From 475aa8faa81b58c4bc31955918b96128bc34d8a1 Mon Sep 17 00:00:00 2001 From: Tim Crombie Date: Tue, 10 Oct 2023 18:33:48 -0400 Subject: [PATCH] update article --- DESCRIPTION | 22 +- R/delta.R | 10 +- R/regEff.R | 2 +- README.md | 38 +- man/delta.Rd | 2 +- man/easyXpress-package.Rd | 6 +- man/regEff.Rd | 2 +- vignettes/articles/Dose Response.Rmd | 166 ------- vignettes/articles/Dose_Response.Rmd | 422 ++++++++++++++++++ .../articles}/checkModels_ex_anno.png | Bin vignettes/articles/viewOverlay.png | Bin 0 -> 4333766 bytes 11 files changed, 458 insertions(+), 212 deletions(-) delete mode 100644 vignettes/articles/Dose Response.Rmd create mode 100644 vignettes/articles/Dose_Response.Rmd rename {man/figures => vignettes/articles}/checkModels_ex_anno.png (100%) create mode 100644 vignettes/articles/viewOverlay.png diff --git a/DESCRIPTION b/DESCRIPTION index 3c8dd6d..e5b6869 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,27 +1,27 @@ Package: easyXpress Title: A Package to Read, Process, and Analyze Worm Data Generated from CellProfiler’s WormToolbox -Version: 1.1.0 +Version: 2.0.0 Authors@R: c( - person(given = "Joy", - family = "Nyaanga", - role = c("aut", "cre"), - email = "jnyaan@gmail.com", - comment = c(ORCID = "0000-0002-1402-9213")), person(given = "Tim", family = "Crombie", - role = "aut", + role = c("aut", "cre"), email = "tim.crombie@gmail.com", comment = c(ORCID = "0000-0002-5645-4154")), + person(given = "Joy", + family = "Nyaanga", + role = "aut", + email = "jnyaan@gmail.com", + comment = c(ORCID = "0000-0002-1402-9213")), person(given = "Sam", family = "Widmayer", role = "aut", email = "sjwidmay@gmail.com", comment = c(ORCID = "0000-0002-1200-4768"))) Description: A workflow for the reading, processing, and visualization - of images obtained from the Molecular Devices ImageExpress Nano Imager - and processed with CellProfiler's WormToolbox. - Includes a powerful suite of functions for the rapid processing - and analysis of large high-throughput image based datasets. + of nematode morphology data extracted from images using + CellProfiler's WormToolbox. + Includes a powerful suite of functions to process + large high-throughput image based datasets. Depends: R (>= 3.5.0) Imports: diff --git a/R/delta.R b/R/delta.R index f94b8ad..ff35265 100644 --- a/R/delta.R +++ b/R/delta.R @@ -6,7 +6,7 @@ #' @param data A data frame output from any \code{WF} function. #' @param ... <[`dynamic-dots`][rlang::dyn-dots]> Variable(s) used to group data. It is recommended to group data to independent bleaches for all strains. Variable names in data are supplied separated by commas and without quotes. #' For example, the typical variables for grouping are \code{Metadata_Experiment, bleach, strain}. -#' @param WF Select \code{"filter"} or \code{"ignore"}. The default, \code{"filter"}, will filter out all flagged wells before calculating the delta from control. +#' @param WF Select \code{"filter"} or \code{"ignore"}. The default, \code{"filter"}, will filter out all flagged wells before calculating the delta from control, if present. #' \code{"ignore"} will calculate the delta including all flagged data. Be careful using \code{"ignore"}, it is only included for diagnostic purposes. #' @param vars The well summary statistics to perform the delta calculation on. These are supplied in a character vector. For example, the default is set to \code{c("median_wormlength_um", "cv_wormlength_um")}. #' @param doseR Logical, is this dose response data? The default, \code{doseR = FALSE}, @@ -51,10 +51,18 @@ delta <- function(data, ..., WF = "filter", vars = c("median_wormlength_um", "cv stop(message(message(paste0(capture.output(knitr::kable(example)), collapse = "\n")))) } + # get any user flags from data + uf1 <- names(data %>% dplyr::select(contains("_WellFlag"))) + # filter wells if needed if(WF == "filter") { + if(length(uf1) == 0) { + message(glue::glue("No flagged wells detected.")) + d <- data + } else { d <- easyXpress::filterWF(data = data, rmVars = T) message(glue::glue("Flagged wells are filtered from the data.")) + } } else { d <- data message(glue::glue('Flagged wells are NOT being filtered from the data. This is NOT the recommended approach. Please consider WF = "filter".')) diff --git a/R/regEff.R b/R/regEff.R index 8d58eec..f765e98 100644 --- a/R/regEff.R +++ b/R/regEff.R @@ -11,7 +11,7 @@ #' \code{_reg_coeff}, and \code{_reg_sig}. #' 2) A diagnostic plot, \code{$p1}, showing the effect of the confounding variable (\code{c.var}) on the dependent variable (\code{d.var}). #' 3) A diagnositc plot, \code{$p2}, showing the regression coefficients of the confounding variable (\code{c.var}) on the y-axis and the dependent variable (\code{d.var}) on the x-axis. -#' 4) A list, \code{$models} of model outputs for each group An ANOVA summary table with the effects. +#' 4) A list, \code{$models} of model objects for each group. #' @export regEff <- function(data, ..., d.var, c.var) { diff --git a/README.md b/README.md index ed54dc6..a4079c4 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,10 @@ # easyXpress hex ## Overview -This package is designed for the reading, processing, and visualization of images obtained from the Molecular Devices ImageExpress Nano Imager, and processed with CellProfiler's WormToolbox. +This package is designed for reading, processing, and visualizing of nematode morphology data extracted from images using CellProfiler's WormToolbox. ## Installation -`easyXpress` is specialized for use with CellProfiler generated worm image data. The package is rather specific to use in the Andersen Lab and, therefore, is not available from CRAN. To install easyXpress you will need the [`devtools`](https://github.com/hadley/devtools) package. You can install `devtools` and `easyXpress` using the commands below: +`easyXpress` is specialized for use with image data produced by the [`cellprofiler-nf` nextflow pipeline](https://github.com/AndersenLab/cellprofiler-nf). To install `easyXpress` you will need the [`devtools`](https://github.com/hadley/devtools) package. You can install `devtools` and `easyXpress` using the commands below: ```r install.packages("devtools") @@ -18,21 +18,16 @@ The functionality of the package can be broken down into three main goals: + Flagging and pruning anomalous data points. -+ Generating diagnositic images. ++ Generating diagnostic images. -For more information about implementing CellProfiler to generate data used by the `easyXpress` package, see [`AndersenLab/cellprofiler-nf`](https://github.com/AndersenLab/cellprofiler-nf). +For more information about implementing `cellprofiler-nf` to generate data used by the `easyXpress` package, see [`AndersenLab/cellprofiler-nf`](https://github.com/AndersenLab/cellprofiler-nf). ## Directory structure -Because so much information must be transferred alongside the plate data, the directory structure -from which you are reading is critically important. Below is an example of a correct project directory structure. -The `cp_data` directory contains an `.RData` file sourced directly from the default output folder for a CellProfiler run. -The `processed_images` directory contains `.png` files from the CellProfiler run. There should be one `.png` file -for each well included in your analysis. The `design` directory contains the `.csv` file having all the variables necessary -to describe your experiment (i.e. drug names, drug concentrations, strain names, food types, etc.). +The directory structure holding data is critically important. Below is an example of a correct project directory structure. +The `cp_data` directory contains an `.RData` file output by `cellprofiler-nf`. The `processed_images` directory contains `_overlay.png` files output by `cellprofiler-nf`. There should be one `.png` file for each well included in your analysis. The `design` directory contains a `.csv` with all the variables necessary to describe your experiment (i.e. experiment names, drug names, drug concentrations, strain names, food types, etc.). -If you do not have condition information (i.e. drug names, drug concentrations, strain names, food types, etc.) -you do not need the `design` directory. +If you do not have condition information you do not need the `design` directory. ``` /projects/20200812_example @@ -64,25 +59,12 @@ and experiment name separated by underscores. ### File naming -The processed image files should be formatted with the experiment data, name of the experiment, the plate number, -the magnification used for imaging, and the well name. All processed image files must be saved as `.png` files. -In the file named `20191119-growth-p01-m2x_A01_overlay.png` the first section `20191119` is the experiment date, -`growth` is the name of the experiment, `p01` is the plate number, `m2x` is the magnification used for imaging, -and `A01` is the well name. +The processed image files should be formatted with the experiment data, name of the experiment, the plate number, the magnification used for imaging, and the well name. All processed image files must be saved as `.png` files. In the file named `20191119-growth-p01-m2x_A01_overlay.png` the first section `20191119` is the experiment date, `growth` is the name of the experiment, `p01` is the plate number, `m2x` is the magnification used for imaging, and `A01` is the well name. ## Package Overview -The complete easyXpress package consists of nine functions: -`readXpress`, `modelSelection`, `edgeFlag`, `setFlags`, `process`, `Xpress`, `viewPlate`, `viewWell`, and `viewDose`. +The `easyXpress` package consists of six function classes that work together to clean and process experimental data. The `tidy` functions will help pre-process raw images to get them ready for submission to the `cellprofiler-nf` pipeline. The `ObjectFlag` or `OF` functions help to flag problematic data output from `cellprofiler-nf`. The `WellFlag` or `WF` functions work to flag anomalous summary statistics for micro-plate wells. Throughout the data cleaning workflow, the `check` and `view` function classes are used to validate whether the flag functions are properly applied. All other functions serve to facilitate the cleaning process and do not have a standardized naming convention. -For more detailed information regarding use of these functions, see the vignette: **A walk-through of easyXpress**. -This can be done in R - - -```r -library(easyXpress) -browseVignettes(package = "easyXpress") -``` - - +For more detailed information regarding use of these functions, see the article: **Dose Response Processing**. ### Citation diff --git a/man/delta.Rd b/man/delta.Rd index 54c1dfb..12fa8fa 100644 --- a/man/delta.Rd +++ b/man/delta.Rd @@ -18,7 +18,7 @@ delta( \item{...}{<[`dynamic-dots`][rlang::dyn-dots]> Variable(s) used to group data. It is recommended to group data to independent bleaches for all strains. Variable names in data are supplied separated by commas and without quotes. For example, the typical variables for grouping are \code{Metadata_Experiment, bleach, strain}.} -\item{WF}{Select \code{"filter"} or \code{"ignore"}. The default, \code{"filter"}, will filter out all flagged wells before calculating the delta from control. +\item{WF}{Select \code{"filter"} or \code{"ignore"}. The default, \code{"filter"}, will filter out all flagged wells before calculating the delta from control, if present. \code{"ignore"} will calculate the delta including all flagged data. Be careful using \code{"ignore"}, it is only included for diagnostic purposes.} \item{vars}{The well summary statistics to perform the delta calculation on. These are supplied in a character vector. For example, the default is set to \code{c("median_wormlength_um", "cv_wormlength_um")}.} diff --git a/man/easyXpress-package.Rd b/man/easyXpress-package.Rd index 4bb9eff..a163a34 100644 --- a/man/easyXpress-package.Rd +++ b/man/easyXpress-package.Rd @@ -8,17 +8,17 @@ \description{ \if{html}{\figure{logo.png}{options: align='right' alt='logo' width='120'}} -A workflow for the reading, processing, and visualization of images obtained from the Molecular Devices ImageExpress Nano Imager and processed with CellProfiler's WormToolbox. Includes a powerful suite of functions for the rapid processing and analysis of large high-throughput image based datasets. +A workflow for the reading, processing, and visualization of nematode morphology data extracted from images using CellProfiler's WormToolbox. Includes a powerful suite of functions to process large high-throughput image based datasets. } \details{ For a quick introduction to \pkg{easyXpress} see the vignette: \href{../doc/easyXpress.html}{A walk-through of easyXpress}. } \author{ -\strong{Maintainer}: Joy Nyaanga \email{jnyaan@gmail.com} (\href{https://orcid.org/0000-0002-1402-9213}{ORCID}) +\strong{Maintainer}: Tim Crombie \email{tim.crombie@gmail.com} (\href{https://orcid.org/0000-0002-5645-4154}{ORCID}) Authors: \itemize{ - \item Tim Crombie \email{tim.crombie@gmail.com} (\href{https://orcid.org/0000-0002-5645-4154}{ORCID}) + \item Joy Nyaanga \email{jnyaan@gmail.com} (\href{https://orcid.org/0000-0002-1402-9213}{ORCID}) \item Sam Widmayer \email{sjwidmay@gmail.com} (\href{https://orcid.org/0000-0002-1200-4768}{ORCID}) } diff --git a/man/regEff.Rd b/man/regEff.Rd index de70091..5828763 100644 --- a/man/regEff.Rd +++ b/man/regEff.Rd @@ -21,7 +21,7 @@ A list including four elements. \code{_reg_coeff}, and \code{_reg_sig}. 2) A diagnostic plot, \code{$p1}, showing the effect of the confounding variable (\code{c.var}) on the dependent variable (\code{d.var}). 3) A diagnositc plot, \code{$p2}, showing the regression coefficients of the confounding variable (\code{c.var}) on the y-axis and the dependent variable (\code{d.var}) on the x-axis. -4) A list, \code{$models} of model outputs for each group An ANOVA summary table with the effects. +4) A list, \code{$models} of model objects for each group. } \description{ A function to regress out the effect a confounding variable on a dependent variable. diff --git a/vignettes/articles/Dose Response.Rmd b/vignettes/articles/Dose Response.Rmd deleted file mode 100644 index 2e02106..0000000 --- a/vignettes/articles/Dose Response.Rmd +++ /dev/null @@ -1,166 +0,0 @@ ---- -title: "Dose Response Processing" ---- - -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -``` - -## Introduction - -**easyXpress** is an R package to read, process, and analyze worm morphology data acquired with the -[`cellprofiler-nf nextflow pipeline`](https://github.com/AndersenLab/cellprofiler-nf). - -Below is a detailed walk through applying the easyXpress package to sample dose response data that were -extracted from a series of [toxicant dose responses published in the journal Toxicology](https://www.sciencedirect.com/science/article/pii/S0300483X22002049?via%3Dihub). - -In general, there are four major steps to processing dose response data. - -1. LOAD DATA: Read in CellProfiler data and select the appropriate models for primary objects. -2. FLAG OBJECTS: Flag, check, and filter problematic objects, then summarize object data within wells. -3. FLAG WELLS: Flag, check, and filter problematic wells, then check exprimental design balance after filtering. -4. FINALIZE RESULTS: Calculate control delta's and regress confounding effects if needed. - -```{r setup} -library(easyXpress) -library(tidyverse) -``` - -### Get example dose response data - -This article will use example data that exceeds the typical size for package vignettes, therefore users can pull the example data from a separate repository to recreate the workflow below. - -To get the example data users will need to have `git` installed on their machine. [Follow instructions here if you don't have `git` installed or are unsure.](https://github.com/git-guides/install-git) - -```{r message=FALSE} -# specify a directory to clone the example data repository to. Here we'll clone it to the Desktop. You can use whatever path your like here, just make sure it is valid and ends in / -ex.dir <- "~/Desktop/" - -# clone the repo using a system command -system(paste0("git clone https://github.com/AndersenLab/eXDR.git ", ex.dir, "eXDR")) - -``` - -## Load Data - -Read in CellProfiler data and select the appropriate models for primary objects. - -### `readXpress()` - -This function reads data output from `cellprofiler-nf` into R. - -`readXpress()` takes one or more paths to project directories that hold data exported from `cellprofiler-nf`. Each project directory msut have CellProfiler data in a sub-folder named `cp_data`. Set the `rdafile` argument to the name of the files you plan to use, one per project. If `design = TRUE`, a design file will be joined. The design file should be located in a sub-folder of the experimental directory named `design`. If `design = FALSE`, no design file will be joined. To process dose response data, users must set `doseR = TRUE`. The other arguments are specified by default but can be adjusted if needed. - -For more information regarding project directory structure, see [`R/easyXpress`](https://github.com/AndersenLab/easyXpress). - -If `design = T` the `readXpress()` function returns a list, with the first element named `raw_data` and the second named `design`. - -```{r} -# make a vector of project directories cloned in the example data (eXDR) -filedirs <- c(paste0(ex.dir, "eXDR", "/20201210_toxin14A"), - paste0(ex.dir, "eXDR", "/20201217_toxin15A"), - paste0(ex.dir, "eXDR", "/20210311_toxin22A")) - -# make a vector of the .RData files cloned in the example data (eXDR) -rdafiles <- c("20201210_toxin14A_Analysis-20231003.RData", - "20201217_toxin15A_Analysis-20231003.RData", - "20210311_toxin22A_Analysis-20231003.RData") - -# Read in the data -dr <- easyXpress::readXpress(filedir = filedirs, rdafile = rdafiles, design = T, doseR = T) -``` -### `modelSelection()` - -`modelSelection()` takes the raw data output from the `readXpress()` function. -It will assign the appropriate CellProfiler model to each primary object in the data frame. - -To access the raw data output from the `readXpress()` function call above, we can use `dr$raw_data`. - -In this example, the data was generated using 4 worm models: MDHD, L1, L2L3, and L4, models. - -```{r} -# Use the modelSelection function to select the best model for each primary object -ms <- easyXpress::modelSelection(dr$raw_data) -``` - -## Flag Objects - -This part of the project processing will flag problematic objects in the data using various `ObjectFlag` functions (OFs). Next, users can filter flags or retain them after they have been checked. - -Since all the object data is derived from images, the various `view` functions are used to annotate flag data in the image overlays output by `cellprofiler-nf`, which help determine which flags should be filtered or retained. - -We recommend the `edgeOF()` and `clusterOF()` functions are applied before checking objects. After checking objects, we recommend setting custom flags with the `userOF` function, then applying the `classifierOF()` and `outlierOF()` functions before summarizing the filtered data with the `summarizeWells()` function. - -### edge and cluster OFs -```{r, fig.height = 12, fig.width = 12, fig.align = 'center'} -# Use the edge Object Flag (OF) function to flag objects close to the edge of the well -ef <- edgeOF(data = ms) - -# Use the clusterOF function to flag objects that likely contain overlapping worms. -cf <- clusterOF(data = ef) - -``` - -### check object flags -```{r, fig.height = 12, fig.width = 12, fig.align = 'center'} -# Let's check on the objects to see how the flags look across our desired grouping variables (...) -c1 <- checkOF(data = cf, drug, concentration_um) - -# loop at the plot -c1$p -``` -
The `checkOF` plot shows that there are a lot of objects and many clusters at the higher concentrations of Zinc, which suggests a problem at these concentrations. - -```{r, fig.height = 12, fig.width = 12, fig.align = 'center', warning=FALSE} -# Let's look at the size distributions of the objects by our grouping variables with the checkObjs function. - -# Plot just the noFlag data by setting OF = "filter" -c2 <- checkObjs(data = cf, OF = 'filter', strain, drug, concentration_um) - -# look at the plot object -c2 -``` -
The diagnostic plot made with the `checkObjs()` function shows a strong bimodal distribution in the higher concentrations of zinc that further suggest a problem - probably debris in wells. - -### checkModels - -Let's use the `checkModels()` function to see if the small objects in each drug are real worms or not. This function will produce diagnostic plots for drugs across grouping variables, use `?checkModels()` for more help. - -```{r} -# # Before we can use the checkModels function we need to add a variable that specifies the PATH to the processed images for each project. This is common when data are pulled from 3 independent assays, like in this example. We can use dplyr to help us do that and make a nice label variable to include in our diagnostic overlays too. -# cm <- cf %>% -# dplyr::mutate(i.dir = dplyr::case_when(Metadata_Experiment == "toxin14A" ~ -# paste0(ex.dir, "eXDR/20201210_toxin14A/processed_images/"), -# Metadata_Experiment == "toxin15A" ~ -# paste0(ex.dir, "eXDR/20201217_toxin15A/processed_images/"), -# Metadata_Experiment == "toxin22A" ~ -# paste0(ex.dir, "eXDR/20210311_toxin22A/processed_images/"), -# TRUE ~ NA_character_), # add the proc.img.dir var -# w.lab = paste(drug, strain, concentration_um, sep = "_")) # add the well label var -# -# # now we can use the checkModels function to look across the drugs and assays to see if we should use the MDHD model. Also, we can decide if we want to use a ~100um (30px) or ~165um (50px) size filter. -# cm.out <- checkModels(data = cm, -# Metadata_Experiment, drug, # grouping vars (...) to make a diagnostic plot for each. -# proc.img.dir = "i.dir", -# well.label = "w.lab", -# out.dir = paste0(ex.dir, "eXDR/checkModels/out")) -``` - -```{r fig.cap='checkModels() plot'} -knitr::include_graphics(path = "man/figures/checkModels_ex_anno.png") -``` -
Based on the diagnostic plots from the `checkModels()` function it seems like we should: -1) filter the MDHD model for Zinc dichloride and use a 165um size threshold, see example above. -2) Use the MDHD model for paraquat, but filter out objects with a `worm_length_um` less than 165um, no example shown but you can see the plots in your example directory for yourself. - -## Flag Wells - -Flag, check, and filter problematic wells then check exprimental design balance after filtering. - - -## Finalize Results - -Calculate control delta's and regress confounding effects if needed. diff --git a/vignettes/articles/Dose_Response.Rmd b/vignettes/articles/Dose_Response.Rmd new file mode 100644 index 0000000..c7bcf53 --- /dev/null +++ b/vignettes/articles/Dose_Response.Rmd @@ -0,0 +1,422 @@ +--- +title: "Dose Response Processing" +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +## Introduction + +**easyXpress** is an R package to read, process, and analyze worm morphology data acquired with the +[`cellprofiler-nf`](https://github.com/AndersenLab/cellprofiler-nf) nextflow pipeline. + +Below is a detailed walk through applying the easyXpress package to sample dose response data extracted from a toxicant dose response article published in the journal [Toxicology](https://www.sciencedirect.com/science/article/pii/S0300483X22002049?via%3Dihub). + +There are four major steps to processing dose response data that are described in detail below. + +**1. Load Data** - Read in CellProfiler data and select the appropriate models for primary objects.
+**2. Flag Objects** - Flag, check, and filter problematic objects, then summarize object data within wells.
+**3. Flag Wells** - Flag, check, and filter problematic wells, then check experimental design balance after filtering.
+**4. Finalize Results** - Calculate control delta's and regress confounding effects if needed. + +```{r setup, message = FALSE} +library(easyXpress) +library(tidyverse) +``` + +### Get example dose response data + +This article will use example data that exceeds the typical size for package vignettes, to recreate the workflow below clone the example data from the `AndersenLab/eXDR` repository. + +To clone the example data, users will need to have `git` installed on their machine. Follow the [instructions here](https://github.com/git-guides/install-git) if you don't have `git` installed or are unsure. + +```{r message=FALSE} +# specify a directory to clone the example data repository to. Here we'll clone it to the Desktop. You can use whatever path your like here, just make sure it is valid and ends in / +ex.dir <- "~/Desktop/" + +# clone the repo using a system command +system(paste0("git clone https://github.com/AndersenLab/eXDR.git ", ex.dir, "eXDR")) + +``` + +## Load Data + +Read in CellProfiler data and select the appropriate models for primary objects. + +### `readXpress` + +This function reads data output from `cellprofiler-nf` into R. + +`readXpress()` takes one or more paths to project directories that hold data exported from `cellprofiler-nf`. Each project directory must have CellProfiler data in a sub-folder named `cp_data`. Set the `rdafile` argument to the name of the files you plan to use, one per project. If `design = TRUE`, a design file will be joined. The design file should be located in a sub-folder of the experimental directory named `design`. To process dose response data, users must set `doseR = TRUE`. The other arguments are specified by default but can be adjusted if needed. + +For more information regarding project directory structure, see [`R/easyXpress`](https://github.com/AndersenLab/easyXpress). + +If `design = T` the `readXpress()` function returns a list, with the first element named `raw_data` and the second named `design`. + +```{r} +# make a vector of project directories cloned in the example data (eXDR) +filedirs <- c(paste0(ex.dir, "eXDR", "/20201210_toxin14A"), + paste0(ex.dir, "eXDR", "/20201217_toxin15A"), + paste0(ex.dir, "eXDR", "/20210311_toxin22A")) + +# make a vector of the .RData files cloned in the example data (eXDR) +rdafiles <- c("20201210_toxin14A_Analysis-20231003.RData", + "20201217_toxin15A_Analysis-20231003.RData", + "20210311_toxin22A_Analysis-20231003.RData") + +# Read in the data +dr <- easyXpress::readXpress(filedir = filedirs, rdafile = rdafiles, design = T, doseR = T) +``` +### `modelSelection` + +`modelSelection()` takes the raw data output from the `readXpress()` function. +It will assign the appropriate CellProfiler model to each primary object in the data frame. + +To access the raw data output from the `readXpress()` function call above, we can use `dr$raw_data`. + +In this example, the data was generated using 4 worm models (MDHD, L1, L2L3, and L4). + +```{r} +# Use the modelSelection function to select the best model for each primary object +ms <- easyXpress::modelSelection(dr$raw_data) +``` + +## Flag Objects + +This step of the project processing will flag problematic objects in the data using various `ObjectFlag` functions (OF)s. Users can filter flags or retain them after they have been checked. + +### `edgeOF` and `clusterOF` + +The `edgeOF()` function will flag objects close to the edge of the well that are difficult to segment properly. + +The `clusterOF()` function will flag any objects that are found within the same primary object. These 'clusters' are often debris or improperly segmented worms. +```{r, fig.width = 10, fig.align = 'center'} +# Use the edgeOF fucntion +ef <- edgeOF(data = ms) + +# Use the clusterOF function +cf <- clusterOF(data = ef) +``` + +### `checkOF` + +Use the `checkOF()` function to see how the object flags look across the desired grouping variables, which are specified with `...`. +```{r, fig.width = 10, fig.align = 'center'} +c1 <- checkOF(data = cf, drug, concentration_um) + +# loop at the plot returned +c1$p +``` +
The `checkOF` plot above shows that there are a lot of objects and many clusters at the higher concentrations of Zinc, which suggests a problem at these concentrations. + +### `checkObjs` + +Use the `checkObjs()` function to visualize the size distributions of the objects by grouping variables. +```{r, fig.width = 12, fig.height = 7, fig.align = 'center', warning=FALSE} +# Show just the noFlag data by setting OF = "filter" +c2 <- checkObjs(data = cf, OF = 'filter', drug, concentration_um) + +# look at the plot returned +c2 +``` +
The diagnostic plot made with the `checkObjs()` function above shows a strong bimodal distribution in the higher concentrations of zinc that further suggest a problem - probably debris in wells. + +### `checkModels` + +The `checkModels()` function is used to see if small objects are debris. This function will produce diagnostic plots across grouping variables. The default settings help decide whether to use a 100um (30px) or 165um (50px) size filter for objects, and whether to use the `MDHD` worm model or not. Use `?checkModels()` for more help if needed. + +Before the `checkModels()` function is used, a variable that specifies the PATH to the processed images for each project is needed. Below, the `dplyr` package is used to add that variable and make a well label to include in the diagnostic overlays. + +```{r} +# Add variables that describe the PATH to processed images and desired well labels +cm <- cf %>% + dplyr::mutate(i.dir = dplyr::case_when(Metadata_Experiment == "toxin14A" ~ + paste0(ex.dir, "eXDR/20201210_toxin14A/processed_images/"), + Metadata_Experiment == "toxin15A" ~ + paste0(ex.dir, "eXDR/20201217_toxin15A/processed_images/"), + Metadata_Experiment == "toxin22A" ~ + paste0(ex.dir, "eXDR/20210311_toxin22A/processed_images/"), + TRUE ~ NA_character_), # add the proc.img.dir var + w.lab = paste(drug, strain, concentration_um, sep = "_")) # add the well label var + +# use the checkModels function +cm.out <- checkModels(data = cm, + Metadata_Experiment, drug, # the grouping vars (...), make a plot for each. + proc.img.dir = "i.dir", + well.label = "w.lab", + out.dir = paste0(ex.dir, "eXDR/checkModels/out")) # save in the repo you cloned +``` + +```{r, echo = FALSE, fig.cap = '**checkModels() output for Zinc dichloride** - The MDHD objects are all junk or improperly segemnted worms, objects < 165um are also problematic. These objects should be filtered. The large inset overlaid on the right of this plot is provided to show the objects in more detail, it is not included in the acutal function output.'} +knitr::include_graphics("checkModels_ex_anno.png", dpi = 144) +``` +
Based on the diagnostic plots from the `checkModels()` function, like the example above, the following filters should be applied for each drug:
+**1. Zinc dichloride** - Remove all objects identified by the MDHD model and remove objects with a `worm_length_um` < 165um.
+**2. Paraquat** - Use the MDHD model for paraquat, but remove out objects with a `worm_length_um` < 165um. There is no example shown for paraquat, but you can see the plots in your example directory for yourself. + +### `userOF` + +Use the `userOF()` function to add the size and model flags to the data as an easyXpress compatible object flag. The variable added can be named anything. In this example, it is named 'user' and passed to the `userOF` function through `...`. + +```{r, fig.width = 12, fig.height = 7, fig.align = 'center', warning=FALSE} +# add the user variable that will be converted to an object flag +u = cm %>% + dplyr::mutate(user = dplyr::case_when(drug == "Zinc dichloride" & + model == "MDHD" ~ "junk", + drug == "Zinc dichloride" & + worm_length_um < 165 ~ "junk", + drug == "Paraquat" & + worm_length_um < 165 ~ "junk", + TRUE ~ NA_character_)) +# Run the userOF function and specify user variable as the flag +uf <- easyXpress::userOF(data = u, user) + +# Check the object data again to see if the bimodal distributions are resolved. +checkObjs(data = uf, OF = "filter", drug, concentration_um) +``` +
The diagnostic plot made with the `checkObjs()` function above shows that the bimodal distribution in the higher concentrations of zinc are improved by adding the user object flag. + +### `classifierOF` + +The `classiferOF()` function will flag objects that are likely debris or improperly segmented worms. Currently, this function should only be used on 2X images obtained with the ImageXpress Nano and run through the most recent release of the `cellprofiler-nf` nextflow pipeline. + +Any objects with `NA` for the data variables used by the classifier will be flagged in the output. These are rare, and often problematic to begin with. Check the function output to see how many objects are being flagged as `NA`. +```{r} +# apply the classifierOF function +cl <- classifierOF(data = uf) +``` + +### `outlierOF` + +The `outlierOF` function will flag objects that have extreme `worm_length_um` values relative to the other objects in the same well. + +The `outlierOF` function should be applied after all other object flags have been set. This is because the function ignores previously flagged data when searching for outliers in the well. +```{r} +# apply the outlierOF function +o <- easyXpress::outlierOF(data = cl) +``` + +### `checkObjs` + +Use the `checkObjs()` function again to check the effect of filtering all the object flags. Any strong multi-modal signals that remain in the data should be reviewed to ensure they are the result of biology rather than anomalous features of the well image. +```{r,fig.width = 12, fig.height = 7, fig.align = 'center', warning=FALSE} +# check objects again, notice how there are 5 ObjectFlags detected now. +checkObjs(data = o, OF = 'filter', drug, concentration_um) +``` +
The plot above looks much better than it did for the raw data. For example, the bimodal distributions at the high concentrations of Zinc are largely removed. + +### `checkOF` + +Use the `checkOF` function again to see how much data is being flagged. If certain conditions in the experiment are heavily flagged it suggests that those data may not be worth keeping. +```{r, fig.width = 12, fig.height = 7, fig.align = 'center', warning=FALSE} +# check object flags again +co2 <- checkOF(data = o, drug, concentration_um) + +# check the plot +co2$p +``` +
There are many flagged objects for high concentration of zinc. This is somewhat expected since the wells appear to have a lot of junk in them. + +The `viewOverlay()` function can be used to check these wells and see if the flags are working properly whether the data are worth keeping. + +### `viewOverlay` + +The `viewOverlay()` function will annotate the diagnostic images output from `cellprofiler-nf` with any desired object data. This function is extremely useful for troubleshooting problematic conditions in an experiment. Since this function will build an array of full size images, we recommend sub-setting the data to 48 wells or less to reduce file sizes. + +The `setOF()` function can be used to help annotate object flags. In the example below, the `setOF()` function is used to collapse the five `_ObjectFlag` variables to a single variable named `objectFlag`, which we will use to annotate the overlays. The `dplyr` package is also used to filter the data to problematic conditions and randomly sample eight wells to pass to the `viewOverlay()` function. +```{r, fig.width = 12, fig.height = 8, fig.align = 'center', warning=FALSE} +# IMPORTANT! set the seed to ensure the 8 random wells are found in the example images +set.seed(99) + +# set the flags and filter data +o2 <- easyXpress::setOF(data = o) %>% # set the flags + dplyr::filter(drug == "Zinc dichloride" & concentration_um %in% c(200, 400)) %>% + dplyr::filter(well.id %in% sample(well.id, size = 8)) #randomly sample 8 wells + +# Use the viewOverlay function +vo1 <- easyXpress::viewOverlay(data = o2, + proc.img.dir = "i.dir", + well.label = "w.lab", + obj.label = "model", + text.anno = "objectFlag", + file = paste0(ex.dir, "eXDR/viewOverlay/overlay1.png")) # save to example dir +``` + +```{r, echo = FALSE, fig.align = 'left', fig.cap = '**viewOverlay() output** - The objects are annotated with text showing the objectFlag type. The colors indicate which worm model was selected for the object.'} +knitr::include_graphics("viewOverlay.png", dpi = 144) +``` +
The `noFlag` objects look ok, but the wells are certainly dirty. Since the flags appear to be working we can retain these wells for now and move on. The full sized version of the overlay is saved to the example data directory that was cloned above. + +### `filterOF` + +Use the `filterOF()` function to remove all the flagged objects from the data after the flags have been checked. It is helpful to retain these data as a final processed object data set. +```{r} +# finalize the object data made with outlierOF function above. +proc.objs <- easyXpress::filterOF(o, rmVars = T) +``` + +### `summarizeWells` + +The `summarizeWells()` function will remove flagged objects, summarize the data within each well and drop all object related variables from the data. These actions prepare the data for the next step, which is to flag wells. +```{r} +# remove all flags, summarize wells, and drop object vars all in one function +raw.wells <- easyXpress::summarizeWells(data = o) + +``` + +## Flag Wells + +Flag, check, and filter problematic wells, then check the experimental design balance after filtering. + +### `titerWF` + +Use the `titerWF()` function to check for improperly titered bleaches. This function will look across the control conditions within grouping variables and flag wells that belong to bleaches with extreme variation in worm numbers or `cv.n`. Previous analyses suggest that a `cv.n` > 0.68 is a good threshold. Each set of experiments may require a different threshold, which can be set with the `thresh` parameter. + +The first element of the retruned list `tf$d` is the flagged well data. A diagnotic plot is returned in the second element of the list `tf$p`. + +```{r, fig.align = 'center', warning = FALSE} +# Use the titerWF function within a particular bleach and drug for each strain +tf <- easyXpress::titerWF(data = raw.wells, Metadata_Experiment, bleach, strain, drug, doseR = T) +# look at the diagnostic plot +tf$p +``` +
The diagnostic plot above shows that there is one bleach with large variation in worm number `cv.n > 0.68`. All the wells from this bleach will be flagged. + +### `nWF` + +Use the `nWF()` function to flag wells with too many or too few objects in them. In this example, the `min` argument is set to `3`, which will flag wells with less than 3 objects. The default value is `min = 5`, but because the example data is so sparse at the high concentrations of the toxicants the value is set lower. + +The function will output a flagged data `n$d` and a dignostic plot `n$p` as the first and second elements of a list. The diagnostic plot is faceted by the grouping variables specified with `...`. +```{r, fig.width = 10, fig.align = 'center'} +# use the nWF function with the data output from the titerWF function above +n <- easyXpress::nWF(data = tf$d, drug, concentration_um, max = 30, min = 3) + +# Look at the diagnostic plot +n$p +``` +
The plot shows the distribution of object counts across the grouping variables with the `min` and `max` thresholds ploted as vertical lines. + +### `outlierWF` + +Use the `outlierWF()` function to flag wells with extreme values of `median_wormlength_um` within the grouping variables specified with `...`. Typically the grouping variables should isolate strains, conditions, and independent bleaches, like in the example below. +```{r} +# use the outlierWF function to flag outliers within groups +# then use dplyr::mutate to add a variable to indicate independent bleaches. +ow <- easyXpress::outlierWF(data = n$d, + Metadata_Experiment, bleach, drug, + concentration_um, strain) %>% + dplyr::mutate(assay_bleach = paste(Metadata_Experiment, bleach, sep = "_")) +``` + +### `checkWF` + +Use the `checkWF()` function to see how many wells are being flagged across grouping variables specified with `...`. + +```{r, fig.width = 10, fig.height = 7, fig.align = 'center'} +# Use the checkWF function +cw1 <- easyXpress::checkWF(data = ow, drug, concentration_um) + +# look at the plot +cw1$p +``` +
The diagnostic plot above shows that large fraction of wells are being flagged in the highest Zinc concentration. We can use the`filterWF()` and `checkBalance()` functions to see how much data is retained across grouping variables relative to the complete experimental design after the flags are filtered. + +### `filterWF` + +The `filterWF()` function will remove all the flagged wells from the data. +```{r} +# Use the filterWF function and drop the flagging variables afterward +fw <- filterWF(data = ow, rmVars = T) +``` + +### `checkBalance` + +Use the `checkBalance()` function to see the fraction of data retained after the flags are filtered. A balanced design is critical if the effects of confounding variables are to be regressed with the `regEff()` function. The original design data is sourced from the `dr$design` object produced by the `readXpress()` function above. In the example below, the `dplyr` package is used to add the `assay_bleach` variable to the design data so that it can be joined with the data output form the `outlierWF()` function and used as the x-axis in the resulting plot, see code chunk below. + +The `checkBalance()` function returns a list with summary data for making the plot as `cb$d` and the plot itself as `cb$p`. +```{r, fig.width = 10, fig.height = 7, fig.align = 'center'} +# use the checkBalance function and add assay_bleach var to design +cb <- checkBalance(data = fw, drug, concentration_um, + design = dr$design %>% + dplyr::mutate(assay_bleach = paste(Metadata_Experiment, bleach)), + x = assay_bleach) + +# Look at the plot and add a nicer x-axis with ggplot2 package +cb$p + + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) + + ggplot2::geom_hline(yintercept = 0.75, linetype = 2, color = "red") +``` +
The plot above shows that the flagged data fall in particular bleaches. We recommend that the independent bleaches with less than 75 % of data retained be removed. Based on this plot, the bleaches that should be removed for each drug are listed below.
+**1. Paraquat** - The four bleaches `toxin14A_2`, `toxin14A_3`, `toxin15A_1`, and `toxin15A_3` should be removed.
+**2. Zinc dichloride** - The five bleaches `toxin14A_1`, `toxin14A_2`, `toxin15A_1`, `toxin15A_3`, and `toxin22A_3` should be removed.
+ +### `checkEff` + +The `checkEff()` function is used to look at the data after the problematic bleaches have been removed. For more help with the `checkEff()` function use `?checkEff()`. +```{r, fig.align = 'center'} +# filter the unbalanced bleaches - nearly half the data in this example. +drop <- fw %>% + dplyr::mutate(b.filter = + dplyr::case_when(drug == "Paraquat" & assay_bleach %in% + c("toxin14A_2", + "toxin14A_3", + "toxin15A_1", + "toxin15A_3") ~ "drop", + drug == "Zinc dichloride" & assay_bleach %in% + c("toxin14A_1", + "toxin14A_2", + "toxin15A_1", + "toxin15A_3", + "toxin22A_3") ~ "drop", + TRUE ~ "keep")) %>% + dplyr::filter(b.filter == "keep") %>% + dplyr::select(-b.filter) + +# use the checkEff function +ce1 <- easyXpress::checkEff(data = drop, drug, strain, x = concentration_um, y = median_wormlength_um, fill = assay_bleach, scales = "free_x") + +# look at the plot +ce1 +``` +
The dose effect is as expected for the two strains, increasing concentrations of toxicants reduce the length of animals in the wells. There could be an effect of assay and bleach that can be tested with the `regEff()` function. + +## Finalize Results + +### `regEff` + +The `regEff()` function will regress out the effect of a confounding variable on a dependent variable within groups specified by `...`. The function extracts the residuals of a linear model specified as `d.var ~ c.var - 1` that remove the estimated influence of the confounding variable `c.var` on the dependent variable `d.var`. The function returns a list with four elements. the residuals of the method should be used carefully as it can introduce noise into data awill also produce diagnostic plots and We recommend testing + +Before using the `regEff()` function it is important to have a well balanced design. If the effect of bleach +```{r, fig.align = 'center'} +# Regress the effect of the remaining independent bleaches for each drug using regEff() +reg <- easyXpress::regEff(data = drop, drug, d.var = median_wormlength_um, c.var = assay_bleach) + +# Look at the regression coefficients in the diagnostic plot p2 +reg$p2 +``` +
The diagnostic plot above shows the point estimates of regression coefficients for each of the indepedent bleaches faceted by drug with standard errors as lines. We recommend further filtering bleaches with extreme effects. Here there are no extreme bleach effects to filter. + +### `delta` + +The `delta()` function is used to control for innate differences among strains. This function will calculate the difference in well summary statistics between the experimental condition and the median control condition within a group. It is recommended to group data to independent bleaches for all strains and drugs when processing dose reponse data. + +This is the final step in processing the data. See the help for the `delta()` function with `?delta()`. +```{r} +# use the delta() function +del <- easyXpress::delta(data = reg$d, + assay_bleach, drug, strain, # group with ... + WF = "filter", + doseR = TRUE, + vars = "median_wormlength_um_reg") + +# check the finalized data +checkEff(data = del, drug, strain, x = concentration_um, + y = median_wormlength_um_reg_delta, + fill = assay_bleach, + scales = "free_x") +``` +
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