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File Version Author Date Message
Rmd e1a0085 Michael S. Zens 2024-09-09 msz changes for clustering and tanglegrams

Overview: Maternal Tdap+ vs. Tdap-; Clustering features at the month 7 (vaccinated) and month 19 (boosted) visits

Data are log transformed and scaled AFTER removing the variance related to Total IgG for each feature separately by visit.

Missing data are excluded from this analysis.

Hierarchical clustering is based on a similarity matrix based on absolute rank correlation: 1 - abs(cor(num_tdata_scaled))

By Visit - vaccinated : Results

Heatmap clustering

Cluster Arms

[1] “after_cluster_arms”

Fix Arms

[1] “after_fix_arms”

Number of Clusters

  • Among all indices:

  • 6 proposed 2 as the best number of clusters

  • 12 proposed 3 as the best number of clusters

  • 3 proposed 4 as the best number of clusters

  • 1 proposed 5 as the best number of clusters

  • 1 proposed 14 as the best number of clusters

  • 1 proposed 15 as the best number of clusters

                 ***** Conclusion *****

  • According to the majority rule, the best number of clusters is 3

Dendrogram

Whole feature set clustering: basic clusters and tree branch boot strap tests

[1] “after_dendrogram”

Hierarchical Clusters

The number of clusters shown is optimized using the preponderance of indicies for this purpose.

[1] “after_dendlistnew”

Tree Branch Tests

References

Suzuki, R. and Shimodaira, H. (2006) Pvclust: an R package for assessing the uncertainty in hierarchical clustering, Bioinformatics, 22 (12): 1540-1542.

Shimodaira, H. (2004) Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling, Annals of Statistics, 32, 2616-2641.

Shimodaira, H. (2002) An approximately unbiased test of phylogenetic tree selection, Systematic Biology, 51, 492-508.

Suzuki, R. and Shimodaira, H. (2004) An application of multiscale bootstrap resampling to hierarchical clustering of microarray data: How accurate are these clusters?, The Fifteenth International Conference on Genome Informatics 2004, P034.

UMAP Concordance

Correlation Heatmap: all features

These heatmaps are based on Spearman Rank Correlation.

Correlation Network: all features

The correlation networks are based on Partial Correlation. This means that all the correlation between pairs of features after all the other correlation is accounted for. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight

Cluster Contents

name contents
Cluster_1 DT_ADCD, DT_FcgR2a, DT_FcgR3b, DT_IgG1, DT_IgG3, FHA_ADCD, FHA_ADNP, FHA_FcgR2a, FHA_FcgR3b, FHA_IgG1, FHA_IgG3, PRN_ADCD, PRN_FcgR2a, PRN_FcgR3b, PRN_IgG1, PRN_IgG3, PT_ADCD, PT_ADCP, PT_ADNP, PT_FcgR2a, PT_FcgR3b, PT_IgG1, PT_IgG3, TT_ADCD, TT_ADCP, TT_ADNP, TT_FcgR2a, TT_FcgR3b, TT_IgG1, TT_IgG3
Cluster_2 DT_ADCP, FHA_ADCP, PRN_ADCP
Cluster_3 DT_ADNP, PT_IgG2
Cluster_4 DT_IgG, FHA_IgG, PRN_IgG, PRN_IgG4, TT_IgG
Cluster_5 DT_IgG2, DT_IgG4, FHA_IgG2, FHA_IgG4, PT_IgG4, TT_IgG2, TT_IgG4
Cluster_6 PRN_ADNP
Cluster_7 PRN_IgG2, PT_IgG
Cluster 1

[1] “DT_ADCD” “DT_FcgR2a” “DT_FcgR3b” “DT_IgG1” “DT_IgG3”
[6] “FHA_ADCD” “FHA_ADNP” “FHA_FcgR2a” “FHA_FcgR3b” “FHA_IgG1”
[11] “FHA_IgG3” “PRN_ADCD” “PRN_FcgR2a” “PRN_FcgR3b” “PRN_IgG1”
[16] “PRN_IgG3” “PT_ADCD” “PT_ADCP” “PT_ADNP” “PT_FcgR2a” [21] “PT_FcgR3b” “PT_IgG1” “PT_IgG3” “TT_ADCD” “TT_ADCP”
[26] “TT_ADNP” “TT_FcgR2a” “TT_FcgR3b” “TT_IgG1” “TT_IgG3”

Correlation Heatmap 1

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 1

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 2 — 3 0.38 3 — 4 0.08 3 — 5 0.09 1 — 6 0.06 2 — 8 0.08 3 — 8 0.02 6 — 8 0.04 7 — 8 0.07 1 — 9 -0.02 3 — 9 0.06 6 — 9 0.07 7 — 9 0.1 8 — 9 0.47 6 — 10 0.13 7 — 10 0.19 9 — 10 0.05 5 — 11 0.16 6 — 11 0.03 7 — 11 0.05 9 — 11 0.07 10 — 11 0.07 1 — 12 0.58 6 — 12 0.09 9 — 12 -0.01 2 — 13 0.03 3 — 13 0.04 8 — 13 0.06 9 — 13 0.07 10 — 13 0.03 3 — 14 0.05 5 — 14 0.01 9 — 14 0.09 13 — 14 0.43 4 — 15 0.2 7 — 15 0.08 10 — 15 0.14 5 — 16 0.12 7 — 16 0.03 8 — 16 0.02 9 — 16 0.04 11 — 16 0.18 15 — 16 0.05 6 — 17 0.07 3 — 18 0.01 7 — 18 0.01 8 — 18 0.03 11 — 18 0.03 16 — 18 0.05 17 — 18 0.2 5 — 19 0.04 11 — 19 0.24 14 — 19 0.06 16 — 19 0.06 17 — 19 0.12 18 — 19 0.01 2 — 20 0 8 — 20 0.01 14 — 20 0.04 15 — 20 0.06 17 — 20 0.13 18 — 20 0.02 3 — 21 0.05 5 — 21 0.01 9 — 21 0.06 11 — 21 0.01 13 — 21 0.03 14 — 21 0.06 17 — 21 0.18 18 — 21 0.21 19 — 21 0.02 20 — 21 0.56 4 — 22 0.04 10 — 22 0.23 15 — 22 0.08 16 — 22 0.02 21 — 22 0.07 1 — 23 -0.01 5 — 23 0.22 8 — 23 0.06 9 — 23 0.04 11 — 23 0.07 16 — 23 0.08 17 — 23 0.05 18 — 23 0.12 19 — 23 0.14 21 — 23 0.03 1 — 24 0.24 3 — 24 0.01 6 — 24 0.29 11 — 24 0.02 12 — 24 0.23 13 — 24 0.06 14 — 24 0 17 — 24 0.23 22 — 24 0.11 2 — 25 0.02 3 — 25 0.01 8 — 25 0 13 — 25 0.06 14 — 25 0 15 — 25 0.05 19 — 25 -0.01 22 — 25 0.04 24 — 25 0.06 7 — 26 0.01 13 — 26 0.09 14 — 26 0.08 19 — 26 0.16 25 — 26 0.15 2 — 27 0.21 8 — 27 0.12 13 — 27 0.18 20 — 27 0.14 24 — 27 0.08 25 — 27 0.07 26 — 27 0.01 2 — 28 0.02 3 — 28 0.27 9 — 28 0.1 13 — 28 0.08 14 — 28 0.11 21 — 28 0.06 25 — 28 0.01 26 — 28 0.02 27 — 28 0.27 4 — 29 0.34 15 — 29 0.33 21 — 29 0.03 22 — 29 0.12 25 — 29 0.23 5 — 30 0.27 8 — 30 0 9 — 30 0.03 15 — 30 0.02 16 — 30 0.25 19 — 30 0.09 21 — 30 0 24 — 30 0.03 26 — 30 0.02 28 — 30 0.03 29 — 30 0.05

Heatmap Subjects Clustered 1

Heatmap Arm Assigned 1

Cluster 2

[1] “DT_ADCP” “FHA_ADCP” “PRN_ADCP”

Correlation Heatmap 2

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 2

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 1 — 3 0.22 2 — 3 0.38

Heatmap Subjects Clustered 2

Heatmap Arm Assigned 2

Cluster 3

[1] “DT_ADNP” “PT_IgG2”

Correlation Heatmap 3

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 3

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 1 — 2 0.21

Heatmap Subjects Clustered 3

Heatmap Arm Assigned 3

Cluster 4

[1] “DT_IgG” “FHA_IgG” “PRN_IgG” “PRN_IgG4” “TT_IgG”

Correlation Heatmap 4

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 4

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 1 — 2 0.36 1 — 3 0.23 2 — 3 0.23 2 — 4 -0.11 3 — 4 -0.28 1 — 5 0.36 3 — 5 0.2 4 — 5 -0.08

Heatmap Subjects Clustered 4

Heatmap Arm Assigned 4

Cluster 5

[1] “DT_IgG2” “DT_IgG4” “FHA_IgG2” “FHA_IgG4” “PT_IgG4” “TT_IgG2” “TT_IgG4”

Correlation Heatmap 5

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 5

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 1 — 2 0.28 1 — 3 0.26 2 — 3 0.08 2 — 4 0.43 3 — 4 0.2 2 — 5 0.17 4 — 5 0.21 1 — 6 0.35 1 — 7 -0.11 2 — 7 0.08 4 — 7 0.11 5 — 7 0.18 6 — 7 0.33

Heatmap Subjects Clustered 5

Heatmap Arm Assigned 5

Cluster 6
Correlation Heatmap 6

This cluster is represented by only a single feature: DT_IgG2 This cluster is represented by only a single feature: DT_IgG4 This cluster is represented by only a single feature: FHA_IgG2 This cluster is represented by only a single feature: FHA_IgG4 This cluster is represented by only a single feature: PT_IgG4 This cluster is represented by only a single feature: TT_IgG2 This cluster is represented by only a single feature: TT_IgG4

Cluster 7

[1] “PRN_IgG2” “PT_IgG”

Correlation Heatmap 7

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 7

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 1 — 2 -0.26

Heatmap Subjects Clustered 7

Heatmap Arm Assigned 7

By Visit - boosted : Results

Heatmap clustering

Cluster Arms

[1] “after_cluster_arms”

Fix Arms

[1] “after_fix_arms”

Number of Clusters

  • Among all indices:

  • 11 proposed 2 as the best number of clusters

  • 6 proposed 3 as the best number of clusters

  • 2 proposed 4 as the best number of clusters

  • 1 proposed 5 as the best number of clusters

  • 1 proposed 10 as the best number of clusters

  • 2 proposed 14 as the best number of clusters

  • 1 proposed 15 as the best number of clusters

                 ***** Conclusion *****

  • According to the majority rule, the best number of clusters is 2

Dendrogram

Whole feature set clustering: basic clusters and tree branch boot strap tests

[1] “after_dendrogram”

Hierarchical Clusters

The number of clusters shown is optimized using the preponderance of indicies for this purpose.

[1] “after_dendlistnew”

Tree Branch Tests

References

Suzuki, R. and Shimodaira, H. (2006) Pvclust: an R package for assessing the uncertainty in hierarchical clustering, Bioinformatics, 22 (12): 1540-1542.

Shimodaira, H. (2004) Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling, Annals of Statistics, 32, 2616-2641.

Shimodaira, H. (2002) An approximately unbiased test of phylogenetic tree selection, Systematic Biology, 51, 492-508.

Suzuki, R. and Shimodaira, H. (2004) An application of multiscale bootstrap resampling to hierarchical clustering of microarray data: How accurate are these clusters?, The Fifteenth International Conference on Genome Informatics 2004, P034.

UMAP Concordance

Correlation Heatmap: all features

These heatmaps are based on Spearman Rank Correlation.

Correlation Network: all features

The correlation networks are based on Partial Correlation. This means that all the correlation between pairs of features after all the other correlation is accounted for. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 1 — 11 0.13 14 — 15 0.32 1 — 21 0.02 11 — 21 0.05 12 — 22 0.03 24 — 25 0.26 27 — 37 0.12 1 — 41 0.24 11 — 41 0.11 21 — 41 0.14 4 — 44 0.03 41 — 44 0 5 — 45 0.03 35 — 45 0.13 6 — 46 0.09 16 — 46 0.06 26 — 46 0.05 7 — 47 0.1

Cluster Contents

name contents
Cluster_1 DT_ADCD, DT_FcgR2a, FHA_ADCD, PRN_ADCD, PT_ADCD, PT_FcgR2a, TT_ADCD, TT_ADCP, TT_FcgR2a, TT_IgG2
Cluster_2 DT_ADCP, DT_ADNP, FHA_ADNP, PRN_ADNP, PT_ADNP, TT_ADNP
Cluster_3 DT_FcgR3b, FHA_ADCP, FHA_FcgR2a, FHA_FcgR3b, PRN_ADCP, PRN_FcgR2a, PRN_FcgR3b, PT_ADCP, PT_FcgR3b, TT_FcgR3b
Cluster_4 DT_IgG, FHA_IgG, PRN_IgG, PT_IgG, TT_IgG
Cluster_5 DT_IgG1, FHA_IgG1, PRN_IgG1, PT_IgG1, TT_IgG1
Cluster_6 DT_IgG2, DT_IgG4, FHA_IgG2, FHA_IgG4, PRN_IgG2, PRN_IgG4, PT_IgG2, PT_IgG4, TT_IgG4
Cluster_7 DT_IgG3, FHA_IgG3, PRN_IgG3, PT_IgG3, TT_IgG3
Cluster 1

[1] “DT_ADCD” “DT_FcgR2a” “FHA_ADCD” “PRN_ADCD” “PT_ADCD” “PT_FcgR2a” [7] “TT_ADCD” “TT_ADCP” “TT_FcgR2a” “TT_IgG2”

Correlation Heatmap 1

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 1

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 1 — 3 0.31 1 — 4 0.03 2 — 4 0.2 3 — 4 0.22 1 — 5 0.07 2 — 5 0.03 3 — 5 0.11 4 — 5 0.01 2 — 6 0.31 3 — 6 0 5 — 6 0.16 1 — 7 0.5 3 — 7 0.15 4 — 7 0.3 5 — 7 0.17 1 — 8 -0.02 4 — 8 -0.01 2 — 9 0.36 6 — 9 0.09 7 — 9 0.23 4 — 10 -0.22 8 — 10 0.06

Heatmap Subjects Clustered 1

Heatmap Arm Assigned 1

Cluster 2

[1] “DT_ADCP” “DT_ADNP” “FHA_ADNP” “PRN_ADNP” “PT_ADNP” “TT_ADNP”

Correlation Heatmap 2

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 2

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight

Heatmap Subjects Clustered 2

Heatmap Arm Assigned 2

Cluster 3

[1] “DT_FcgR3b” “FHA_ADCP” “FHA_FcgR2a” “FHA_FcgR3b” “PRN_ADCP”
[6] “PRN_FcgR2a” “PRN_FcgR3b” “PT_ADCP” “PT_FcgR3b” “TT_FcgR3b”

Correlation Heatmap 3

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 3

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 2 — 3 0.06 1 — 4 0.02 2 — 4 0.13 3 — 4 0.78 2 — 5 0.47 4 — 5 0 1 — 6 0.08 3 — 6 0.05 4 — 6 0.03 1 — 7 0.15 3 — 7 0.03 5 — 7 0.07 6 — 7 0.68 1 — 8 0.05 2 — 8 0.27 3 — 8 0.01 4 — 8 0.05 5 — 8 0.01 6 — 8 0.17 1 — 9 0.09 3 — 9 0.01 4 — 9 0.05 8 — 9 0.02 1 — 10 0.31 7 — 10 0.11 9 — 10 0.52

Heatmap Subjects Clustered 3

Heatmap Arm Assigned 3

Cluster 4

[1] “DT_IgG” “FHA_IgG” “PRN_IgG” “PT_IgG” “TT_IgG”

Correlation Heatmap 4

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 4

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 1 — 2 0.16 1 — 3 0.2 2 — 3 0.25 2 — 4 0.28 1 — 5 0.39 2 — 5 0.22 3 — 5 0.28 4 — 5 0.28

Heatmap Subjects Clustered 4

Heatmap Arm Assigned 4

Cluster 5

[1] “DT_IgG1” “FHA_IgG1” “PRN_IgG1” “PT_IgG1” “TT_IgG1”

Correlation Heatmap 5

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 5

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight 1 — 2 0.1 1 — 3 0.21 2 — 3 0.04 2 — 4 0.35 3 — 4 0.54 1 — 5 0.52 2 — 5 0.1 3 — 5 0.14 4 — 5 0.07

Heatmap Subjects Clustered 5

Heatmap Arm Assigned 5

Cluster 6

[1] “DT_IgG2” “DT_IgG4” “FHA_IgG2” “FHA_IgG4” “PRN_IgG2” “PRN_IgG4” “PT_IgG2” [8] “PT_IgG4” “TT_IgG4”

Correlation Heatmap 6

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 6

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight

Heatmap Subjects Clustered 6

Heatmap Arm Assigned 6

Cluster 7

[1] “DT_IgG3” “FHA_IgG3” “PRN_IgG3” “PT_IgG3” “TT_IgG3”

Correlation Heatmap 7

These heatmaps are based on Spearman Rank Correlation.

[1] “after cluster specific correlation heatmap”

[1] “after cluster specific correlation scatter plot grid”

Correlation Network 7

The correlation networks are based on optimal sparse estimates of the partial correlation matrix (Friedman, Hastie and Tibshirani, 2011). Partial correlations represent the remaining association (-1,1) between two nodes after controlling for all other node associations (Epskamp, S. & Fried, Eiko I. (2018)). The lines between nodes are labelled with the partial correlation coefficient. Sometimes there are sparse networks when no significant partial correlation left to plot. Sometimes there are too few samples for the number of features and the correlation matrix becomes indefinite. In either of these circumstances no network plot will be made.

From To Weight

Heatmap Subjects Clustered 7

Heatmap Arm Assigned 7

Compare dendrograms: By Visit

$dend1 ‘dendrogram’ with 2 branches and 50 members total, at height 0.9999847

$dend2 ‘dendrogram’ with 2 branches and 50 members total, at height 0.9996386

attr(,“class”) [1] “dendlist”

CRI: 0.133

MASTxCF: 0.041

RFS: 0.577

CRI (Clade Retention Index): The CRI quantifies agreement between two dendograms by counting the number of shared nodes and measures disagreement by counting the number of leaves that originate from an unshared node. The difference between these two counts is calculated and scaled relative to the maximum possible agreement.

MASTxCF (Maximum Agreement Subtree x Consensus Fork): MASTxCF begins by constructing the largest subtree that is fully agreed upon by two dendrograms, known as the Maximum Agreement Subtree (MAST). It then calculates the proportion of nodes from the original dendrogram that are included in this subtree. This value is multiplied by the proportion of nodes shared between the two trees, as measured by Colless’s Consensus Fork (CF).

RFS (Robinson-Foulds Similarity): RFS first calculates the Robinson-Foulds (RF) distance between two dendograms by counting the number of branch modifications (removals and additions) required to transform one tree into the other. This distance is scaled relative to the maximum possible RF distance for the given trees, then inverted so that a higher value indicates greater similarity between the dendrograms.All metrics quantify tree similarity on a scale from 0 to 1, where 1 corresponds to two trees being identical in structure.

Vidovic, S. U. (2019). Tree congruence: quantifying similarity between dendrogram topologies. ePrints|Soton. https://doi.org/10.5258/SOTON/D1069


sessionInfo()
R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] broom_1.0.5        e1071_1.7-13       glue_1.6.2         RPostgres_1.4.5   
 [5] RColorBrewer_1.1-3 phangorn_2.11.1    TreeDist_2.9.0     ape_5.8           
 [9] gt_0.10.1          ggforce_0.4.2      ggrepel_0.9.3      umap_0.2.10.0     
[13] ggfortify_0.4.16   ppcor_1.1          MASS_7.3-60.0.1    phdcocktail_0.1.0 
[17] gtsummary_1.7.2    ggpubr_0.6.0       rstatix_0.7.2      mvtnorm_1.2-3     
[21] GGally_2.2.1       pvclust_2.2-0      rcompanion_2.4.34  polycor_0.8-1     
[25] boot_1.3-28.1      qgraph_1.9.8       janitor_2.2.0      dendextend_1.17.1 
[29] pheatmap_1.0.12    skimr_2.1.5        kableExtra_1.3.4   rmarkdown_2.25    
[33] papeR_1.0-5        xtable_1.8-4       car_3.1-2          carData_3.0-5     
[37] mclust_6.0.1       kohonen_3.0.12     clValid_0.7        cluster_2.1.6     
[41] NbClust_3.0.1      data.table_1.14.8  DT_0.28            DBI_1.1.3         
[45] fpc_2.2-10         extrafont_0.19     lubridate_1.9.2    forcats_1.0.0     
[49] stringr_1.5.0      dplyr_1.1.2        purrr_1.0.1        readr_2.1.4       
[53] tidyr_1.3.0        tibble_3.2.1       ggplot2_3.4.4      tidyverse_2.0.0   
[57] here_1.0.1        

loaded via a namespace (and not attached):
  [1] bitops_1.0-7         fs_1.6.3             matrixStats_1.0.0   
  [4] httr_1.4.7           webshot_0.5.5        prabclus_2.3-3      
  [7] repr_1.1.6           tools_4.3.1          backports_1.4.1     
 [10] utf8_1.2.3           R6_2.5.1             nortest_1.0-4       
 [13] withr_3.0.0          gridExtra_2.3        fdrtool_1.2.17      
 [16] cli_3.6.1            shinyjs_2.1.0        sandwich_3.1-0      
 [19] labeling_0.4.3       sass_0.4.9           diptest_0.77-0      
 [22] robustbase_0.99-1    proxy_0.4-27         pbapply_1.7-2       
 [25] askpass_1.2.0        pbivnorm_0.6.0       systemfonts_1.0.5   
 [28] foreign_0.8-84       R.utils_2.12.2       svglite_2.1.2       
 [31] readxl_1.4.3         rstudioapi_0.15.0    generics_0.1.3      
 [34] gtools_3.9.4         Matrix_1.6-1.1       fansi_1.0.4         
 [37] DescTools_0.99.52    abind_1.4-5          R.methodsS3_1.8.2   
 [40] lifecycle_1.0.4      whisker_0.4.1        multcomp_1.4-25     
 [43] yaml_2.3.7           snakecase_0.11.1     blob_1.2.4          
 [46] grid_4.3.1           lavaan_0.6-16        PlotTools_0.3.1     
 [49] promises_1.2.0.1     gdata_2.19.0         lattice_0.21-8      
 [52] pillar_1.9.0         knitr_1.47           gld_2.6.6           
 [55] corpcor_1.6.10       admisc_0.33          codetools_0.2-19    
 [58] fastmatch_1.1-4      V8_4.4.1             broom.helpers_1.14.0
 [61] Rdpack_2.6.1         vctrs_0.6.3          png_0.1-8           
 [64] cellranger_1.1.0     gtable_0.3.4         kernlab_0.9-32      
 [67] cachem_1.0.8         xfun_0.45            mime_0.12           
 [70] rbibutils_2.2.16     libcoin_1.0-10       survival_3.5-5      
 [73] ellipsis_0.3.2       TH.data_1.1-2        nlme_3.1-164        
 [76] bit64_4.0.5          gmodels_2.18.1.1     rprojroot_2.0.4     
 [79] R.cache_0.16.0       bslib_0.7.0          TreeTools_1.12.0    
 [82] rpart_4.1.19         colorspace_2.1-0     Hmisc_5.1-1         
 [85] nnet_7.3-19          Exact_3.2            mnormt_2.1.1        
 [88] tidyselect_1.2.0     curl_5.1.0           bit_4.0.5           
 [91] compiler_4.3.1       extrafontdb_1.0      git2r_0.32.0        
 [94] rvest_1.0.3          htmlTable_2.4.1      expm_0.999-7        
 [97] xml2_1.3.5           checkmate_2.2.0      scales_1.3.0        
[100] DEoptimR_1.1-3       psych_2.3.9          lmtest_0.9-40       
[103] quadprog_1.5-8       multcompView_0.1-9   digest_0.6.33       
[106] htmltools_0.5.8      pkgconfig_2.0.3      jpeg_0.1-10         
[109] base64enc_0.1-3      highr_0.11           fastmap_1.1.1       
[112] rlang_1.1.1          htmlwidgets_1.6.2    shiny_1.7.4.1       
[115] farver_2.1.1         jquerylib_0.1.4      zoo_1.8-12          
[118] jsonlite_1.8.7       R.oo_1.25.0          RCurl_1.98-1.13     
[121] magrittr_2.0.3       modeltools_0.2-23    Formula_1.2-5       
[124] munsell_0.5.0        Rcpp_1.0.11          viridis_0.6.4       
[127] reticulate_1.37.0    stringi_1.7.12       rootSolve_1.8.2.4   
[130] plyr_1.8.9           flexmix_2.3-19       ggstats_0.5.1       
[133] parallel_4.3.1       lmom_3.0             splines_4.3.1       
[136] hms_1.1.3            igraph_1.5.1         ggsignif_0.6.4      
[139] reshape2_1.4.4       stats4_4.3.1         evaluate_0.23       
[142] tzdb_0.4.0           tweenr_2.0.3         httpuv_1.6.11       
[145] Rttf2pt1_1.3.12      openssl_2.1.1        polyclip_1.10-6     
[148] coin_1.4-3           RSpectra_0.16-1      later_1.3.1         
[151] glasso_1.11          viridisLite_0.4.2    class_7.3-22        
[154] memoise_2.0.1        workflowr_1.7.1      timechange_0.2.0    
[157] concaveman_1.1.0