Effects of normalisation on false positives and false negatives when applying t-test for equality of the mean.

<p>(A) We consider responses to eight conditions with log-normal distributions with CV of 0.2 and means of the conditions from 1 to 8 equal to: 1, 2, 2, 4, 7, 7, 18, 18. A number n = 5 of sampled replicates are obtained from these distributions and normalised using the normalisations above. Using these replicates before and after normalisation, conditions are tested using a two-tailed t-test with threshold p-value of 0.05. We repeat this procedure a large number of times and estimate the percentage of false positives. (B) In analogy with (A), we estimate the number of false negatives considering means of the conditions from 1 to 8 equal to: 1, 2, 3, 4, 7, 10.5, 18, 27. Notice that for a fair comparison, when testing two conditions, one has a mean that is always 2/3 the mean of the other, e.g. Condition 5 has mean 7 and Condition 6 has mean 10.5, with 7/10.5 = 2/3.</p

Normalisations of Western blot replicates in the literature.

<p>We divide the normalisations found in literature into three categories: (A) normalisation by fixed normalisation point or control; (B) normalisation by sum of the replicate; (C) normalisation by optimal alignment. For illustration purposes we do not use actual Western blot data. Each normalisation is presented using three cartoon Western blots, representing three replicates, and highlighting with red circles the data points used in the normalisation procedure. The graphs show the normalised data, where the points belonging to the same replicate are connected with lines.</p

Identification of potential new treatment response markers and therapeutic targets using a Gaussian process-based method in lapatinib insensitive breast cancer models

<div><p>Molecularly targeted therapeutics hold promise of revolutionizing treatments of advanced malignancies. However, a large number of patients do not respond to these treatments. Here, we take a systems biology approach to understand the molecular mechanisms that prevent breast cancer (BC) cells from responding to lapatinib, a dual kinase inhibitor that targets human epidermal growth factor receptor 2 (HER2) and epidermal growth factor receptor (EGFR). To this end, we analysed temporal gene expression profiles of four BC cell lines, two of which respond and the remaining two do not respond to lapatinib. For this analysis, we developed a Gaussian process based algorithm which can accurately find differentially expressed genes by analysing time course gene expression profiles at a fraction of the computational cost of other state-of-the-art algorithms. Our analysis identified 519 potential genes which are characteristic of lapatinib non-responsiveness in the tested cell lines. Data from the Genomics of Drug Sensitivity in Cancer (GDSC) database suggested that the basal expressions 120 of the above genes correlate with the response of BC cells to HER2 and/or EGFR targeted therapies. We selected 27 genes from the larger panel of 519 genes for experimental verification and 16 of these were successfully validated. Further bioinformatics analysis identified vitamin D receptor (VDR) as a potential target of interest for lapatinib non-responsive BC cells. Experimentally, calcitriol, a commonly used reagent for VDR targeted therapy, in combination with lapatinib additively inhibited proliferation in two HER2 positive cell lines, lapatinib insensitive MDA-MB-453 and lapatinib resistant HCC 1954-L cells.</p></div

Effect of the normalisation on the CV of the normalised data.

<p>(A) Distribution of the data in a simulated scenario. In our theoretical analysis of the effects of the normalisation on the variability of the normalised data we consider a distribution of the response to eight conditions. We use log-normal distributions with CV 0.2 and mean of the response to the conditions from 1 to 8 as 1, 2, 3, 4, 7, 10.5, 18, 27. (B) CVs are shown for the distribution of the simulated data before normalisation, after normalisation by first condition, after normalisation by sum of all data points in a replicate and after normalisation by least squared differences. The mean CV is computed as the average across the eight conditions. (C) Data from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087293#pone.0087293.s003" target="_blank">Figure S3</a> of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087293#pone.0087293-Rauch1" target="_blank">[25]</a> (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087293#pone.0087293.s005" target="_blank">Figure S5</a> in this publication) were normalised using different normalisation strategies and the mean CV of the resulting normalised data is shown. As the mean CV obtained by the normalisation by fixed point depends on the choice of normalisation point, we report the mean and standard deviation obtained. We also report the mean CV obtained using ppERK and pAkt data and we compare them with the theoretical results of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087293#pone-0087293-g003" target="_blank">Figure 3B</a>. (D) Before normalisation, the response to Condition 2 has a CV of 0.2, as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087293#pone-0087293-g003" target="_blank">Figure 3A</a>. Condition 2 is then normalised by fixed point, with Condition 1 as normalisation point. Here we show how the CV of normalised Condition 2 changes for increasing CV of the normalisation point Condition 1.</p

Decomposition of the human signaling network.

<p>(<b>A</b>) Human signaling network. (<b>B</b>) Evolvable core. (<b>C</b>) Robust neighbor. (<b>D</b>) Attractor landscape of the human signaling network. (<b>E</b>) Attractor landscape of the evolvable core.</p

Bioinformatics analysis of the selected genes.

<p>(A) Enriched gene ontology terms are summarized and visualized by REVIGO. Here, each circle represents a gene ontology term and the size of the circle represents the extent of enrichment. (B) Pathway enrichment analysis of the selected genes. Pathways are shown in X-axis and the enrichment scores are shown in Y-axis. (C) Transcriptional module found in the identified genes. (D) Transcriptional activity of VDR. (E) PPI network induced by the identified genes. (F,G) PPIs of VDR and EIF2S2.</p

Signal linearity obtained by different Western blot detection systems.

<p>Representative experiments of Western blots containing 2-fold serial dilution of BSA. Shown are the representative results from 3 independent experiments. BSA was detected by (A,C) ECL with X-ray film and (B,D) ECL with CCD imager. Blue squares indicate data points that are linear, while red triangles indicate data points outside the linear range of detection. To highlight linear and non-linear data we use linear trend lines, reporting the coefficient of determination . In (A,B) data are in log-log scale to improve visualisation.</p

Topological characteristics of the evolvable core and robust neighbor sub-network.

<p>(<b>A</b>) Number of self-loops. (<b>B</b>) Number of two-node feedbacks. (<b>C</b>) Number of three-node feedbacks of the original network, evolvable core sub-network, random-deletion sub-network, robust neighbor sub-network, and random-selection sub-network. (<b>D</b>) Degree heterogeneity of the original network, evolvable core sub-network, and random-deletion sub-network. (<b>E</b>) Degree distribution of the original network and evolvable core sub-network. (<b>F</b>) The ratio of robust neighbor links to the whole links for the low-degree, middle-degree, and high-degree nodes, respectively. (<b>G</b>) Characteristic path length of the original network, evolvable core sub-network, and random-deletion sub-network. (<b>H</b>) Number of connected components of the robust neighbor and random-selection sub-network. (<b>I</b>) Characteristic path lengths of the robust neighbor and random-selection sub-network. Error bars denote the standard errors of the average values.</p

Genetic properties of the network nodes in terms of evolvability and robustness scores.

<p>(<b>A</b>) The correlation between evolutionary rate and evolvability score. (<b>B</b>) The correlation between species broadness and evolvability score. (<b>C</b>) The normalized average evolvability and robustness scores of the genes related to immune system. (<b>D</b>) The normalized average evolvability and robustness scores of the oncogenes.</p

Clinico-pathological subtypes of and receptor expressions in MDA-MB-453 and JIMT1 cell lines.

<p>Clinico-pathological subtypes of and receptor expressions in MDA-MB-453 and JIMT1 cell lines.</p