| Characteristics of host proteins interacting with known B. mal/ei virulence factors | Table 2 shows that these virulence factors interacted with a statistically significant number of human proteins that were associated with 1) protein ubiquitination and ubiquitin ligase activity, 2) vesicle organization, and 3) protein complexes located in the cytoskeleton, in lysosomes, and in the nuclear lumen. |
| Characteristics of host proteins interacting with known B. mal/ei virulence factors | The other 11 connected components consisted of five or fewer proteins, an observation that was not statistically significant from a random selection of proteins (data not shown). |
| Gene set functional enrichment analyses | We retained only annotations that were enriched at an FDR control level of 0.05, i.e., there is a less than 5% chance that the obtained p-values are not statistically significant . |
| Gene set functional enrichment analyses | We evaluated whether the observed IMt interaction modules were statistically significant as follows. |
| Human-B. mal/ei interactions and their effect on the crosstalk between different biological processes | The largest statistically significant interaction module, represented by red stars, contained proteins previously identified as bacterial targets vital for host actin cytoskeleton rearrangement, e.g., membrane-associated small GTPases (CDC42 and RALA), Filamin-A (FILA), and Rho GDP-dissociation inhibitor (ARHGDIB) [2, 33]. |
| Introduction | Using the HPIA algorithm, we identified a statistically significant number of functionally similar host-pathogen interactions between these three PPI datasets. |
| Putative B. mallei virulence factors improve characterization of B. mallei targets | We identified 75 statistically significant interaction modules whose GO biological process annotations largely overlapped with the ones identified for interacting partners of known virulence factors only. |
| Putative B. mallei virulence factors improve characterization of B. mallei targets | Although the number of statistically significant interaction modules was smaller than above (an increase in the number of host proteins dilutes the enrichment), the addition of new host proteins increased the size (in terms of proteins and interactions) of previously identified interaction modules (S4 Table). |
| Putative B. mallei virulence factors improve characterization of B. mallei targets | We identified two statistically significantly enriched host pathways: bacterial invasion of epithelial cells and focal adhesion. |
| Summary | pestis and human-S. enterica and identified a statistically significant number of aligned interactions. |
| Topological properties of human proteins interacting with B. mal/ei in the human PPI network | We evaluated whether the observed values for each of the five properties were statistically significant as follows. |
| Acknowledgments | We would like to thank Matthew Stephens for advice about evaluating statistical significance and multiple hypothesis correction. |
| Author Summary | In this paper, we improve on a method used to identify cycling time series by better estimating the statistical significance of periodic patterns and, in turn, by searching for a Wider range of patterns than traditionally investigated. |
| Overview | In this case, each time point is a different group, and ANOVA is equivalent to testing for any statistically significant variation across the time points. |
| Overview | Because eXpression measurements are averages over many cells and different time points come from different samples (as the measurement is destructive), only synchronized, consistent variation across all samples can generate a statistically significant trend. |
| ANG-2 and PIGF survival analysis on breast cancer patients | Interestingly, we observed that the same analysis repeated for patients with high and low levels of ANG-2 and CD14 or PIGF and CD14 (and not for the remaining gene) resulted on p-values not statistically significant (0.0587 and 0.521 respectively, Fig. |
| Abstract | Finally, the re-lapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. |
| Statistical analysis and data treatments | A p value < 0.05 was considered statistically significant . |
| Discussion | The power of a statistical test generally depends on three factors: first, the sample size; second, statistical significance as measured by the threshold p-value used to assess significance; and third, the effect size, which quantifies departures from the null hypothesis. |
| Power calculation for fixed non-neutral model parameters | For the LOGS metacommunity, and when the local dynamics are strictly neutral (7/ = 0 for model HL or c = 0 for model PC), the models are equivalent to the SNM, and the power is equal to the threshold p-value for statistical significance (0.05 in our study). |
| Testing the neutral null model | The neutral model is rejected if the p-value is less than the chosen threshold for statistical significance , which we take to be 0.05. |
| Quantifying accuracy and detecting functional connections | We then consider the effect of the presynaptic input to be statistically significant when the ZZ-test gives p < 0.05. |
| Quantifying accuracy and detecting functional connections | In evaluating statistical significance , we limit our analysis to the pairwise models and use the un-cross-validated likelihood-ratio test. |
| input experiments. | Comparison of the distributions obtained using the observed vs shuffled spike trains allows us to test whether an input has a statistically significant effect on the firing of the postsynaptic neuron (see Methods). |