We first compared our proteomic dataset to data from previous microarray studies measuring transcriptional changes due to rapamycin treatment in yeast [6], [7].
Moreover, this pattern is consistent across independent microarray studies measuring response to different artemisinin derivatives (Fig. 1a).
We can integrate different experiments that measure the same biological entity, such as microarray studies measuring tumor versus normal gene expression differences on different experimental platforms.
Divergence was reported in a microarray study measuring gene expression across five sets of environmental perturbation (heat shock, oxidative stress, nitrogen starvation, DNA damage, and carbon source switching) between S. cerevisiae, S. paradoxus, S. mikatae, and S. kudriazevii.
While microarray studies assess gene expression levels by measuring hybridization intensities to the relevant probes [1], SAGE studies use portions of cDNA transcripts known as SAGE tags that are concatenated, cloned, and sequenced to provide a quantitative measure of the transcripts levels in the cell [2].
To assess Keratin 18 expression in a panel of cell lines, we used the same samples as in Figure 4F (i.e. independent samples from 3 IPF and 3 control fibroblast lines which were part of the microarray study) and measured protein abundance by immunoblot (mammary epithelial cells served as a positive control, Figure 6B).
To assess whether relationships existed between expression pattern and the P. infestans core motifs, we used data from a microarray study that measured mRNA at five sequential life stages [ 47].
DNA microarrays have been used to measure changes in host cell gene transcription during infection, with an aim to infer the mechanisms and strategies applied by Ap [ 19- 24], but no microarray studies that directly measure Ap transcription have been published.
In this article, we focus on microarray studies where gene expressions are measured along with certain cancer clinical outcomes.
The importance of quality control (QC) measures in microarray studies, including pre-chip (RNA quality of samples) and post-chip outcomes of the data, has been described previously [ 19].
We will go back to this theme in the last paragraph of the results section, here we will concentrate on the falsification of the second assumption, i.e. the supposed irrelevance of cell-cell interactions necessary for concentrating only on single cell explanations of population based measures like microarray studies.
