Merge pull request #33 from genesofeve/multivariateAnalysisR-testthat

Merge Request: Integration of Updated Test Code and Test Data from multivariateAnalysisR-testthat to Master

DESCRIPTION

@@ -2,7 +2,7 @@ Package: projectR
 Type: Package
 Title: Functions for the projection of weights from PCA, CoGAPS, NMF,
     correlation, and clustering
-Version: 1.19.01
+Version: 1.20.01
 Author: Gaurav Sharma, Charles Shin, Jared Slosberg, Loyal Goff, Genevieve Stein-O'Brien
 Maintainer: Genevieve Stein-O'Brien <[email protected]>
 Description: Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering.
@@ -37,13 +37,15 @@ Suggests:
     knitr,
     rmarkdown,
     ComplexHeatmap,
-    gplots
+    gplots,
+    SeuratObject
 LazyData: TRUE
+LazyDataCompression: gzip
 RoxygenNote: 7.2.3
 Encoding: UTF-8
 VignetteBuilder: knitr
 biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion, Software
 BugReports: https://support.bioconductor.org/t/projectR/
 URL: https://github.com/genesofeve/projectR/
 Depends: 
-    R (>= 4.0.1)
+    R (>= 4.0.0)

R/multivariateAnalysisR.R

@@ -18,7 +18,7 @@
 #' @param CIheight height of CI png
 #' @param CIspacing spacing between each CI in CI graph
 #'
-#' @return none; ANOVA and Confidence Intervals are visualized and exported in both PNG and CSV
+#' @return a sorted list of ANOVA and CI results; ANOVA and Confidence Intervals are visualized and exported in both PNG and CSV
 #' @export
 #'
 
@@ -387,5 +387,6 @@ multivariateAnalysisR <- function(
   )
   CI_export_path <- file.path(getwd(), exportFolder, "multivariateAnalysisR_CI.csv")
   write.csv(CI_CSV_df, CI_export_path, row.names = FALSE)
-
-}
+
+  return(sortedGrossList)
+}

R/package.R

@@ -117,3 +117,9 @@
 #' @format A gene (rows) by cell (column) matrix
 "glial_counts"
 
+#' Truncated Seurat Object with latent space projection
+#' done to unspecified cells in different stages for multivariateAnalysisR analysis
+#' @name multivariateAnalysisR_seurat_test
+#' @docType data
+#' @format A Seurat Object with 31034 observations of 4 variables in meta.data:
+"multivariateAnalysisR_seurat_test"

README.md

@@ -1,6 +1,6 @@
 # Overview
 
-[![R-CMD-check](https://github.com/genesofeve/projectR/actions/workflows/R-CMD-check.yml/badge.svg)](https://github.com/genesofeve/projectR/actions/workflows/R-CMD-check.yml)
+[![R-CMD-check](https://github.com/genesofeve/projectR/actions/workflows/r-build-check.yml/badge.svg)](https://github.com/genesofeve/projectR/actions/workflows/r-build-check.yml)
 
 An R package for creating projection matrices from gene weights extracted from
 high dimensional genomic analysis.

data/.Rapp.history

@@ -0,0 +1 @@
+load("/Users/macintosh/Desktop/projectR/data/multivariateAnalysisR-seurat-test.RData")

tests/testthat/test_projectR.R

@@ -17,6 +17,7 @@ test_that("data is proper",{
 	expect_that(pd.RNAseq6l3c3t,is_a('data.frame'))
 	expect_true(all(dim(pd.RNAseq6l3c3t) == c(54,38)))
 	expect_true(all(dim(CR.RNAseq6l3c3t) == c(54,5)))
+	expect_that(multivariateAnalysisR_seurat_test, is_a('Seurat'))
 	})
 
 test_that("results are as expected",{
@@ -48,4 +49,19 @@ test_that("results are as expected",{
 	expect_true(all(dim(pca.ESepiGen4c1l) == c(54,9)))
 	expect_true(all(pca.ESepiGen4c1l != 0))
 	expect_true(all(!is.na(pca.ESepiGen4c1l)))
+
+	#multivariateAnalysisR check
+	output <- multivariateAnalysisR(seuratobj = multivariateAnalysisR_seurat_test, 
+	                                patternKeys = list("Pattern_1", "Pattern_2"), 
+	                                dictionaries = list(
+	                                  list("stage" = "E18"), 
+	                                  list("stage" = "Adult")
+	                                  )
+	                                )
+	expect_is(output, "list")
+	expect_length(output, 2)
+	expect_true("patternKey" %in% names(output[[1]]))
+	expect_true("ANOVA" %in% names(output[[1]]))
+	expect_true("CI" %in% names(output[[1]]))
+
 	})

vignettes/projectR.Rmd

@@ -518,7 +518,7 @@ The arguments for `multivariateAnalysisR` are:
 
 
 ### Output 
-The outputs of `multivariateAnalysisR` are two pairs of exported PNG/CSV files: one for ANOVA analysis, another for CI. No values are returned from the function. From the ANOVA analysis, researchers can see the general ranking of differential uses of patterns across the specified clusters. From the CI analysis, researchers can identify the specific differential use cases between every pair of clusters. 
+`multivariateAnalysisR` returns a sorted list of the generated ANOVA and CI values. It also exports two pairs of exported PNG/CSV files: one for ANOVA analysis, another for CI. From the ANOVA analysis, researchers can see the general ranking of differential uses of patterns across the specified clusters. From the CI analysis, researchers can identify the specific differential use cases between every pair of clusters. 
 
 ### Comparing differential uses of patterns across different clusters
 Demonstrative example will be added soon.