From 23b78cce10032e230b76dabaf99640065861f587 Mon Sep 17 00:00:00 2001 From: Rohit Farmer Date: Thu, 5 Oct 2023 13:43:21 -0400 Subject: [PATCH] R-CMD workflow yaml update --- .github/workflows/r_all.yml | 2 +- README.md | 1 - comments.md | 3 ++- docs/comments.html | 2 +- docs/index.html | 1 - docs/pkgdown.yml | 2 +- docs/search.json | 2 +- 7 files changed, 6 insertions(+), 7 deletions(-) diff --git a/.github/workflows/r_all.yml b/.github/workflows/r_all.yml index 912bd37..14d1648 100644 --- a/.github/workflows/r_all.yml +++ b/.github/workflows/r_all.yml @@ -32,7 +32,7 @@ jobs: steps: - uses: actions/checkout@v2 - - uses: r-lib/actions/setup-r@master + - uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} diff --git a/README.md b/README.md index 46d5517..496de16 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,6 @@ [![Actions Status](https://github.com/niaid/HDStIM/workflows/R-CMD-check/badge.svg)](https://github.com/niaid/HDStIM/actions?query=workflow%3AR-CMD-check) -[![pkgdown](https://github.com/niaid/HDStIM/workflows/pkgdown/badge.svg)](https://github.com/niaid/HDStIM/actions?query=workflow%3Apkgdown) The goal of this package is to identify response to a stimulant in diff --git a/comments.md b/comments.md index d7b650b..f3c9dd0 100644 --- a/comments.md +++ b/comments.md @@ -23,7 +23,8 @@ If the option to update Roxygen2 documentation is enabled in Rstudio settings th `devtools::document()` ## Pkgdown Website -If changes are made to README.Rmd or vignettes, build pkgdown website before pushing the code to GitHub. +If changes are made to README.Rmd or vignettes, build pkgdown website before pushing the code to GitHub. +[https://pkgdown.r-lib.org/reference/build_site_github_pages.html](https://pkgdown.r-lib.org/reference/build_site_github_pages.html). `pkgdown::build_site_github_pages()` diff --git a/docs/comments.html b/docs/comments.html index cb77e9d..05f7469 100644 --- a/docs/comments.html +++ b/docs/comments.html @@ -74,7 +74,7 @@

Updating Documentation

Pkgdown Website

-

If changes are made to README.Rmd or vignettes, build pkgdown website before pushing the code to GitHub.

+

If changes are made to README.Rmd or vignettes, build pkgdown website before pushing the code to GitHub.
https://pkgdown.r-lib.org/reference/build_site_github_pages.html.

pkgdown::build_site_github_pages()

diff --git a/docs/index.html b/docs/index.html index 0bff1ae..4a6c0af 100644 --- a/docs/index.html +++ b/docs/index.html @@ -122,7 +122,6 @@

Developers

Dev status

  • Actions Status
    • -
  • pkgdown
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index b3ae8fe..ad35f25 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.0.6 pkgdown_sha: ~ articles: HDStIM: HDStIM.html -last_built: 2023-10-05T16:36Z +last_built: 2023-10-05T17:38Z urls: reference: https://niaid.github.io/HDStIM/reference article: https://niaid.github.io/HDStIM/articles diff --git a/docs/search.json b/docs/search.json index 7b07976..d942f66 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"to-run-the-main-hdstim-function","dir":"Articles","previous_headings":"","what":"To Run The Main HDStIM Function","title":"HDStIM","text":"stated , HDStIM() primary function HDStIM package. use example data set chi11 (mass cytometry) included package. Note:chi11 minimal dataset included unit testing . Therefore, represent typical mass/flow cytometry assay.","code":"library(HDStIM) mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = TRUE, umap_cells = 500, verbose = FALSE) class(mapped_data) #> [1] \"list\" attributes(mapped_data) #> $names #> [1] \"response_mapping_main\" \"stacked_bar_plot_data\" \"state_markers\" #> [4] \"cellpop_col\" \"stim_label\" \"unstim_label\" #> [7] \"seed_val\" \"all_fisher_p_val\" \"all_k_means_data\" #> [10] \"umap_plot_data\" \"umap\" \"umap_cells\""},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"output","dir":"Articles","previous_headings":"To Run The Main HDStIM Function","what":"Output","title":"HDStIM","text":"HDStIM() returns list mapped expression data, data plot stacked bar plots visualize K-means Fisher’s exact test results, data plot optional UMAPs. list also includes tables containing statistical information K-means Fisher’s exact test information passed function attributes.","code":"head(mapped_data$response_mapping_main) #> # A tibble: 6 × 41 #> cluster_id sample_id condition patient_id stim_type cell_population CD45 #> #> 1 79 CHI-011_1_2_G CHI CHI-011 G CD11c CD14 CD38 3.48 #> 2 69 CHI-011_2_4_G CHI CHI-011 G CD11c CD14 CD38 2.22 #> 3 69 CHI-011_4_13_G CHI CHI-011 G CD11c CD14 CD38 3.03 #> 4 79 CHI-011_4_11_G CHI CHI-011 G CD11c CD14 CD38 2.82 #> 5 69 CHI-011_2_5_G CHI CHI-011 G CD11c CD14 CD38 2.07 #> 6 69 CHI-011_1_1_G CHI CHI-011 G CD11c CD14 CD38 3.39 #> # ℹ 34 more variables: CD7 , CD19 , pPLCg2 , CD4 , #> # IgD , CD20 , CD25 , pSTAT5 , CD123 , AKT , #> # pSTAT1 , CD27 , pP38 , CD24 , pSTAT3 , #> # CD11c , CD14 , CD56 , IkBa , pCREB , CD16 , #> # CD38 , CD8 , CD45RA , CD3 , pERK1_2 , #> # HLA_DR , pS6 , CD127 , ncount , k_cluster_id , #> # responding_cluster , response_status , comb_no head(mapped_data$stacked_bar_plot_data) #> # A tibble: 6 × 7 #> cell_population stim_type f_p_val stim_clust stim_status k_cluster #> #> 1 CD11c CD14 CD38 A 3.05e-26 1 unstim cluster1 #> 2 CD11c CD14 CD38 A 3.05e-26 1 unstim cluster2 #> 3 CD11c CD14 CD38 A 3.05e-26 1 stim cluster1 #> 4 CD11c CD14 CD38 A 3.05e-26 1 stim cluster2 #> 5 CD19 CD20 CD45RA HLA-DR C… A 5.56e-18 2 unstim cluster1 #> 6 CD19 CD20 CD45RA HLA-DR C… A 5.56e-18 2 unstim cluster2 #> # ℹ 1 more variable: cell_count_perc head(mapped_data$umap_plot_data) #> # A tibble: 6 × 8 #> cell_population stim_type condition tot_of_cells no_of_cells UMAP1 UMAP2 #> #> 1 CD11c CD14 CD38 A CHI 200 200 0.938 -0.149 #> 2 CD11c CD14 CD38 A CHI 200 200 1.92 1.09 #> 3 CD11c CD14 CD38 A CHI 200 200 -1.91 -0.812 #> 4 CD11c CD14 CD38 A CHI 200 200 0.773 0.447 #> 5 CD11c CD14 CD38 A CHI 200 200 1.24 0.926 #> 6 CD11c CD14 CD38 A CHI 200 200 -1.48 1.08 #> # ℹ 1 more variable: response_status head(mapped_data$all_fisher_p_val) #> stim_type cell_population stim_clust1 stim_clust2 unstim_clust1 #> 1 A CD11c CD14 CD38 83 17 11 #> 2 A CD11c HLA-DR 43 57 50 #> 3 A CD19 CD20 CD45RA HLA-DR CD24 23 77 83 #> 4 A CD3 CD27 CD127 35 65 32 #> 5 A CD3 CD4 CD27 CD45RA 34 66 42 #> 6 A CD3 CD4 HLA-DR 37 63 39 #> unstim_clust2 estimate p.value conf.low conf.high #> 1 89 38.31592946 3.052288e-26 16.41025641 97.9574110 #> 2 50 0.75546384 3.950464e-01 0.41598165 1.3672058 #> 3 17 0.06232828 5.555794e-18 0.02850666 0.1296101 #> 4 68 1.14345766 7.645958e-01 0.60968684 2.1500051 #> 5 58 0.71262870 3.078444e-01 0.38474999 1.3129671 #> 6 61 0.91899062 8.842353e-01 0.49842428 1.6921035 #> method alternative f_p_adj #> 1 Fisher's Exact Test for Count Data two.sided 2.441830e-25 #> 2 Fisher's Exact Test for Count Data two.sided 7.023047e-01 #> 3 Fisher's Exact Test for Count Data two.sided 2.539791e-17 #> 4 Fisher's Exact Test for Count Data two.sided 9.212285e-01 #> 5 Fisher's Exact Test for Count Data two.sided 6.156888e-01 #> 6 Fisher's Exact Test for Count Data two.sided 9.781822e-01 head(mapped_data$all_k_means_data) #> stim_type cell_population pPLCg2 pSTAT5 AKT #> 1 A CD11c CD14 CD38 0.6036923 1.5150197 0.19882950 #> 2 A CD11c CD14 CD38 0.3433208 0.5100987 0.14467489 #> 3 A CD11c HLA-DR 0.2754466 0.2628448 0.08187613 #> 4 A CD11c HLA-DR 0.9878153 0.5947371 0.10871637 #> 5 A CD19 CD20 CD45RA HLA-DR CD24 0.2804838 0.2853633 0.07871608 #> 6 A CD19 CD20 CD45RA HLA-DR CD24 0.3600085 1.9650785 0.12718000 #> pSTAT1 pP38 pSTAT3 IkBa pCREB pERK1_2 pS6 size #> 1 2.46662032 0.85286553 1.4007937 1.8315034 2.752456 0.10329694 0.27964780 94 #> 2 0.80050991 0.69575908 0.2100968 1.4439438 2.346255 0.04306572 0.16080807 106 #> 3 0.09136274 0.03271188 0.0532651 0.8501084 0.437598 0.03960397 0.07132055 93 #> 4 0.65155894 0.13219535 0.2322881 2.4118800 2.118350 0.02017302 0.12388073 107 #> 5 0.51540424 0.12034285 0.1396368 2.7656495 1.401279 0.13669583 0.10065605 106 #> 6 1.79297154 0.21265052 0.7480344 3.1434614 1.702514 0.20580153 0.18568148 94 #> withinss cluster #> 1 342.6724 1 #> 2 340.0997 2 #> 3 174.5916 1 #> 4 266.5471 2 #> 5 255.0538 1 #> 6 236.2004 2"},{"path":[]},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"plots-explaining-k-means-clustering-and-fishers-exact-test","dir":"Articles","previous_headings":"To Plot Diagnostic Figures","what":"Plots Explaining K-means Clustering And Fisher’s Exact Test","title":"HDStIM","text":"Using stacked_bar_plot_data, plot_K_Fisher() generates bar plots showing percentage cells stimulated unstimulated samples clustered two K-means clusters given cell population stimulation type. plot_K_Fisher() returns list ggplot objects. path specified, can also render save plots PNG format.","code":"k_plots <- plot_K_Fisher(mapped_data, path = NULL, verbose = FALSE) k_plots[[1]]"},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"umap-plots-to-visually-inspect-responding-and-non-responding-cell-mapping","dir":"Articles","previous_headings":"To Plot Diagnostic Figures","what":"UMAP Plots To Visually Inspect Responding and Non-Responding Cell Mapping","title":"HDStIM","text":"Note: can generate plots asked UMAPs calculated HDStIM() function. UMAP plots can helpful visually inspecting well HDStIM() mapped responding vs. non-responding cells cell population stimulation type. plot_umap() also returns list ggplot objects path specified, render save plots PNG format.","code":"u_plots <- plot_umap(mapped_data, path = NULL, verbose = FALSE) u_plots[[1]]"},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"distribution-plots-for-individual-state-marker-before-and-after-mapping","dir":"Articles","previous_headings":"To Plot Diagnostic Figures","what":"Distribution Plots for Individual State Marker before And After Mapping","title":"HDStIM","text":"state/signaling markers distribution plots shows kernel density estimation pre HDStIM() data stimulated unstimulated samples along density cells stimulated samples mapped responding. plot_exprs() also returns list ggplot objects path specified, render save plots PNG format.","code":"e_plots <- plot_exprs(mapped_data, path = NULL,verbose = FALSE) library(ggplot2) e_plots[[1]] + theme(text = element_text(size = 11)) #> Picking joint bandwidth of 0.0611 #> Picking joint bandwidth of 0.274 #> Picking joint bandwidth of 0.21 #> Picking joint bandwidth of 0.0301 #> Picking joint bandwidth of 0.23 #> Picking joint bandwidth of 0.191 #> Picking joint bandwidth of 0.102 #> Picking joint bandwidth of 0.179 #> Picking joint bandwidth of 0.172 #> Picking joint bandwidth of 0.288"},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"to-rank-statesignaling-markers-according-to-their-contribution-to-the-response","dir":"Articles","previous_headings":"","what":"To Rank State/Signaling Markers According To Their Contribution To The Response","title":"HDStIM","text":"marker_ranking_boruta() function runs Boruta stimulation - cell population combinations passed Fisher’s exact test rank markers according contribution response. function returns list tibble containing attribute statistics calculated Boruta ggplot objects. path NULL, plots also rendered saved specified folder PNG format.","code":"m_ranks <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = NULL, max_runs = 100, seed_val = 123, verbose = FALSE) head(m_ranks$attribute_stats) #> # A tibble: 6 × 9 #> stim_type cell_population state_marker meanImp medianImp minImp maxImp #> #> 1 A CD11c CD14 CD38 pP38 0.0606 0.109 -1.46 2.07 #> 2 A CD11c CD14 CD38 AKT 0.0420 0.197 -1.87 1.60 #> 3 A CD11c CD14 CD38 pERK1_2 2.50 2.66 -0.690 4.92 #> 4 A CD11c CD14 CD38 pPLCg2 2.87 2.80 0.719 5.10 #> 5 A CD11c CD14 CD38 pS6 2.75 2.90 0.229 4.99 #> 6 A CD11c CD14 CD38 pCREB 4.52 4.44 2.04 7.26 #> # ℹ 2 more variables: normHits , decision m_ranks$plots[[1]]"},{"path":"https://niaid.github.io/HDStIM/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Rohit Farmer. Author, maintainer. Richard Apps. Author. John Tsang. Author, project director.","code":""},{"path":"https://niaid.github.io/HDStIM/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Farmer R, Apps R, Tsang J (2023). HDStIM: High Dimensional Stimulation Immune Mapping ('HDStIM'). https://github.com/niaid/HDStIM, https://niaid.github.io/HDStIM/.","code":"@Manual{, title = {HDStIM: High Dimensional Stimulation Immune Mapping ('HDStIM')}, author = {Rohit Farmer and Richard Apps and John Tsang}, year = {2023}, note = {https://github.com/niaid/HDStIM, https://niaid.github.io/HDStIM/}, }"},{"path":[]},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"main-hdstim-function","dir":"","previous_headings":"","what":"Main HDStIM Function","title":"For Testing","text":"","code":"mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"marker-ranking-by-boruta","dir":"","previous_headings":"","what":"Marker Ranking by Boruta","title":"For Testing","text":"","code":"attribute_stats <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = NULL, max_runs = 1000, seed_val = 123, verbose = FALSE)"},{"path":[]},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"readmermd","dir":"","previous_headings":"","what":"README.Rmd","title":"For Testing","text":"Changes made README.Rmd overwritten README.md build. Therefore, edit README.md directly. making changes README.Rmd run knitr.","code":""},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"updating-documentation","dir":"","previous_headings":"","what":"Updating Documentation","title":"For Testing","text":"option update Roxygen2 documentation enabled Rstudio settings get triggered default package build/install/restart/check. However, use following command. devtools::document()","code":""},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"pkgdown-website","dir":"","previous_headings":"","what":"Pkgdown Website","title":"For Testing","text":"changes made README.Rmd vignettes, build pkgdown website pushing code GitHub. pkgdown::build_site_github_pages()","code":""},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"win-builder","dir":"","previous_headings":"","what":"Win Builder","title":"For Testing","text":"","code":"devtools::check_win_release()"},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"check-as-cran","dir":"","previous_headings":"","what":"Check as CRAN","title":"For Testing","text":"","code":"devtools::check(args = c('--as-cran')) devtools::check(args = c('--as-cran'), env_vars = c(NOT_CRAN = \"true\", \"_R_CHECK_LIMIT_CORES_\" = TRUE))"},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"submit-to-cran","dir":"","previous_headings":"","what":"Submit to CRAN","title":"For Testing","text":"","code":"devtools::submit_cran()"},{"path":"https://niaid.github.io/HDStIM/index.html","id":"hdstim-","dir":"","previous_headings":"","what":"High Dimensional Stimulation Immune Mapping (HDStIM)","title":"High Dimensional Stimulation Immune Mapping (HDStIM)","text":"goal package identify response stimulant CyTOF/Flow cytometry stimulation assays labeling cells responded based unsupervised high dimensional approach. Starting annotated cell populations either automated clustering FlowSOM traditional cell gating, primary function HDStIM() follows heuristic approach label cells responding non-responding. combination cell population stimulation type (e.g., CD127+ T-helper cells interferon-alpha), HDStIM() starts performing k-means clustering combined set cells stimulated unstimulated samples. K-means clustering performed expression data state markers combined. Upon clustering using contingency table, Fisher’s exact test determines effect size statistical significance partitioning. Cells form combinations pass Fisher’s exact test labelled responding.","code":""},{"path":"https://niaid.github.io/HDStIM/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"High Dimensional Stimulation Immune Mapping (HDStIM)","text":"can install released version stimcellselector CRAN : development version GitHub :","code":"install.packages(\"HDStIM\") # install.packages(\"devtools\") devtools::install_github(\"niaid/HDStIM\")"},{"path":"https://niaid.github.io/HDStIM/index.html","id":"contact","dir":"","previous_headings":"","what":"Contact","title":"High Dimensional Stimulation Immune Mapping (HDStIM)","text":"Rohit Farmer: rohit.farmer@nih.gov, rohit.farmer@gmail.com","code":""},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":null,"dir":"Reference","previous_headings":"","what":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"Function select cells stimulated samples likely responded stimulant.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"","code":"HDStIM( dat, state_markers, cellpop_col, stim_lab, unstim_lab, p_value = 0.05, seed_val = NULL, umap = FALSE, umap_cells = NULL, verbose = FALSE )"},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"dat tibble single cell data. Cells rows variables/markers columns. state_markers character vector labels state markers stimulation panel. cellpop_col Column tibble cell population IDs. stim_lab character vector stim label(s). unstim_lab character unstim label(s). p_value P-value Fisher's exact test. Default 0.05. seed_val Seed value (integer) kmeans clustering. Default NULL seed value. umap Boolean (T/F) carry UMAP selected cells. Default FALSE skip UMAP calculation. umap_cells integer; calculating UMAPs take minimum umap_cells per cluster total number cells cluster size smaller umap_cells. Default NULL. verbose Logical. make function verbose. Default FALSE.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"list tibbles expression data selected cells, data plot stacked bar plots, data plot UMAP plots, parameters passed function.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"","code":"mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/chi11.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data set for CyTOF Stimulation Assay — chi11","title":"Sample data set for CyTOF Stimulation Assay — chi11","text":"list CyTOF stimulation assay data.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/chi11.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data set for CyTOF Stimulation Assay — chi11","text":"","code":"chi11"},{"path":"https://niaid.github.io/HDStIM/reference/chi11.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sample data set for CyTOF Stimulation Assay — chi11","text":"list one tibble containig CyTOF expression data. four character vectors arguments HDStIM function. chi11$expr_data 7,000 X 36 tibble. Cells rows variables columns. first 6 columns contain cell cluster_id (FlowSOM clustering), sample_id (unique FSC file), condition (comparison groups), patient_id (unique subject), stim_type (labels types stimulation assays including unstim), merging1 (meta culster labels ConsensusClusterPlus). last 30 columns contain archsinh transformed CyTOF expression values 30 markers (20 type 10 state) used sitmulation panel. chi11$type_markers character vector labels type markers used stimulation panel. chi11$state_markers character vector labels state markers used stimulation panel. chi11$cluster_col character label meta-cluster/cluster ID column chi11$expr_dat tibble. chi11$stim_label character vector label(s) stimulation types corresponding labels thestim_type column chi11$expr_data. chi11$unstim_label character label unstim cells corresponding labels thestim_type column chi11$expr_data.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":null,"dir":"Reference","previous_headings":"","what":"Marker Ranking by Boruta — marker_ranking_boruta","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"Function run Boruta stimulation - cell population combinations passed Fisher's exact test rank markers according contribution response.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"","code":"marker_ranking_boruta( mapped_data, path = NULL, n_cells = NULL, max_runs = 100, seed_val = 123, verbose = 0 )"},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"mapped_data Returned list HDStIM function. path Path folder save figures generated function. n_cells Number cells sample data. Default NULL include cells. max_runs Maximum number runs random forest algorithm. Default 100. seed_val Seed value Boruta. Default 123. verbose 0, 1, 2. Default 0.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"list tibble containing attribute statistics calculated Boruta ggplot objects. path NULL, plots also rendered saved specified folder PNG format.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"","code":"# \\donttest{ mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE) marker_ranking <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = NULL, max_runs = 1000, seed_val = 123, verbose = 0) # }"},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"Diagnostic plots explaining K-means clustering Fisher's exact test carried HDStIM","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"","code":"plot_K_Fisher(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"mapped_data Returned list HDStIM function. path Path folder save figures generated function NULL default. verbose Logical. make function verbose. Default FALSE.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"list ggplot objects. path NULL, PNG files plots saved specified folder.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"","code":"mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE) pk <- plot_K_Fisher(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"Diagnostic plots showing individual marker distribution mapping HDStIM","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"","code":"plot_exprs(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"mapped_data List output HDStIM function. path Path folder save figures generated function. verbose Logical. make function verbose. Default FALSE.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"list ggplot objects. path NULL, PNG files plots saved specified folder.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"","code":"mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE) pe <- plot_exprs(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Marker ranking heatmap — plot_marker_ranking_heatmap","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"consolidated heatmap showing importance scores state markers (X-axis) stimulation-cell population combinations passed Fisher's exact test (Y-axis).","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"","code":"plot_marker_ranking_heatmap(marker_ranking)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"marker_ranking Returned list marker_ranking_boruta function.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"ComplexHeatmap object","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"","code":"# \\donttest{ mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = TRUE, umap_cells = 50, verbose = FALSE) marker_ranking <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = NULL, max_runs = 1000, seed_val = 123, verbose = 0) pht <- plot_marker_ranking_heatmap(marker_ranking) # }"},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"Diagnostic UMAP plots showing partitioning cells responding non-responding groups HDStIM","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"","code":"plot_umap(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"mapped_data Returned list HDStIM function. path Path folder save figures generated function. verbose Logical. make function verbose. Default FALSE.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"list ggplot objects. path NULL, PNG files plots saved specified folder.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"","code":"# \\donttest{ mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = TRUE, umap_cells = 50, verbose = FALSE) pu <- plot_umap(mapped_data, path = NULL, verbose = FALSE) # }"}] +[{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"to-run-the-main-hdstim-function","dir":"Articles","previous_headings":"","what":"To Run The Main HDStIM Function","title":"HDStIM","text":"stated , HDStIM() primary function HDStIM package. use example data set chi11 (mass cytometry) included package. Note:chi11 minimal dataset included unit testing . Therefore, represent typical mass/flow cytometry assay.","code":"library(HDStIM) mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = TRUE, umap_cells = 500, verbose = FALSE) class(mapped_data) #> [1] \"list\" attributes(mapped_data) #> $names #> [1] \"response_mapping_main\" \"stacked_bar_plot_data\" \"state_markers\" #> [4] \"cellpop_col\" \"stim_label\" \"unstim_label\" #> [7] \"seed_val\" \"all_fisher_p_val\" \"all_k_means_data\" #> [10] \"umap_plot_data\" \"umap\" \"umap_cells\""},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"output","dir":"Articles","previous_headings":"To Run The Main HDStIM Function","what":"Output","title":"HDStIM","text":"HDStIM() returns list mapped expression data, data plot stacked bar plots visualize K-means Fisher’s exact test results, data plot optional UMAPs. list also includes tables containing statistical information K-means Fisher’s exact test information passed function attributes.","code":"head(mapped_data$response_mapping_main) #> # A tibble: 6 × 41 #> cluster_id sample_id condition patient_id stim_type cell_population CD45 #> #> 1 79 CHI-011_1_2_G CHI CHI-011 G CD11c CD14 CD38 3.48 #> 2 69 CHI-011_2_4_G CHI CHI-011 G CD11c CD14 CD38 2.22 #> 3 69 CHI-011_4_13_G CHI CHI-011 G CD11c CD14 CD38 3.03 #> 4 79 CHI-011_4_11_G CHI CHI-011 G CD11c CD14 CD38 2.82 #> 5 69 CHI-011_2_5_G CHI CHI-011 G CD11c CD14 CD38 2.07 #> 6 69 CHI-011_1_1_G CHI CHI-011 G CD11c CD14 CD38 3.39 #> # ℹ 34 more variables: CD7 , CD19 , pPLCg2 , CD4 , #> # IgD , CD20 , CD25 , pSTAT5 , CD123 , AKT , #> # pSTAT1 , CD27 , pP38 , CD24 , pSTAT3 , #> # CD11c , CD14 , CD56 , IkBa , pCREB , CD16 , #> # CD38 , CD8 , CD45RA , CD3 , pERK1_2 , #> # HLA_DR , pS6 , CD127 , ncount , k_cluster_id , #> # responding_cluster , response_status , comb_no head(mapped_data$stacked_bar_plot_data) #> # A tibble: 6 × 7 #> cell_population stim_type f_p_val stim_clust stim_status k_cluster #> #> 1 CD11c CD14 CD38 A 3.05e-26 1 unstim cluster1 #> 2 CD11c CD14 CD38 A 3.05e-26 1 unstim cluster2 #> 3 CD11c CD14 CD38 A 3.05e-26 1 stim cluster1 #> 4 CD11c CD14 CD38 A 3.05e-26 1 stim cluster2 #> 5 CD19 CD20 CD45RA HLA-DR C… A 5.56e-18 2 unstim cluster1 #> 6 CD19 CD20 CD45RA HLA-DR C… A 5.56e-18 2 unstim cluster2 #> # ℹ 1 more variable: cell_count_perc head(mapped_data$umap_plot_data) #> # A tibble: 6 × 8 #> cell_population stim_type condition tot_of_cells no_of_cells UMAP1 UMAP2 #> #> 1 CD11c CD14 CD38 A CHI 200 200 0.938 -0.149 #> 2 CD11c CD14 CD38 A CHI 200 200 1.92 1.09 #> 3 CD11c CD14 CD38 A CHI 200 200 -1.91 -0.812 #> 4 CD11c CD14 CD38 A CHI 200 200 0.773 0.447 #> 5 CD11c CD14 CD38 A CHI 200 200 1.24 0.926 #> 6 CD11c CD14 CD38 A CHI 200 200 -1.48 1.08 #> # ℹ 1 more variable: response_status head(mapped_data$all_fisher_p_val) #> stim_type cell_population stim_clust1 stim_clust2 unstim_clust1 #> 1 A CD11c CD14 CD38 83 17 11 #> 2 A CD11c HLA-DR 43 57 50 #> 3 A CD19 CD20 CD45RA HLA-DR CD24 23 77 83 #> 4 A CD3 CD27 CD127 35 65 32 #> 5 A CD3 CD4 CD27 CD45RA 34 66 42 #> 6 A CD3 CD4 HLA-DR 37 63 39 #> unstim_clust2 estimate p.value conf.low conf.high #> 1 89 38.31592946 3.052288e-26 16.41025641 97.9574110 #> 2 50 0.75546384 3.950464e-01 0.41598165 1.3672058 #> 3 17 0.06232828 5.555794e-18 0.02850666 0.1296101 #> 4 68 1.14345766 7.645958e-01 0.60968684 2.1500051 #> 5 58 0.71262870 3.078444e-01 0.38474999 1.3129671 #> 6 61 0.91899062 8.842353e-01 0.49842428 1.6921035 #> method alternative f_p_adj #> 1 Fisher's Exact Test for Count Data two.sided 2.441830e-25 #> 2 Fisher's Exact Test for Count Data two.sided 7.023047e-01 #> 3 Fisher's Exact Test for Count Data two.sided 2.539791e-17 #> 4 Fisher's Exact Test for Count Data two.sided 9.212285e-01 #> 5 Fisher's Exact Test for Count Data two.sided 6.156888e-01 #> 6 Fisher's Exact Test for Count Data two.sided 9.781822e-01 head(mapped_data$all_k_means_data) #> stim_type cell_population pPLCg2 pSTAT5 AKT #> 1 A CD11c CD14 CD38 0.6036923 1.5150197 0.19882950 #> 2 A CD11c CD14 CD38 0.3433208 0.5100987 0.14467489 #> 3 A CD11c HLA-DR 0.2754466 0.2628448 0.08187613 #> 4 A CD11c HLA-DR 0.9878153 0.5947371 0.10871637 #> 5 A CD19 CD20 CD45RA HLA-DR CD24 0.2804838 0.2853633 0.07871608 #> 6 A CD19 CD20 CD45RA HLA-DR CD24 0.3600085 1.9650785 0.12718000 #> pSTAT1 pP38 pSTAT3 IkBa pCREB pERK1_2 pS6 size #> 1 2.46662032 0.85286553 1.4007937 1.8315034 2.752456 0.10329694 0.27964780 94 #> 2 0.80050991 0.69575908 0.2100968 1.4439438 2.346255 0.04306572 0.16080807 106 #> 3 0.09136274 0.03271188 0.0532651 0.8501084 0.437598 0.03960397 0.07132055 93 #> 4 0.65155894 0.13219535 0.2322881 2.4118800 2.118350 0.02017302 0.12388073 107 #> 5 0.51540424 0.12034285 0.1396368 2.7656495 1.401279 0.13669583 0.10065605 106 #> 6 1.79297154 0.21265052 0.7480344 3.1434614 1.702514 0.20580153 0.18568148 94 #> withinss cluster #> 1 342.6724 1 #> 2 340.0997 2 #> 3 174.5916 1 #> 4 266.5471 2 #> 5 255.0538 1 #> 6 236.2004 2"},{"path":[]},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"plots-explaining-k-means-clustering-and-fishers-exact-test","dir":"Articles","previous_headings":"To Plot Diagnostic Figures","what":"Plots Explaining K-means Clustering And Fisher’s Exact Test","title":"HDStIM","text":"Using stacked_bar_plot_data, plot_K_Fisher() generates bar plots showing percentage cells stimulated unstimulated samples clustered two K-means clusters given cell population stimulation type. plot_K_Fisher() returns list ggplot objects. path specified, can also render save plots PNG format.","code":"k_plots <- plot_K_Fisher(mapped_data, path = NULL, verbose = FALSE) k_plots[[1]]"},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"umap-plots-to-visually-inspect-responding-and-non-responding-cell-mapping","dir":"Articles","previous_headings":"To Plot Diagnostic Figures","what":"UMAP Plots To Visually Inspect Responding and Non-Responding Cell Mapping","title":"HDStIM","text":"Note: can generate plots asked UMAPs calculated HDStIM() function. UMAP plots can helpful visually inspecting well HDStIM() mapped responding vs. non-responding cells cell population stimulation type. plot_umap() also returns list ggplot objects path specified, render save plots PNG format.","code":"u_plots <- plot_umap(mapped_data, path = NULL, verbose = FALSE) u_plots[[1]]"},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"distribution-plots-for-individual-state-marker-before-and-after-mapping","dir":"Articles","previous_headings":"To Plot Diagnostic Figures","what":"Distribution Plots for Individual State Marker before And After Mapping","title":"HDStIM","text":"state/signaling markers distribution plots shows kernel density estimation pre HDStIM() data stimulated unstimulated samples along density cells stimulated samples mapped responding. plot_exprs() also returns list ggplot objects path specified, render save plots PNG format.","code":"e_plots <- plot_exprs(mapped_data, path = NULL,verbose = FALSE) library(ggplot2) e_plots[[1]] + theme(text = element_text(size = 11)) #> Picking joint bandwidth of 0.0611 #> Picking joint bandwidth of 0.274 #> Picking joint bandwidth of 0.21 #> Picking joint bandwidth of 0.0301 #> Picking joint bandwidth of 0.23 #> Picking joint bandwidth of 0.191 #> Picking joint bandwidth of 0.102 #> Picking joint bandwidth of 0.179 #> Picking joint bandwidth of 0.172 #> Picking joint bandwidth of 0.288"},{"path":"https://niaid.github.io/HDStIM/articles/HDStIM.html","id":"to-rank-statesignaling-markers-according-to-their-contribution-to-the-response","dir":"Articles","previous_headings":"","what":"To Rank State/Signaling Markers According To Their Contribution To The Response","title":"HDStIM","text":"marker_ranking_boruta() function runs Boruta stimulation - cell population combinations passed Fisher’s exact test rank markers according contribution response. function returns list tibble containing attribute statistics calculated Boruta ggplot objects. path NULL, plots also rendered saved specified folder PNG format.","code":"m_ranks <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = NULL, max_runs = 100, seed_val = 123, verbose = FALSE) head(m_ranks$attribute_stats) #> # A tibble: 6 × 9 #> stim_type cell_population state_marker meanImp medianImp minImp maxImp #> #> 1 A CD11c CD14 CD38 pP38 0.0606 0.109 -1.46 2.07 #> 2 A CD11c CD14 CD38 AKT 0.0420 0.197 -1.87 1.60 #> 3 A CD11c CD14 CD38 pERK1_2 2.50 2.66 -0.690 4.92 #> 4 A CD11c CD14 CD38 pPLCg2 2.87 2.80 0.719 5.10 #> 5 A CD11c CD14 CD38 pS6 2.75 2.90 0.229 4.99 #> 6 A CD11c CD14 CD38 pCREB 4.52 4.44 2.04 7.26 #> # ℹ 2 more variables: normHits , decision m_ranks$plots[[1]]"},{"path":"https://niaid.github.io/HDStIM/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Rohit Farmer. Author, maintainer. Richard Apps. Author. John Tsang. Author, project director.","code":""},{"path":"https://niaid.github.io/HDStIM/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Farmer R, Apps R, Tsang J (2023). HDStIM: High Dimensional Stimulation Immune Mapping ('HDStIM'). https://github.com/niaid/HDStIM, https://niaid.github.io/HDStIM/.","code":"@Manual{, title = {HDStIM: High Dimensional Stimulation Immune Mapping ('HDStIM')}, author = {Rohit Farmer and Richard Apps and John Tsang}, year = {2023}, note = {https://github.com/niaid/HDStIM, https://niaid.github.io/HDStIM/}, }"},{"path":[]},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"main-hdstim-function","dir":"","previous_headings":"","what":"Main HDStIM Function","title":"For Testing","text":"","code":"mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"marker-ranking-by-boruta","dir":"","previous_headings":"","what":"Marker Ranking by Boruta","title":"For Testing","text":"","code":"attribute_stats <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = NULL, max_runs = 1000, seed_val = 123, verbose = FALSE)"},{"path":[]},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"readmermd","dir":"","previous_headings":"","what":"README.Rmd","title":"For Testing","text":"Changes made README.Rmd overwritten README.md build. Therefore, edit README.md directly. making changes README.Rmd run knitr.","code":""},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"updating-documentation","dir":"","previous_headings":"","what":"Updating Documentation","title":"For Testing","text":"option update Roxygen2 documentation enabled Rstudio settings get triggered default package build/install/restart/check. However, use following command. devtools::document()","code":""},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"pkgdown-website","dir":"","previous_headings":"","what":"Pkgdown Website","title":"For Testing","text":"changes made README.Rmd vignettes, build pkgdown website pushing code GitHub.https://pkgdown.r-lib.org/reference/build_site_github_pages.html. pkgdown::build_site_github_pages()","code":""},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"win-builder","dir":"","previous_headings":"","what":"Win Builder","title":"For Testing","text":"","code":"devtools::check_win_release()"},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"check-as-cran","dir":"","previous_headings":"","what":"Check as CRAN","title":"For Testing","text":"","code":"devtools::check(args = c('--as-cran')) devtools::check(args = c('--as-cran'), env_vars = c(NOT_CRAN = \"true\", \"_R_CHECK_LIMIT_CORES_\" = TRUE))"},{"path":"https://niaid.github.io/HDStIM/comments.html","id":"submit-to-cran","dir":"","previous_headings":"","what":"Submit to CRAN","title":"For Testing","text":"","code":"devtools::submit_cran()"},{"path":"https://niaid.github.io/HDStIM/index.html","id":"hdstim-","dir":"","previous_headings":"","what":"High Dimensional Stimulation Immune Mapping (HDStIM)","title":"High Dimensional Stimulation Immune Mapping (HDStIM)","text":"goal package identify response stimulant CyTOF/Flow cytometry stimulation assays labeling cells responded based unsupervised high dimensional approach. Starting annotated cell populations either automated clustering FlowSOM traditional cell gating, primary function HDStIM() follows heuristic approach label cells responding non-responding. combination cell population stimulation type (e.g., CD127+ T-helper cells interferon-alpha), HDStIM() starts performing k-means clustering combined set cells stimulated unstimulated samples. K-means clustering performed expression data state markers combined. Upon clustering using contingency table, Fisher’s exact test determines effect size statistical significance partitioning. Cells form combinations pass Fisher’s exact test labelled responding.","code":""},{"path":"https://niaid.github.io/HDStIM/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"High Dimensional Stimulation Immune Mapping (HDStIM)","text":"can install released version stimcellselector CRAN : development version GitHub :","code":"install.packages(\"HDStIM\") # install.packages(\"devtools\") devtools::install_github(\"niaid/HDStIM\")"},{"path":"https://niaid.github.io/HDStIM/index.html","id":"contact","dir":"","previous_headings":"","what":"Contact","title":"High Dimensional Stimulation Immune Mapping (HDStIM)","text":"Rohit Farmer: rohit.farmer@nih.gov, rohit.farmer@gmail.com","code":""},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":null,"dir":"Reference","previous_headings":"","what":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"Function select cells stimulated samples likely responded stimulant.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"","code":"HDStIM( dat, state_markers, cellpop_col, stim_lab, unstim_lab, p_value = 0.05, seed_val = NULL, umap = FALSE, umap_cells = NULL, verbose = FALSE )"},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"dat tibble single cell data. Cells rows variables/markers columns. state_markers character vector labels state markers stimulation panel. cellpop_col Column tibble cell population IDs. stim_lab character vector stim label(s). unstim_lab character unstim label(s). p_value P-value Fisher's exact test. Default 0.05. seed_val Seed value (integer) kmeans clustering. Default NULL seed value. umap Boolean (T/F) carry UMAP selected cells. Default FALSE skip UMAP calculation. umap_cells integer; calculating UMAPs take minimum umap_cells per cluster total number cells cluster size smaller umap_cells. Default NULL. verbose Logical. make function verbose. Default FALSE.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"list tibbles expression data selected cells, data plot stacked bar plots, data plot UMAP plots, parameters passed function.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/HDStIM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"HDStIM: High Dimensional Stimulation Immune Mapping — HDStIM","text":"","code":"mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/chi11.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data set for CyTOF Stimulation Assay — chi11","title":"Sample data set for CyTOF Stimulation Assay — chi11","text":"list CyTOF stimulation assay data.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/chi11.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data set for CyTOF Stimulation Assay — chi11","text":"","code":"chi11"},{"path":"https://niaid.github.io/HDStIM/reference/chi11.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sample data set for CyTOF Stimulation Assay — chi11","text":"list one tibble containig CyTOF expression data. four character vectors arguments HDStIM function. chi11$expr_data 7,000 X 36 tibble. Cells rows variables columns. first 6 columns contain cell cluster_id (FlowSOM clustering), sample_id (unique FSC file), condition (comparison groups), patient_id (unique subject), stim_type (labels types stimulation assays including unstim), merging1 (meta culster labels ConsensusClusterPlus). last 30 columns contain archsinh transformed CyTOF expression values 30 markers (20 type 10 state) used sitmulation panel. chi11$type_markers character vector labels type markers used stimulation panel. chi11$state_markers character vector labels state markers used stimulation panel. chi11$cluster_col character label meta-cluster/cluster ID column chi11$expr_dat tibble. chi11$stim_label character vector label(s) stimulation types corresponding labels thestim_type column chi11$expr_data. chi11$unstim_label character label unstim cells corresponding labels thestim_type column chi11$expr_data.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":null,"dir":"Reference","previous_headings":"","what":"Marker Ranking by Boruta — marker_ranking_boruta","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"Function run Boruta stimulation - cell population combinations passed Fisher's exact test rank markers according contribution response.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"","code":"marker_ranking_boruta( mapped_data, path = NULL, n_cells = NULL, max_runs = 100, seed_val = 123, verbose = 0 )"},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"mapped_data Returned list HDStIM function. path Path folder save figures generated function. n_cells Number cells sample data. Default NULL include cells. max_runs Maximum number runs random forest algorithm. Default 100. seed_val Seed value Boruta. Default 123. verbose 0, 1, 2. Default 0.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"list tibble containing attribute statistics calculated Boruta ggplot objects. path NULL, plots also rendered saved specified folder PNG format.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/marker_ranking_boruta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Marker Ranking by Boruta — marker_ranking_boruta","text":"","code":"# \\donttest{ mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE) marker_ranking <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = NULL, max_runs = 1000, seed_val = 123, verbose = 0) # }"},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"Diagnostic plots explaining K-means clustering Fisher's exact test carried HDStIM","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"","code":"plot_K_Fisher(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"mapped_data Returned list HDStIM function. path Path folder save figures generated function NULL default. verbose Logical. make function verbose. Default FALSE.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"list ggplot objects. path NULL, PNG files plots saved specified folder.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_K_Fisher.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic plots explaining K-means clustering and Fisher's exact test carried out by HDStIM — plot_K_Fisher","text":"","code":"mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE) pk <- plot_K_Fisher(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"Diagnostic plots showing individual marker distribution mapping HDStIM","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"","code":"plot_exprs(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"mapped_data List output HDStIM function. path Path folder save figures generated function. verbose Logical. make function verbose. Default FALSE.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"list ggplot objects. path NULL, PNG files plots saved specified folder.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_exprs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic plots showing individual marker distribution before and after mapping by HDStIM — plot_exprs","text":"","code":"mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = FALSE, umap_cells = NULL, verbose = FALSE) pe <- plot_exprs(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Marker ranking heatmap — plot_marker_ranking_heatmap","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"consolidated heatmap showing importance scores state markers (X-axis) stimulation-cell population combinations passed Fisher's exact test (Y-axis).","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"","code":"plot_marker_ranking_heatmap(marker_ranking)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"marker_ranking Returned list marker_ranking_boruta function.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"ComplexHeatmap object","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_marker_ranking_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Marker ranking heatmap — plot_marker_ranking_heatmap","text":"","code":"# \\donttest{ mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = TRUE, umap_cells = 50, verbose = FALSE) marker_ranking <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = NULL, max_runs = 1000, seed_val = 123, verbose = 0) pht <- plot_marker_ranking_heatmap(marker_ranking) # }"},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"Diagnostic UMAP plots showing partitioning cells responding non-responding groups HDStIM","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"","code":"plot_umap(mapped_data, path = NULL, verbose = FALSE)"},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"mapped_data Returned list HDStIM function. path Path folder save figures generated function. verbose Logical. make function verbose. Default FALSE.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"list ggplot objects. path NULL, PNG files plots saved specified folder.","code":""},{"path":"https://niaid.github.io/HDStIM/reference/plot_umap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic UMAP plots showing the partitioning of cells into responding and non-responding groups by HDStIM — plot_umap","text":"","code":"# \\donttest{ mapped_data <- HDStIM(chi11$expr_data, chi11$state_markers, chi11$cluster_col, chi11$stim_label, chi11$unstim_label, seed_val = 123, umap = TRUE, umap_cells = 50, verbose = FALSE) pu <- plot_umap(mapped_data, path = NULL, verbose = FALSE) # }"}]