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User:HassanForoughiAsl/1 July 2010
From 2010.igem.org
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| - | "Despite the described interest, coexpression studies done at global “omic” scale are not focused in many cases on human samples [5], and, when they correspond to human, very often they include heterogeneous datasets, mixing “normal” samples with “disease altered” samples from patients suffering from some kind of pathological state. This is the case, for example, in several human gene expression large studies [2], [6]. The inclusion of many disease datasets (mainly from cancer) in such meta-analyses may introduce strong bias and produce a lot of biological noise in the results. In fact, it is well known that cancer cells have altered genomes. Therefore, these kind of studies cannot be used to clarify how a normal-healthy human cellular system works, and they cannot be used to draw a reliable map of the human gene coexpression landscape." <ref> | + | "Despite the described interest, coexpression studies done at global “omic” scale are not focused in many cases on human samples [5], and, when they correspond to human, very often they include heterogeneous datasets, mixing “normal” samples with “disease altered” samples from patients suffering from some kind of pathological state. This is the case, for example, in several human gene expression large studies [2], [6]. The inclusion of many disease datasets (mainly from cancer) in such meta-analyses may introduce strong bias and produce a lot of biological noise in the results. In fact, it is well known that cancer cells have altered genomes. Therefore, these kind of studies cannot be used to clarify how a normal-healthy human cellular system works, and they cannot be used to draw a reliable map of the human gene coexpression landscape." <ref> doi:10.1371/journal.pone.0003911</ref> |
Revision as of 10:15, 1 July 2010
"Despite the described interest, coexpression studies done at global “omic” scale are not focused in many cases on human samples [5], and, when they correspond to human, very often they include heterogeneous datasets, mixing “normal” samples with “disease altered” samples from patients suffering from some kind of pathological state. This is the case, for example, in several human gene expression large studies [2], [6]. The inclusion of many disease datasets (mainly from cancer) in such meta-analyses may introduce strong bias and produce a lot of biological noise in the results. In fact, it is well known that cancer cells have altered genomes. Therefore, these kind of studies cannot be used to clarify how a normal-healthy human cellular system works, and they cannot be used to draw a reliable map of the human gene coexpression landscape." <ref> doi:10.1371/journal.pone.0003911</ref>
