update ALL help documents

.Rproj.user/E4C83D23/console06/INDEX001

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.Rproj.user/E4C83D23/pcs/files-pane.pper

@@ -1,5 +1,5 @@
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-    "path" : "~/Repositary/visual16S",
+    "path" : "~/xviz",
     "sortOrder" : [
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.Rproj.user/E4C83D23/pcs/source-pane.pper

@@ -1,3 +1,3 @@
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.Rproj.user/E4C83D23/pcs/windowlayoutstate.pper

@@ -1,13 +1,13 @@
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-        "splitterpos" : 546,
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+        "splitterpos" : 543,
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         "panelheight" : 1325,
-        "splitterpos" : 543,
+        "splitterpos" : 542,
         "topwindowstate" : "NORMAL",
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.Rproj.user/E4C83D23/pcs/workbench-pane.pper

@@ -1,5 +1,5 @@
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.Rproj.user/E4C83D23/persistent-state

@@ -1,8 +1,8 @@
 build-last-errors="[]"
-build-last-errors-base-dir="~/Repositary/visual16S/"
-build-last-outputs="[{\"output\":\"==> R CMD INSTALL --no-multiarch --with-keep.source visual16S\\n\\n\",\"type\":0},{\"output\":\"No protocol specified\\n\",\"type\":1},{\"output\":\"\",\"type\":1},{\"output\":\"* installing to library ‘/home/yeguanhua/R/x86_64-pc-linux-gnu-library/3.5’\\n\",\"type\":1},{\"output\":\"* installing *source* package ‘visual16S’ ...\\n\",\"type\":1},{\"output\":\"\",\"type\":1},{\"output\":\"** R\\n\",\"type\":1},{\"output\":\"** data\\n\",\"type\":1},{\"output\":\"*** moving datasets to lazyload DB\\n\",\"type\":1},{\"output\":\"\",\"type\":1},{\"output\":\"** byte-compile and prepare package for lazy loading\\n\",\"type\":1},{\"output\":\"\",\"type\":1},{\"output\":\"** help\\n\",\"type\":1},{\"output\":\"\",\"type\":1},{\"output\":\"*** installing help indices\\n\",\"type\":1},{\"output\":\"\",\"type\":1},{\"output\":\"** building package indices\\n\",\"type\":1},{\"output\":\"** testing if installed package can be loaded\\n\",\"type\":1},{\"output\":\"\",\"type\":1},{\"output\":\"No protocol specified\\n\",\"type\":1},{\"output\":\"\",\"type\":1},{\"output\":\"* DONE (visual16S)\\n\",\"type\":1},{\"output\":\"\",\"type\":1}]"
+build-last-errors-base-dir=""
+build-last-outputs="[]"
 compile_pdf_state="{\"errors\":[],\"output\":\"\",\"running\":false,\"tab_visible\":false,\"target_file\":\"\"}"
-files.monitored-path=""
+files.monitored-path="~/BJCancer/analysis"
 find-in-files-state="{\"handle\":\"\",\"input\":\"\",\"path\":\"\",\"regex\":true,\"results\":{\"file\":[],\"line\":[],\"lineValue\":[],\"matchOff\":[],\"matchOn\":[]},\"running\":false}"
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 saveActionState="0"

.Rproj.user/E4C83D23/sources/prop/INDEX

@@ -1,14 +1,28 @@
+~%2FBJCancer%2FMJ%2FMJ20190403%2Fanalysis%2FMJ_190403_analysis.Rmd="2E7B383F"
 ~%2FBJCancer%2FMJ%2Fanalysis%2Fanalysis_mj2.Rmd="3715EBC3"
 ~%2FBJCancer%2FMJ20190403011%2Fanalysis%2FMJ_190403_analysis.Rmd="5616A48D"
+~%2FBJCancer%2Fanalysis%2Fanalysis.R="312F76A"
+~%2FBJCancer%2Fanalysis%2Fsubject_extract.R="46FD7822"
+~%2FBJCancer%2Fmetadata%2Fprocessing.R="298A5E2"
 ~%2FR%2F16S-analysis-visualization%2FDESCRIPTION="8FFE7823"
 ~%2FR%2F16S-analysis-visualization%2FREADME.Rmd="5DA43DB2"
 ~%2FR%2FXbiome16SVisualization%2FDESCRIPTION="EAFED69B"
+~%2FRepositary%2F16S_analysis_functions_Yip.R="C94066A8"
 ~%2FRepositary%2Fvisual16S%2FDESCRIPTION="718DAA5C"
 ~%2FRepositary%2Fvisual16S%2FR%2Falpha_diversity_plot.R="AD67D71C"
+~%2FRepositary%2Fvisual16S%2FR%2Fconstruct_lefse_table.R="E2E70350"
+~%2FRepositary%2Fvisual16S%2FR%2Fconstruct_otu_table.R="9F3D6689"
+~%2FRepositary%2Fvisual16S%2FR%2Flog2fc.R="80BEE29D"
+~%2FRepositary%2Fvisual16S%2FR%2Fplot_alpha_diversity.R="C34039C6"
+~%2FRepositary%2Fvisual16S%2FR%2Fplot_beta_diversity.R="2E6CF992"
+~%2FRepositary%2Fvisual16S%2FR%2Fplot_correlation.R="C6A8435F"
+~%2FRepositary%2Fvisual16S%2FR%2Fplot_sparsity.R="B8AEF7F3"
 ~%2FRepositary%2Fvisual16S%2FR%2Fplot_stacked_bar.R="9D821D65"
+~%2FRepositary%2Fvisual16S%2FR%2Ftrack_reads_dada2.R="D1F8C44E"
 ~%2FRepositary%2Fvisual16S%2FREADME.Rmd="B4C7C8F"
 ~%2FRepositary%2Fvisual16S%2FREADME.md="CDBA173"
 ~%2FRepositary%2Fvisualization416S%2FREADME.Rmd="A92803F3"
+~%2Fxviz%2FR%2Fconstruct_otu_table.R="844B86E3"
 ~%2Fxviz%2FR%2Fplot_alpha_diversity.R="27BC8921"
 ~%2Fxviz%2FR%2Fplot_beta_diversity.R="E3BEF9E6"
 ~%2Fxviz%2FR%2Fplot_stacked_bar.R="BDEA371A"

.Rproj.user/shared/notebooks/paths

@@ -1,7 +1,21 @@
+/home/yeguanhua/BJCancer/MJ/MJ20190403/analysis/MJ_190403_analysis.Rmd="960A7DF"
+/home/yeguanhua/BJCancer/analysis/analysis.R="C933209"
+/home/yeguanhua/BJCancer/analysis/subject_extract.R="C8C14983"
+/home/yeguanhua/BJCancer/metadata/processing.R="5E210D39"
+/home/yeguanhua/Repositary/16S_analysis_functions_Yip.R="193FF21C"
 /home/yeguanhua/Repositary/visual16S/DESCRIPTION="D6FAFC4C"
+/home/yeguanhua/Repositary/visual16S/R/construct_lefse_table.R="FE82BBBF"
+/home/yeguanhua/Repositary/visual16S/R/construct_otu_table.R="1CB7F17F"
+/home/yeguanhua/Repositary/visual16S/R/log2fc.R="25C0CF72"
+/home/yeguanhua/Repositary/visual16S/R/plot_alpha_diversity.R="80616A8C"
+/home/yeguanhua/Repositary/visual16S/R/plot_beta_diversity.R="CEFFBA5"
+/home/yeguanhua/Repositary/visual16S/R/plot_correlation.R="8C1009F"
+/home/yeguanhua/Repositary/visual16S/R/plot_sparsity.R="4BF1C0A9"
 /home/yeguanhua/Repositary/visual16S/R/plot_stacked_bar.R="C1BF12DA"
+/home/yeguanhua/Repositary/visual16S/R/track_reads_dada2.R="B6F5D662"
 /home/yeguanhua/Repositary/visual16S/README.Rmd="EFE45572"
 /home/yeguanhua/Repositary/visual16S/README.md="802CD8B6"
+/home/yeguanhua/xviz/R/construct_otu_table.R="7A12CFBD"
 /home/yeguanhua/xviz/R/plot_alpha_diversity.R="33D2C56B"
 /home/yeguanhua/xviz/R/plot_beta_diversity.R="41254790"
 /home/yeguanhua/xviz/R/plot_stacked_bar.R="6591DBD3"

R/construct_lefse_table.R

@@ -5,15 +5,14 @@
 #' Format Data for LEfSe for more details.
 #'
 #' @param phyloseq A phyloseq object contain otu table, taxonomy table, sample
-#'                 metadata and phylogenetic tree.
+#' metadata and phylogenetic tree.
 #' @param feature The column name of the feature you want to select. In final
-#'                table, feature will be the first row.
+#' table, feature will be the first row.
 #' @param level The coloumn name of the level wanted to select. Default is
-#'              "all". If "all" then retain all taxonomy level, else retain the
-#'              taxonomy from Kingdom to selected level, drop everything else.
-#'              Level name should be one of "all", "Kingdom", "Phylum", "Class",
-#'              "Order", "Family", "Genus", "Species". Taxonomy will be
-#'              seperated by "|".
+#' "all". If "all" then retain all taxonomy level, else retain the taxonomy
+#' from Kingdom to selected level, drop everything else. Level name should be
+#' one of c("all", "Kingdom", "Phylum", "Class", "Order", "Family", "Genus",
+#' "Species"). Taxonomy will be seperated by "|".
 #' @export
 #' @examples
 #' construct_lefse_table(demo_phyloseq_object, feature = "diagnosis",

R/construct_otu_table.R

@@ -3,12 +3,12 @@
 #' construct_otu_table can construct a OTU table with a phyloseq object.
 #'
 #' @param phyloseq A phyloseq object contain otu table, taxonomy table, sample
-#'                 metadata and phylogenetic tree.
+#' metadata and phylogenetic tree.
 #' @param level The coloumn name of the level wanted to select. Default is
-#'              "all". If "all" then retain all taxonomy level and seperate by
-#'              "; ", else ONLY retain the given taxonomy level, drop
-#'              everything else. Level name should be one of "all", "Kingdom",
-#'              "Phylum", "Class", "Order", "Family", "Genus", "Species".
+#' "all". If "all" then retain all taxonomy level and seperate by "; ", else
+#' ONLY retain the given taxonomy level, drop everything else. Level name
+#' should be one of "all", "Kingdom", "Phylum", "Class", "Order", "Family",
+#' "Genus", "Species".
 #' @export
 #' @examples
 #' construct_otu_table(demo_phyloseq_object, level = "Genus") %>% .[,1:5]
@@ -19,8 +19,8 @@ construct_otu_table <- function(phyloseq, level = "all") {
   # Check if input 'level' is correct
   if (!level %in% c("all", "Kingdom", "Phylum", "Class", "Order", "Family",
                     "Genus", "Species")) {
-    stop('level should be one of "all", "Kingdom", "Phylum", "Class", "Order",
-         "Family", "Genus", "Species".')}
+    stop(paste0('Argument "level" should be one of c("all", "Kingdom", ',
+                '"Phylum" "Class", "Order", "Family", "Genus", "Species").'))}
   # Read in sequence table and taxonomy table from phyloseq
   otu <- otu_table(phyloseq) %>% as.data.frame() %>% t() %>%
     as.data.frame() %>% rownames_to_column(var = "OTU_ID")

R/convert_to_percentage.R

@@ -4,10 +4,9 @@
 #'
 #' @param df a input data frame.
 #' @param row_sum Default is TRUE. If row_sum == TRUE, then will take every
-#'                value of a row and divide by the summary of this row, and
-#'                apply this to every row. If row_sum == FALSE, then will take
-#'                every value of a column and divide by the summary of this
-#'                column, and apply this to every column.
+#' value of a row and divide by the summary of this row, and apply this to
+#' every row. If row_sum == FALSE, then will take every value of a column and
+#' divide by the summary of this column, and apply this to every column.
 #' @export
 #' @examples
 #' convert_to_percentage(demo_dada2_result$seq_tab, row_sum = TRUE) %>% .[,1:5]

R/extract_metadata_phyloseq.R

@@ -8,10 +8,10 @@
 #' column.
 #'
 #' @param phyloseq A phyloseq object contain otu table, taxonomy table, sample
-#'                 metadata and phylogenetic tree.
+#' metadata and phylogenetic tree.
 #' @param feature The column name of the feature you want to select. Default is
-#'                NA. If NA, will return the complete metadata, else will
-#'                return subject id and feature column that's given.
+#' NA. If NA, will return the complete metadata, else will return subject id
+#' and feature column that's given.
 #' @export
 #' @examples
 #' extract_metadata_phyloseq(demo_phyloseq_object)

R/log2fc.R

@@ -3,16 +3,15 @@
 #' This is a function for plotting log2 fold change.
 #'
 #' @param phyloseq A phyloseq object contain otu table, taxonomy table, sample
-#'                 metadata and phylogenetic tree.
-#' @param feature The column name of the feature you want to select from metadata,
-#'                e.g. "Phenotype".
-#' @param level Which taxonomy level to calculate fold change. Default is NA. If
-#'              level is given, will use construct_otu_table function to construct
-#'              OTU table, and use DESeq to calculate fold change.
-#'
+#' metadata and phylogenetic tree.
+#' @param feature The column name of the feature you want to select from
+#' metadata, e.g. "Phenotype".
+#' @param level Which taxonomy level to calculate fold change. Default is NA.
+#' If level is given, will use construct_otu_table function to construct OTU
+#' table, and use DESeq to calculate fold change.
 #' @param p_value The cut off P value for the fold change. Default is 0.05.
 #' @param save_res Default is FALSE. If TRUE, will save original result
-#'                 DESeq2_result.rds to current working directory.
+#' DESeq2_result.rds to current working directory.
 #' @param reference The control group. Default is NA.
 #' @param treatment The treatment group. Default is NA.
 #' @export

R/plot_alpha_diversity.R

@@ -3,19 +3,18 @@
 #' This is a function for plotting alpha diversity.
 #'
 #' @param phyloseq A phyloseq object contain otu table, taxonomy table, sample
-#'                 metadata and phylogenetic tree.
+#' metadata and phylogenetic tree.
 #' @param feature The column name of the feature you want to select from
-#'                metadata.
+#' metadata.
 #' @param feature2 The column name of another feature you want to select from
-#'                metadata, e.g. "Gender", which will make the plots draw in
-#'                different shapes. Default is NA.
+#' metadata, e.g. "Gender", which will make the plots draw in different shapes.
+#' Default is NA.
 #' @param measures The measures to calculate alpha diversity. Default is NA. If
-#'                 NA, all available alpha diversity measures will be
-#'                 calculated and generate a table. If not NA, measures
-#'                 should be one of "Observed", "Chao1", "ACE", "Shannon",
-#'                 "Simpson", "InvSimpson", "Fisher".
+#' NA, all available alpha diversity measures will be calculated and generate a
+#' table. If not NA, measures should be one of c("Observed", "Chao1", "ACE",
+#' "Shannon", "Simpson", "InvSimpson", "Fisher").
 #' @param p_test The p-value to test alpha diversity. p_test should be either
-#'               "wilcox" or "kruskal".
+#' "wilcox" or "kruskal". PS: "wilcox" can only work with two groups.
 #' @export
 #' @examples
 #' plot_alpha_diversity(demo_phyloseq_object, feature = "diagnosis",
@@ -27,15 +26,15 @@ plot_alpha_diversity <- function (phyloseq, feature, feature2 = NA,
   if (!is.na(measures)) {
     if (!measures %in% c("Observed", "Chao1", "ACE", "Shannon", "Simpson",
                          "InvSimpson", "Fisher")) {
-      stop('measures should be one of c("Observed", "Chao1", "ACE", "Shannon",
-           "Simpson", "InvSimpson", "Fisher").')
+      stop(paste0('Argument "measures" should be one of c("Observed", "Chao1"',
+                  ', "ACE", "Shannon", "Simpson", "InvSimpson", "Fisher").'))
     } else {
       ## Step 1: Use plot_richness function to calculate alpha diversity
       alpha_diversity <- plot_richness(phyloseq, x = feature,
                                        measures = measures)
       ## Step 2: Calculate p-value
       if (p_test == "wilcox") {
-        # Prepare feature table for calculating Mann-Whitney U test(2 groups only)
+        # Prepare feature table for calculating Mann-Whitney U test
         feature_tab_4_MWtest <- extract_metadata_phyloseq(phyloseq, feature)
         # Extract feature levels
         feature_0 <- feature_tab_4_MWtest[[feature]] %>% unique() %>% .[1]
@@ -48,11 +47,11 @@ plot_alpha_diversity <- function (phyloseq, feature, feature2 = NA,
         p_value <- wilcox.test(alpha_diversity$data$value ~
                                  feature_tab_4_MWtest[[feature]])$p.value
       } else if (p_test == "kruskal") {
-        # Kruskal test(for 2 or more groups)
+        # Kruskal test
         p_value <- kruskal.test(alpha_diversity$data$value,
                                 factor(alpha_diversity$data[,feature]))$p.value
       } else {
-        stop("The input p_test is not supported")
+        stop("The input p_test is not supported.")
       }
       ## Step 3: Plot alpha diversity
       y <- "value"

R/plot_beta_diversity.R

@@ -11,17 +11,20 @@
 #' @param method The method to calculate beta diversity. Method should be one
 #' of "bray", "jaccard", "unifrac", "wunifrac". Default is "bray".
 #' PS: "unifrac" and "wunifrac" require a phylogenetic tree.
+#' @param colors A color vector for the plot, the number of colors need to
+#' match the number of feature. Default is NULL, if NULL, plot_alpha_diversity
+#' will use ggsci::scale_color_jco for the plot.
 #' @export
 #' @examples
 #' plot_beta_diversity(demo_phyloseq_object, feature = "diagnosis")
 
 plot_beta_diversity <- function(phyloseq, feature, feature2 = NA,
-                                method = "bray"){
+                                method = "bray", colors = NULL){
   set.seed(99)
   ## Step 1: Calculate beta diversity
   if (!method %in% c("bray", "jaccard", "unifrac", "wunifrac")) {
-    stop('Beta diversity method should be one of "bray", "jaccard", "unifrac",
-         "wunifrac".')
+    stop(paste0('Beta diversity method should be one of c("bray", "jaccard", ',
+                '"unifrac", "wunifrac").'))
   } else if (method %in% c("unifrac", "wunifrac")) {
     # Requires phyloseq-class that contains both an otu_table and a
     # phylogenetic tree
@@ -41,7 +44,11 @@ plot_beta_diversity <- function(phyloseq, feature, feature2 = NA,
   # Join two tables
   beta_plot <- left_join(PC, metadata)
   # Print beta-diversity table
-  select(beta_plot, SampleID, !!feature, PC1, PC2) %>% print()
+  if (is.na(feature2)) {
+    select(beta_plot, SampleID, !!feature, PC1, PC2) %>% print()
+  } else {
+    select(beta_plot, SampleID, !!feature, !!feature2, PC1, PC2) %>% print()
+  }
   ## Step 3: Plot beta diversity
   # Make x-axis and y-axis names for aes_string
   x_name <- "PC1"
@@ -66,7 +73,6 @@ plot_beta_diversity <- function(phyloseq, feature, feature2 = NA,
             axis.text.x = element_text(size = 12),
             legend.text = element_text(size = 12),
             strip.text.x = element_text(size = 14))
-    p + ggsci::scale_color_jco() + ggsci::scale_fill_jco()
   } else {
     p <- ggplot(data = beta_plot,
                 # Use aes_string() to pass variables to ggplot
@@ -85,6 +91,13 @@ plot_beta_diversity <- function(phyloseq, feature, feature2 = NA,
             axis.text.x = element_text(size = 12),
             legend.text = element_text(size = 12),
             strip.text.x = element_text(size = 14))
+  }
+  if (is.null(colors)) {
     p + ggsci::scale_color_jco() + ggsci::scale_fill_jco()
+  } else if (length(colors) != length(unique(beta_plot[[feature]]))) {
+    stop(paste0("The number of colors and the number of ", feature,
+                " does not mtach."))
+  } else {
+    p + scale_color_manual(values = colors)
   }
 }

R/plot_correlation.R

@@ -13,8 +13,8 @@
 plot_correlation <- function (cor_tab, x, y, method = "pearson") {
   # Notice: Colnames of the input table can only be letters or numbers.
   if (any(str_detect(c(x, y), '\\W'))) {
-    stop("Colnames of the input columns can only contain letters or numbers,
-         or it can't be recognized when plotting.")
+    stop(paste0("Colnames of the input columns can only contain letters or',
+                ' numbers, or it can't be recognized when plotting."))
   }
   if (method == "pearson") {
     unit <- "Pearson's r"