“1.7.0”

Author: Khodadadi-Jamayran

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: iCellR
 Type: Package
 Title: Analyzing High-Throughput Single Cell Sequencing Data
-Version: 1.6.6
+Version: 1.7.0
 Authors@R: c(
   person(given = 'Alireza', family = 'Khodadadi-Jamayran',role = c('aut','cre'), email = 'alireza.khodadadi.j@gmail.com', comment = c(ORCID = '0000-0003-2495-7504')),
   person(given = 'Joseph', family = 'Pucella',role = c('aut','ctb'), comment = c(ORCID = '0000-0003-0875-8046')),
@@ -17,17 +17,17 @@ Description: A toolkit that allows scientists to work with data from single cell
 Depends: R (>= 3.3.0), ggplot2, plotly
 Imports: Matrix, Rtsne, gridExtra, ggrepel, ggpubr, scatterplot3d,
         RColorBrewer, knitr, NbClust, shiny, pheatmap, ape, ggdendro,
-        plyr, reshape, Hmisc, htmlwidgets, methods, uwot, hdf5r,
-        progress, igraph, data.table, Rcpp, RANN, jsonlite, png
+        plyr, reshape, Hmisc, htmlwidgets, methods, uwot, progress,
+        igraph, data.table, Rcpp, hdf5r, RANN, jsonlite, png
 License: GPL-2
 LinkingTo: Rcpp
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.1.1
+RoxygenNote: 7.1.2
 BugReports: https://github.com/rezakj/iCellR/issues
 URL: https://github.com/rezakj/iCellR
 NeedsCompilation: yes
-Packaged: 2021-11-09 18:05:17 UTC; khodaa01
+Packaged: 2025-10-28 14:48:40 UTC; khodaa01
 Author: Alireza Khodadadi-Jamayran [aut, cre]
     (<https://orcid.org/0000-0003-2495-7504>),
   Joseph Pucella [aut, ctb] (<https://orcid.org/0000-0003-0875-8046>),

NAMESPACE

@@ -5,6 +5,7 @@ export(add.10x.image)
 export(add.adt)
 export(add.vdj)
 export(adt.rna.merge)
+export(bubble.gg.plot)
 export(capture.image.10x)
 export(cc)
 export(cell.cycle)

R/F0018.R

@@ -7,6 +7,7 @@
 #' @param pval.test Choose from "t.test", "wilcox.test", default = "t.test".
 #' @param p.adjust.method Correction method. Choose from "holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none", default = "hochberg".
 #' @param padjval Minimum adjusted p value for out put, default = 0.1.
+#' @param low.cell.filt filter out clusters with low number of cells, default = 5.
 #' @param Inf.FCs If set to FALSE the infinite fold changes would be filtered from out put, default = FALSE.
 #' @param uniq If set to TRUE only genes that are a marker for only one cluster would be in the out put, default = FALSE.
 #' @param positive If set to FALSE both the up regulated (positive) and down regulated (negative) markers would be in the out put, default = TRUE.
@@ -18,6 +19,7 @@ findMarkers <- function (x = NULL,
           p.adjust.method = "hochberg",
           fold.change = 2,
           padjval = 0.1,
+          low.cell.filt = 5,
           Inf.FCs = FALSE,
           uniq = FALSE,
           positive = TRUE) {
@@ -42,7 +44,7 @@ findMarkers <- function (x = NULL,
   }
   # get cluster data
   # get avrages
-    x <- clust.avg.exp(x, data.type = data.type)
+    x <- clust.avg.exp(x, data.type = data.type, low.cell.filt = low.cell.filt)
 ##########
   DATA <- x@best.clust
   if(!is.numeric(DATA$clusters)){

R/F0028.R

@@ -224,6 +224,10 @@ heatmap.gg.plot <- function (x = NULL,
   }
   ### plot
   data$expression <- as.numeric(as.character(data$expression))
+
+  ######### cluster order (this makes cluster numeric factors)
+  CL=as.numeric(data$clusters)
+  data$clusters=factor(CL, levels=sort(unique(CL)))
   #############
   heatmap <- ggplot(data, aes(x = cell, y = gene, fill = expression, text=clusters)) + geom_tile() +
     scale_fill_gradient2(low = col.low, mid = col.mid, high = col.high, name = "",

R/F0030.R

@@ -9,9 +9,6 @@ add.adt <- function (x = NULL, adt.data = "data.frame") {
   if ("iCellR" != class(x)[1]) {
     stop("x should be an object of class iCellR")
   }
-  if (class(adt.data) != "data.frame") {
-    stop("ADT data should be a data frame object")
-  }
   # Add ADT to gene names
   row.names(adt.data) <- paste("ADT", row.names(adt.data), sep = "_")
   row.names(adt.data) <- gsub("-",".",row.names(adt.data))

R/F0036.R

@@ -8,7 +8,7 @@
 prep.vdj <- function (vdj.data = "data.frame", cond.name = "NULL") {
 # read VDJ data
   my.vdj <- vdj.data
-  my.vdj <- subset(my.vdj, productive == "True")
+  # my.vdj <- subset(my.vdj, productive == "True")
   my.vdj <- subset(my.vdj, raw_clonotype_id != "None")
   cell.barcodes <- my.vdj$barcode
   cell.barcodes <- gsub("-",".",cell.barcodes)

R/F0039.R

@@ -9,9 +9,6 @@ add.vdj <- function (x = NULL, vdj.data = "data.frame") {
   if ("iCellR" != class(x)[1]) {
     stop("x should be an object of class iCellR")
   }
-  if (class(vdj.data) != "data.frame") {
-    stop("VDJ data should be a data frame object")
-  }
   attributes(x)$vdj.data <- vdj.data
   return(x)
 }

R/F0040.R

@@ -3,5 +3,5 @@
 #' A dataset containing the genes for S phase
 #'
 #' @format A character with 43 genes
-#' @source \url{https://www.science.org/doi/abs/10.1126/science.aad0501}
+#' @source Paper: Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.
 "s.phase"

R/F0051.R

@@ -3,5 +3,5 @@
 #' A dataset containing the genes for G2 and M phase
 #'
 #' @format A character with 54 genes
-#' @source \url{https://www.science.org/doi/abs/10.1126/science.aad0501}
+#' @source Paper: Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.
 "g2m.phase"