a significant improve-
ment over two bins. The improvement of using four bins
over three bins is marginal, and we chose to use three bins
for the entire analysis, since using fewer bins provides
more statistical power when comparing the analysis with
shuffled data. As the number of bins is increased beyond
four, the correlation between estimated and true Spec
values decreases, due to under-sampling of bins which
results in spurious specificity values. Matlab and Perl
code for Spec are available upon request.
Expression domains and cell-type networks
The cell-type network was generated by identifying
all genes whose expression domain size was 2 or 3 cell
types (raw data, plant Supplementary Table S6, mouse
Supplementary Table S7). By delineating the ‘fingers’ in
Figure 2 that emanate from integral Nspecvalues along the
dSpec=0 axis, genes were identified for expression
domains of size d=2 or 3, according to the respective
criteria 1.5?Nspec<2.5 or 2.5?Nspec<3.5. We also
required a dSpec level <0.4, to avoid the false positive
regions in Figure 2. Genes were then grouped into
patterns based on their expression domain size d: the d
cell types with the highest Spec values were labeled 1,
and all other cell types were labeled 0. The number of
genes g that comprised a significantly enriched pattern
was identified by permuting gene expression values,
generating Nspec values, and repeating the expression
domain identification procedure. The observed value of
g for each pattern was used to detect significantly
enriched patterns, by requiring a value of g that was
beyond the 95% percentile expected by chance assessed
Figure 2. Genomic distribution of cell specificity. Microarray data from 12 neuronal cell types and 11789 genes in mouse and from 13 root tip cell
types and 17 270 genes in Arabidopsis were used after filtered for uniquely mapping probes. The cell specificity index (Spec) was computed for each
gene in each cell type. The cell type y* with highest Spec value was found for each gene. To assess significance for each dataset, we generated a
shuffled dataset, by randomly permuting expression levels within each cell type across all genes. The distribution of [Nspec(y*),dSpec(y*)] over all
genes is shown in shades of red (observed); the same distribution computed over a shuffled dataset is shown in shades of green (expected). Since the
distributions overlap, each bin is colored according to the distribution whose value is larger, by a factor of two or more. Bins in which the observed
and expected distributions do not differ by this criterion are colored dark blue; likewise in dark blue are bins which did not show a significant
difference between the two distributions, based on a P-value<0.001 criterion, computed using the Poisson distribution with the expected mean.
Black bins are exclusively those for which both distributions are zero.
Nucleic Acids Research, 2011,Vol.39, No. 219095
using the permuted data. This corresponded to having at
least five genes display a pattern for the plant data and at
least three genes for the mouse data; patterns satisfying
this criterion were used as follows. The data was converted
to an n-by-n matrix A, by summing the number of genes
contributing to each pair of cell types occurring in each
pattern. For example, for n=5 cell types, if 10 genes
shared pattern (1, 1, 0, 0, 1) a value of 10 was added to
the entries A12, A15and A25, and so on for all significant
patterns. To visualize major patterns, only edges with
>100 genes for plants and >50 genes for mouse were
drawn. The network was generated from the cell type by
cell-type matrix using the Matlab biograph function with a
generated using hierarchical clustering using Pearson
average linkage, after filtering out the 25% least varying
genes in the dataset based on the variance of their average
expression level across all cell types.
Gene ontology analysis
Lists of genes as described in the text were tested for
over-representation of Gene ontology (GO) terms using
Virtual Plant (13) (http://virtualplant.bio.nyu.edu) for
Arabidopsis and GOrilla (14,15) for mouse. The back-
(Supplementary Tables S1–S3), which excluded probes
that mapped to two or more different transcripts. In
each case, the hypergeometric distribution was used to
in theSpec analysis
The data for hormone marker analysis included all treat-
ments for each hormone without corresponding controls
(raw data; Supplementary Table S8). Treatments for
any given hormone were used as replicates regardless of
whether they were generated in the same experimental
series or lab in order to test performance in a metadata
analysis. Spec values were generated as described above
with each hormone treated as a separate category
(Supplementary Table S9). GenePattern scores were
generated using the Comparative Marker Selection tool
with a ‘One versus All’ analysis for each hormone
category using default settings (Supplementary Table
S10). Genes were ranked by their overall ‘score’ statistic,
as outputfrom GenePattern.
auxin-responsive gene list was gathered from the literature
and GO annotations (Supplementary Table S4).
Microarray datasets and probe-to-locus mapping
For the cell-type network analysis, microarray datasets for
mouse neuronal cell types were obtained from (4) and
Arabidopsis root tip cell types were obtained from
microarray probes and genomic loci were used for
computed for each locus based on a unique probe. In
the rare cases when multiple probes mapped to a single
genetic locus, the probe with the lowest Nspecvalue was
most cases,Spec was
used. All Affymetrix cell files were normalized with the
MAS5 average intensity normalization method as imple-
mented in the Affymetrix software. At a target intensity of
250, we determined empirically, using known markers,
that a hybridization value of 50 represented a reliable ex-
pression signal. Thus, genes which did not express >50
were excluded from the analysis.
Hormone and white blood cell datasets
Published microarray datasets used for the hormone
analysis were as follows (names of laboratories/datasets
follow the conventions established in http://affymetrix.
Gibberellins (GA)—De Grauwe, Griffiths, Riken-Goda,
RIKEN-LI; GA inhibitor—Riken-Goda;
Riken-Goda, Raghavan; Auxin inhibitor—Riken-Goda;
Sakakibara; Ethylene—Millenaar, Riken-Goda; Ethylene
St Clair. While all 13 types of hormones or inhibitors
were used in the Spec analysis, Figure 7 displays the
results for a subset of 10 selected hormones. For Human
white blood cell profiles, all data and probe annotations
were taken from Supplementary Data in (17). We used the
quantile normalized, log2 transformed data in the file
was obtained from ArrayExpress (E-TABM-633), con-
verting values back to their original scale (anti-log2).
We used a summary of well-documented CD marker by
cell type expression (18) to infer a pattern of expression for
51 CD markers that we extracted from gene annotation
files and manually annotated for presence/absence in
seven cell types, filling a matrix with +1 (positive
marker), ?1 (negative marker), and 0 (unspecified
marker) for each cell type. The megakaryocyte cell type
was left out of the analysis because it was not specifically
annotated in the summary table. A corresponding matrix
of the Nspecand dSpec values was computed based on the
mRNA expression profiles using each of the probes
mapping to the known markers. The corresponding rows
(CD markers) of the known expression table and the Nspec
table were compared using Pearson correlation.
The specificity measure
We begin by addressing the problem of cell-type specificity
abstractly, and then tailor our approach to various kinds
of data. We assume that a gene is characterized by a dis-
tribution of its expression level x, in each cell type y (out
of n different cell types) and label this distribution P(x|y).
The question of how to measure P(x|y) in practice is
addressed in ‘Materials and Methods’ section. For now,
let us imagine that P(x|y) is known to us and to a
colleague, and suppose we play the following game.
In a given cell type y, we make a single measurement of
9096Nucleic Acids Research, 2011,Vol.39, No. 21
the gene expression level. We present this measurement x
to our colleague, but we do not reveal the cell identity y.
We would like to know, when presenting the measurement
from cell type y, on average how much information
regarding the cell type have we given to our colleague.
For example, if there are many cell types, and a gene is
expressed only in a single cell type y0, a measurement of
the gene’s level in y0will be highly informative for our
colleague, while a measurement from a different cell type
will be uninformative. The measure Spec(y), defined
below, quantifies this notion precisely.
To define Spec(y), we first invert the distribution using
Bayes’ rule (19), to obtain P(y|x), i.e. the probability that
a cell is of type y given that the gene’s level is observed
to be x (Figure 1b),
where we assume that our colleague has no a priori
knowledge about cell types, i.e. P(y)=1/n, though one
can easily incorporate a non-uniform prior. We then ask
how informative is expression level x regarding the cell
type. Figure 1b presents an idealized example, where a
single gene has been measured in six cell types, and
expression levels are binned into three possible states.
Measurements in the ‘low’ range are uninformative,
since five of the cell types are found to exhibit such
levels. Measurements in the ‘medium’ and ‘high’ ranges
are partially informative, since only two cell types have
these levels, with the high range being more informative
than the medium range. No measurement is maximally
informative, since some ambiguity about the cell type
remains no matter which level is measured.
We compute the ‘information’ (20), I(x), of gene
expression level x:
By using the base of the logarithm to be n (the number
of cell types), possible values of I(x) lie in the range from
0 (uninformative) to 1 (maximally informative). To obtain
the ‘specificity value’, Spec(y), we average the values of
I(x) over the distribution P(x|y) of gene expression levels
for each cell type:
Intuitively, Spec(y) indicates the average amount of
information about the cell’s identity that is provided by
a measurement of the gene’s expression level in cell type y.
In the Figure 1b example, Spec(y) is found to be highest in
cell type C, intermediate in cell types E and B, and low in
the other three types; I(x) indicates that this gene is not
maximally informative at any level, and therefore Spec
is not maximally high in any cell type.
We also define the quantity Nspec(y)?n1?Spec(y)which is
the effective number of cell types (not necessarily integral)
specified by the gene’s expression level in cell type y. This
is equivalent to the effective number of states specified by
IðxÞ ¼ 1+
a probability distribution using the exponential of the
distribution’s entropy (20). To summarize specificity of a
gene, we will omit y, writing Nspecto indicate the maximal
value of Nspec(y) over all cell types. A maximally specific
gene has Nspec=1, while a minimally specific gene has
an Nspec=n, the number of cell types.
Spec is related to a well-known information-theoretic
measure, the mutual information (20), Imutual, which in
our above formulation is given by
where P(x,y)=P(x)P(y|x) is the joint probability of x
and y. If we average the values of Spec(y) over the cell
type distribution P(y), we find
PðxÞPðyjxÞlognPðyjxÞ ¼ Imutual
Thus, the average value of Spec(y) over all cell types
gives the mutual information between cell type and gene
expression level. Mutual information provides an overall
measure of the specificity of a gene, but is not a cell-type
specific measure and can give counter-intuitive results for
highly specific genes. To see this, we return to the example
of the gene that is expressed only in cell type y0. It is clear
that the larger the number of cell types n, the lower the
mutual information between this gene’s expression level
and the cell type; Spec(y0), on the other hand, will be
1 regardless of the number of cell types. Spec(y) is a
Effect of noise on specificity (dSpec)
Depending on experimental approach, different types of
noise exist in each data set. Typical sources of noise
include (i) technical noise, due to the experimental prep-
aration and measurement technique, (ii) sample compos-
ition noise, due to replicate samples, which may differ
depending on how they were collected, and (iii) single-cell
biological noise, due to inherently random processes
within single cells. For the datasets we examine, which
consist of large pools of cells, the main sources of noise
are (i) and (ii). Our approach does not attempt to disen-
tangle these different sources, and their sum total effect
results in the distribution of expression levels P(x|y). We
now consider how specificity is affected in genes with dif-
ferent levels of noise.
Consider Scenario A (low noise) in which two different
genes are measured in 12 cell types (Supplementary
Figure S1). Gene 1 is expressed at high level in two cell
types, and at low level in the 10 other cell types. This gene
has a Spec value of 0.72 in the two high-expression cell
types, with a corresponding Nspecvalue of 2. Gene 2 is
expressed at high level in three cell types, and at low
levels in nine other cell types. Accordingly, in the three
high-expression cell types, its Spec value is 0.56 with an
Nspecvalue of 3. With low noise in each case, high Spec
Nucleic Acids Research, 2011,Vol.39, No. 21 9097
values identify the cell types that share a specific expres-
sion level, i.e. the gene’s ‘expression domain’. However, in
a separate Scenario B (high noise), expression levels in the
first gene are noisy at low levels and its Spec value is thus
significantly <0.72, with a value of 0.52 and an Nspecvalue
of 3.3. Adding noise to a gene with an expression domain
of two cell types leads to Spec values resembling a gene
with an expression domain of three cell types.
To distinguish the lack of specificity due to larger
expression domains from lack of specificity due to noise
we generalize the Spec measure. We analyze the case of
presenting our colleague with two independent measure-
ments, x1and x2, from the same cell type. In Scenario A
above, two measurements provide no more information
than one measurement, since low noise implies that both
measurements will record similar values. In Scenario B,
however, the two measurements are likely to differ in
cell types in which expression is noisy, and to be identical
in cell types with less noise. This results in excess informa-
tion of two measurements versus one, which our colleague
can use to distinguish among cell types. The lower the
excess information, the more difficult it is to distinguish
among cell types within the gene’s expression domain, and
the more specific of the domain as a unit is the gene.
The measure dSpec(y), below, quantifies this notion.
To define dSpec, we first use Bayes’ rule to obtain the
distribution of y conditional on the two measurements,
The information is likewise generalized as I(x1,x2)=
based on the two measurements in cell type y:
We then define dSpec as the relative excess information:
yPðyjx1,x2ÞlognPðyjx1,x2Þ, and we define the quan-
tity Spec2(y), which is the amount of information expected
dSpecðyÞ ¼Spec2ðyÞ ? SpecðyÞ
In Scenario B (Supplementary Figure S1), dSpec detects
that the gene’s specificity is affected by noise, with dSpec
values of 0.21 in cell types 2 and 4, and dSpec values of
0.91 in types 5 and 11. In Scenario C, however, expression
in three cell types is noisy, but with very similar noise
profiles in all three types. Although noise is present,
expression is nevertheless specific of three cell types (2, 4
and 7), resulting in an Nspecvalue of 3 (Spec=0.56), and a
dSpec value of 0. Thus, noise does not necessarily reduce
quantifies the effect of noise on specificity.
It is natural to consider further generalization, in which
m independent measurements from cell type y are pre-
sented to our colleague, and a more general quantity
Specm(y) is computed, for m>2. Clearly, the larger the
number of measurements, the finer the differences in
gene expression profiles P(x|y) that can be distinguished.
While it is straightforward to work out the amount of
information obtained in the limit of large m, this limit is
not useful for our purposes, because it is dominated by the
process of distinguishing among the most similar cell
types, typically those belonging to the gene’s expression
domain. For example, for a gene expressed only in cell
type y0, any noise in its expression level reduces its speci-
ficity in a way that is detected by dSpec(y0) and thus
attributed to noise rather than to having a larger expres-
sion domain. If instead of m=2, we use a much larger
number of measurements, we will find that Specm(y0)&1,
i.e. noise no longer reduces specificity, and for large
enough m we may even have Specm(y)&1 in many
other cell types y6¼y0. The
appropriate for assessing the impact of noise on expres-
sion domains. One can think of dSpec as measuring the
first-order or principal effect of noise on domains, whereas
larger values of m can detect higher order or secondary
value m=2 is thus
Expression domains and the genomic distribution
We applied Spec to quantify transcriptome structure over
a broad taxonomic range, using two datasets that profiled
a large number of cell types within a specific organ, and
calculated Spec for each gene in each cell type. The first
dataset included profiles of 13 morphologically identifi-
able cell types in the Arabidopsis root (5,6,16), obtained
by Fluorescence Activated Cell Sorting (FACS) of
cell-type specific marker lines and profiled on the ATH1
microarray (Affymetrix). The second dataset consisted of
12 different types of mouse neurons (4), which were
manually sorted, pooled, and applied to the MOE430A
We define the gene’s expression domain as the set of
cell types for which the gene’s expression level is most
informative (see ‘Materials and Methods’ section). Nspec
measures the size of the gene’s expression domain, i.e. the
effective number of cell types which the gene specifies in its
expression pattern. For example, the gene in Scenario B
(Supplementary Figure S1) has an Nspec value of 3.3,
indicating that the gene is effectively informative of
between three and four cell types, without forming
a perfect pattern. The value of dSpec&0.2 in the two
highest Spec cell types (types 2 and 4) indicates the
presence of noise in other cell types (types 5 and 11)
which reduces specificity. Alternatively, for genes that
display perfect patterns, dSpec values will be 0, and
Nspecvalues will be integral.
The global distribution of specificity (Figure 2) displays
the expression domain sizes and their noise levels across
the genome. The bright spot at Nspec=1 indicates the
existence of a large set of ‘private transcripts’, genes
whose expression domain is exclusive to single cell types.
One striking feature of the data is that a majority of genes
have larger expression domains, i.e. they are not specific to
a single cell type but are rather shared by at least two to
five other cell types (Figure 2). These expression domains
appear as bright fingers along the left side of the Figure 2
9098Nucleic Acids Research, 2011,Vol.39, No. 21
in the low-noise part of the distribution, which demon-
strates that these are essentially clean patterns, with a
small amount of gene expression noise. The shuffling
analysis described in the caption to Figure 2 shows that
these patterns are significant and indeed more prevalent
than single cell-type specificity. While previous studies of
genomic datasets identified particular expression patterns
using clustering methods (5,6), the Spec-based analysis
shows rigorously that the vast majority of the transcrip-
tome exhibits multi cell-type expression domains, a feature
that appears to be shared across the wide taxonomic
distance between plants and mice.
Cell types have a large component of neighbor- and
The Spec measure provides a way to systematically study
cell-type specificity across the genome by examining genes
with increasingly larger expression domains, as shown in
Figure 3 for two different cell types. The different Nspec
intervals identify genes with different size expression
domains that overlap a given cell type. The number of
unique and shared transcripts in each cell type are
shown in Supplementary Figure S3. We used the Spec
measure to map expression domains onto specific cells
using a network representation (Spec network), shown in
Figure 4, in which each edge denotes a sufficiently large
number of genes whose domains include the two cell types
(see ‘Materials and Methods’ section). For comparison,
we also generated a similarity tree based on averages of
the normalized data.
The analysis reveals the transcriptional programs that
are shared by neighboring tissues. For example, in plants,
the Spec network reveals a strong transcriptional link
between phloem cells and a subset of pericycle cells that
neighbor them in the central cylinder (Figure 4); this
connection is not apparent in the similarity tree, yet is
supported by both the spatial proximity of the cell types
and genetic perturbations that simultaneously affect both
phloem and pericycle identity adjacent to the phloem (21).
In mouse, the similarity tree is unable to establish the
affinity between amygdala cells collected from adjacent
layers in the brain, possibly because they were harvested
from mice of different genetic backgrounds (Figure 4).
However, the Spec network reveals a large set of
transcripts shared between them.
Spatially separated cell types can be linked by shared
We also find that transcripts can be shared by cell types
that are separated spatially across organs. For example,
quiescent center (QC), which supports the growth of stem
cells in the root niche (22,23), was connected largely to
adjacent cell types, as in the similarity tree (Figure 4).
However, the QC also shared one major edge with pro-
genitor cells of the lateral root meristem, a distant cell type
that is nonetheless also associated with root growth
(24,25) (Figure 4). A few critical regulators for meristem
function, such as PLETHORA2 (26), are known to be
specifically expressed in both stem cell populations.
However, the Spec network identifies a more global
functional link between these cell types. For example,
genes that share a high Spec in QC and lateral root
meristem were over-represented in plastid formation
(plastid fission, P<10?2; Supplementary Table S1). In
support of this functional link, mutants in plastid forma-
tion have been shown to have stem cell-specific defects in
plant roots (27). Once again, the specific connection
between the two cell types was not obvious from the simi-
larity tree or common clustering routines (Figure 4 and
Supplementary Figure S2). In the mouse brain, the Spec
network linked together four cell types or subtypes within
a part of the classically defined limbic system, the hippo-
campus and amygdala, for which functional and synaptic
ties have been noted by recent morphological and molecu-
lar genetic studies (28). Interestingly, in both organisms,
we found that certain functional categories of genes
tended to have similar domain sizes even if they were ex-
pressed in different cell types (Supplementary Tables S2
Using Spec for biomarker identification
As shown in Figures 2 and 3, high Spec values identify
highly specific gene expression patterns, with dSpec assess-
ing the noisiness of the pattern. These metrics should also
be useful for biomarker discovery, as is frequently sought
in cancer diagnostics (29,30). We tested this possibility
by using two case studies in which large datasets and
known markers for specific conditions were available.
The first dataset consisted of expression profiles of eight
different white blood cell types from seven healthy
subjects analyzed on the Illumina BeadChip platform
(17). This dataset provided an opportunity to test Spec
against the large and well-documented set of cluster of
differentiation (CD) markers, whose specificity varies ex-
pression in one to many of the cell types (31). We reasoned
that, in a large-scale validation using markers selected to
reflect on–off states, mRNA expression patterns should
broadly reflect the protein levels measured by cell-surface
markers. In addition, the samples in this dataset repre-
sented individuals, permitting us to use dSpec to assess
robustness of expression in replicate cell types, a
measure of cell-type plasticity among individuals that
has clinical relevance.
We coded known positive and negative markers for
each cell type into a table of +1 and ?1 values, respect-
ively, and correlated the table to Nspecvalues generated
from the empirical data in the expression study (see
‘Materials and Methods’ section, Figure 5). The highest
correlation was among the narrowly expressed markers,
those present in one, two or three cell types (average
r=0.72, 0.85, 0.63 respectively), with 12 out of these
26 markershaving correlation
Correlation drops off rapidly for markers that were
broadly expressed (i.e. in four, five, six, or seven cell
types). The results showed that Spec is highly effective in
identifying known markers, including markers with more
complex patterns of expression in several cell types and
absence in others.
The analysis also illustrates the potential of dSpec to
assess robustness for a given marker at high resolution.
Nucleic Acids Research, 2011,Vol.39, No. 21 9099
For example, CD14 is specific to monocytes and granulo-
cytes, as known from its use as a cell surface marker and
predicted by its Nspecvalues. The marker’s dSpec values
are relatively low in its negative cell types but moderately
high in one of its positive cell types (granulocytes). Such a
noise profile suggests that the marker is prone to false
negatives but not false positives, a case where noise may
be tolerable when multiple markers are used. The marker
exhibits a reliable-when-present plasticity such that when
it is present, it identifies granulocytes with high confi-
dence. However, not all individuals express the marker
in granulocytes. On the other hand, CD123 is highly
specific to granulocytes, where it exhibits low noise (low
dSpec), while its dSpec is high in all cell types other than
granulocytes. Such a profile suggests that CD123 is highly
informative of granulocytes but its high noise in the other
cell types, where its expression levels vary among individ-
uals, makes it potentially prone to false positives. The
marker exhibits a plasticity in which virtually all individ-
uals express CD123 at a specific level in granulocytes yet
individuals show variable expression levels in almost all
other cell types.
SpecExpression Spec ExpressionSpec Expression
Domain Size = 2
4 = ez iS n i amoD 3 = ez iS n i amoD
Spec: n o i ss e r pxE:
G30 Amygdala-Specific Genes
Domain Size = 2
4 = ez iS n i amoD 3 = ez iS n i amoD
Spec Expression SpecExpression SpecExpression
Figure 3. Expression domains of genes across cell types. Data is shown for Arabidopsis phloem companion cells and for the mouse G30-amygdala
cells. For each cell type, three plots are given, representing domains of size 2, 3 and 4. For domain size D, we included all genes whose D lowest
Nspec(y) values were <D+0.5, and which had all other Nspec(y) values greater than D+1. We required that the D cell types with lowest Nspec(y)
values include the given cell type (phloem companion or G30-amygdala). Both Spec and raw expression data are shown. Genes are sorted according
to the order (left to right) of the D cell types in the gene’s domain, and further sorted according to the Spec value of the left-most cell type in the
domain. Additionally, genes exhibiting low expression in the cell type of maximal Spec are sorted to the bottom of the plot, allowing these genes to
be easily noticed visually.
9100 Nucleic Acids Research, 2011,Vol.39, No. 21
lat root meri
Lat Root Cap (J3411)
Quiescent Center (AGL42)
Phl Pole Pericycle (S17)
Lat Root Meristem (RM1000)
Dev Xylem (S4)
root stem cells
Figure 4. Cell-type affinities in the Arabidopsis root and mouse brain. In Spec network representations, each edge represents a major pattern that
linked the two cell types via a large number of genes (>100 genes in Arabidopsis; >50 genes in mouse) whose expression domains overlapped both
cell types (see ‘Materials and Methods’ section). Dendrograms depict cell-type affinities using a similarity matrix of Pearson correlation of overall
gene expression values. Gray edges in network represent cell types with high similarity in the tree where their ancestral node meets less than half the
Nucleic Acids Research, 2011,Vol.39, No. 21 9101
Overall, we find that mRNA expression plasticity as
measured by dSpec increases for markers transcribed in
an increasingly larger number of cell types (Figure 5).
Such a trend, as detected by Spec’s quantification of
specificity and noise, has practical implications. For
example, if a negative marker is needed to sort against a
cell population, that population will be more reliably
identified by a set of narrow markers than a single
Figure 4. Continued
maximal distance. Black edges show cellular affinities that are distant in the similarity tree where their ancestral node meets at greater than half the
maximal distance. Broken lines are longest distance relationships in the tree where their ancestral node is basal. (A) Phloem cells (red arrowheads)
share a gene regulatory set (red circle) with adjoining pericycle cells (asterisks), showing a molecular domain of radial asymmetry (red square); radial
asymmetry subfigure is reproduced from Figure 1b of (44); root subfigure was previously published in (3). QC cells, which support the growth of the
primary meristem, share a strong affinity with lateral root meristem cells, which support the growth of lateral roots (blue circle). (B) Cells in the core
of the limbic system of mouse, amygdala and hippocampus (blue circle), show strong affinities in the Spec network despite differences in the genetic
background from which they came. In the similarity tree, some of the same cell types (e.g. amygdala cell samples) show distant relationships; brain
subfigure is reproduced from http://stuff4educators.com/web_images/amygdala_hippocampus.jpg.
1 2 3 4 5 6 7
0 0.51.0 >1.5
Figure 5. Validation of Spec against well-documented white blood cell markers. (left panels) The heatmap depicts the known expression profile (18)
of 51 CD markers in the seven cell types tested (see ‘Materials and Methods’ section). Positive markers (blue), negative markers (white) and
unspecified markers (gray) are indicated. From top to bottom, groups of rows show markers expressed in an increasing number of cell types,
from one cell type (top rows) to seven cell types (bottom rows). Three markers that had two probes each are listed twice (CD163, probes
ILMN_1722622/ILMN_1733270; CD74, probes ILMN_1736567/ILMN_1761464; and CD86, probes ILMN_1651349/ILMN_1714602). (Middle
panels) The heatmap shows the Nspecvalues for the makers calculated from expression data (17) (‘Materials and Methods’ section). The Pearson
correlations, r, between marker values (?1, 0, 1) and Nspecvalues are listed, and indicate a high level of concordance for markers expressed in up to
three cell types between known cell-surface expression patterns (left panels) and Nspecvalues calculated from expression profiles (middle panels).
A heatmap of dSpec values (right panels) for each gene in each cell type shows the level and cell-type distribution of noise for each gene, indicating a
trend of decreasing robustness for more widely expressed genes. Arrows indicate the two examples that are discussed in the text.
9102 Nucleic Acids Research, 2011,Vol.39, No. 21
broad marker (assuming that mRNA noise levels reflect
protein-level noise in the case of cell-surface markers).
Thus, Spec and dSpec provide a way to assess marker
specificity and plasticity, respectively; in particular,
dSpec can detect whether specificity is reduced due to
noise inside or outside of the gene’s expression domain.
In the second dataset, we used a large compendium
of hormone treatment microarray data from many
different labs (http://affymetrix.arabidopsis.info/narrays/
experimentbrowse.pl). The use of this dataset permitted
us to benchmark Spec against one other common
biomarker discovery tool using a large set of genes
known to respond in one condition, auxin treatment
(Supplementary Table S4). We asked whether Spec
could make use of the information in a meta-analysis
consisting of 250 samples collected in 14 different labs.
A number of quantitative approaches have been applied
to finding biomarkers, including support vector machines,
neural networks and others (32–36). We tested Spec
against one highly cited and well-documented tool,
GenePattern (1,37), which bases pattern discovery on
a t-test with calculated false discovery rates. The test set
contained treated samples for 13 hormones or hormone
inhibitors, with no controls included (since the 12 other
classes served as background or non-target classes). In
each case, wetested for
examining the top ranked genes for each algorithm to com-
pare performance of the two approaches (Figure 6A).
Spec was notably precise at stringent levels. Among the
top 20 ranked markers identified by each method, Spec
identified 10 known markers while GenePattern identified
two (Supplementary Table S5). Both methods capture
highly specific markers with consistently high expression
in the target class and low expression in the non-target
class, although we note this is a relatively small percentage
of the known markers.
(>250 highest ranked), GenePattern was able to find
more markers than Spec (24 versus 15 in the top 500
ranked) but precision at these stringency levels was very
Another unique property of Spec as a biomarker
discovery tool is its ability to identify complex markers
that are informative of more than one condition, as
At low stringency levels
Figure 6. Biomarker discovery performance for Spec and GenePattern. (A) The graph shows the precision (true positive/total positive cases) of each
biomarker approach in identifying 221 documented auxin-responsive markers among profiles of 17 285 genes in a series of 13 different hormone
treatments. The identity of markers was obtained from literature, not from the data itself. For every gene, each method generates a marker score for
each hormone [e.g. Spec(auxin)] and genes were ranked from highest to lowest score in the auxin category, using only those genes in which the auxin
score was highest among all hormones. To obtain the precision of each method at a given ranked gene list size (i.e. top 20 genes, top 40 genes, etc),
the number of true hits in the list were tallied and divided by the list size. (B) Within the top 500 ranking genes for each approach, graphs show the
expression patterns of the highest-ranking documented auxin markers discovered by one method but not the other. The graph shows expression in all
the experiments used by each method to evaluate the markers, with the auxin experiments highlighted nearest to the origin.
Nucleic Acids Research, 2011,Vol.39, No. 21 9103
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
Figure 7. Hormone-specific gene expression. Data from 250 experiments conducted in many different laboratories, in which specific hormone
treatments were administered, was analyzed with hormone types taking the place of cell types in the analysis (see ‘Materials and Methods’
section). In each panel, labeled by the hormone type y, each gene was plotted as a single blue point at position [Spec(y),dSpec(y)]; red points
represent the shuffled dataset. A brassinosteroid-responsive protein (brassinosteroid-6-oxidase 2, green circle) and an auxin-responsive protein
9104 Nucleic Acids Research, 2011,Vol.39, No. 21
documented auxin-responsive transcript (AT4G34770)
has a relatively high Spec for auxin and for ethylene
(Figure 7, purple circle). Interestingly, its noise within
the auxin data is relatively high and it is not likely to be
identified as an auxin marker using traditional statistical
methods. Similarly, brassinosteroid-6-oxidase 2, which is
involved in brassinosteroid biosynthesis, is induced by
both brassinosteroid and GA inhibitors, the latter of
which exhibits high variance within treatments and
would likely have been missed by statistical methods
(Figure 7, green circle). Thus, analogous to its potential
in linking the function of cells, the quantification of spe-
cificity provides a way of linking common responses to
We have described a rigorous approach that uses informa-
tion theory to formalize the concept of specificity in gene
expression and to quantify cell identity in the presence of
noise. More generally, the approach is applicable when
levels, protein abundances, epigenetic modifications, etc.)
are mapped onto a biological organization y (e.g. cell
types, spatial structure, treatments, disease states, etc.)—
and the mapping is given by a probability distribution
P(x|y). Information-based approaches in developmental
biology have previously focused on transmission of infor-
mation within developmental regulatory circuits (38,39).
Our application of Spec here addresses a novel question,
namely how much information does a gene’s expression
level provide about a cell’s identity. As such, Spec
provides both a unifying conceptual framework and a
measurement tool in the study of cell identity, and with
it the ability to quantify on a genome-wide scale this
central concept of developmental biology.
The formulation presented here makes it possible to
distinguish noise that detracts from information on
cell-type specificity from noise which does not, by using
the measure dSpec. This unique feature of the method is
generally missing in purely statistical approaches, such as
analysis of variance or measures based on the t-test. The
ability to cope with various sources of noise in data reveals
critical connections between cell types, as well as unique
and promising classes of biomarkers. When technical rep-
licate data are used, dSpec detects cases when variability
among samples detracts from specificity. When samples
from different individuals are used, dSpec provides
a measure of transcriptional plasticity for each gene in
each cell type. In its biomarker applications, dSpec there-
fore makes it possible to pinpoint cell types in which plas-
ticity is most likely to result in false positives, and to
identify markers that are more likely to be reliable for
a given target class.
The Spec analysis provides a new tool to explore the
mosaic character of gene expression in a multicellular
organism. In the plant and animal examples examined,
domains containing between two and five cell types
(Figures 2 and 3), with shared gene expression describing
potentially novel mechanistic links between cell types.
The quantification of specificity and noise also reveals
the limits of complex pattern detection, i.e. patterns con-
sisting of more than five or six cell-type domains could
not be precisely delineated given the noise in the data
(Figure 2). The genomic signature of cell-type specificity,
i.e. the bright fingers in Figure 2, is notably absent in
the hormonedataset (Supplementary
demonstrating that the signature is neither a generic
feature of microarray data nor an artifact of the
approach. In addition, cell identity has a component of
specifically absent gene activity, i.e. transcripts expressed
in all but one or a few cell types (Figure 3).
As a quantitative measure of specificity, Spec also opens
possibilities for phylogenetic studies to map changes in
cellular complexity during evolution (3,40–43). The next
generation of genomics may allow entire transcriptomes to
be routinely measured in individual cells, rather than in
pooled samples. It will be particularly interesting to see
individual differences among single cells using Spec, and
to determine which parts of the overall genomic distribu-
tion of specificity, shown in Figure 2, are maintained at
the single cell level, and which new aspects of specificity
are revealed. By virtue of having information theory at its
basis, Spec providesa
comparing our current measurements of specificity, with
those enabled by future technological advances in
Spec’s significantly higher precision in biomarker iden-
tification is due to its handling of noise, which permits
markers to exhibit significant variability within the
target class (Figure 6B). This is arguably a common
property of many otherwise reliable markers, which are
specific to the target condition but variable in their
response. On the other hand, most of the markers
missed by Spec and identified by GenePattern showed
high levels of noise in the non-target set but a consistently
higher level of expression in the target set (Figure 6B).
Such markers could be prone to false positive tests and
may pose a problem for diagnostics.
We have described the formulation and application of
Spec, an information-theoretic specificity measure that
allows a rigorous quantification of cell identity in biologic-
al systems. Using information theory as the basis for
measuring specificity allows both ease of interpretation
Figure 7. Continued
(AT4G34770, purple circle) are highlighted for illustrative purposes. The auxin panel is shown in an enlarged view below, and the highly specific
genes are labeled according to their genomic annotations. Several of the well-characterized auxin-responsive genes (IAA’s) are seen to exhibit a high
amount of noise across the multiple datasets used here; they are nevertheless among the most specifically expressed genes as measured by Spec. We
note that each laboratory’s control (null treatment) experiments were not used as part of this analysis as the meta-analysis, in effect, used all data for
comparisons. Spec and dSpec values were computed using three expression level bins.
Nucleic Acids Research, 2011,Vol.39, No. 219105
and flexibility of application. Moreover, it necessitates the
incorporation of noise as an integral component of the
specificity measure. As we have shown, this critical facet
of our approach allows features of genomic data that are
typically ignored or discarded due to variability to be
meaningfully analyzed and quantified. Without our
explicit accounting of noise, many of the structures
revealed in the datasets we have analyzed (transcription
patterns, biomarkers, cell-type connections) would have
been entirely missed. The flexibility and generality of the
method, combined with its rigorous treatment of noise,
provide a powerful approach for quantitative analysis of
specificity in biology.
Supplementary Data are available at NAR Online.
The authors would like to thank Mark Siegal, Erik van
Nimwegen and three anonymous referees for their
comments and careful reading of the manuscript.
Burroughs Wellcome Career Award at the Scientific
Interface (to E.K.); National Institutes of Health (grant
R01 GM078279 to K.D.B.). Funding for open access
Institutes of Health.
Conflict of interest statement. None declared.
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