Extract.ConsensusPartitionList.RdSubset a ConsensusPartitionList object
# S3 method for ConsensusPartitionList
[(x, i, j, drop = TRUE)A ConsensusPartitionList-class object.
Index for top-value methods, character or nummeric.
Index for partitioning methods, character or nummeric.
Whether drop class
For a specific combination of top-value method and partitioning method, you can also
subset by e.g. x['SD:hclust'].
A ConsensusPartitionList-class object or a ConsensusPartition-class object.
data(golub_cola)
golub_cola[c("SD", "MAD"), c("hclust", "kmeans")]
#> A 'ConsensusPartitionList' object with 4 methods.
#> On a matrix with 4116 rows and 72 columns.
#> Top rows are extracted by 'SD, MAD' methods.
#> Subgroups are detected by 'hclust, kmeans' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 1000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "functional_enrichment"
#> [7] "get_anno" "get_anno_col" "get_classes"
#> [10] "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol"
#> [16] "nrow" "rownames" "show"
#> [19] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap"
#> [22] "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "MAD")], c("hclust", "kmeans")]
golub_cola["SD", "kmeans"] # a ConsensusPartition object
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 4116 rows and 72 columns.
#> Top rows (328) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes"
#> [3] "collect_plots" "collect_stats"
#> [5] "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap"
#> [9] "dimension_reduction" "functional_enrichment"
#> [11] "get_anno" "get_anno_col"
#> [13] "get_classes" "get_consensus"
#> [15] "get_matrix" "get_membership"
#> [17] "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k"
#> [21] "is_stable_k" "membership_heatmap"
#> [23] "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes"
#> [27] "rownames" "select_partition_number"
#> [29] "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
golub_cola["SD:kmeans"] # a ConsensusPartition object
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 4116 rows and 72 columns.
#> Top rows (328) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes"
#> [3] "collect_plots" "collect_stats"
#> [5] "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap"
#> [9] "dimension_reduction" "functional_enrichment"
#> [11] "get_anno" "get_anno_col"
#> [13] "get_classes" "get_consensus"
#> [15] "get_matrix" "get_membership"
#> [17] "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k"
#> [21] "is_stable_k" "membership_heatmap"
#> [23] "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes"
#> [27] "rownames" "select_partition_number"
#> [29] "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
golub_cola[["SD:kmeans"]] # a ConsensusPartition object
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 4116 rows and 72 columns.
#> Top rows (328) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes"
#> [3] "collect_plots" "collect_stats"
#> [5] "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap"
#> [9] "dimension_reduction" "functional_enrichment"
#> [11] "get_anno" "get_anno_col"
#> [13] "get_classes" "get_consensus"
#> [15] "get_matrix" "get_membership"
#> [17] "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k"
#> [21] "is_stable_k" "membership_heatmap"
#> [23] "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes"
#> [27] "rownames" "select_partition_number"
#> [29] "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
golub_cola["SD", "kmeans", drop = FALSE] # still a ConsensusPartitionList object
#> A 'ConsensusPartitionList' object with 1 methods.
#> On a matrix with 4116 rows and 72 columns.
#> Top rows are extracted by 'SD' methods.
#> Subgroups are detected by 'kmeans' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 250 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "functional_enrichment"
#> [7] "get_anno" "get_anno_col" "get_classes"
#> [10] "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol"
#> [16] "nrow" "rownames" "show"
#> [19] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap"
#> [22] "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "kmeans"] or object["SD:kmeans"]
golub_cola["SD:kmeans", drop = FALSE] # still a ConsensusPartitionList object
#> A 'ConsensusPartitionList' object with 1 methods.
#> On a matrix with 4116 rows and 72 columns.
#> Top rows are extracted by 'SD' methods.
#> Subgroups are detected by 'kmeans' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 250 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "functional_enrichment"
#> [7] "get_anno" "get_anno_col" "get_classes"
#> [10] "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol"
#> [16] "nrow" "rownames" "show"
#> [19] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap"
#> [22] "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "kmeans"] or object["SD:kmeans"]
golub_cola["SD", ]
#> A 'ConsensusPartitionList' object with 5 methods.
#> On a matrix with 4116 rows and 72 columns.
#> Top rows are extracted by 'SD' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 1250 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "functional_enrichment"
#> [7] "get_anno" "get_anno_col" "get_classes"
#> [10] "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol"
#> [16] "nrow" "rownames" "show"
#> [19] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap"
#> [22] "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object["SD"], c("hclust", "kmeans")]
golub_cola[, "hclust"]
#> A 'ConsensusPartitionList' object with 4 methods.
#> On a matrix with 4116 rows and 72 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 1000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "functional_enrichment"
#> [7] "get_anno" "get_anno_col" "get_classes"
#> [10] "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol"
#> [16] "nrow" "rownames" "show"
#> [19] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap"
#> [22] "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], "hclust"]
golub_cola[1:2, 1:2]
#> A 'ConsensusPartitionList' object with 4 methods.
#> On a matrix with 4116 rows and 72 columns.
#> Top rows are extracted by 'SD, CV' methods.
#> Subgroups are detected by 'hclust, kmeans' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 1000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "functional_enrichment"
#> [7] "get_anno" "get_anno_col" "get_classes"
#> [10] "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol"
#> [16] "nrow" "rownames" "show"
#> [19] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap"
#> [22] "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]