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helperFlowCell

This is a general purpose library to assist with handling flow cytometry data.

  1. build_design_matrix.R takes a text input of markers (panel.csv) and compares it to reference files ('antibody_inventory.xlsx' and instrument_config.xlsx') and outputs a succinct Excel spreadsheet in which each row is an antibody and each column is a distinct channel on your flow instrument. Positive cells are shaded gray for easy identification.
  2. plot_spectra.R takes a text input of markers (panel.csv) and plots the fluorophores against channels available on your instrument (instrument_config.xlsx') to show how well the fluorophores align with the detectors.
  3. reshape_compensation.R takes the compensation exported from Flowjo (eg. Acquisition-defined.csv) and generates compensation_matrix.txt so that it can be imported into FACSDiva. It also allows you to rearrange the channels by providing a list in fluorochrome_order.txt'.
  4. analyze_counts.R is the main analysis script. It takes raw exported counts from Flowjo's table editor (counts.csv) and user-defined metadata.csv, merges and reshapes them into a single table, then for each cell type in each organ, calculates the p values for all pairs of groups. If provided, mice.csv can also be merged in, enabling the calculation of a weeks_old parameter. Finally, if a total_viable_cells column is added to metadata.csv, it will also plot the absolute counts. See below for how to export data from Flowjo and supply the metadata file.
  5. analyze_mfi.R is similar to analyze_counts.R, but takes mfi (mfi.csv). If sdev (sdev.csv) is also supplied, this script will also plot modal histograms of simulated fluorescence data.

Installation

Install the following packages in R:

install.packages('import')
install.packages("optparse")
install.packages("logr")
install.packages("zeallot")  # %<-% operator
install.packages('magrittr')  # %>% operator
install.packages("progress")

# file IO
install.packages("openxlsx")
install.packages("readxl")
install.packages("XML")
install.packages("jsonlite")

# data
install.packages('plyr')
install.packages('dplyr')
install.packages('reshape2')
install.packages('tidyr')
install.packages('wrapr')
install.packages("stringr")
install.packages("stringi")

# plotting
install.packages('ggplot2')
install.packages('superb')
install.packages('cowplot')
install.packages('plotly')
install.packages('htmlwidgets')
install.packages('pandoc')  # save static plotly images
install.packages('ggprism')
install.packages('svglite')
update.packages('systemfonts')

In python, create a conda environment ('r-reticulate') and install plotly and kaleido:

conda create -n r-reticulate python=3.9
conda activate r-reticulate
pip install plotly==5.3.1
pip3 install -U kaleido==0.2.1
pip install numpy==1.24.2

If you're on linux, additionally you'll need to install pandoc using the command line:

sudo apt install pandoc

To help R find your conda environment, create the .Renviron hidden file on in your home directory and include the following three lines:

LD_LIBRARY_PATH="/path/to/miniconda3/envs/r-reticulate/lib/libpython3.9.dylib"
RETICULATE_MINICONDA_PATH="/path/to/miniconda3"
RETICULATE_PYTHON_ENV="/path/to/miniconda3/envs/r-reticulate/"

Note that if you have a higher version of plotly and kaleido in your conda environment, you will see the following entirely unhelpful error. See this Stack Overflow link for more details.

Error in save_image(): ! {reticulate} wasn't able to find a Python environment. ℹ If you have an existing Python installation, use reticulate::use_python() to inform {reticulate} of it. ℹ To have {reticulate} install Python for you, reticulate::install_python().
Backtrace:
    ▆ 1. └─save_fig(...)
      2. ├─base::suppressWarnings(...)
      3. │ └─base::withCallingHandlers(...)=
      4. └─plotly::save_image(...)
      5. └─plotly::kaleido()
      6. └─rlang::abort(...)
      Error in save_image(fig, file = file.path(file.path(dirpath, "png"), paste0(filename, : Calls: save_fig

Lastly, put this in your bashrc:

# mac
RSTUDIO_PANDOC="/Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools"

# linux
RSTUDIO_PANDOC="/usr/lib/rstudio/resources/app/bin/quarto/bin/tools/x86_64"

Data Requirements

Set up the following files and place them in the ref directory.

  1. antibody_inventory.xlsx: This file should have at minimum the antibody and fluorophore columns. You should probably also include other identifying information, such as the company and catalog_no, but for the purposes of this repo, that's optional.

  2. instrument_config.csv: This file should have the following columns: id, pmt, laser, fluorochromes, where each row is a separate laser/detector combination and the fluorochromes column should contain a comma-separated list of all the fluorophores that laser/detector combo should be able to detect. For the purposes of this repo, it's okay to leave fluorophores out, but do not duplicate a fluorophore, even if it is detected across multiple channels.

  3. spectra.csv: Download the the fluorescence intensity data from FPBase by selecting your desired fluorophores, then clicking the "Share" button on the bottom right of the lower toolbar. An example file was included, since this is publicly available data.

How to Set Up Files for Analysis Scripts

To get the counts, open Flowjo's Table Editor and drag in all the gates you want for the organ that you want. Change the metric to Count instead of "Freq. of Parent". An easy way to do this is to open the workspace file using Sublime or other text editor, then go to the TableEditor section. Under your table of interest, replace statistic="fj.stat.freqofparent" with statistic="Count" for all rows.

To get the mfi and sdev, do the same thing as above in Sublime, but instead, use regex to search and replace this block: name=.*?rename="". For mfi, replace with name="Geometric Mean : GFP" identifier="Geometric Mean" statistic="Geometric Mean" parameterName="Comp-GFP-A :: GFP" rename="". For sdev, replace with name="Robust SD : GFP" identifier="Robust SD" statistic="Robust SD" parameterName="Comp-GFP-A :: GFP" rename="". Make sure you name your file so you know what kind of mean you use. For example, I name my mfi files gmfi.csv and rsdev.csv to denote geometric mean and robust sd, respectively.

Getting Started

  1. All scripts are meant to be run from the command line. For example:

    Rscript R/design_flow_panel.R

Contributing

Please create a Github issue or reach out to me directly.

Copyright

This code is copyright by Harrison Wang in 2023.