export volcano plot func & add Github Action

combiz · Jun 2, 2021 · 063f1e0 · 063f1e0
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diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
@@ -0,0 +1,33 @@
+# For help debugging build failures open an issue on the RStudio community with the 'github-actions' tag.
+# https://community.rstudio.com/new-topic?category=Package%20development&tags=github-actions
+on:
+  push:
+    branches:
+      - main
+      - master
+      - dev-am
+  pull_request:
+    branches:
+      - main
+      - master
+
+name: R-CMD-check
+
+jobs:
+  R-CMD-check:
+    runs-on: macOS-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+      - uses: actions/checkout@v2
+      - uses: r-lib/actions/setup-r@v1
+      - name: Install dependencies
+        run: |
+          install.packages(c("remotes", "rcmdcheck"))
+          remotes::install_deps(dependencies = TRUE)
+        shell: Rscript {0}
+      - name: Check
+        run: |
+          options(crayon.enabled = TRUE)
+          rcmdcheck::rcmdcheck(args = "--no-manual", error_on = "error")
+        shell: Rscript {0}
diff --git a/NAMESPACE b/NAMESPACE
@@ -52,6 +52,7 @@ export(report_impacted_pathway)
 export(report_integrated_sce)
 export(report_merged_sce)
 export(report_qc_sce)
+export(volcano_plot)
 export(write_celltype_mappings)
 export(write_sce)
 export(write_sparse_matrix)

diff --git a/R/cluster_sce.R b/R/cluster_sce.R
@@ -2,9 +2,8 @@
 #' Cluster SingleCellExperiment with monocle3::cluster_cells
 #'
 #' @param sce a SingleCellExperiment object
-#' @param reduction_methods one or more of "PCA", "tSNE", "UMAP", "UMAP3D"
-#' @param pca_dims the number of pca dimensions used
-#' @param ... see uwot::umap for umap options
+#' @param ... see uwot::umap for umap options. Includes reduction_methods one 
+#' or more of "PCA", "tSNE", "UMAP", "UMAP3D"
 #'
 #' @return sce a SingleCellExperiment object annotated with reducedDims
 #'

diff --git a/R/diffexp_models.R b/R/diffexp_models.R
@@ -664,15 +664,23 @@ perform_de <- function(sce,
 }
 
 
-#' volcano plot
+#' Plot volcano plot for differential expression analysis
 #'
+#' @param dt Differential expression result table from perform_de() function.
+#' @param fc_threshold Fold change threshold for the volcano plot. This will be adjusted and plotted as the log2 fold change.  Default is 1.05.
+#' @param pval_cutoff The adjusted p-value cut-off for the volcano plot. Default is 0.05.
+#' @param n_label The number of top up & down differentially expressed genes to be labeled. Default is 10.
+#' 
+#' @return No return. The plot is printed out.
+#' 
+#' @family differential gene expression
+#' 
 #' @importFrom ggplot2 ggplot geom_point aes coord_cartesian
 #' @importFrom ggrepel geom_text_repel
 #' @importFrom dplyr %>% filter top_n
 #'
-#' @keywords internal
-
-.volcano_plot <- function(dt,
+#' @export
+volcano_plot <- function(dt,
                           fc_threshold = 1.05,
                           pval_cutoff = 0.05,
                           n_label = 10) {

diff --git a/R/find_cells_sce.R b/R/find_cells_sce.R
@@ -8,7 +8,7 @@
 #' @param lower see [DropletUtils::emptyDrops()]
 #' @param retain see [DropletUtils::emptyDrops()].  Use "auto" to calculate
 #' the parameter from the top `expect_cells` cells (cellranger method).
-#' @param alpha minimum FDR for [DropletUtils::emptyDrops()]
+# #' @param alpha minimum FDR for [DropletUtils::emptyDrops()]
 #' @param niters the number of Monte Carlo iterations
 #' @param expect_cells the number of cells expected for auto retain
 #' @param alpha_cutoff The alpha cutoff used for Monte Carlo signficance.

diff --git a/R/integrate_sce.R b/R/integrate_sce.R
@@ -5,9 +5,9 @@
 #'
 #' @param sce a SingleCellExperiment object or merged sce objects
 #' @param method the integration method to use
-#' @param k Inner dimension of factorization (number of factors).
 #'   Set to k=30 as default.
-#' @param ... Additional arguments
+#' @param ... Additional arguments. Such as k Inner dimension of factorization 
+#' (number of factors).
 #'
 #' @return sce SingleCellExperiment object annotated with reducedDims
 #'

diff --git a/R/liger_preprocess.R b/R/liger_preprocess.R
@@ -4,9 +4,11 @@
 #' Split merged object into multiple sce objects and extract sparse matrices:
 #' @param sce SingleCellExperiment object or merged objects
 #' @param k Inner dimension of factorization (number of factors).
+#' @param unique_id_var the colData variable identifying unique samples. 
+#' Default is "manifest".
 #'
 #' Make a Liger object:
-#' @param take.gene.union Whether to fill out raw.data matrices with union
+#' @param take_gene_union Whether to fill out raw.data matrices with union
 #'   of genes across all datasets (filling in 0 for missing data)
 #'   (requires make.sparse=T) (default FALSE).
 #' @param remove.missing Whether to remove cells not expressing any
@@ -15,7 +17,7 @@
 #'   not expressed in any dataset) (default TRUE).
 #'
 #'  Select informative genes:
-#' @param num.genes Number of genes to find for each dataset.
+#' @param num_genes Number of genes to find for each dataset.
 #'   Set to 3000 as default.
 #' @param combine How to combine variable genes across experiments.
 #'   Either "union" or "intersect".
@@ -26,7 +28,10 @@
 #'  Scale genes by root-mean-square across cells:
 #'
 #' Remove cells/genes with no expression across any genes/cells:
-#' @param use.cols Treat each column as a cell (default TRUE)
+#' @param use_cols Treat each column as a cell (default TRUE)
+#' @param num_cores Number of cores used on user's machine to run function. 
+#' Default fouynd by `future::availableCores()`
+#' @param ... Additional arguments.
 #'
 #' @return liger preprocessed object.
 #'

diff --git a/R/liger_reduce_dims.R b/R/liger_reduce_dims.R
@@ -19,9 +19,9 @@
 #'   this increments the random seed by 1 for each consecutive restart,
 #'   so future factorizations of the same dataset can be run with
 #'   one rep if necessary. (default 1)
-#' @param H.init Initial values to use for H matrices. (default NULL)
-#' @param W.init Initial values to use for W matrix (default NULL)
-#' @param V.init Initial values to use for V matrices (default NULL)
+#' @param h_init Initial values to use for H matrices. (default NULL)
+#' @param w_init Initial values to use for W matrix (default NULL)
+#' @param v_init Initial values to use for V matrices (default NULL)
 #' @param rand_seed Random seed to allow reproducible results (default 1).
 #' @param print_obj Print objective function values after convergence
 #'   (default FALSE).
@@ -40,6 +40,7 @@
 #'   sparse modalities like methylation data). (default FALSE)
 #' @param resolution Controls the number of communities detected.
 #'   Higher resolution -> more communities. (default 1)
+#' @param ... Additional arguments.
 #'
 #' @return liger object with H, H.norm, W, and V slots sets.
 #'

diff --git a/R/model_celltype_freqs.R b/R/model_celltype_freqs.R
@@ -6,6 +6,8 @@
 #' @param celltype_var the colData variable specifying celltype or subtype
 #' @param dependent_var the name of the colData variable for contrasts
 #' @param ref_class the class of dependent_var used as reference
+#' @param var_order Optional re-ordering of subset_group factor levels. Default 
+#' NULL. 
 #' @param ... Additional arguments
 #'
 #' @return results_l a list of results

diff --git a/R/plot_expr_by_numeric_var.r b/R/plot_expr_by_numeric_var.r
@@ -9,7 +9,7 @@
 #'
 #'
 #' @param sce a SingleCellExperiment object
-#' @param group_var The colData variable for x-axis groups
+#' @param numeric_var The colData variable for x-axis groups. Default is p_tau
 #' @param subset_var The colData variable to subset on
 #' @param subset_group The specific subset_var group to subset
 #' @param gene The gene of interest

diff --git a/R/report_de.R b/R/report_de.R
@@ -27,7 +27,7 @@ report_de <- function(res,
 
   cli::cli_h2("Generating report for differential expression analysis")
 
-  p <- .volcano_plot(
+  p <- volcano_plot(
     dt = res,
     fc_threshold = fc_threshold,
     pval_cutoff = pval_cutoff,

diff --git a/README.Rmd b/README.Rmd
@@ -28,6 +28,7 @@ knitr::opts_chunk$set(
 [![Manual](https://img.shields.io/badge/manual-passing-brightgreen.svg?style=flat)](https://combiz.github.io/scflow-manual/)
 [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/combiz/scFlow/issues)
 [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-green.svg)](https://www.gnu.org/licenses/gpl-3.0)
+[![R-CMD-check](https://github.com/combiz/scFlow/workflows/R-CMD-check/badge.svg)](https://github.com/combiz/scFlow/actions)
 <!-- badges: end -->
 
 The goal of scFlow is to provide tools in R to build a complete analysis workflow for single-cell/nuclei RNA sequencing data.

diff --git a/man/cluster_sce.Rd b/man/cluster_sce.Rd
diff --git a/man/dot-filter_sce_genes_for_de.Rd b/man/dot-filter_sce_genes_for_de.Rd
diff --git a/man/dot-generate_model_from_vars.Rd b/man/dot-generate_model_from_vars.Rd
diff --git a/man/dot-perform_de_with_mast.Rd b/man/dot-perform_de_with_mast.Rd
diff --git a/man/dot-preprocess_sce_for_de.Rd b/man/dot-preprocess_sce_for_de.Rd
diff --git a/man/dot-volcano_plot.Rd b/man/dot-volcano_plot.Rd
diff --git a/man/find_cells.Rd b/man/find_cells.Rd
diff --git a/man/integrate_sce.Rd b/man/integrate_sce.Rd
diff --git a/man/liger_preprocess.Rd b/man/liger_preprocess.Rd
diff --git a/man/liger_reduce_dims.Rd b/man/liger_reduce_dims.Rd
diff --git a/man/model_celltype_freqs.Rd b/man/model_celltype_freqs.Rd
diff --git a/man/perform_de.Rd b/man/perform_de.Rd
diff --git a/man/plot_expr_by_numeric_var.Rd b/man/plot_expr_by_numeric_var.Rd