version 1.0.1

cran · May 24, 2022 · 4997735 · 4997735
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,8 +2,8 @@ Package: ZetaSuite
 Type: Package
 Title: Analyze High-Dimensional High-Throughput Dataset and Quality
         Control Single-Cell RNA-Seq
-Version: 1.0.0
-Date: 2022-03-26
+Version: 1.0.1
+Date: 2022-05-22
 Authors@R: c(
     person(given = "Yajing", family = "Hao", email = "yahao@health.ucsd.edu", role  = c("aut"), comment = c(ORCID = "0000-0003-1384-4176")),
     person(given = "Shuyang", family = "Zhang", email = "s4zhang@ucsd.edu", role  = "ctb", comment=c(ORCID="0000-0002-8428-1828")),
@@ -13,7 +13,7 @@ Authors@R: c(
 		)
 Maintainer: Junhui Li <ljh.biostat@gmail.com>
 Description: The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In 'ZetaSuite', we  have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.
-Imports: RColorBrewer, Rtsne, dplyr, e1071, ggplot2, reshape2, scater,
+Imports: RColorBrewer, Rtsne, dplyr, e1071, ggplot2, reshape2,
         gridExtra, mixtools
 License: GPL-2 | GPL-3
 Depends: R (>= 2.10)
@@ -26,6 +26,6 @@ Author: Yajing Hao [aut] (<https://orcid.org/0000-0003-1384-4176>),
   Guofeng Zhao [ctb],
   Xiang-Dong Fu [cph, fnd] (<https://orcid.org/0000-0001-5499-8732>)
 NeedsCompilation: no
-Packaged: 2022-03-30 19:46:31 UTC; junhuili
+Packaged: 2022-05-23 01:59:24 UTC; junhuili
 Repository: CRAN
-Date/Publication: 2022-03-31 07:30:14 UTC
+Date/Publication: 2022-05-24 19:40:02 UTC
diff --git a/MD5 b/MD5
@@ -1,8 +1,8 @@
-d371374e3b9d443a26b42374f38e2501 *DESCRIPTION
-81062333bac36b619dbb3ca0d23632bd *NAMESPACE
+916dfec258df8e8a03802adca4c0ee36 *DESCRIPTION
+f946ffb73cb7af6350cdca137f297d50 *NAMESPACE
 6c316c4d23354b81736e8f62ca9b0007 *R/EventCoverage.R
-6d975ace319f3327fdb2a6ac50f578df *R/FDRcutoff.R
-8a13bcbd65cc640831f25c8f070362f9 *R/QC.R
+59b02431290ebe35f8f4e0b9ca35109a *R/FDRcutoff.R
+ce030e4346692e1c20a1720044ca9a79 *R/QC.R
 7109fb14e7a3d6ec13567dba926785bd *R/SVM.R
 1775b212021da036e1054acf55c4f712 *R/Zeta.R
 6eba0dbdf1f491a662165b8f3366fe3a *R/ZetaSuitSC.R
@@ -18,7 +18,7 @@ a0b10a210c5a53361288482324ef08ff *data/nonExpGene.rda
 56d8d91c5e8141a9fb21c2291e4dece6 *data/posGene.rda
 2005cb01b0f6bb9761d4d5057ed8653d *inst/doc/ZetaSuite.R
 6d25f7132fe0a056782f7abf654a85f1 *inst/doc/ZetaSuite.Rmd
-e85e678b69a41db0886b358b93dad998 *inst/doc/ZetaSuite.html
+89ce83fe82f17751ccb4dec4c98713e4 *inst/doc/ZetaSuite.html
 fd41dd347e0ccf74ee5f73e00bed97e6 *man/EventCoverage.Rd
 62365f0fcb49d150289d356cadfb5c9c *man/FDRcutoff.Rd
 102ad4d57173ddbbcba646149934d4da *man/QC.Rd

diff --git a/NAMESPACE b/NAMESPACE
@@ -13,7 +13,6 @@ import(e1071)
 import(ggplot2)
 import(mixtools)
 import(reshape2)
-import(scater)
 importFrom(grDevices,colorRampPalette)
 importFrom(grDevices,dev.off)
 importFrom(grDevices,pdf)

diff --git a/R/FDRcutoff.R b/R/FDRcutoff.R
@@ -32,7 +32,7 @@
 #'
 #' @keywords ZetaSuite FDR cutoff
 #'
-#' @import ggplot2 scater
+#' @import ggplot2
 #'
 #' @importFrom grDevices dev.off pdf
 #'
@@ -63,7 +63,7 @@ FDRcutoff <- function (zetaData, negGene, posGene, nonExpGene, combine = FALSE)
       numNexp <- sum(zetaData[, "Zeta_D"] >= num & zetaData[, "type"] %in% c("non_exp"))
       FDR_Nexp <- numNexp/totalNum
       screen_Stress <- (iFDR - FDR_Nexp)/iFDR
-      FDR_cutOff_de[index, ] <- c(num, totalNum, numNexp, FDR_Nexp, screen_Stress, "Decrease")
+      FDR_cutOff_de[index, ] <- c(num, FDR_Nexp,screen_Stress,totalNum, numNexp, "Decrease")
       index <- index + 1
     }
     seqI <- seq(minI, maxI, stepI)
@@ -102,23 +102,23 @@ FDRcutoff <- function (zetaData, negGene, posGene, nonExpGene, combine = FALSE)
   FDR_cutOff$SS <- as.numeric(FDR_cutOff$SS)
   FDR_cutOff$Cut_Off <- as.numeric(FDR_cutOff$Cut_Off)
   zetaData_NS <- zetaData[zetaData$type != "NS_mix", ]
-  
+
   p1 <- ggplot(zetaData_NS) + geom_jitter(aes_string(x = "type", y = "Zeta_D", col = "type")) + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + xlab("") + scale_color_manual(values = c("#c994c7", "#67a9cf", "#ef8a62"))
   p2 <- ggplot(zetaData_NS) + geom_jitter(aes_string(x = "type", y = "Zeta_I", col = "type")) + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + xlab("") + scale_color_manual(values = c("#c994c7", "#67a9cf", "#ef8a62"))
   p_Zeta_type <- gridExtra::grid.arrange(p1, p2, nrow = 1)
-  
+
   if (combine == FALSE) {
     fdtss <- FDR_cutOff[FDR_cutOff$SS < 0.9, ]
     Dec <- fdtss[fdtss$Type == "Decrease", ]
     Inc <- fdtss[fdtss$Type == "Increase", ]
-    
+
     p1 <- ggplot(Dec, aes_string(x = "Cut_Off", y = "SS", col = "Type")) + geom_point() + geom_smooth(span = 0.2) + theme_bw() + xlab("Zeta Score") + ylab("Screen strength") + theme(legend.position = c(0.8, 0.2), legend.title = element_blank())
     p2 <- ggplot(Inc, aes_string(x = "Cut_Off", y = "SS", col = "Type")) + geom_point() + geom_smooth(span = 0.2) + theme_bw() + xlab("Zeta Score") + ylab("Screen strength") + theme(legend.position = c(0.8, 0.2), legend.title = element_blank())
     p_SS_cutOff <- gridExtra::grid.arrange(p1, p2, nrow = 1)
-    
+
   } else {
     fdtss <- FDR_cutOff[FDR_cutOff$SS < 1, ]
-    
+
     p_SS_cutOff <- ggplot(fdtss, aes_string(x = "Cut_Off", y = "SS", col = "Type")) + geom_point() + geom_smooth(span = 0.2) + theme_bw() + xlab("Zeta Score") + ylab("Screen strength") + theme(legend.position = c(0.8, 0.2), legend.title = element_blank())
   }
   plotList <- list()

diff --git a/R/QC.R b/R/QC.R
@@ -28,7 +28,7 @@
 #'
 #' @keywords ZetaSuite quality
 #'
-#' @import ggplot2 reshape2 scater Rtsne
+#' @import ggplot2 reshape2 Rtsne
 #'
 #' @importFrom grDevices colorRampPalette dev.off pdf png
 #'