cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14551646
title: Additional file 1 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 1: Figure S1. Clustering of cohort transcriptomes reveals profiles consistent with molecular subtypes. We applied unsupervised learning to the expression data used for each cohort considered in this study in order to remove unwanted variation associated with molecular subtypes from our alteration divergence analysis. In conjunction with information on known molecular subtypes present in these cohorts, we identified cases such as METABRIC and TCGA-LGG in which these subtypes clearly overlapped with distinguishable transcriptomic profiles. This contrasted with cohorts such as TCGA-STAD in which subtypes were present but could not be unambiguously linked with unique transcriptomic profiles, and those like TCGA-LUSC in which neither molecular subtypes nor expression clusters were present. The counts of cohort samples with each subtype are listed in the plot legends. In cohorts where molecular subtypes were found to have identifiable transcriptomic profiles we created sub-cohorts that only included samples from a particular subtype or set of subtypes. Unsupervised learning on these sub-cohorts’ transcriptomes revealed that they did not exhibit the large-scale clusters of samples observed in the original cohorts and were thus much more suitable as input for our mutation classification pipelines. We include here these UMAP clusterings for the entire METABRIC cohort and the METABRIC-(LumA) cohort and for the remaining cohorts at our data portal under Figures/S1 - Cohort UMAP Clustering. The names of these figures have the format (expr-source)__(cohort)__UMAP_comps-0_1.svg.
DOI: 10.6084/m9.figshare.14551649
title: Additional file 2 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 2: Table S1. Inventories of subgrouping task information. For each cohort and sub-cohort in which subgroupings were enumerated, we list here information for the subgroupings of point mutations (Base), the subgroupings constructed by adding CNAs (Copy), as well as the cohort-specific and gene-specific randomly-chosen subgroupings (RandCoh and RandGene). We include here these files for the METABRIC(LumA) cohort: mtype-tbl for an inventory of the subgroupings tested, auc-mat for subgrouping perfomances and Delong divergence relative to the corresponding gene-wide task, coef-means for averaged logistic ridge regression model coefficients, and trnsf-aucs for transfer performances when applied to the remaining cohorts, as well as a README describing the format of each file. These files for the remaining cohorts can be found at our data portal under Datasets/Output Summaries/Ridge.
DOI: 10.6084/m9.figshare.14551646.v1
title: Additional file 1 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 1: Figure S1. Clustering of cohort transcriptomes reveals profiles consistent with molecular subtypes. We applied unsupervised learning to the expression data used for each cohort considered in this study in order to remove unwanted variation associated with molecular subtypes from our alteration divergence analysis. In conjunction with information on known molecular subtypes present in these cohorts, we identified cases such as METABRIC and TCGA-LGG in which these subtypes clearly overlapped with distinguishable transcriptomic profiles. This contrasted with cohorts such as TCGA-STAD in which subtypes were present but could not be unambiguously linked with unique transcriptomic profiles, and those like TCGA-LUSC in which neither molecular subtypes nor expression clusters were present. The counts of cohort samples with each subtype are listed in the plot legends. In cohorts where molecular subtypes were found to have identifiable transcriptomic profiles we created sub-cohorts that only included samples from a particular subtype or set of subtypes. Unsupervised learning on these sub-cohorts’ transcriptomes revealed that they did not exhibit the large-scale clusters of samples observed in the original cohorts and were thus much more suitable as input for our mutation classification pipelines. We include here these UMAP clusterings for the entire METABRIC cohort and the METABRIC-(LumA) cohort and for the remaining cohorts at our data portal under Figures/S1 - Cohort UMAP Clustering. The names of these figures have the format (expr-source)__(cohort)__UMAP_comps-0_1.svg.
DOI: 10.6084/m9.figshare.14551652
title: Additional file 3 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 3: Figures S2. Comparing GATA3 subgrouping model coefficients identifies genes implicated in transcriptional divergences. We compare the model gene coefficients for the gene-wide GATA3 task in METABRIC(LumA) to coefficients for selected GATA3 subgroupings, including the best performing subgrouping as measured by AUC (frameshift_variant-5-6-splice_acceptor_variant–). Genes whose coefficients are in the top ten or bottom according to coefficient value for either model are highlighted in each plot, as are genes in the top twenty-five according to absolute difference between the coefficient values of the two task models. Genes discussed in the text are given bolded labels where applicable. We include here these files for subgroupings of GATA3 cv-significantly better than the GATA3 gene-wide task in METABRIC(LumA) cohort and for similarly significant subgroupings of the remaining genes and cohorts at our data portal under Figures/S2 - Subgrouping Coefficient Comparisons. The names of these figures have the format (expr-source)__(cohort)__(gene)__(subgrouping)__divergence-scatter_Ridge.svg.
DOI: 10.6084/m9.figshare.14551652.v1
title: Additional file 3 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 3: Figures S2. Comparing GATA3 subgrouping model coefficients identifies genes implicated in transcriptional divergences. We compare the model gene coefficients for the gene-wide GATA3 task in METABRIC(LumA) to coefficients for selected GATA3 subgroupings, including the best performing subgrouping as measured by AUC (frameshift_variant-5-6-splice_acceptor_variant–). Genes whose coefficients are in the top ten or bottom according to coefficient value for either model are highlighted in each plot, as are genes in the top twenty-five according to absolute difference between the coefficient values of the two task models. Genes discussed in the text are given bolded labels where applicable. We include here these files for subgroupings of GATA3 cv-significantly better than the GATA3 gene-wide task in METABRIC(LumA) cohort and for similarly significant subgroupings of the remaining genes and cohorts at our data portal under Figures/S2 - Subgrouping Coefficient Comparisons. The names of these figures have the format (expr-source)__(cohort)__(gene)__(subgrouping)__divergence-scatter_Ridge.svg.
DOI: 10.6084/m9.figshare.14551649.v1
title: Additional file 2 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 2: Table S1. Inventories of subgrouping task information. For each cohort and sub-cohort in which subgroupings were enumerated, we list here information for the subgroupings of point mutations (Base), the subgroupings constructed by adding CNAs (Copy), as well as the cohort-specific and gene-specific randomly-chosen subgroupings (RandCoh and RandGene). We include here these files for the METABRIC(LumA) cohort: mtype-tbl for an inventory of the subgroupings tested, auc-mat for subgrouping perfomances and Delong divergence relative to the corresponding gene-wide task, coef-means for averaged logistic ridge regression model coefficients, and trnsf-aucs for transfer performances when applied to the remaining cohorts, as well as a README describing the format of each file. These files for the remaining cohorts can be found at our data portal under Datasets/Output Summaries/Ridge.
DOI: 10.6084/m9.figshare.14551655
title: Additional file 4 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 4: Figure S3. Clustering subgrouping model coefficients reveals structure of mutation heterogeneity. We applied hierarchical clustering to examine the average regression model gene coefficients across all forty cross-validation folds for each of our subgrouping tasks. Subgroupings with task AUCs of below 0.7 were omitted, as were genes that did not have an absolute model coefficient ranked in the top five for any of the remaining tasks. Distances between subgroupings were computed by taking the inverse of the Spearman correlation across all gene coefficients; these were then used to cluster subgroupings into five groups. To facilitate presentation, here we only show these clusterings for subgroupings which did not have another subgrouping in the same cluster with a higher AUC and a Jaccard index of at least 0.9 with respect to the subgroupings’ mutated samples. The subgroupings with the highest AUC in each cluster are bolded, as is the gene-wide task. An asterisk is placed next to the AUCs of subgroupings with cv-significantly better performance than that of the gene-wide task. We include here these heatmaps for GATA3, TP53, and PIK3CA in METABRIC-(LumA) as well as KRAS in TCGA-LUAD. The corresponding figures for the remaining cases can be found at our data portal under Figures/S3 - Gene Coefficient Heatmaps. The names of these figures have the format (expr-source)__(cohort)__(gene)_auto-heatmap_Ridge.svg.
DOI: 10.6084/m9.figshare.14551655.v1
title: Additional file 4 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 4: Figure S3. Clustering subgrouping model coefficients reveals structure of mutation heterogeneity. We applied hierarchical clustering to examine the average regression model gene coefficients across all forty cross-validation folds for each of our subgrouping tasks. Subgroupings with task AUCs of below 0.7 were omitted, as were genes that did not have an absolute model coefficient ranked in the top five for any of the remaining tasks. Distances between subgroupings were computed by taking the inverse of the Spearman correlation across all gene coefficients; these were then used to cluster subgroupings into five groups. To facilitate presentation, here we only show these clusterings for subgroupings which did not have another subgrouping in the same cluster with a higher AUC and a Jaccard index of at least 0.9 with respect to the subgroupings’ mutated samples. The subgroupings with the highest AUC in each cluster are bolded, as is the gene-wide task. An asterisk is placed next to the AUCs of subgroupings with cv-significantly better performance than that of the gene-wide task. We include here these heatmaps for GATA3, TP53, and PIK3CA in METABRIC-(LumA) as well as KRAS in TCGA-LUAD. The corresponding figures for the remaining cases can be found at our data portal under Figures/S3 - Gene Coefficient Heatmaps. The names of these figures have the format (expr-source)__(cohort)__(gene)_auto-heatmap_Ridge.svg.
DOI: 10.6084/m9.figshare.14551658
title: Additional file 5 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 5: Figure S4. Subgrouping prediction tasks outperform cohort-specific random background prediction tasks. For each of the learning tasks completed in METABRIC(LumA) to predict the presence of an actual mutation or subgrouping of mutations, five additional tasks were performed based on predicting a simulated set of point mutations. These sets were constructed by randomly selecting a group of samples from the entire cohort of the same size as the group of samples affected by the original “real” mutation in the cohort. In addition to these cohort-specific random subgroupings, we also constructed five gene-specific random subgroupings for each task by selecting samples as above using only those samples carrying any point mutation of the gene associated with the task’s subgrouping. (left) We compare the AUCs of cohort-specific random subgroupings to the AUCs of the original subgroupings. (right) We compare the AUC of each original subgrouping to the best AUC across the gene-specific random subgroupings of the same number of “mutated” samples. We include here these figures for the METABRIC-(LumA) and TCGA-BRCA(LumA) cohorts and for the remaining cohorts at our data portal under Figures/S4 - Cohort Null Background AUC Comparisons. The names of these figures have the format (expr-source)__(cohort)__cohort-comparison_Ridge.svg.
DOI: 10.6084/m9.figshare.14551661
title: Additional file 6 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 6: Figure S5. Subgrouping prediction tasks outperform gene-specific random background prediction tasks. In addition to our cohort-specific null background tasks, we also constructed gene-specific random tasks for each tested subgrouping by randomly selecting five size-matched groups from the set of samples carrying any point mutation of the gene in the cohort. The AUCs of these gene-specific random tasks (grey distributions) tended to be lower than the AUCs of subgroupings (colorful distributions) in genes that had at least one subgrouping task with an AUC of 0.7. Distributions of subgroupings containing at least one subgrouping with performance cv-significantly better than that of the gene-wide task (dashed line) are annotated with an asterisk; genes in which at least one random subgrouping was cv-significantly better than the best original subgrouping are also annotated with an asterisk. We include here these figures for the METABRIC-(LumA) and TCGA-BRCA(LumA) cohorts and for the remaining cohorts at our data portal under Figures/S5 - Gene Null Background AUC Comparisons. The names of these figures have the format (expr-source)__(cohort)__gene-comparisons_Ridge.svg.
DOI: 10.6084/m9.figshare.14551658.v1
title: Additional file 5 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 5: Figure S4. Subgrouping prediction tasks outperform cohort-specific random background prediction tasks. For each of the learning tasks completed in METABRIC(LumA) to predict the presence of an actual mutation or subgrouping of mutations, five additional tasks were performed based on predicting a simulated set of point mutations. These sets were constructed by randomly selecting a group of samples from the entire cohort of the same size as the group of samples affected by the original “real” mutation in the cohort. In addition to these cohort-specific random subgroupings, we also constructed five gene-specific random subgroupings for each task by selecting samples as above using only those samples carrying any point mutation of the gene associated with the task’s subgrouping. (left) We compare the AUCs of cohort-specific random subgroupings to the AUCs of the original subgroupings. (right) We compare the AUC of each original subgrouping to the best AUC across the gene-specific random subgroupings of the same number of “mutated” samples. We include here these figures for the METABRIC-(LumA) and TCGA-BRCA(LumA) cohorts and for the remaining cohorts at our data portal under Figures/S4 - Cohort Null Background AUC Comparisons. The names of these figures have the format (expr-source)__(cohort)__cohort-comparison_Ridge.svg.
DOI: 10.6084/m9.figshare.14551661.v1
title: Additional file 6 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 6: Figure S5. Subgrouping prediction tasks outperform gene-specific random background prediction tasks. In addition to our cohort-specific null background tasks, we also constructed gene-specific random tasks for each tested subgrouping by randomly selecting five size-matched groups from the set of samples carrying any point mutation of the gene in the cohort. The AUCs of these gene-specific random tasks (grey distributions) tended to be lower than the AUCs of subgroupings (colorful distributions) in genes that had at least one subgrouping task with an AUC of 0.7. Distributions of subgroupings containing at least one subgrouping with performance cv-significantly better than that of the gene-wide task (dashed line) are annotated with an asterisk; genes in which at least one random subgrouping was cv-significantly better than the best original subgrouping are also annotated with an asterisk. We include here these figures for the METABRIC-(LumA) and TCGA-BRCA(LumA) cohorts and for the remaining cohorts at our data portal under Figures/S5 - Gene Null Background AUC Comparisons. The names of these figures have the format (expr-source)__(cohort)__gene-comparisons_Ridge.svg.
DOI: 10.6084/m9.figshare.14551664.v1
title: Additional file 7 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 7: Figure S6. Increasing computational complexity does not change or improve upon classification performance. We observed similar subgrouping classification performance in METABRIC(LumA) when we repeated our prediction tasks with (a) a support vector machine classifier and (b) a random forest classifier in place of the logistic ridge regression classifier that was originally used. (c) Using these more computationally complex classifiers did not result in improved classification performance across all non-random classification tasks, nor did tuning using a greater number of potential hyper-parameter values.
DOI: 10.6084/m9.figshare.14551664
title: Additional file 7 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 7: Figure S6. Increasing computational complexity does not change or improve upon classification performance. We observed similar subgrouping classification performance in METABRIC(LumA) when we repeated our prediction tasks with (a) a support vector machine classifier and (b) a random forest classifier in place of the logistic ridge regression classifier that was originally used. (c) Using these more computationally complex classifiers did not result in improved classification performance across all non-random classification tasks, nor did tuning using a greater number of potential hyper-parameter values.
DOI: 10.6084/m9.figshare.14551667
title: Additional file 8 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 8: Figure S7. Applying an expanded search space to subgrouping enumeration and classification in METABRIC(LumA). The task enumeration step in METABRIC(LumA) was modified to allow for subgroupings of up to three branches each containing at least five samples for a total of at least twenty samples. This resulted in an expanded search space of 6483 subgroupings. (a) The AUCs of the optimal subgroupings found for each gene are shown in the same style as in Fig. 1. (b) The AUCs of all subgroupings enumerated using the original search criteria (left) and the additional subgroupings enumerated using the expanded search criteria (right). Distributions of subgroupings containing at least one subgrouping with performance cv-significantly better than that of the gene-wide task are annotated with an asterisk; genes in which at least one subgrouping enumerated using the expanded criteria was cv-significantly better than the best original subgrouping are also annotated with an asterisk.
DOI: 10.6084/m9.figshare.14551670
title: Additional file 9 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 9: Figure S8. Adding copy number alterations to subgrouping classifiers. We augmented our classification tasks by adding deep amplifications and deep deletions to each subgrouping where there were at least five of one of these two types of mutations present in the corresponding gene within the cohort. Here we compare the classification performance of the best found subgrouping containing CNAs (y-axis) to the gene-wide task (x-axis) for each cancer gene with enumerated subgroupings in each cohort. We include here these figures for the METABRIC-(LumA) and TCGA-BRCA(LumA) cohorts and for the remaining cohorts at our data portal under Figures/S8 - CNA Subgrouping AUC Comparisons. The names of these figures have the format (expr-source)__(cohort)__copy-comparisons_Ridge.svg.
DOI: 10.6084/m9.figshare.14551670.v1
title: Additional file 9 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 9: Figure S8. Adding copy number alterations to subgrouping classifiers. We augmented our classification tasks by adding deep amplifications and deep deletions to each subgrouping where there were at least five of one of these two types of mutations present in the corresponding gene within the cohort. Here we compare the classification performance of the best found subgrouping containing CNAs (y-axis) to the gene-wide task (x-axis) for each cancer gene with enumerated subgroupings in each cohort. We include here these figures for the METABRIC-(LumA) and TCGA-BRCA(LumA) cohorts and for the remaining cohorts at our data portal under Figures/S8 - CNA Subgrouping AUC Comparisons. The names of these figures have the format (expr-source)__(cohort)__copy-comparisons_Ridge.svg.
DOI: 10.6084/m9.figshare.14551667.v1
title: Additional file 8 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

description: Additional file 8: Figure S7. Applying an expanded search space to subgrouping enumeration and classification in METABRIC(LumA). The task enumeration step in METABRIC(LumA) was modified to allow for subgroupings of up to three branches each containing at least five samples for a total of at least twenty samples. This resulted in an expanded search space of 6483 subgroupings. (a) The AUCs of the optimal subgroupings found for each gene are shown in the same style as in Fig. 1. (b) The AUCs of all subgroupings enumerated using the original search criteria (left) and the additional subgroupings enumerated using the expanded search criteria (right). Distributions of subgroupings containing at least one subgrouping with performance cv-significantly better than that of the gene-wide task are annotated with an asterisk; genes in which at least one subgrouping enumerated using the expanded criteria was cv-significantly better than the best original subgrouping are also annotated with an asterisk.