comparing Pearson correlation between 0 h (X ðt0Þ) and all time points (Y~Xðt0Þ,Xðt1Þ,X ðt4Þ) at 0 h, 1 h, 4 h of the same sample vectors, auto-correlation. Therefore, the auto-correlation profiles measures progressive divergence of expression from t0 for each genotype in terms of decreasing correlation in time, if response of LPS stimulation occurs. Since Pearson correlations of whole genome for all condition are close to one (i.e., h>0), we need to distinguish whether a = 1 or not. We add 0 h vector elements of X into both X and Y, resulting in 2n-dimension; X = (X(t0), X(t0)) and Y = (X(t), X(t0)). If X(t) = X(t0), that is, no response, then X :Y~2jX ðt0Þj, jX j~pﬃ2ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ0ﬃÞﬃﬃjﬃ and jY j~pﬃ2ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ0ﬃÞﬃﬃjﬃ, thus, jXðt jX ðt auto-correlation = 1 (h = 0). On the other hand, if X(t) = a X(t0) (a?1 & a.0), auto-correlation, r?1 (i.e. h?0). Biologically, auto- correlation with control profiles will show progressive divergence from 0 h expression for each genotype if dynamic response to LPS exists,. Similarly to auto-correlation, cross-correlation is a temporal Pearson correlation measure. However, instead of measuring between the same genotypes with different time points, cross-correlation measures between different genotypes from wildtype at the same time points. Linear regressions analyses To obtain reliable linear regressions in the analysis of expression change distributions (Figure 8A–C) for the collective mode, we determined the minimal number of ORFs (N) per group, to observe the formation of a Gaussian distribution centering on the average expression change of the group of ORFs in all genotypes. Shapiro-Wilk test was performed to assess the normality of the distribution of expression changes in each group of N (0,N,2000 by step of 10) ORFs, which provides a statistic W coupled with p- value of the data (Higher W and lower p-values for normally distributed data). The average of p-values computed were lower than 0.1 (2-tailed) for groups of about N.300 for MyD88 KO distribution, N.800 for TRIF KO and N.1000 for DKO (Figure S5). We therefore used N = 1000 to determine the linear regressions of expression changes distributions in collective mode. Functional enrichment analyses DAVID functional annotation platform [28] was used to identify the functional categories (Gene Ontology (GO) [38], Panther gene classification [39] or KEGG pathways [40]) enriched in groups of ORFs. Among the 22690 ORFs, 10264 genes with annotations in Gene Ontology, 13824 genes with annotations in Panther gene classification, and 3778 genes with annotations in KEGG pathways were identified. To evaluate functional category enrichment under control of False Discovery Rate, Benjamini- Hochberg adjusted p-values were obtained for each term (GO, Panther, KEGG), and terms scoring p-values,0.05 were retained. Supporting Information Table S1 List of immune-related genes. List of 157 immune- related genes selected from GenMAPP used for analysis. Found at: doi:10.1371/journal.pone.0004905.s001 (0.16 MB DOC) Table S2 Differential activation of groups of ORFs between genotypes. Biological processes (Panther) and pathways (KEGG) enriched (p,0.05, Fisher-exact p-value) in the top 400 ORFs upregulated in each genotype. Found at: doi:10.1371/journal.pone.0004905.s002 (0.12 MB DOC) References Macrophage Genome-Wide Control Figure S1 Temporal Pearson correlation using MAS5 normaliza- tion. A) Auto- and B) cross-correlations for whole genome (22690 ORFs). C) Auto- and D) cross-correlations for immune-related genes. Found at: doi:10.1371/journal.pone.0004905.s003 (0.08 MB DOC) Figure S2 Genome-wide expression changes between time points. Genome-wide expression changes (Dx) between time points, 0–1 h (x-axis) vs. 1–4 h (y-axis) for groups of N ORFs (N = 10, 50, 80, 200) in A) wildtype, B) MyD88 KO, C) TRIF KO, D) and DKO. Group of n ORFs are sorted by their 0–1 h expression change (x-axis). Each point represents the average of Dx for n ORFs. + and - indicate average of expression change of the upregulated and downregulated ORFs in each group, respectively. Found at: doi:10.1371/journal.pone.0004905.s004 (0.08 MB DOC) Figure S3 Grouping of expression forms Gaussian distribution. Density distribution of all group of A) 50, B) 500 and C) 1000 ORFs sorted from highest to lowest for 0–1 h for each genotype. The density distribution of each of these groups in 1–4 h shows Gaussian distribution with decreasing fluctuations when group size increases (lighter color for increasing upregulated groups and darker color for increasing downregulated groups). x-axis represents Dx for 1–4 h and y-axis represents the density of ORFs. Found at: doi:10.1371/journal.pone.0004905.s005 (0.11 MB DOC) Figure S4 Genome-wide expression changes between genotypes. Genome-wide expression changes (Dx) for 0–1 h between genotypes: A) wildtype vs. MyD88 KO, B) wildtype vs. TRIF KO, C) wildtype vs. DKO, D) TRIF KO vs. MyD88 KO, E) TRIF KO vs. DKO, F) MyD88 KO vs. DKO for groups of N ORFs (N = 10, 50, 80, 200). Group of N ORFs are sorted by their 0–1 h expression change (x-axis). Each point represents the average of Dx for N ORFs. + and 2 indicate average of expression change of the upregulated and downregulated ORFs in each group. Found at: doi:10.1371/journal.pone.0004905.s006 (0.10 MB DOC) Figure S5 Test for normality of genome-wide expression changes profiles. Average of p-values obtained from Shapiro-Wilk test for all groups of ORFs of wildtype collective mode in the MyD88 KO, TRIF KO, and DKO expression changes distribu- tion when varying number of genes in the group. Found at: doi:10.1371/journal.pone.0004905.s007 (0.03 MB DOC) Acknowledgments We thank Osamu Takeuchi of Osaka University, Japan and Systems Immunology members (Kentaro Hayashi, Daiki Yamada, Midori Hashi- moto). Our gratitude goes to our family members, Takako, Kimiko, Kazumi Tsuchiya and Kris, Lucas, Davisha Kumar for continuous support throughout the project. Author Contributions Conceived and designed the experiments: MT MT AG KS. Performed the experiments: SA. Analyzed the data: MT VP SC AG KS. Wrote the paper: MT VP SC AG KS. 1. Akira S, Uematsu S, Takeuchi O (2006) Pathogen recognition and innate immunity. Cell 124: 783–801. 2. McKenna K, Beignon AS, Bhardwaj N (2005) Plasmacytoid dendritic cells: linking innate and adaptive immunity. J Virol 79: 17–27. PLoS ONE | www.plosone.org 12 March 2009 | Volume 4 | Issue 3 | e4905