From 6a5cdded94db01b32eaa7f790b5578f0462fb16b Mon Sep 17 00:00:00 2001 From: Zuguang Date: Fri, 16 Apr 2021 11:27:57 +0200 Subject: [PATCH] update website --- DESCRIPTION | 4 +- docs/404.html | 2 +- docs/LICENSE-text.html | 2 +- docs/articles/index.html | 2 +- docs/articles/interactive.html | 4 +- docs/articles/simplifyEnrichment.html | 199 +++++++++--------- .../figure-html/unnamed-chunk-12-1.png | Bin 403411 -> 403595 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 3377887 -> 3366402 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 466967 -> 477425 bytes docs/articles/word_cloud_anno.html | 4 +- .../figure-html/unnamed-chunk-2-1.png | Bin 267481 -> 269952 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 257396 -> 259261 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 325288 -> 327536 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 356829 -> 357168 bytes docs/authors.html | 21 +- docs/index.html | 14 +- docs/pkgdown.yml | 2 +- docs/reference/DO_similarity.html | 2 +- docs/reference/GO_similarity.html | 4 +- docs/reference/all_clustering_methods.html | 2 +- docs/reference/anno_word_cloud.html | 4 +- docs/reference/anno_word_cloud_from_GO.html | 2 +- docs/reference/binary_cut.html | 2 +- docs/reference/cluster_by_MCL.html | 2 +- docs/reference/cluster_by_apcluster.html | 2 +- docs/reference/cluster_by_dynamicTreeCut.html | 2 +- docs/reference/cluster_by_hdbscan.html | 2 +- docs/reference/cluster_by_igraph.html | 2 +- docs/reference/cluster_by_kmeans.html | 2 +- docs/reference/cluster_by_mclust.html | 2 +- docs/reference/cluster_terms.html | 2 +- docs/reference/cmp_make_clusters.html | 22 +- docs/reference/cmp_make_plot.html | 22 +- .../reference/compare_clustering_methods.html | 42 ++-- docs/reference/count_word.html | 2 +- docs/reference/count_word_from_GO.html | 2 +- docs/reference/dend_node_apply.html | 2 +- docs/reference/difference_score.html | 2 +- docs/reference/edit_node.html | 2 +- docs/reference/export_to_shiny_app.html | 2 +- docs/reference/guess_ont.html | 2 +- docs/reference/heightDetails.word_cloud.html | 2 +- docs/reference/ht_clusters-1.png | Bin 382867 -> 398617 bytes docs/reference/ht_clusters-2.png | Bin 384198 -> 399974 bytes docs/reference/ht_clusters.html | 12 +- docs/reference/index.html | 2 +- docs/reference/partition_by_hclust.html | 2 +- docs/reference/partition_by_kmeans.html | 2 +- docs/reference/partition_by_kmeanspp.html | 2 +- docs/reference/partition_by_pam.html | 2 +- docs/reference/plot_binary_cut.html | 2 +- docs/reference/random_DO.html | 42 ++-- docs/reference/random_GO.html | 42 ++-- .../register_clustering_methods.html | 2 +- docs/reference/remove_clustering_methods.html | 2 +- docs/reference/reset_clustering_methods.html | 2 +- docs/reference/scale_fontsize.html | 4 +- docs/reference/select_cutoff.html | 2 +- docs/reference/simplifyEnrichment.html | 2 +- docs/reference/simplifyGO-1.png | Bin 418573 -> 442796 bytes docs/reference/simplifyGO.html | 4 +- .../simplifyGOFromMultipleLists.html | 23 +- docs/reference/subset_enrichResult.html | 2 +- docs/reference/term_similarity.html | 2 +- docs/reference/term_similarity_from_KEGG.html | 2 +- .../term_similarity_from_MSigDB.html | 2 +- .../term_similarity_from_Reactome.html | 2 +- .../term_similarity_from_enrichResult.html | 2 +- docs/reference/term_similarity_from_gmt.html | 2 +- docs/reference/widthDetails.word_cloud.html | 2 +- docs/reference/word_cloud_grob.html | 2 +- vignettes/simplifyEnrichment.Rmd | 8 +- 72 files changed, 298 insertions(+), 265 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 045777b..c283fc4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: simplifyEnrichment Type: Package Title: Simplify Functional Enrichment Results -Version: 1.1.4.9012 -Date: 2021-4-11 +Version: 1.1.5 +Date: 2021-4-16 Author: Zuguang Gu Maintainer: Zuguang Gu Depends: R (>= 3.6.0), BiocGenerics, grid diff --git a/docs/404.html b/docs/404.html index 3c775ed..928c847 100644 --- a/docs/404.html +++ b/docs/404.html @@ -71,7 +71,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 9d06ca0..a70d02a 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -71,7 +71,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/articles/index.html b/docs/articles/index.html index 7b490af..7eb5385 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -71,7 +71,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/articles/interactive.html b/docs/articles/interactive.html index b42f515..f71132f 100644 --- a/docs/articles/interactive.html +++ b/docs/articles/interactive.html @@ -31,7 +31,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 @@ -88,7 +88,7 @@

A Shiny app to interactively visualize clustering results

Zuguang Gu ()

-

2021-03-29

+

2021-04-16

Source: vignettes/interactive.Rmd diff --git a/docs/articles/simplifyEnrichment.html b/docs/articles/simplifyEnrichment.html index 1a5b2ad..1836685 100644 --- a/docs/articles/simplifyEnrichment.html +++ b/docs/articles/simplifyEnrichment.html @@ -31,7 +31,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 @@ -88,7 +88,7 @@

Simplify Functional Enrichment Results

Zuguang Gu ()

-

2021-03-29

+

2021-04-16

Source: vignettes/simplifyEnrichment.Rmd @@ -115,7 +115,7 @@

With the similarity matrix mat, users can directly apply simplifyGO() function to perform the clustering as well as visualizing the results.

 df = simplifyGO(mat)
-
## Cluster 500 terms by 'binary_cut'... 43 clusters, used 1.126213 secs.
+
## Cluster 500 terms by 'binary_cut'... 43 clusters, used 1.124932 secs.

On the right side of the heatmap there are the word cloud annotations which summarize the functions with keywords in every GO cluster. Note there is no word cloud for the cluster that is merged from small clusters (size < 5).

The returned variable df is a data frame with GO IDs, GO terms and the cluster labels:

@@ -199,34 +199,34 @@

  • Number of clusters: For each clustering, there are two numbers: the number of total clusters and the number of clusters with size >= 5 (only the big clusters).
  • -Block mean: The mean similarity values of the blocks in the similarity heatmap. Similar denotation as difference score, for term \(i\) and \(j\) where \(i\) can be the same as \(j\) (values on the diagonal are also used), the similarity value \(x_{i,j}\) is saved to the vector \(\mathbf{x_3}\) only when term \(i\) and \(j\) are in a same cluster. The block mean is the mean value over \(\mathbf{x_3}\).
  • +Block mean: Mean similarity values of the diagonal blocks in the similarity heatmap. Using the same convention as for the difference score, the block mean is the mean value of \(\mathbf{x_1}\). -
    ## Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.830595 secs.
    -## Cluster 500 terms by 'kmeans'... 15 clusters, used 3.410228 secs.
    -## Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.1339529 secs.
    -## Cluster 500 terms by 'apcluster'... 39 clusters, used 0.504811 secs.
    -## Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.1745231 secs.
    -## Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.09497118 secs.
    -## Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2554171 secs.
    -## Cluster 500 terms by 'louvain'... 29 clusters, used 0.06092119 secs.
    -## Cluster 500 terms by 'walktrap'... 26 clusters, used 0.4917281 secs.
    -## Cluster 500 terms by 'MCL'... 28 clusters, used 2.2424 secs.
    +
    ## Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.8137581 secs.
    +## Cluster 500 terms by 'kmeans'... 16 clusters, used 3.310741 secs.
    +## Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.1416259 secs.
    +## Cluster 500 terms by 'apcluster'... 39 clusters, used 0.5024211 secs.
    +## Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.1711211 secs.
    +## Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.1007149 secs.
    +## Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.220422 secs.
    +## Cluster 500 terms by 'louvain'... 29 clusters, used 0.06802702 secs.
    +## Cluster 500 terms by 'walktrap'... 26 clusters, used 0.2167759 secs.
    +## Cluster 500 terms by 'MCL'... 28 clusters, used 2.415706 secs.

    If plot_type argument is set to heatmap. There are heatmaps for the similarity matrix under different clusterings methods. The last panel is a table with the number of clusters.

     compare_clustering_methods(mat, plot_type = "heatmap")
    -
    ## Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.8308511 secs.
    -## Cluster 500 terms by 'kmeans'... 20 clusters, used 3.325294 secs.
    -## Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.1394169 secs.
    -## Cluster 500 terms by 'apcluster'... 39 clusters, used 0.556318 secs.
    -## Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.1673472 secs.
    -## Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.07141685 secs.
    -## Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2307508 secs.
    -## Cluster 500 terms by 'louvain'... 29 clusters, used 0.07669187 secs.
    -## Cluster 500 terms by 'walktrap'... 26 clusters, used 0.238606 secs.
    -## Cluster 500 terms by 'MCL'... 28 clusters, used 2.471999 secs.
    +
    ## Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.8636141 secs.
    +## Cluster 500 terms by 'kmeans'... 14 clusters, used 3.30296 secs.
    +## Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.129946 secs.
    +## Cluster 500 terms by 'apcluster'... 39 clusters, used 0.515388 secs.
    +## Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.1674349 secs.
    +## Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.07290006 secs.
    +## Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2432129 secs.
    +## Cluster 500 terms by 'louvain'... 29 clusters, used 0.09060407 secs.
    +## Cluster 500 terms by 'walktrap'... 26 clusters, used 0.2347581 secs.
    +## Cluster 500 terms by 'MCL'... 28 clusters, used 1.971701 secs.

    Please note, the clustering methods might have randomness, which means, different runs of compare_clustering_methods() may generate different clusterings (slightly different). Thus, if users want to compare the plots between compare_clustering_methods(mat) and compare_clustering_methods(mat, plot_type = "heatmap"), they should set the same random seed before executing the function.

    @@ -329,7 +329,7 @@ 

    ## Use column 'ID' as `go_id_column`.
     ## Use column 'p.adjust' as `padj_column`.
     ## 861/6488 GO IDs left for clustering.
    -## Cluster 861 terms by 'binary_cut'... 35 clusters, used 0.9187369 secs.
    +## Cluster 861 terms by 'binary_cut'... 35 clusters, used 0.9460809 secs.

    Next we demonstrate two other data types for simplifyGOFromMultipleLists(). Both usages are straightforward. The first is a list of numeric vectors:

    @@ -339,8 +339,8 @@ 

     lt3 = lapply(lt, function(x) x$ID[x$p.adjust < 0.001])
     simplifyGOFromMultipleLists(lt3)
    -

    The process of this analysis is as follows. Let’s assume there are \(n\) GO lists, we first construct a global matrix where columns correspond to the \(n\) GO lists and rows correspond to the “union” of all GO IDs in the \(n\) lists. The value for the ith GO ID and in the jth list are taken from the corresponding numeric vector in lt. If the jth vector in lt does not contain the ith GO ID, the value defined by default argument is taken there (e.g. in most cases the numeric values are adjusted p-values, default is set to 1). Let’s call this matrix as \(M_0\).

    -

    Next step is to filter \(M_0\) so that we only take a subset of GO IDs of interest. We define a proper function via argument filter to remove GO IDs that are not important for the analysis. Functions for filter is applied to every row in \(M_0\) and filter function needs to return a logical value to decide whether to remove the current GO ID. For example, if the values in lt are adjusted p-values, the filter function can be set as function(x) any(x < padj_cutoff) so that the GO ID is kept as long as it is signfiicant in at least one list. After the filtering, let’s call the filtered matrix \(M_1\).

    +

    The process of this analysis is as follows. Let’s assume there are \(n\) GO lists, we first construct a global matrix where columns correspond to the \(n\) GO lists and rows correspond to the “union” of all GO IDs in the \(n\) lists. The value for the ith GO ID and in the jth list are taken from the corresponding numeric vector in lt. If the jth vector in lt does not contain the ith GO ID, the value defined by default argument is taken there (e.g. in most cases the numeric values are adjusted p-values, thus default is set to 1). Let’s call this matrix as \(M_0\).

    +

    Next step is to filter \(M_0\) so that we only take a subset of GO IDs of interest. We define a proper function via argument filter to remove GO IDs that are not important for the analysis. Function for filter is applied to every row in \(M_0\) and filter function needs to return a logical value to decide whether to keep or remove the current GO ID. For example, if the values in lt are adjusted p-values, the filter function can be set as function(x) any(x < padj_cutoff) so that the GO ID is kept as long as it is signfiicant in at least one list. After the filtering, let’s call the filtered matrix \(M_1\).

    GO IDs in \(M_1\) (row names of \(M_1\)) are used for clustering. A heatmap of \(M_1\) is attached to the left of the GO similarity heatmap so that the group-specific (or list-specific) patterns can be easily observed and to corresponded to GO functions.

    Argument heatmap_param controls several parameters for heatmap \(M_1\):

      @@ -370,87 +370,86 @@

      ## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib ## ## locale: -## [1] C/en_US.UTF-8/C/C/C/C +## [1] C/UTF-8/C/C/C/C ## ## attached base packages: ## [1] stats4 grid parallel stats graphics grDevices utils ## [8] datasets methods base ## ## other attached packages: -## [1] hu6800.db_3.2.3 org.Hs.eg.db_3.12.0 -## [3] AnnotationDbi_1.52.0 IRanges_2.24.1 -## [5] S4Vectors_0.28.1 Biobase_2.50.0 -## [7] cola_1.9.0.1016 simplifyEnrichment_1.1.4.9002 -## [9] BiocGenerics_0.36.0 knitr_1.31 +## [1] hu6800.db_3.2.3 org.Hs.eg.db_3.12.0 AnnotationDbi_1.52.0 +## [4] IRanges_2.24.1 S4Vectors_0.28.1 Biobase_2.50.0 +## [7] cola_1.9.0.9018 simplifyEnrichment_1.1.5 BiocGenerics_0.36.0 +## [10] knitr_1.31 ## ## loaded via a namespace (and not attached): -## [1] shadowtext_0.0.7 circlize_0.4.12 -## [3] fastmatch_1.1-0 systemfonts_1.0.1 -## [5] plyr_1.8.6 igraph_1.2.6 -## [7] proxyC_0.1.5 splines_4.0.4 -## [9] BiocParallel_1.24.1 MCL_1.0 -## [11] ggplot2_3.3.3 digest_0.6.27 -## [13] foreach_1.5.1 htmltools_0.5.1.1 -## [15] GOSemSim_2.16.1 viridis_0.5.1 -## [17] magick_2.7.1 GO.db_3.12.1 -## [19] fansi_0.4.2 magrittr_2.0.1 -## [21] memoise_2.0.0 tm_0.7-8 -## [23] cluster_2.1.1 doParallel_1.0.16 -## [25] graphlayouts_0.7.1 ComplexHeatmap_2.7.9.1002 -## [27] annotate_1.68.0 RcppParallel_5.0.3 -## [29] matrixStats_0.58.0 pkgdown_1.6.1 -## [31] enrichplot_1.10.2 colorspace_2.0-0 -## [33] ggrepel_0.9.1 blob_1.2.1 -## [35] textshaping_0.3.3 xfun_0.22 -## [37] dplyr_1.0.5 crayon_1.4.1 -## [39] microbenchmark_1.4-7 jsonlite_1.7.2 -## [41] scatterpie_0.1.5 genefilter_1.72.1 -## [43] impute_1.64.0 brew_1.0-6 -## [45] survival_3.2-10 iterators_1.0.13 -## [47] glue_1.4.2 polyclip_1.10-0 -## [49] gtable_0.3.0 GetoptLong_1.0.5 -## [51] shape_1.4.5 apcluster_1.4.8 -## [53] scales_1.1.1 DOSE_3.16.0 -## [55] DBI_1.1.1 Rcpp_1.0.6 -## [57] gridtext_0.1.4 viridisLite_0.3.0 -## [59] xtable_1.8-4 clue_0.3-58 -## [61] bit_4.0.4 mclust_5.4.7 -## [63] httr_1.4.2 fgsea_1.16.0 -## [65] RColorBrewer_1.1-2 ellipsis_0.3.1 -## [67] pkgconfig_2.0.3 XML_3.99-0.6 -## [69] farver_2.1.0 sass_0.3.1 -## [71] utf8_1.2.1 dynamicTreeCut_1.63-1 -## [73] tidyselect_1.1.0 labeling_0.4.2 -## [75] rlang_0.4.10 reshape2_1.4.4 -## [77] munsell_0.5.0 tools_4.0.4 -## [79] cachem_1.0.4 downloader_0.4 -## [81] dbscan_1.1-6 generics_0.1.0 -## [83] RSQLite_2.2.4 evaluate_0.14 -## [85] stringr_1.4.0 fastmap_1.1.0 -## [87] yaml_2.2.1 ragg_1.1.2 -## [89] bit64_4.0.5 fs_1.5.0 -## [91] tidygraph_1.2.0 purrr_0.3.4 -## [93] ggraph_2.0.5 slam_0.1-48 -## [95] DO.db_2.9 xml2_1.3.2 -## [97] compiler_4.0.4 png_0.1-7 -## [99] tweenr_1.0.1 tibble_3.1.0 -## [101] bslib_0.2.4 stringi_1.5.3 -## [103] highr_0.8 desc_1.3.0 -## [105] lattice_0.20-41 Matrix_1.3-2 -## [107] markdown_1.1 vctrs_0.3.6 -## [109] pillar_1.5.1 lifecycle_1.0.0 -## [111] BiocManager_1.30.10 jquerylib_0.1.3 -## [113] eulerr_6.1.0 GlobalOptions_0.1.2 -## [115] data.table_1.14.0 cowplot_1.1.1 -## [117] qvalue_2.22.0 R6_2.5.0 -## [119] gridExtra_2.3 codetools_0.2-18 -## [121] MASS_7.3-53.1 assertthat_0.2.1 -## [123] rprojroot_2.0.2 rjson_0.2.20 -## [125] expm_0.999-6 clusterProfiler_3.18.1 -## [127] tidyr_1.1.3 rvcheck_0.1.8 -## [129] rmarkdown_2.7 skmeans_0.2-13 -## [131] Cairo_1.5-12.2 ggforce_0.3.3 -## [133] NLP_0.2-1

    +## [1] shadowtext_0.0.7 circlize_0.4.12 +## [3] fastmatch_1.1-0 systemfonts_1.0.1 +## [5] plyr_1.8.6 igraph_1.2.6 +## [7] proxyC_0.1.5 splines_4.0.4 +## [9] BiocParallel_1.24.1 MCL_1.0 +## [11] ggplot2_3.3.3 digest_0.6.27 +## [13] foreach_1.5.1 htmltools_0.5.1.1 +## [15] GOSemSim_2.16.1 viridis_0.5.1 +## [17] magick_2.7.1 GO.db_3.12.1 +## [19] fansi_0.4.2 magrittr_2.0.1 +## [21] memoise_2.0.0 tm_0.7-8 +## [23] cluster_2.1.1 doParallel_1.0.16 +## [25] graphlayouts_0.7.1 ComplexHeatmap_2.7.10.9001 +## [27] annotate_1.68.0 RcppParallel_5.0.3 +## [29] matrixStats_0.58.0 pkgdown_1.6.1 +## [31] enrichplot_1.10.2 colorspace_2.0-0 +## [33] ggrepel_0.9.1 blob_1.2.1 +## [35] textshaping_0.3.3 xfun_0.22 +## [37] dplyr_1.0.5 crayon_1.4.1 +## [39] microbenchmark_1.4-7 jsonlite_1.7.2 +## [41] scatterpie_0.1.5 genefilter_1.72.1 +## [43] impute_1.64.0 brew_1.0-6 +## [45] survival_3.2-10 iterators_1.0.13 +## [47] glue_1.4.2 polyclip_1.10-0 +## [49] gtable_0.3.0 GetoptLong_1.0.5 +## [51] shape_1.4.5 apcluster_1.4.8 +## [53] scales_1.1.1 DOSE_3.16.0 +## [55] DBI_1.1.1 Rcpp_1.0.6 +## [57] gridtext_0.1.4 viridisLite_0.3.0 +## [59] xtable_1.8-4 clue_0.3-58 +## [61] bit_4.0.4 mclust_5.4.7 +## [63] httr_1.4.2 fgsea_1.16.0 +## [65] RColorBrewer_1.1-2 ellipsis_0.3.1 +## [67] pkgconfig_2.0.3 XML_3.99-0.6 +## [69] farver_2.1.0 sass_0.3.1 +## [71] utf8_1.2.1 dynamicTreeCut_1.63-1 +## [73] tidyselect_1.1.0 labeling_0.4.2 +## [75] rlang_0.4.10 reshape2_1.4.4 +## [77] munsell_0.5.0 tools_4.0.4 +## [79] cachem_1.0.4 downloader_0.4 +## [81] dbscan_1.1-6 generics_0.1.0 +## [83] RSQLite_2.2.5 evaluate_0.14 +## [85] stringr_1.4.0 fastmap_1.1.0 +## [87] yaml_2.2.1 ragg_1.1.2 +## [89] bit64_4.0.5 fs_1.5.0 +## [91] tidygraph_1.2.0 purrr_0.3.4 +## [93] ggraph_2.0.5 slam_0.1-48 +## [95] DO.db_2.9 xml2_1.3.2 +## [97] compiler_4.0.4 png_0.1-7 +## [99] tweenr_1.0.2 tibble_3.1.0 +## [101] bslib_0.2.4 stringi_1.5.3 +## [103] highr_0.8 desc_1.3.0 +## [105] lattice_0.20-41 Matrix_1.3-2 +## [107] markdown_1.1 vctrs_0.3.7 +## [109] pillar_1.5.1 lifecycle_1.0.0 +## [111] BiocManager_1.30.12 jquerylib_0.1.3 +## [113] eulerr_6.1.0 GlobalOptions_0.1.2 +## [115] data.table_1.14.0 cowplot_1.1.1 +## [117] irlba_2.3.3 qvalue_2.22.0 +## [119] R6_2.5.0 gridExtra_2.3 +## [121] codetools_0.2-18 MASS_7.3-53.1 +## [123] assertthat_0.2.1 rprojroot_2.0.2 +## [125] rjson_0.2.20 expm_0.999-6 +## [127] clusterProfiler_3.18.1 tidyr_1.1.3 +## [129] rvcheck_0.1.8 rmarkdown_2.7 +## [131] skmeans_0.2-13 Cairo_1.5-12.2 +## [133] ggforce_0.3.3 NLP_0.2-1
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    Word Cloud Annotation

    Zuguang Gu ()

    -

    2021-03-29

    +

    2021-04-16

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simplifyEnrichment @@ -39,7 +39,7 @@ - + @@ -58,7 +58,7 @@ -

    +
    @@ -135,7 +135,7 @@

    Calculate Gene Ontology (GO) semantic similarity matrix

    Calculate Gene Ontology (GO) semantic similarity matrix

    -
    GO_similarity(go_id, ont, db = 'org.Hs.eg.db', measure = "Rel")
    +
    GO_similarity(go_id, ont = NULL, db = 'org.Hs.eg.db', measure = "Rel")

    Arguments

    diff --git a/docs/reference/all_clustering_methods.html b/docs/reference/all_clustering_methods.html index 33608a1..640a781 100644 --- a/docs/reference/all_clustering_methods.html +++ b/docs/reference/all_clustering_methods.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/anno_word_cloud.html b/docs/reference/anno_word_cloud.html index 3c3587e..771fecc 100644 --- a/docs/reference/anno_word_cloud.html +++ b/docs/reference/anno_word_cloud.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 @@ -197,7 +197,7 @@

    Examp require(ComplexHeatmap)
    #> Loading required package: ComplexHeatmap
    #> ======================================== -#> ComplexHeatmap version 2.7.9.1002 +#> ComplexHeatmap version 2.7.10.9001 #> Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/ #> Github page: https://github.com/jokergoo/ComplexHeatmap #> Documentation: http://jokergoo.github.io/ComplexHeatmap-reference diff --git a/docs/reference/anno_word_cloud_from_GO.html b/docs/reference/anno_word_cloud_from_GO.html index 3a5abea..181b1ec 100644 --- a/docs/reference/anno_word_cloud_from_GO.html +++ b/docs/reference/anno_word_cloud_from_GO.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5
    diff --git a/docs/reference/binary_cut.html b/docs/reference/binary_cut.html index 1b195f4..6e6e7fe 100644 --- a/docs/reference/binary_cut.html +++ b/docs/reference/binary_cut.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/cluster_by_MCL.html b/docs/reference/cluster_by_MCL.html index 20b5d0b..761f224 100644 --- a/docs/reference/cluster_by_MCL.html +++ b/docs/reference/cluster_by_MCL.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/cluster_by_apcluster.html b/docs/reference/cluster_by_apcluster.html index f161b2a..b272d4a 100644 --- a/docs/reference/cluster_by_apcluster.html +++ b/docs/reference/cluster_by_apcluster.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/cluster_by_dynamicTreeCut.html b/docs/reference/cluster_by_dynamicTreeCut.html index 3c74946..793166f 100644 --- a/docs/reference/cluster_by_dynamicTreeCut.html +++ b/docs/reference/cluster_by_dynamicTreeCut.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/cluster_by_hdbscan.html b/docs/reference/cluster_by_hdbscan.html index eb48d00..2344923 100644 --- a/docs/reference/cluster_by_hdbscan.html +++ b/docs/reference/cluster_by_hdbscan.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/cluster_by_igraph.html b/docs/reference/cluster_by_igraph.html index a6b2f68..53439c4 100644 --- a/docs/reference/cluster_by_igraph.html +++ b/docs/reference/cluster_by_igraph.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/cluster_by_kmeans.html b/docs/reference/cluster_by_kmeans.html index 017feb6..34cfdd3 100644 --- a/docs/reference/cluster_by_kmeans.html +++ b/docs/reference/cluster_by_kmeans.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/cluster_by_mclust.html b/docs/reference/cluster_by_mclust.html index bd10e97..c1ae833 100644 --- a/docs/reference/cluster_by_mclust.html +++ b/docs/reference/cluster_by_mclust.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/cluster_terms.html b/docs/reference/cluster_terms.html index 7b40ac1..245f2bb 100644 --- a/docs/reference/cluster_terms.html +++ b/docs/reference/cluster_terms.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/cmp_make_clusters.html b/docs/reference/cmp_make_clusters.html index 93c2ef2..553de38 100644 --- a/docs/reference/cmp_make_clusters.html +++ b/docs/reference/cmp_make_clusters.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 @@ -183,16 +183,16 @@

    Examp mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds", package = "simplifyEnrichment")) clt = cmp_make_clusters(mat) -
    #> Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.856966 secs. -#> Cluster 500 terms by 'kmeans'... 14 clusters, used 3.534226 secs. -#> Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.145431 secs. -#> Cluster 500 terms by 'apcluster'... 39 clusters, used 0.646719 secs. -#> Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.185468 secs. -#> Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.117954 secs. -#> Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.551507 secs. -#> Cluster 500 terms by 'louvain'... 29 clusters, used 0.0563972 secs. -#> Cluster 500 terms by 'walktrap'... 26 clusters, used 0.22858 secs. -#> Cluster 500 terms by 'MCL'... 28 clusters, used 2.122988 secs.
    # } +
    #> Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.84921 secs. +#> Cluster 500 terms by 'kmeans'... 14 clusters, used 3.243423 secs. +#> Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.1395221 secs. +#> Cluster 500 terms by 'apcluster'... 39 clusters, used 0.6052079 secs. +#> Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.172935 secs. +#> Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.4142361 secs. +#> Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2246912 secs. +#> Cluster 500 terms by 'louvain'... 29 clusters, used 0.05425 secs. +#> Cluster 500 terms by 'walktrap'... 26 clusters, used 0.5262959 secs. +#> Cluster 500 terms by 'MCL'... 28 clusters, used 2.052602 secs.
    # }
    @@ -179,16 +179,16 @@

    Examp mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds", package = "simplifyEnrichment")) clt = cmp_make_clusters(mat) -
    #> Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.8914831 secs. -#> Cluster 500 terms by 'kmeans'... 18 clusters, used 3.665325 secs. -#> Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.1293108 secs. -#> Cluster 500 terms by 'apcluster'... 39 clusters, used 0.5937471 secs. -#> Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.1682239 secs. -#> Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.06073713 secs. -#> Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2633681 secs. -#> Cluster 500 terms by 'louvain'... 29 clusters, used 0.06282496 secs. -#> Cluster 500 terms by 'walktrap'... 26 clusters, used 0.5658419 secs. -#> Cluster 500 terms by 'MCL'... 28 clusters, used 2.104845 secs.
    cmp_make_plot(mat, clt) +
    #> Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.873457 secs. +#> Cluster 500 terms by 'kmeans'... 18 clusters, used 3.535221 secs. +#> Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.128432 secs. +#> Cluster 500 terms by 'apcluster'... 39 clusters, used 0.5327232 secs. +#> Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.1651649 secs. +#> Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.05728006 secs. +#> Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2227449 secs. +#> Cluster 500 terms by 'louvain'... 29 clusters, used 0.06401205 secs. +#> Cluster 500 terms by 'walktrap'... 26 clusters, used 0.219851 secs. +#> Cluster 500 terms by 'MCL'... 28 clusters, used 2.058679 secs.
    cmp_make_plot(mat, clt)
    cmp_make_plot(mat, clt, plot_type = "heatmap")
    # }
    diff --git a/docs/reference/compare_clustering_methods.html b/docs/reference/compare_clustering_methods.html index 4fd8336..1de7e9b 100644 --- a/docs/reference/compare_clustering_methods.html +++ b/docs/reference/compare_clustering_methods.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 @@ -196,26 +196,26 @@

    Examp mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds", package = "simplifyEnrichment")) compare_clustering_methods(mat) -
    #> Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.9059761 secs. -#> Cluster 500 terms by 'kmeans'... 18 clusters, used 3.550738 secs. -#> Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.1377971 secs. -#> Cluster 500 terms by 'apcluster'... 39 clusters, used 0.479248 secs. -#> Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.173533 secs. -#> Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.06257701 secs. -#> Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2325771 secs. -#> Cluster 500 terms by 'louvain'... 29 clusters, used 0.05866504 secs. -#> Cluster 500 terms by 'walktrap'... 26 clusters, used 0.2246361 secs. -#> Cluster 500 terms by 'MCL'... 28 clusters, used 1.981292 secs.
    compare_clustering_methods(mat, plot_type = "heatmap") -
    #> Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.932358 secs. -#> Cluster 500 terms by 'kmeans'... 18 clusters, used 3.267643 secs. -#> Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.1589811 secs. -#> Cluster 500 terms by 'apcluster'... 39 clusters, used 0.4682629 secs. -#> Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.1746161 secs. -#> Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.06367111 secs. -#> Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2351241 secs. -#> Cluster 500 terms by 'louvain'... 29 clusters, used 0.056499 secs. -#> Cluster 500 terms by 'walktrap'... 26 clusters, used 0.2404039 secs. -#> Cluster 500 terms by 'MCL'... 28 clusters, used 2.281958 secs.
    # } +
    #> Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.8463531 secs. +#> Cluster 500 terms by 'kmeans'... 18 clusters, used 3.180219 secs. +#> Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.1369441 secs. +#> Cluster 500 terms by 'apcluster'... 39 clusters, used 0.4699709 secs. +#> Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.1674678 secs. +#> Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.39766 secs. +#> Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2361381 secs. +#> Cluster 500 terms by 'louvain'... 29 clusters, used 0.05693007 secs. +#> Cluster 500 terms by 'walktrap'... 26 clusters, used 0.221807 secs. +#> Cluster 500 terms by 'MCL'... 28 clusters, used 1.952941 secs.
    compare_clustering_methods(mat, plot_type = "heatmap") +
    #> Cluster 500 terms by 'binary_cut'... 43 clusters, used 0.795332 secs. +#> Cluster 500 terms by 'kmeans'... 18 clusters, used 3.087361 secs. +#> Cluster 500 terms by 'dynamicTreeCut'... 58 clusters, used 0.1364391 secs. +#> Cluster 500 terms by 'apcluster'... 39 clusters, used 0.4592068 secs. +#> Cluster 500 terms by 'hdbscan'... 13 clusters, used 0.172581 secs. +#> Cluster 500 terms by 'fast_greedy'... 29 clusters, used 0.05868912 secs. +#> Cluster 500 terms by 'leading_eigen'... 30 clusters, used 0.2194998 secs. +#> Cluster 500 terms by 'louvain'... 29 clusters, used 0.08828497 secs. +#> Cluster 500 terms by 'walktrap'... 26 clusters, used 0.225198 secs. +#> Cluster 500 terms by 'MCL'... 28 clusters, used 1.94868 secs.
    # }
    diff --git a/docs/reference/count_word_from_GO.html b/docs/reference/count_word_from_GO.html index 3a4be68..a3b71bd 100644 --- a/docs/reference/count_word_from_GO.html +++ b/docs/reference/count_word_from_GO.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/dend_node_apply.html b/docs/reference/dend_node_apply.html index 715e1c2..b992bef 100644 --- a/docs/reference/dend_node_apply.html +++ b/docs/reference/dend_node_apply.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/difference_score.html b/docs/reference/difference_score.html index efeb4f8..628c7d9 100644 --- a/docs/reference/difference_score.html +++ b/docs/reference/difference_score.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/edit_node.html b/docs/reference/edit_node.html index 2f086b7..de2e5d4 100644 --- a/docs/reference/edit_node.html +++ b/docs/reference/edit_node.html @@ -72,7 +72,7 @@ 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zELhtk82`n##$qJ4pf>FOH|^;6O=@}iWayFpPHPIx|9*zPY0`O9rA77KJXnNeCuXS1h|%yGJbqW8#Ul7zk*SEgVD!GjMvY(8HUyjXr7- z$V}gRHwq-!Nx4vcw&?%>zwgpa5QX) zC81Y!=>-qlpPt=Q(c4!!UDD87nuMK@o^z&_&W(c4`0K+w6X$*Wr}JQktkRn;OW

    - - - - @@ -230,6 +226,10 @@

    Arg

    + + + + diff --git a/docs/reference/index.html b/docs/reference/index.html index d26116b..a8a7992 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -71,7 +71,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/partition_by_hclust.html b/docs/reference/partition_by_hclust.html index b4bc287..e948a40 100644 --- a/docs/reference/partition_by_hclust.html +++ b/docs/reference/partition_by_hclust.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/partition_by_kmeans.html b/docs/reference/partition_by_kmeans.html index fe91537..52db32c 100644 --- a/docs/reference/partition_by_kmeans.html +++ b/docs/reference/partition_by_kmeans.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/partition_by_kmeanspp.html b/docs/reference/partition_by_kmeanspp.html index e0fa7d4..2badf78 100644 --- a/docs/reference/partition_by_kmeanspp.html +++ b/docs/reference/partition_by_kmeanspp.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/partition_by_pam.html b/docs/reference/partition_by_pam.html index 5c9b665..6025d2f 100644 --- a/docs/reference/partition_by_pam.html +++ b/docs/reference/partition_by_pam.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/plot_binary_cut.html b/docs/reference/plot_binary_cut.html index 939238c..78c1501 100644 --- a/docs/reference/plot_binary_cut.html +++ b/docs/reference/plot_binary_cut.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/random_DO.html b/docs/reference/random_DO.html index eabcfa9..37bec69 100644 --- a/docs/reference/random_DO.html +++ b/docs/reference/random_DO.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 @@ -156,26 +156,26 @@

    Value

    Examples

    # \donttest{ random_DO(100) -
    #> [1] "DOID:11372" "DOID:7241" "DOID:9279" "DOID:14068" "DOID:227" -#> [6] "DOID:8000" "DOID:3918" "DOID:0050916" "DOID:9173" "DOID:5457" -#> [11] "DOID:4953" "DOID:6868" "DOID:10845" "DOID:13767" "DOID:10286" -#> [16] "DOID:57" "DOID:8886" "DOID:14400" "DOID:3407" "DOID:0060095" -#> [21] "DOID:0060208" "DOID:13760" "DOID:5997" "DOID:4715" "DOID:4873" -#> [26] "DOID:9378" "DOID:6776" "DOID:6938" "DOID:4959" "DOID:1129" -#> [31] "DOID:0050484" "DOID:6432" "DOID:5628" "DOID:3437" "DOID:8663" -#> [36] "DOID:0050622" "DOID:2723" "DOID:476" "DOID:0050135" "DOID:2093" -#> [41] "DOID:14764" "DOID:5178" "DOID:5287" "DOID:5364" "DOID:3933" -#> [46] "DOID:0050881" "DOID:0050722" "DOID:4908" "DOID:0050720" "DOID:3603" -#> [51] "DOID:3374" "DOID:2745" "DOID:9352" "DOID:6559" "DOID:217" -#> [56] "DOID:6469" "DOID:4423" "DOID:14555" "DOID:5083" "DOID:12157" -#> [61] "DOID:1768" "DOID:10648" "DOID:1168" "DOID:8644" "DOID:201" -#> [66] "DOID:7750" "DOID:2986" "DOID:7512" "DOID:11847" "DOID:4719" -#> [71] "DOID:4989" "DOID:10575" "DOID:4557" "DOID:0050641" "DOID:11302" -#> [76] "DOID:4407" "DOID:6682" "DOID:5016" "DOID:10230" "DOID:4147" -#> [81] "DOID:974" "DOID:3209" "DOID:321" "DOID:0060194" "DOID:13450" -#> [86] "DOID:0050486" "DOID:0060031" "DOID:4418" "DOID:1542" "DOID:854" -#> [91] "DOID:5485" "DOID:3052" "DOID:13730" "DOID:3908" "DOID:3504" -#> [96] "DOID:6838" "DOID:12162" "DOID:0060329" "DOID:13823" "DOID:4971"
    # } +
    #> [1] "DOID:3179" "DOID:7602" "DOID:6032" "DOID:1862" "DOID:10606" +#> [6] "DOID:5349" "DOID:5232" "DOID:14392" "DOID:4114" "DOID:3047" +#> [11] "DOID:0050145" "DOID:7840" "DOID:9740" "DOID:9410" "DOID:3827" +#> [16] "DOID:0050877" "DOID:13655" "DOID:7598" "DOID:2762" "DOID:4848" +#> [21] "DOID:11246" "DOID:9775" "DOID:3819" "DOID:332" "DOID:8331" +#> [26] "DOID:9877" "DOID:0050704" "DOID:6501" "DOID:0060326" "DOID:6016" +#> [31] "DOID:3850" "DOID:3240" "DOID:8956" "DOID:4489" "DOID:13046" +#> [36] "DOID:223" "DOID:138" "DOID:2444" "DOID:4901" "DOID:8476" +#> [41] "DOID:678" "DOID:5170" "DOID:1936" "DOID:8552" "DOID:4919" +#> [46] "DOID:4136" "DOID:3447" "DOID:1934" "DOID:0080021" "DOID:9744" +#> [51] "DOID:3260" "DOID:0080031" "DOID:7512" "DOID:4731" "DOID:3401" +#> [56] "DOID:10550" "DOID:13089" "DOID:0050791" "DOID:1998" "DOID:566" +#> [61] "DOID:14397" "DOID:11817" "DOID:0060332" "DOID:5729" "DOID:7752" +#> [66] "DOID:119" "DOID:14731" "DOID:8712" "DOID:5982" "DOID:12143" +#> [71] "DOID:8233" "DOID:5593" "DOID:5193" "DOID:5525" "DOID:9810" +#> [76] "DOID:14780" "DOID:5662" "DOID:10881" "DOID:11385" "DOID:5274" +#> [81] "DOID:321" "DOID:2614" "DOID:2843" "DOID:0050883" "DOID:5997" +#> [86] "DOID:10235" "DOID:6271" "DOID:13787" "DOID:0050735" "DOID:3535" +#> [91] "DOID:0050933" "DOID:4702" "DOID:8224" "DOID:3459" "DOID:0050656" +#> [96] "DOID:12282" "DOID:5062" "DOID:5916" "DOID:7633" "DOID:0050640"
    # }
    @@ -161,26 +161,26 @@

    Value

    Examples

    # \donttest{ random_GO(100) -
    #> [1] "GO:0071899" "GO:0045585" "GO:0007063" "GO:0051036" "GO:1901731" -#> [6] "GO:1904631" "GO:1904292" "GO:0002262" "GO:0072340" "GO:0035066" -#> [11] "GO:1905317" "GO:0061051" "GO:0006581" "GO:0051150" "GO:0043315" -#> [16] "GO:0008104" "GO:0040029" "GO:0060452" "GO:0009069" "GO:0070179" -#> [21] "GO:0050942" "GO:1903798" "GO:0002331" "GO:0061508" "GO:0000726" -#> [26] "GO:0045324" "GO:0099178" "GO:0006542" "GO:0032788" "GO:0036257" -#> [31] "GO:0033189" "GO:1902459" "GO:0075523" "GO:0006679" "GO:0007080" -#> [36] "GO:0043012" "GO:0019243" "GO:1900163" "GO:0044351" "GO:0048245" -#> [41] "GO:0061537" "GO:0006270" "GO:0009756" "GO:0000023" "GO:0014886" -#> [46] "GO:0032764" "GO:0035352" "GO:0006577" "GO:2000525" "GO:1902747" -#> [51] "GO:0071466" "GO:0031441" "GO:0051023" "GO:1904947" "GO:0042474" -#> [56] "GO:0042311" "GO:0019673" "GO:0070126" "GO:0051881" "GO:0006312" -#> [61] "GO:1901841" "GO:0090647" "GO:0060488" "GO:0032535" "GO:1902512" -#> [66] "GO:0002377" "GO:0006597" "GO:0015819" "GO:0031408" "GO:0002541" -#> [71] "GO:0046031" "GO:0006984" "GO:1990253" "GO:0016488" "GO:0060667" -#> [76] "GO:0006335" "GO:0016101" "GO:2000330" "GO:0098736" "GO:0061756" -#> [81] "GO:2000659" "GO:0042754" "GO:0042274" "GO:0007518" "GO:0001798" -#> [86] "GO:2000065" "GO:0048378" "GO:1990182" "GO:0070914" "GO:0070286" -#> [91] "GO:0032455" "GO:0099564" "GO:0007339" "GO:0035544" "GO:0050999" -#> [96] "GO:0008064" "GO:0021562" "GO:2000650" "GO:0051791" "GO:0043584"
    # } +
    #> [1] "GO:0032329" "GO:0050732" "GO:0006425" "GO:1904354" "GO:0080163" +#> [6] "GO:2000392" "GO:0060428" "GO:0003182" "GO:0090172" "GO:0090384" +#> [11] "GO:0080154" "GO:0001779" "GO:0048641" "GO:0060071" "GO:0072593" +#> [16] "GO:1905642" "GO:1905676" "GO:0043242" "GO:0070472" "GO:0071260" +#> [21] "GO:0051209" "GO:0036179" "GO:0000019" "GO:0070207" "GO:0043968" +#> [26] "GO:1901909" "GO:0032960" "GO:0032687" "GO:0010575" "GO:1903608" +#> [31] "GO:0002329" "GO:1905684" "GO:0098789" "GO:1990798" "GO:0070295" +#> [36] "GO:0010826" "GO:0003168" "GO:0099538" "GO:0051026" "GO:0051491" +#> [41] "GO:0051295" "GO:0002769" "GO:0045662" "GO:1902553" "GO:0045617" +#> [46] "GO:0060234" "GO:0048285" "GO:1904649" "GO:0090084" "GO:0036006" +#> [51] "GO:0030183" "GO:0032696" "GO:0032348" "GO:1905167" "GO:0061041" +#> [56] "GO:0036344" "GO:0071900" "GO:1905062" "GO:0035112" "GO:0060806" +#> [61] "GO:1902440" "GO:1905216" "GO:0043318" "GO:0009749" "GO:0044334" +#> [66] "GO:0006541" "GO:0060546" "GO:0010748" "GO:0051573" "GO:0032400" +#> [71] "GO:0048713" "GO:0043117" "GO:0097119" "GO:1902905" "GO:0072679" +#> [76] "GO:0007196" "GO:2000431" "GO:0045187" "GO:0017038" "GO:0071218" +#> [81] "GO:0045184" "GO:1904708" "GO:0099083" "GO:0008045" "GO:0010916" +#> [86] "GO:0060242" "GO:0060333" "GO:0061290" "GO:1904353" "GO:0055111" +#> [91] "GO:0072173" "GO:0060523" "GO:0072425" "GO:1904695" "GO:0033564" +#> [96] "GO:0045955" "GO:0120009" "GO:0090280" "GO:0070230" "GO:0008154"
    # }
    diff --git a/docs/reference/remove_clustering_methods.html b/docs/reference/remove_clustering_methods.html index b41ef78..12d426c 100644 --- a/docs/reference/remove_clustering_methods.html +++ b/docs/reference/remove_clustering_methods.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/reset_clustering_methods.html b/docs/reference/reset_clustering_methods.html index e35b601..319ed14 100644 --- a/docs/reference/reset_clustering_methods.html +++ b/docs/reference/reset_clustering_methods.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/scale_fontsize.html b/docs/reference/scale_fontsize.html index e957e56..1713fb3 100644 --- a/docs/reference/scale_fontsize.html +++ b/docs/reference/scale_fontsize.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 @@ -165,7 +165,7 @@

    Examp
    x = runif(10, min = 1, max = 20) # scale x to fontsize 4 to 16. scale_fontsize(x) -
    #> [1] 8 10 11 5 7 11 10 5 9 4
    +
    #> [1] 11 6 8 11 6 5 11 10 8 7
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    Examp go_id = random_GO(500) mat = GO_similarity(go_id)
    #> You haven't provided value for `ont`, guess it as `BP`.
    df = simplifyGO(mat, word_cloud_grob_param = list(max_width = 80)) -
    #> Cluster 500 terms by 'binary_cut'... 20 clusters, used 0.795145 secs.
    head(df) +
    #> Cluster 500 terms by 'binary_cut'... 20 clusters, used 0.774152 secs.
    head(df)
    #> id #> 1 GO:0010046 #> 2 GO:0006977 diff --git a/docs/reference/simplifyGOFromMultipleLists.html b/docs/reference/simplifyGOFromMultipleLists.html index ae9b3cf..251a2fd 100644 --- a/docs/reference/simplifyGOFromMultipleLists.html +++ b/docs/reference/simplifyGOFromMultipleLists.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5
    @@ -137,6 +137,7 @@

    Perform simplifyGO analysis with multiple lists of GO IDs

    simplifyGOFromMultipleLists(lt, go_id_column = NULL, padj_column = NULL, padj_cutoff = 1e-2,
         filter = function(x) any(x < padj_cutoff), default = 1,
    +    ont = NULL, db = 'org.Hs.eg.db', measure = "Rel",
         heatmap_param = list(NULL),
         method = "binary_cut", control = list(partial = TRUE),
         min_term = NULL, verbose = TRUE, column_title = NULL, ...)
    @@ -168,6 +169,18 @@

    Arg

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    + + + + + + + + + + + + @@ -233,7 +246,7 @@

    Examp # perform functional enrichment on the signatures genes from cola anlaysis require(cola)
    #> Loading required package: cola
    #> ======================================== -#> cola version 1.9.0.1016 +#> cola version 1.9.0.9018 #> Bioconductor page: http://bioconductor.org/packages/cola/ #> Github page: https://github.com/jokergoo/cola #> Documentation: https://jokergoo.github.io/cola/ @@ -268,15 +281,15 @@

    Examp # a list of data frames simplifyGOFromMultipleLists(lt, padj_cutoff = 0.001)

    #> Use column 'ID' as `go_id_column`. -#> Use column 'p.adjust' as `padj_column`.
    #> Loading required namespace: gridtext
    #> 861/6488 GO IDs left for clustering.
    #> You haven't provided value for `ont`, guess it as `BP`.
    #> Cluster 861 terms by 'binary_cut'... 35 clusters, used 0.996567 secs.
    +#> Use column 'p.adjust' as `padj_column`.
    #> Loading required namespace: gridtext
    #> 861/6488 GO IDs left for clustering.
    #> You haven't provided value for `ont`, guess it as `BP`.
    #> Cluster 861 terms by 'binary_cut'... 35 clusters, used 0.9628239 secs.
    # a list of numeric values lt2 = lapply(lt, function(x) structure(x$p.adjust, names = x$ID)) simplifyGOFromMultipleLists(lt2, padj_cutoff = 0.001) -
    #> 861/6488 GO IDs left for clustering.
    #> You haven't provided value for `ont`, guess it as `BP`.
    #> Cluster 861 terms by 'binary_cut'... 35 clusters, used 0.9934809 secs.
    +
    #> 861/6488 GO IDs left for clustering.
    #> You haven't provided value for `ont`, guess it as `BP`.
    #> Cluster 861 terms by 'binary_cut'... 35 clusters, used 0.963834 secs.
    # a list of GO IDS lt3 = lapply(lt, function(x) x$ID[x$p.adjust < 0.001]) simplifyGOFromMultipleLists(lt3) -
    #> 861/861 GO IDs left for clustering.
    #> You haven't provided value for `ont`, guess it as `BP`.
    #> Cluster 861 terms by 'binary_cut'... 35 clusters, used 0.908818 secs.
    # } +
    #> 861/861 GO IDs left for clustering.
    #> You haven't provided value for `ont`, guess it as `BP`.
    #> Cluster 861 terms by 'binary_cut'... 35 clusters, used 0.874691 secs.
    # }
    diff --git a/docs/reference/term_similarity.html b/docs/reference/term_similarity.html index fb88074..ea568d2 100644 --- a/docs/reference/term_similarity.html +++ b/docs/reference/term_similarity.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/term_similarity_from_KEGG.html b/docs/reference/term_similarity_from_KEGG.html index 4e6b0c6..7920da5 100644 --- a/docs/reference/term_similarity_from_KEGG.html +++ b/docs/reference/term_similarity_from_KEGG.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/term_similarity_from_MSigDB.html b/docs/reference/term_similarity_from_MSigDB.html index 2fcfc63..567b29c 100644 --- a/docs/reference/term_similarity_from_MSigDB.html +++ b/docs/reference/term_similarity_from_MSigDB.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/term_similarity_from_Reactome.html b/docs/reference/term_similarity_from_Reactome.html index 37470c0..69262d6 100644 --- a/docs/reference/term_similarity_from_Reactome.html +++ b/docs/reference/term_similarity_from_Reactome.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/term_similarity_from_enrichResult.html b/docs/reference/term_similarity_from_enrichResult.html index 3bc4b99..cc07c47 100644 --- a/docs/reference/term_similarity_from_enrichResult.html +++ b/docs/reference/term_similarity_from_enrichResult.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/term_similarity_from_gmt.html b/docs/reference/term_similarity_from_gmt.html index 026077a..c852234 100644 --- a/docs/reference/term_similarity_from_gmt.html +++ b/docs/reference/term_similarity_from_gmt.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/widthDetails.word_cloud.html b/docs/reference/widthDetails.word_cloud.html index fc90bf7..b9b0ced 100644 --- a/docs/reference/widthDetails.word_cloud.html +++ b/docs/reference/widthDetails.word_cloud.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/docs/reference/word_cloud_grob.html b/docs/reference/word_cloud_grob.html index f25f2ad..8efbe6e 100644 --- a/docs/reference/word_cloud_grob.html +++ b/docs/reference/word_cloud_grob.html @@ -72,7 +72,7 @@ simplifyEnrichment - 1.1.4.9002 + 1.1.5 diff --git a/vignettes/simplifyEnrichment.Rmd b/vignettes/simplifyEnrichment.Rmd index 21710d8..0eb3c54 100644 --- a/vignettes/simplifyEnrichment.Rmd +++ b/vignettes/simplifyEnrichment.Rmd @@ -177,12 +177,8 @@ In the barplots, the three metrics are defined as follows: 2. **Number of clusters**: For each clustering, there are two numbers: the number of total clusters and the number of clusters with size >= 5 (only the big clusters). -3. **Block mean**: The mean similarity values of the blocks in the similarity - heatmap. Similar denotation as difference score, for term $i$ and $j$ where - $i$ can be the same as $j$ (values on the diagonal are also used), the - similarity value $x_{i,j}$ is saved to the vector $\mathbf{x_3}$ only when - term $i$ and $j$ are in a same cluster. The block mean is the mean value - over $\mathbf{x_3}$. +3. **Block mean**: Mean similarity values of the diagonal blocks in the similarity + heatmap. Using the same convention as for the difference score, the block mean is the mean value of $\mathbf{x_1}$. ```{r, fig.width = 10, fig.height = 7} compare_clustering_methods(mat)

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z)=tA}reQ#MlP=FM(SuuK;El9H_r~{DR)mx4^U*;NpD34FK=`imiK5^$2nD&8$M5py>zr69ZyGI6i3UjFH21SL1xTJO4N$^(Q80jz+XwEU^Y3E_*dqvcTX4L4wtKpA80 z!piDYne@3Mcc8ZM=lJ*$wCXyAs;Vl$1=kKT9RW7rT;KRp(LnQ?tl02L&sv(37+q*Y z1X6AzxUd;;mq9X1UJw0hY+c*~fdF{jXfN-7wovyC7Y%mT|F-;-qpi|Ehu|-f+>Q8i imj6qE|BIeBZg4KU9)I simplifyEnrichment - 1.1.4.9002 + 1.1.5 @@ -146,7 +146,6 @@

    Visualize the similarity matrix and the clustering

    term = NULL, min_term = round(nrow(mat)*0.01), order_by_size = FALSE, - cluster_slices = FALSE, exclude_words = character(0), max_words = 10, word_cloud_grob_param = list(), @@ -157,6 +156,7 @@

    Visualize the similarity matrix and the clustering

    column_title = NULL, ht_list = NULL, use_raster = TRUE, + run_draw = TRUE, ...)

    Arguments

    @@ -194,10 +194,6 @@

    Arg

    order_by_size

    Whether to reorder clusters by their sizes. The cluster that is merged from small clusters (size < min_term) is always put to the bottom of the heatmap.

    cluster_slices

    Whether to cluster slices.

    exclude_words

    Words that are excluded in the word cloud.

    use_raster

    Whether to write the heatmap as a raster image.

    run_draw

    Internally used.

    ...

    Other arguments passed to draw,HeatmapList-method.

    default

    The default value for the adjusted p-values. See Details.

    ont

    GO ontology. Value should be one of "BP", "CC" or "MF". If it is not specified, the function automatically identifies it by random sampling 10 IDs from go_id (see guess_ont).

    db

    Annotation database. It should be from https://bioconductor.org/packages/3.10/BiocViews.html#___OrgDb

    measure

    Semantic measure for the GO similarity, pass to termSim.

    heatmap_param

    Parameters for controlling the heatmap, see Details.

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