Clustering features by arm - IgG Standardized - AP vs WP only
Excluding total IgG - 6 response clusters
Lisa Gallagher, David Kook, Scot Zens
2024-09-12
Last updated: 2024-09-12
Checks: 6 1
Knit directory:
madi-project3-sdy8003-pertussis-thailand/
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Overview: AP vs. WP - 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))
vaccinated : Cluster analysis and contents
Heatmap clustering maternal Tdap - infant ap
Cluster Arms
[1] “character levels for anno_colors: vaccinated”
Fix Arms
[1] “character levels for anno_colors: vaccinated”
Dendrogram maternal Tdap - infant ap
Whole feature set clustering: basic clusters and tree branch boot strap tests
Hierarchical Clusters
The number of clusters shown is optimized using the preponderance of indicies for this purpose.
The number of clusters shown is optimized using the preponderance of indicies for this purpose.
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.
Cluster Contents maternal Tdap - infant ap
| name | contents |
|---|---|
| Cluster_1 | DT_ADCD, DT_ADCP, DT_ADNP, DT_FcgR2a, DT_FcgR3b, DT_IgG1 |
| Cluster_2 | DT_IgG2, DT_IgG3, DT_IgG4, FHA_ADCD, FHA_ADCP, FHA_ADNP, FHA_FcgR2a, FHA_FcgR3b, FHA_IgG1, FHA_IgG2, FHA_IgG3, FHA_IgG4, PRN_ADCD, PRN_ADCP, PRN_ADNP, PRN_FcgR2a, PRN_FcgR3b, PRN_IgG1, PRN_IgG2 |
| Cluster_3 | PRN_IgG3, PRN_IgG4, PT_ADCD, PT_ADCP, PT_ADNP, PT_FcgR2a |
| Cluster_4 | PT_FcgR3b, PT_IgG1, PT_IgG2, PT_IgG3, PT_IgG4, TT_ADCD |
| Cluster_5 | TT_ADCP, TT_ADNP |
| Cluster_6 | TT_FcgR2a, TT_FcgR3b, TT_IgG1, TT_IgG2, TT_IgG3, TT_IgG4 |
Cluster 1
Correlation Heatmap 1
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 — 2 -0.13 4 — 5 0.41
Heatmap Subjects Clustered 1
Heatmap Arm Assigned 1
Cluster 2
Correlation Heatmap 2
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 7 — 8 0.28 4 — 13 0.1 16 — 17 0.23
Heatmap Subjects Clustered 2
Heatmap Arm Assigned 2
Cluster 3
Correlation Heatmap 3
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 — 3 0.2
Heatmap Subjects Clustered 3
Heatmap Arm Assigned 3
Cluster 4
Correlation Heatmap 4
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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
Heatmap Subjects Clustered 4
Heatmap Arm Assigned 4
Cluster 5
Correlation Heatmap 5
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 0.54
Heatmap Subjects Clustered 5
Heatmap Arm Assigned 5
Cluster 6
Correlation Heatmap 6
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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
Heatmap clustering maternal Tdap - infant wp
Cluster Arms
[1] “character levels for anno_colors: vaccinated”
Fix Arms
[1] “character levels for anno_colors: vaccinated”
Dendrogram maternal Tdap - infant wp
Whole feature set clustering: basic clusters and tree branch boot strap tests
Hierarchical Clusters
The number of clusters shown is optimized using the preponderance of indicies for this purpose.
The number of clusters shown is optimized using the preponderance of indicies for this purpose.
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.
Cluster Contents maternal Tdap - infant wp
| name | contents |
|---|---|
| Cluster_1 | DT_ADCD, DT_ADCP, DT_ADNP, DT_FcgR2a |
| Cluster_2 | DT_FcgR3b, DT_IgG1, DT_IgG2, DT_IgG3, DT_IgG4, FHA_ADCD, FHA_ADCP, FHA_ADNP |
| Cluster_3 | FHA_FcgR2a, FHA_FcgR3b, FHA_IgG1, FHA_IgG2, FHA_IgG3, FHA_IgG4, PRN_ADCD, PRN_ADCP |
| Cluster_4 | PRN_ADNP, PRN_FcgR2a, PRN_FcgR3b, PRN_IgG1, PRN_IgG2, PRN_IgG3, PRN_IgG4, PT_ADCD |
| Cluster_5 | PT_ADCP, PT_ADNP, PT_FcgR2a, PT_FcgR3b, PT_IgG1 |
| Cluster_6 | PT_IgG2, PT_IgG3, PT_IgG4, TT_ADCD, TT_ADCP, TT_ADNP, TT_FcgR2a, TT_FcgR3b, TT_IgG1, TT_IgG2, TT_IgG3, TT_IgG4 |
Cluster 1
Correlation Heatmap 1
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 0.13 1 — 4 0.66 3 — 4 0.18
Heatmap Subjects Clustered 1
Heatmap Arm Assigned 1
Cluster 2
Correlation Heatmap 2
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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
Correlation Heatmap 3
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 0.59 1 — 3 0.04 2 — 3 0.24 2 — 5 0.09 4 — 6 0.16
Heatmap Subjects Clustered 3
Heatmap Arm Assigned 3
Cluster 4
Correlation Heatmap 4
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 2 — 3 0.42 2 — 8 0.15 3 — 8 0.08
Heatmap Subjects Clustered 4
Heatmap Arm Assigned 4
Cluster 5
Correlation Heatmap 5
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 0.06 1 — 4 0.27 2 — 4 0.28 3 — 4 0.8 1 — 5 0.06 2 — 5
-0.08 3 — 5 0.15 4 — 5 0.13
Heatmap Subjects Clustered 5
Heatmap Arm Assigned 5
Cluster 6
Correlation Heatmap 6
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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
Arm Matching: Tanglegram
$dend1
‘dendrogram’ with 2 branches and 45 members total, at height
0.9995092
$dend2 ‘dendrogram’ with 2 branches and 45 members total, at height 0.9995493
attr(,“class”) [1] “dendlist”
CRI: 0.148
MASTxCF: 0.034
RFS: 0.563
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
boosted : Cluster analysis and contents
Heatmap clustering maternal Tdap - infant ap
Cluster Arms
[1] “character levels for anno_colors: boosted”
Fix Arms
[1] “character levels for anno_colors: boosted”
Dendrogram maternal Tdap - infant ap
Whole feature set clustering: basic clusters and tree branch boot strap tests
Hierarchical Clusters
The number of clusters shown is optimized using the preponderance of indicies for this purpose.
The number of clusters shown is optimized using the preponderance of indicies for this purpose.
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.
Cluster Contents maternal Tdap - infant ap
| name | contents |
|---|---|
| Cluster_1 | DT_ADCD, DT_ADCP, DT_ADNP, DT_FcgR2a, DT_FcgR3b, DT_IgG1 |
| Cluster_2 | DT_IgG2, DT_IgG3, DT_IgG4, FHA_ADCD, FHA_ADCP, FHA_ADNP, FHA_FcgR2a, FHA_FcgR3b, FHA_IgG1, FHA_IgG2, FHA_IgG3, FHA_IgG4, PRN_ADCD, PRN_ADCP, PRN_ADNP, PRN_FcgR2a |
| Cluster_3 | PRN_FcgR3b, PRN_IgG1, PRN_IgG2, PRN_IgG3, PRN_IgG4, PT_ADCD, PT_ADCP |
| Cluster_4 | PT_ADNP, PT_FcgR2a, PT_FcgR3b, PT_IgG1 |
| Cluster_5 | PT_IgG2, PT_IgG3, PT_IgG4, TT_ADCD, TT_ADCP |
| Cluster_6 | TT_ADNP, TT_FcgR2a, TT_FcgR3b, TT_IgG1, TT_IgG2, TT_IgG3, TT_IgG4 |
Cluster 1
Correlation Heatmap 1
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 0.18 1 — 4 0.14 4 — 5 0.41 5 — 6 0.3
Heatmap Subjects Clustered 1
Heatmap Arm Assigned 1
Cluster 2
Correlation Heatmap 2
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 7 — 8 0.13 4 — 13 0.32 7 — 16 0.15
Heatmap Subjects Clustered 2
Heatmap Arm Assigned 2
Cluster 3
Correlation Heatmap 3
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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
Heatmap Subjects Clustered 3
Heatmap Arm Assigned 3
Cluster 4
Correlation Heatmap 4
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 — 3 0.15 2 — 3 0.37 3 — 4 0.26
Heatmap Subjects Clustered 4
Heatmap Arm Assigned 4
Cluster 5
Correlation Heatmap 5
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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
Heatmap Subjects Clustered 5
Heatmap Arm Assigned 5
Cluster 6
Correlation Heatmap 6
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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
Heatmap clustering maternal Tdap - infant wp
Cluster Arms
[1] “character levels for anno_colors: boosted”
Fix Arms
[1] “character levels for anno_colors: boosted”
Dendrogram maternal Tdap - infant wp
Whole feature set clustering: basic clusters and tree branch boot strap tests
Hierarchical Clusters
The number of clusters shown is optimized using the preponderance of indicies for this purpose.
The number of clusters shown is optimized using the preponderance of indicies for this purpose.
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.
Cluster Contents maternal Tdap - infant wp
| name | contents |
|---|---|
| Cluster_1 | DT_ADCD, DT_ADCP, DT_ADNP, DT_FcgR2a, DT_FcgR3b |
| Cluster_2 | DT_IgG1, DT_IgG2, DT_IgG3, DT_IgG4, FHA_ADCD, FHA_ADCP |
| Cluster_3 | FHA_ADNP, FHA_FcgR2a, FHA_FcgR3b, FHA_IgG1, FHA_IgG2, FHA_IgG3, FHA_IgG4, PRN_ADCD, PRN_ADCP, PRN_ADNP |
| Cluster_4 | PRN_FcgR2a, PRN_FcgR3b, PRN_IgG1, PRN_IgG2, PRN_IgG3, PRN_IgG4 |
| Cluster_5 | PT_ADCD, PT_ADCP, PT_ADNP, PT_FcgR2a, PT_FcgR3b, PT_IgG1, PT_IgG2, PT_IgG3 |
| Cluster_6 | PT_IgG4, TT_ADCD, TT_ADCP, TT_ADNP, TT_FcgR2a, TT_FcgR3b, TT_IgG1, TT_IgG2, TT_IgG3, TT_IgG4 |
Cluster 1
Correlation Heatmap 1
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 4 — 5 0.33
Heatmap Subjects Clustered 1
Heatmap Arm Assigned 1
Cluster 2
Correlation Heatmap 2
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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
Correlation Heatmap 3
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 0.35
Heatmap Subjects Clustered 3
Heatmap Arm Assigned 3
Cluster 4
Correlation Heatmap 4
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 0.83 2 — 3 0.12 2 — 4 0.02 3 — 4 0.15 1 — 5 -0.06 2 — 6
0.04 4 — 6 0.56
Heatmap Subjects Clustered 4
Heatmap Arm Assigned 4
Cluster 5
Correlation Heatmap 5
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 — 4 0.09 2 — 5 0.38 4 — 5 0.38
Heatmap Subjects Clustered 5
Heatmap Arm Assigned 5
Cluster 6
Correlation Heatmap 6
These heatmaps are based on Spearman Rank Correlation.
[1]
“after cluster specific correlation heatmap”
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 2 — 3 -0.12 2 — 5 0.33 1 — 10 0.3 8 — 10 0.18
Heatmap Subjects Clustered 6
Heatmap Arm Assigned 6
Arm Matching: Tanglegram
$dend1
‘dendrogram’ with 2 branches and 45 members total, at height
0.9998223
$dend2 ‘dendrogram’ with 2 branches and 45 members total, at height 0.9988193
attr(,“class”) [1] “dendlist”
CRI: 0.148
MASTxCF: 0.034
RFS: 0.563
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 workflowr_1.7.1
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 getPass_0.2-2 V8_4.4.1
[61] broom.helpers_1.14.0 Rdpack_2.6.1 vctrs_0.6.3
[64] png_0.1-8 cellranger_1.1.0 gtable_0.3.4
[67] kernlab_0.9-32 cachem_1.0.8 xfun_0.45
[70] mime_0.12 rbibutils_2.2.16 libcoin_1.0-10
[73] survival_3.5-5 ellipsis_0.3.2 TH.data_1.1-2
[76] nlme_3.1-164 bit64_4.0.5 gmodels_2.18.1.1
[79] rprojroot_2.0.4 R.cache_0.16.0 bslib_0.7.0
[82] TreeTools_1.12.0 rpart_4.1.19 colorspace_2.1-0
[85] Hmisc_5.1-1 nnet_7.3-19 Exact_3.2
[88] mnormt_2.1.1 tidyselect_1.2.0 processx_3.8.2
[91] curl_5.1.0 bit_4.0.5 compiler_4.3.1
[94] extrafontdb_1.0 git2r_0.32.0 rvest_1.0.3
[97] htmlTable_2.4.1 expm_0.999-7 xml2_1.3.5
[100] checkmate_2.2.0 scales_1.3.0 DEoptimR_1.1-3
[103] psych_2.3.9 lmtest_0.9-40 quadprog_1.5-8
[106] callr_3.7.5 multcompView_0.1-9 digest_0.6.33
[109] htmltools_0.5.8 pkgconfig_2.0.3 jpeg_0.1-10
[112] base64enc_0.1-3 highr_0.11 fastmap_1.1.1
[115] rlang_1.1.1 htmlwidgets_1.6.2 shiny_1.7.4.1
[118] farver_2.1.1 jquerylib_0.1.4 zoo_1.8-12
[121] jsonlite_1.8.7 R.oo_1.25.0 RCurl_1.98-1.13
[124] magrittr_2.0.3 modeltools_0.2-23 Formula_1.2-5
[127] munsell_0.5.0 Rcpp_1.0.11 viridis_0.6.4
[130] reticulate_1.37.0 stringi_1.7.12 rootSolve_1.8.2.4
[133] plyr_1.8.9 flexmix_2.3-19 ggstats_0.5.1
[136] parallel_4.3.1 lmom_3.0 splines_4.3.1
[139] hms_1.1.3 ps_1.7.5 igraph_1.5.1
[142] ggsignif_0.6.4 reshape2_1.4.4 stats4_4.3.1
[145] evaluate_0.23 tweenr_2.0.3 tzdb_0.4.0
[148] httpuv_1.6.11 Rttf2pt1_1.3.12 polyclip_1.10-6
[151] openssl_2.1.1 coin_1.4-3 RSpectra_0.16-1
[154] later_1.3.1 glasso_1.11 viridisLite_0.4.2
[157] class_7.3-22 memoise_2.0.1 timechange_0.2.0
[160] concaveman_1.1.0