BACKGROUND
the liver is considered one of the vital organs in the body.
its has several major functions including the production of bile to break down fat, glycogen storage, decomposition of red blood cells, production of cholesterol, plasma protein synthesis and drug metabolism just to name a few.
the latter takes place by a host of specialized detoxification enzymes and pathways that biochemically modify or metabolize xenobiotics to harmless metabolites and other byproducts for clearance from the body  <cit> .
however, the metabolism of some drugs and compounds leads to toxic intermediates that can harm the liver and severely disrupt its function  <cit> .
drug-induced liver injury  is the leading cause of liver failure in the united states  and is quickly becoming a major concern world-wide  <cit> .
in fact, dili accounts for more than  <dig> percent of the cases of acute liver failure in the us and more than  <dig> percent of cases of adverse drug reactions result in liver transplantation or death  <cit> .
drug-induced hepatotoxicity is the most frequent cause for a drug to be withdrawn from the market, restricted in its use or have a warning associated with it due to adverse drug reactions  <cit> .
a better understanding of the pathophysiology of dili and the mechanisms involved in the manifestation of hepatotoxicity are critical to improving human health and public awareness of potentially harmful toxicants  <cit> .
microarray gene expression analysis has been used to study the effects of toxicants and other environmental stressors on biological systems  <cit> .
recently, lobenhofer et al.
<cit>  used gene expression data from rats exposed to a compendium of hepatotoxicants to show that blood gene expression patterns could be used not only to classify animals based on the compound they were exposed to, but also to provide a reasonable indication of the severity level of liver injury.
in addition, bushel et al.
<cit>  demonstrated that gene expression profiles from rat blood samples could accurately predict exposure levels of acetaminophen to the rat liver better than traditional clinical panels.
furthermore, human subjects treated with acetaminophen could be classified based on blood gene expression levels.
although these efforts led to informative conclusions about the genes expression changes related to drug-induced hepatotoxicity, they did not capture the breath of the biological mechanisms altered by the toxic insult.
while hierarchical clustering groups genes and samples by the similarity of expression across all the elements in a data matrix, biclustering  <cit>  aims to identify sub-matrices  of gene expression that are coherent and possess homogeneity of co-expression.
hence, the biclusters contain co-expressed genes that represent distinct biological responses related to mechanistic changes.
in this paper we used a biclustering method to analyze a compendium gene expression dataset containing  <dig> liver samples from rats exposed to hepatotoxicants  and leveraged alanine aminotransferase , a serum enzyme indicative of liver injury, as the phenotypic marker in order to identify biclusters of co-expressed genes that over-represent biological processes and pathways related to hepatotoxicity.
RESULTS
experimental design
male  <dig> week old f <dig> fischer rats were exposed to one of the following chemicals:  <dig> -dichlorobenzene,  <dig> -dichlorobenzene, bromobenzene, monocrotaline, n-nitrosomorpholine, thioacetamide, galactosamine or diquat dibromide  <cit> .
for each chemical, four to six rats , were exposed to three or four different dose levels  of the compound and sacrificed at  <dig>   <dig> and  <dig> hrs later to extract liver rna for microarray hybridization.
the  <dig> samples and exposure conditions are listed in table  <dig>  the number of samples in each group varied from  <dig> to  <dig>  liver necrosis was observed in all the rats exposed to one of the eight chemicals at high doses .
however,  <dig> -dichlorobenzene is an isomer of  <dig> -dichlorobenzene and is non-toxic at low and mid doses.
since each compound has its unique chemical structure and properties, the activated biological processes, molecular pathways, extent of toxicity in the liver and injury could be highly similar or dissimilar.
considering these variables, we partitioned the columns of the gene expression data matrix into eight groups  for analysis using coherent co-expression -biclustering.
identification of co-expressed genes based on alt enzyme levels
each group of chemicals included a set of samples with different exposure conditions .
we used alt as a profile to supervise the extraction of the genes that are expressed similarly and correlated with the phenotypic marker.
alt values in each of the chemical exposures were generally elevated from  <dig>   <dig>  to  <dig> hrs and from low doses to high doses.
for a probability threshold  value of  <dig> , supervised cc-biclustering extracted  <dig> biclusters in which the genes were correlated  to alt and  <dig> biclusters in which the genes were anti-correlated  to alt.
the number of genes in each bicluster varied.
the largest number of genes in a bicluster was  <dig>  the number of groups  in the biclusters had a range from a minimum of  <dig> to a maximum of  <dig>  the biclusters which contained only one chemical could represent the uniqueness in gene expression for the agent and the biclusters which contained all eight chemicals could provide some clues to the common responses to the exposures across the agents.
the gene ontology analysis of three biclusters from two chemicals, galactosamine and thioacetamide, revealed interesting categorization of the genes .
the  <dig> genes in a bicluster subset by galactosamine over-represented a kegg pathway related to extracellular matrix  receptor interaction .
another bicluster of  <dig> genes subset by thioacetamide had a different set of significantly over-represented categories representing the wnt signalling pathway and glycerophospholipid metabolism .
the third bicluster of  <dig> genes subset by both chemicals had biological processes and kegg pathways related to neuron development and cell adhesion as over-represented categories .
identification of co-expressed genes that discern isomers and an early response
although the blood serum level of alt is a good indicator of liver injury, it is not considered a prognosticator of toxic insult as the true nature and extent of the liver damage is not proportional to the elevation in the serum enzyme activity  <cit> .
for instance, a normal alt level does not necessarily mean that the liver is definitely normal and high levels of alt in the blood doesn't necessarily indicate the extent to which the liver is inflamed or damaged.
in addition, it is likely that alt levels elevate well after the mechanistic changes have occurred that led to the liver injury from toxic exposure.
therefore, we set out to use cc-biclustering in an unsupervised fashion  so we could find biclusters of genes that may be unrelated to alt or respond before alt elevation and yet are very informative in terms of the manifestation of hepatotoxicity.
we obtained more biclusters  using unsupervised cc-biclustering when the same threshold pt was used as in the supervised case.
similar to the biclusters in figure  <dig>  the genes in the heat maps shown in figure  <dig> are subset by all eight chemicals.
there are  <dig> genes up-regulated as shown in figure 3a  and  <dig> down-regulated genes as shown in figure 3b .
compared to the number of genes in the bicluster shown in figure  <dig>  the bicluster in figure  <dig> contained about four to five times more genes and more significant categories .
the set of genes that are up-regulated were found to over-represent biological processes and kegg pathways representative of a more toxic response .
apoptosis, an inflammatory response and glycolysis/gluconeogenesis were key mechanisms impacted.
the set of down-regulated genes contained over-represented categories related to cholesterol biosynthesis, fatty acid metabolism, alkaloid biosynthesis and the peroxisome proliferator-activated receptors  signaling pathway.
ppar-α is mainly expressed in the liver and activation of the receptor has been associated with suppression of apoptosis and induction of cell proliferation  <cit> .
heat maps of the gene expression from three biclusters subset by seven chemicals , are shown in figure  <dig>  figure 4a  shows  <dig> co-expressed genes up-regulated at a later time and figure 4b   <dig> co-expressed genes down-regulated.
the set of up-regulated genes significantly over-represented categories that are suggestive of liver regeneration   <cit> .
angiogenesis, the regulation of actin cytoskeleton, regulation of adherens  junctions and the toll-like receptor  signaling pathway were impacted.
the down-regulated genes over-represented mechanisms related to energy producing pathways .
comparisons of the over-represented categories between the genes in figure  <dig> and  <dig> clearly differentiate  <dig> -dichlorobenzene from the other chemicals since the biclusters in figure  <dig> do not contain genes that are co-expressed in the samples exposed to  <dig> -dichlorobenzene.
figure 4c  had  <dig> genes up-regulated early and significantly over-represented categories pointing to a negative regulation of protein kinase activity, the mitogen-activated protein kinase  signaling pathway, and apoptosis .
discussion and
CONCLUSIONS
over the recent years several ways of investigating drug-induced hepatotoxicity has been explored.
using gene expression analysis of samples exposed to toxicants offers a genome-wide assessment of the transcriptional changes that occur from the insult.
however, current methodologies for analyzing the data are limited in that they typically do not "anchor" the changes in gene expression to the phenotype of toxicity nor do they constrain them by the experimental design.
case in point is hierarchical clustering of genes and samples based on gene expression data.
although the method provides an overall view of the clusters of genes that are co-expressed within a group of highly similar samples, it does not extract subclusters of co-expressed genes that are related to a given phenotype, end-point measure of the samples or subset of experiments.
yoon et al.
<cit>  proposed a method for discovering coherent biclusters from gene expression data using decision diagrams constituted from binary representations of a set of samples in which the expression of a subset of genes are highly similar .
however, the method does not integrate phenotypic data nor is it guided by the experimental design .
linking co-expressed genes to a phenotype of interest or set of experimental conditions can potentially enhance the interpretation of the biological systems that are impacted from the manifestation of an outcome  <cit> .
we analyzed a compendium gene expression dataset containing  <dig> liver samples from rats exposed to hepatotoxicants and leveraged alanine aminotransferase , a serum enzyme indicative of liver injury as the phenotypic marker, to identify several biological processes and molecular pathways that may be associated with mechanisms of hepatotoxicity.
our analysis used an approach to biclustering called coherent co-expression biclustering  for clustering of a subset of genes through a coherent  measure within each group of samples representing a subset of experimental conditions.
existing biclustering methods use some measure of merit to determine whether a row  or column  should be included or excluded from a bicluster  <cit> .
cc-biclustering uses a given coherent measure  to determine whether a gene or a group of samples is included or excluded from a bicluster.
the cm used between pairs of gene vectors  is flexible.
depending on a research interest, cm can be chosen to be pearson correlation, euclidean distance or some other measure of similarity.
unsupervised cc-biclustering uses a pairwise comparison of the gene expression profiles to extract biclusters.
in the case of supervised cc-biclustering, we correlated the co-expression of the subset of genes within a bicluster with alt.
the overlap between the up-regulated genes from the supervised and unsupervised cc-biclustering methods is  <dig> genes  where as the overlap between the down-regulated genes from the two methods is  <dig> genes .
the sharp contrast between biclusters in figure  <dig> and figure  <dig> with respect to the over-represented biological categories exhibited by the genes positively correlated with alt clearly differentiated  <dig> -dichlorobenzene from the other seven chemicals.
this finding is consistent with a study to predict the levels of necrosis in the rat liver using this same compendium of hepatotoxicants  <cit> .
it was shown that  <dig> -dichlorobenzene was the only chemical with an observed necrosis contained in less than 25% of the hepatocytes .
all the other chemicals caused observed necrosis in > 50% of the hepatocytes in one or more of the exposed samples .
moreover, a bicluster with samples exposed to bromobenzene, diquat dibromide, galactosamine, monocrotaline or n-nitrosomorpholine and  <dig> genes  correlated with alt and contained over-represented categories related to an inflammatory response, glycolysis/gluconeogenesis and apoptosis .
these are biological processes known to be involved in hepatotoxicity  <cit> .
comparison of the over-represented categories between the genes in the biclusters shown in figures  <dig> and  <dig>  also differentiate  <dig> -dichlorobenzene from the other chemicals quite well.
in addition,  <dig> genes in a bicluster were found to be up-regulated early in response to the hepatotoxicant exposure  and significantly over-represented categories related to negative regulation of protein kinase activity, the mapk signaling pathway, and apoptosis .
many genes in this set  have been extensively studied in terms of their response to toxicants or other external stimuli  <cit> .
most of these genes are targets of p <dig> and cause dna damage.
our finding suggests that the liver injury caused by  <dig> -dichlorobenzene could be non-genotoxic while the other toxicants could be genotoxic.
this result is consistent with the current finding that early responses of some genes at  <dig> hr differentiate  <dig> -dichlorobenzene from the other seven more toxic chemicals where it is evident that transcriptional regulation by jun and tp <dig> leads to necrosis  <cit> .
although the data from each array is independently collected, in a vast number of cases, many of the related biological samples are highly correlated.
for example, some samples are from the same tissue, or treated by the same chemical, or a time series.
same samples are merely biological replicates.
in our study we analyzed rat liver samples exposed to one of eight chemicals.
each of the hepatotoxicants has its unique chemical structure and causes different levels and extent of liver injury .
the activated biological processes and molecular pathways can be highly similar or dissimilar.
the gene sets involved in responding to each of the hepatotoxicants can overlap to a large extent or be somewhat unique.
using our biclustering approach we were able to capitalize on the experimental design and/or phenotypic measure to discern possible mechanisms of hepatotoxicity through the over-representation of pathways and biological processes determined by co-expressed genes in cc-biclusters.
more work is underway to efficiently process the wealth of biclusters extracted for a more informed interpretation of the mechanistic changes that take place during the formation of hepatotoxicity.

 the liver is considered one of the vital organs in the body. its has several major functions including the production of bile to break down fat, glycogen storage, decomposition of red blood cells, production of cholesterol, plasma protein synthesis and drug metabolism just to name a few. the latter takes place by a host of specialized detoxification enzymes and pathways that biochemically modify or metabolize xenobiotics to harmless metabolites and other byproducts for clearance from the body. however, the metabolism of some drugs and compounds leads to toxic intermediates that can harm the liver and severely disrupt its function. drug-induced liver injury is the leading cause of liver failure in the united states and is quickly becoming a major concern world-wide. in fact, dili accounts for more than percent of the cases of acute liver failure in the us and more than percent of cases of adverse drug reactions result in liver transplantation or death. drug-induced hepatotoxicity is the most frequent cause for a drug to be withdrawn from the market, restricted in its use or have a warning associated with it due to adverse drug reactions. a better understanding of the pathophysiology of dili and the mechanisms involved in the manifestation of hepatotoxicity are critical to improving human health and public awareness of potentially harmful toxicants. microarray gene expression analysis has been used to study the effects of toxicants and other environmental stressors on biological systems. recently, lobenhofer et al. used gene expression data from rats exposed to a compendium of hepatotoxicants to show that blood gene expression patterns could be used not only to classify animals based on the compound they were exposed to, but also to provide a reasonable indication of the severity level of liver injury. in addition, bushel et al. demonstrated that gene expression profiles from rat blood samples could accurately predict exposure levels of acetaminophen to the rat liver better than traditional clinical panels. furthermore, human subjects treated with acetaminophen could be classified based on blood gene expression levels. although these efforts led to informative conclusions about the genes expression changes related to drug-induced hepatotoxicity, they did not capture the breath of the biological mechanisms altered by the toxic insult. while hierarchical clustering groups genes and samples by the similarity of expression across all the elements in a data matrix, biclustering aims to identify sub-matrices of gene expression that are coherent and possess homogeneity of co-expression. hence, the biclusters contain co-expressed genes that represent distinct biological responses related to mechanistic changes. in this paper we used a biclustering method to analyze a compendium gene expression dataset containing liver samples from rats exposed to hepatotoxicants and leveraged alanine aminotransferase, a serum enzyme indicative of liver injury, as the phenotypic marker in order to identify biclusters of co-expressed genes that over-represent biological processes and pathways related to hepatotoxicity.  experimental design male week old f fischer rats were exposed to one of the following chemicals: -dichlorobenzene, -dichlorobenzene, bromobenzene, monocrotaline, n-nitrosomorpholine, thioacetamide, galactosamine or diquat dibromide. for each chemical, four to six rats, were exposed to three or four different dose levels of the compound and sacrificed at and hrs later to extract liver rna for microarray hybridization. the samples and exposure conditions are listed in table the number of samples in each group varied from to liver necrosis was observed in all the rats exposed to one of the eight chemicals at high doses. however, -dichlorobenzene is an isomer of -dichlorobenzene and is non-toxic at low and mid doses. since each compound has its unique chemical structure and properties, the activated biological processes, molecular pathways, extent of toxicity in the liver and injury could be highly similar or dissimilar. considering these variables, we partitioned the columns of the gene expression data matrix into eight groups for analysis using coherent co-expression -biclustering. identification of co-expressed genes based on alt enzyme levels each group of chemicals included a set of samples with different exposure conditions. we used alt as a profile to supervise the extraction of the genes that are expressed similarly and correlated with the phenotypic marker. alt values in each of the chemical exposures were generally elevated from to hrs and from low doses to high doses. for a probability threshold value of, supervised cc-biclustering extracted biclusters in which the genes were correlated to alt and biclusters in which the genes were anti-correlated to alt. the number of genes in each bicluster varied. the largest number of genes in a bicluster was the number of groups in the biclusters had a range from a minimum of to a maximum of the biclusters which contained only one chemical could represent the uniqueness in gene expression for the agent and the biclusters which contained all eight chemicals could provide some clues to the common responses to the exposures across the agents. the gene ontology analysis of three biclusters from two chemicals, galactosamine and thioacetamide, revealed interesting categorization of the genes. the genes in a bicluster subset by galactosamine over-represented a kegg pathway related to extracellular matrix receptor interaction. another bicluster of genes subset by thioacetamide had a different set of significantly over-represented categories representing the wnt signalling pathway and glycerophospholipid metabolism. the third bicluster of genes subset by both chemicals had biological processes and kegg pathways related to neuron development and cell adhesion as over-represented categories. identification of co-expressed genes that discern isomers and an early response although the blood serum level of alt is a good indicator of liver injury, it is not considered a prognosticator of toxic insult as the true nature and extent of the liver damage is not proportional to the elevation in the serum enzyme activity. for instance, a normal alt level does not necessarily mean that the liver is definitely normal and high levels of alt in the blood doesn't necessarily indicate the extent to which the liver is inflamed or damaged. in addition, it is likely that alt levels elevate well after the mechanistic changes have occurred that led to the liver injury from toxic exposure. therefore, we set out to use cc-biclustering in an unsupervised fashion so we could find biclusters of genes that may be unrelated to alt or respond before alt elevation and yet are very informative in terms of the manifestation of hepatotoxicity. we obtained more biclusters using unsupervised cc-biclustering when the same threshold pt was used as in the supervised case. similar to the biclusters in figure the genes in the heat maps shown in figure are subset by all eight chemicals. there are genes up-regulated as shown in figure 3a and down-regulated genes as shown in figure 3b. compared to the number of genes in the bicluster shown in figure the bicluster in figure contained about four to five times more genes and more significant categories. the set of genes that are up-regulated were found to over-represent biological processes and kegg pathways representative of a more toxic response. apoptosis, an inflammatory response and glycolysis/gluconeogenesis were key mechanisms impacted. the set of down-regulated genes contained over-represented categories related to cholesterol biosynthesis, fatty acid metabolism, alkaloid biosynthesis and the peroxisome proliferator-activated receptors signaling pathway. ppar-α is mainly expressed in the liver and activation of the receptor has been associated with suppression of apoptosis and induction of cell proliferation. heat maps of the gene expression from three biclusters subset by seven chemicals, are shown in figure figure 4a shows co-expressed genes up-regulated at a later time and figure 4b co-expressed genes down-regulated. the set of up-regulated genes significantly over-represented categories that are suggestive of liver regeneration. angiogenesis, the regulation of actin cytoskeleton, regulation of adherens junctions and the toll-like receptor signaling pathway were impacted. the down-regulated genes over-represented mechanisms related to energy producing pathways. comparisons of the over-represented categories between the genes in figure and clearly differentiate -dichlorobenzene from the other chemicals since the biclusters in figure do not contain genes that are co-expressed in the samples exposed to -dichlorobenzene. figure 4c had genes up-regulated early and significantly over-represented categories pointing to a negative regulation of protein kinase activity, the mitogen-activated protein kinase signaling pathway, and apoptosis. discussion and  over the recent years several ways of investigating drug-induced hepatotoxicity has been explored. using gene expression analysis of samples exposed to toxicants offers a genome-wide assessment of the transcriptional changes that occur from the insult. however, current methodologies for analyzing the data are limited in that they typically do not "anchor" the changes in gene expression to the phenotype of toxicity nor do they constrain them by the experimental design. case in point is hierarchical clustering of genes and samples based on gene expression data. although the method provides an overall view of the clusters of genes that are co-expressed within a group of highly similar samples, it does not extract subclusters of co-expressed genes that are related to a given phenotype, end-point measure of the samples or subset of experiments. yoon et al. proposed a method for discovering coherent biclusters from gene expression data using decision diagrams constituted from binary representations of a set of samples in which the expression of a subset of genes are highly similar. however, the method does not integrate phenotypic data nor is it guided by the experimental design. linking co-expressed genes to a phenotype of interest or set of experimental conditions can potentially enhance the interpretation of the biological systems that are impacted from the manifestation of an outcome. we analyzed a compendium gene expression dataset containing liver samples from rats exposed to hepatotoxicants and leveraged alanine aminotransferase, a serum enzyme indicative of liver injury as the phenotypic marker, to identify several biological processes and molecular pathways that may be associated with mechanisms of hepatotoxicity. our analysis used an approach to biclustering called coherent co-expression biclustering for clustering of a subset of genes through a coherent measure within each group of samples representing a subset of experimental conditions. existing biclustering methods use some measure of merit to determine whether a row or column should be included or excluded from a bicluster. cc-biclustering uses a given coherent measure to determine whether a gene or a group of samples is included or excluded from a bicluster. the cm used between pairs of gene vectors is flexible. depending on a research interest, cm can be chosen to be pearson correlation, euclidean distance or some other measure of similarity. unsupervised cc-biclustering uses a pairwise comparison of the gene expression profiles to extract biclusters. in the case of supervised cc-biclustering, we correlated the co-expression of the subset of genes within a bicluster with alt. the overlap between the up-regulated genes from the supervised and unsupervised cc-biclustering methods is genes where as the overlap between the down-regulated genes from the two methods is genes. the sharp contrast between biclusters in figure and figure with respect to the over-represented biological categories exhibited by the genes positively correlated with alt clearly differentiated -dichlorobenzene from the other seven chemicals. this finding is consistent with a study to predict the levels of necrosis in the rat liver using this same compendium of hepatotoxicants. it was shown that -dichlorobenzene was the only chemical with an observed necrosis contained in less than 25% of the hepatocytes. all the other chemicals caused observed necrosis in > 50% of the hepatocytes in one or more of the exposed samples. moreover, a bicluster with samples exposed to bromobenzene, diquat dibromide, galactosamine, monocrotaline or n-nitrosomorpholine and genes correlated with alt and contained over-represented categories related to an inflammatory response, glycolysis/gluconeogenesis and apoptosis. these are biological processes known to be involved in hepatotoxicity. comparison of the over-represented categories between the genes in the biclusters shown in figures and also differentiate -dichlorobenzene from the other chemicals quite well. in addition, genes in a bicluster were found to be up-regulated early in response to the hepatotoxicant exposure and significantly over-represented categories related to negative regulation of protein kinase activity, the mapk signaling pathway, and apoptosis. many genes in this set have been extensively studied in terms of their response to toxicants or other external stimuli. most of these genes are targets of p and cause dna damage. our finding suggests that the liver injury caused by -dichlorobenzene could be non-genotoxic while the other toxicants could be genotoxic. this result is consistent with the current finding that early responses of some genes at hr differentiate -dichlorobenzene from the other seven more toxic chemicals where it is evident that transcriptional regulation by jun and tp leads to necrosis. although the data from each array is independently collected, in a vast number of cases, many of the related biological samples are highly correlated. for example, some samples are from the same tissue, or treated by the same chemical, or a time series. same samples are merely biological replicates. in our study we analyzed rat liver samples exposed to one of eight chemicals. each of the hepatotoxicants has its unique chemical structure and causes different levels and extent of liver injury. the activated biological processes and molecular pathways can be highly similar or dissimilar. the gene sets involved in responding to each of the hepatotoxicants can overlap to a large extent or be somewhat unique. using our biclustering approach we were able to capitalize on the experimental design and/or phenotypic measure to discern possible mechanisms of hepatotoxicity through the over-representation of pathways and biological processes determined by co-expressed genes in cc-biclusters. more work is underway to efficiently process the wealth of biclusters extracted for a more informed interpretation of the mechanistic changes that take place during the formation of hepatotoxicity. 
through the analysis of a gene expression dataset containing liver samples from rats exposed to hepatotoxicants and leveraging alanine aminotransferase, a serum enzyme indicative of liver injury as the phenotypic marker, we identified biological processes and molecular pathways that may be associated with mechanisms of hepatotoxicity. we obtained clusters of co-expressed genes that over-represented biological processes and molecular pathways related to hepatotoxicity in the rat. our analysis used an approach called coherent co-expression biclustering for clustering a subset of genes through a coherent measure within each group of samples representing a subset of experimental conditions. unsupervised biclustering of the data resulted in 1) four to five times more genes within the bicluster containing all the samples exposed to the chemicals, 2) biclusters with co-expression of genes that discerned dichlorobenzene from the other chemicals, pathways and biological processes that underlie liver injury and 3) a bicluster with genes up-regulated in an early response to toxic exposure. in addition, key pathways and biological processes representative of an inflammatory response, energy production and apoptosis were impacted by the hepatotoxicant exposures that manifested liver injury in the rat. however, there are no straightforward ways of using co-expressed genes anchored to a phenotype or constrained by the experimental design for discerning mechanisms of a biological response. hepatotoxicity is a form of liver injury caused by exposure to stressors. none of the over-represented pathways related to liver injury.
 hepatotoxicity is a form of liver injury caused by exposure to stressors. genomic-based approaches have been used to detect changes in transcription in response to hepatotoxicants. however, there are no straightforward ways of using co-expressed genes anchored to a phenotype or constrained by the experimental design for discerning mechanisms of a biological response.  through the analysis of a gene expression dataset containing liver samples from rats exposed to hepatotoxicants and leveraging alanine aminotransferase, a serum enzyme indicative of liver injury as the phenotypic marker, we identified biological processes and molecular pathways that may be associated with mechanisms of hepatotoxicity. our analysis used an approach called coherent co-expression biclustering for clustering a subset of genes through a coherent measure within each group of samples representing a subset of experimental conditions. supervised biclustering identified genes co-expressed and correlated with alt in all the samples exposed to the chemicals. none of the over-represented pathways related to liver injury. however, biclusters with subsets of samples exposed to one of the hepatotoxicants, but not to a non-toxic isomer, contained co-expressed genes that represented pathways related to a stress response. unsupervised biclustering of the data resulted in 1) four to five times more genes within the bicluster containing all the samples exposed to the chemicals, 2) biclusters with co-expression of genes that discerned dichlorobenzene from the other chemicals, pathways and biological processes that underlie liver injury and 3) a bicluster with genes up-regulated in an early response to toxic exposure.  we obtained clusters of co-expressed genes that over-represented biological processes and molecular pathways related to hepatotoxicity in the rat. the mechanisms involved in the response of the liver to the exposure to -dichlorobenzene suggest non-genotoxicity whereas the exposure to the hepatotoxicants could be dna damaging leading to overall genomic instability and activation of cell cycle check point signaling. in addition, key pathways and biological processes representative of an inflammatory response, energy production and apoptosis were impacted by the hepatotoxicant exposures that manifested liver injury in the rat. 