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Results: 14

1.
Figure 5

Figure 5. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Fountain plot comparing DAVID file-based to query-based ID mappings.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
2.
Figure 4

Figure 4. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Fountain plot comparing web interfaces: DAVID_Q vs EnVision_Q.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
3.
Figure 3

Figure 3. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Fountain plot comparing web interfaces: NetAffx_Q vs EnVision_Q.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
4.
Figure 12

Figure 12. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

QQ plot of total spectral counts by protein.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
5.
Figure 6

Figure 6. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Scatterplot, 213503_x_at transcript signals versus Annexin 2 spectral counts, E = endometrioid cancer, S = serous cancer, N = normal.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
6.
Figure 7

Figure 7. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Scatterplot, 1568126 _at transcript signals versus Annexin 2 spectral counts, E = endometrioid cancer, S = serous cancer, N = normal.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
7.
Figure 14

Figure 14. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Standard deviations of correlations. Standard deviations of the correlations between transcript signals and spectral counts, by bootstrap with R = 200, with a nonparametric smooth, compared to the normal theory formula.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
8.
Figure 1

Figure 1. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Distribution of the number of probesets retrieved for each UniProt ACC. Complementary empirical cumulative distribution functions, plotted on a vertical log scale to emphasize differences.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
9.
Figure 13

Figure 13. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

QQ plot for Pearson correlations between spectral counts and mRNA signal,; restricting to 1573 pairs discussed above and shown in Figures 8, 9, 10, and 11.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
10.
Figure 11

Figure 11. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Distributions of the "large correlation component" probability, for each match group. Transformation of Figure 8, replacing the vertical correlation axis by the estimated probability of belonging to the second ("large correlation") component of the mixture shown in green in Figure 10. Horizontal line corresponds to a probability of 50%.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
11.
Figure 9

Figure 9. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Estimated correlation distributions, nonparametric. Nonparametric smooth density estimates for selected ID pair subsets; for example, "DQ" labels the density estimate for the union of these disjoint groups from Figure 8: "D_Q only", "Affx &D_Q", "D_Q&EnV", and "all". The label "union" is all pairs regardless of which mapping resource was the source of the match.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
12.
Figure 8

Figure 8. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Correlation distributions by match group. Correlations between log(mRNA) levels and spectral counts. Box extends to the first and third quartile, with thick horizontal line at the median. The group "all" constitutes matches that all three resources returned (i.e., the intersection); "Affx only" constitutes matches returned only by NetAffx Q; "EnV&D_Q" constitutes matches returned by EnVision_Q and DAVID Q but not by NetAffx_Q; etcetera.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
13.
Figure 2

Figure 2. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Fountain plot comparing retrievals from NetAffx_Q and DAVID_Q. Horizontal axis: For each UniProt ACC, the number of probesets retrieved from NetAffx _Q (left of zero) and DAVID _Q (right of zero). Vertical axis: Each horizontal slice is one ACC (11,879 slices are stacked). ACCs were categorized, and the probeset counts for each ACC graphed by category, sorted within category by intersection size, then by total size. Category definition example: "excess left" is the set of ACCs such that both probeset retrievals were non-empty, and {probesets retrieved by NetAffx _Q} is a strict subset of {probesets retrieved by DAVID_Q}.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.
14.
Figure 10

Figure 10. From: Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

Estimated correlation distributions, mixture model. The correlation distribution smoothed, as a mixture. The black line is the estimated mixture distribution; the red and green are the estimated mixture components. For comparison, the orange line is an nonparametric smooth density estimate. The rug ticks are observed correlations, and the tick colors indicate group assignment by maximum posterior probability. Correlations greater than 0.217 have Pr(mixture component #2) > 0.5, and corresponding tick are colored green for component #2.

Roger S Day, et al. BMC Bioinformatics. 2011;12:213-213.

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