Merge pull request #9 from lorenzobonaguro/dev

Update to version 0.2.0

Author: lorenzobonaguro

.Rhistory

@@ -1,7 +1,7 @@
 Package: cyCONDOR
 Type: Package
 Title: Flow Cytometry data analysis toolbox
-Version: 0.1.6
+Version: 0.2.0
 Authors@R: c(
   person(given = "Lorenzo", family = "Bonaguro", email = "lorenzo.bonaguro@dzne.de", role = c("aut", "cre")),
   person(given = "Charlotte", family = "Kroeger", email = "charlotte.kroeger@dzne.de", role = "aut"),

DESCRIPTION

@@ -53,6 +53,7 @@ export(run_cytonorm)
 export(runtSNE)
 export(scaleColors)
 export(subset_fcd)
+export(subset_fcd_byparam)
 export(train_classifier_model)
 export(train_cytonorm)
 export(train_transfer_model)

NAMESPACE

@@ -1,3 +1,10 @@
+# cyCONDOR 0.2.0
+* Bump of major version for official release and publication
+* Changed order of events in the `prep_fcd` function. This makes the process faster (especially in smaller machines) and reduces the usage of memory.
+* Added function `subset_fcd_byparam`. This function allows to randomly subset a `condor` object proportionally across a selected parameter. For example is not possible to randomly subset `n` cells from each samples.
+* Included more professionalization in the `read_fcd` function. This allows the user to customize few aspects of the `condor` object
+* Included `cyCONDOR` version in the `condor` object under the `extras` slot. This enables the user to trace the version used for the analysis.
+
 # cyCONDOR 0.1.6
 
 * Reorganization of existing visualization functions including harmonization of function names and function arguments, utilization of `condor` object as main input object and addition of more extensive documentation and error messages.

NEWS.md

@@ -85,6 +85,8 @@ filter_fcd <- function(fcd, cell_ids) {
 
   }
 
+  class(new_fcd) <- "flow_cytometry_dataframe"
+
   return(new_fcd)
 
 }
@@ -257,6 +259,53 @@ subset_fcd <- function(fcd, size, seed = 91) {
   return(condor_filter)
 }
 
+#' subset_fcd_byparam
+#'
+#' @title subset_fcd_byparam
+#' @description Performs a random subset of the fcd
+#'
+#' @param fcd flow cytometry dataset.
+#' @param param Name of the parameter to be used to equaly subset the fcd. This should be a columns in the cell annotation table.
+#' @param size Numeric: size of the sub-sampling for each element in `param`.
+#' @param seed A seed is set for reproducibility.
+#'
+#' @return subset_fcd_byparam
+#'
+#' @export
+subset_fcd_byparam <- function(fcd,
+                               param,
+                               size,
+                               seed = 91) {
+
+  # Store the annotation and prepare a container
+
+  anno <- fcd$anno$cell_anno
+
+  selected_cells <- c()
+
+  # Define the cell IDs to keep
+
+  for (single_param in unique(fcd$anno$cell_anno[[param]])) {
+
+    set.seed(seed)
+
+    single_param_cells <- sample(x = rownames(anno[anno[[param]] == single_param,]),
+                                 size = size,
+                                 replace = F)
+
+    selected_cells <- c(selected_cells, single_param_cells)
+
+  }
+
+  # Filter the fcd
+
+  fcd_filter <- filter_fcd(fcd = fcd,
+                           cell_ids = selected_cells)
+
+  # return the filtered object
+  return(fcd_filter)
+
+}
 
 #' df_frequency
 #'

R/data_load_and_transform.R

@@ -9,9 +9,9 @@ We developed an easy-to-use computational framework (condor) covering not only a
 
 **You can find the detailed documentation of cyCONDOR [here](https://lorenzobonaguro.github.io/cyCONDOR/)**
 
-We recommend using `cyCONDOR` from our pre-build `Docker` container [lorenzobonaguro/condor](https://hub.docker.com/r/lorenzobonaguro/condor), the latest version of the image can be pulled with:
+We recommend using `cyCONDOR` from our pre-build `Docker` container [lorenzobonaguro/cycondor](https://hub.docker.com/r/lorenzobonaguro/cycondor), the latest version of the image can be pulled with:
 ```
-docker pull lorenzobonaguro/cycondor:v016
+docker pull lorenzobonaguro/cycondor:v020
 ```
 
 To run the image you can then follow the following script
@@ -21,7 +21,7 @@ docker run -dp [YOURPORT]:8787 \
 -e USER=[YOURUSERNAME] -e PASSWORD=[YOURPASSWORD] \
 --name condor_analysis \
 -v [PATHTODATA]:/home/[YOURUSERNAME]/data/ \
-lorenzobonaguro/cycondor:v016
+lorenzobonaguro/cycondor:v020
 ```
 You can then access RStudio from your web browser at the address
 
@@ -57,7 +57,7 @@ devtools::install_github(repo = c("JinmiaoChenLab/Rphenograph", "stuchly/Rphenoa
 
 Finally we install cyCONDOR, here we manually provide the link to the Bioconductor repositories.
 ```
-devtools::install_url("https://github.com/lorenzobonaguro/cyCONDOR/releases/download/v016/cyCONDOR_0.1.6.tar.gz",
+devtools::install_url("https://github.com/lorenzobonaguro/cyCONDOR/releases/download/v020/cyCONDOR_0.2.0.tar.gz",
                       repos = BiocManager::repositories())
 ```
 
@@ -110,3 +110,5 @@ follow me:
 <img src="man/figures/twitter.png" width="8%" style="float: left;"/>
 
 [@LorenzoBonaguro](<https://twitter.com/LorenzoBonaguro>)
+
+If you want to directly chat with us join our [Slack Workspace](https://join.slack.com/t/cycondor/shared_invite/zt-2keb5ztaa-0aNKxP3OCglOTUiXDrtkhg)

R/misc.R

@@ -95,6 +95,7 @@ reference:
   - filter_fcd
   - merge_condor
   - subset_fcd
+  - subset_fcd_byparam
   - checkInput
   - measured_markers