homeworks week 9

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "githubPullRequests.ignoredPullRequestBranches": [
+        "main"
+    ]
+}

Announcements.qmd

@@ -34,4 +34,10 @@ For previous course announcements emails, see the links below.
 
 - April 12, 2026 [Cytometry in R - Week # 09 - It's Raining Functions!](https://github.com/UMGCCCFCSR/CytometryInR/discussions/162) 
 
+- April 21, 2026 [No Class This Week](https://github.com/UMGCCCFCSR/CytometryInR/discussions/174)
+
+- April 28, 2026 [Cytometry in R - Week # 10 - Downsampling and Concatenation](https://github.com/UMGCCCFCSR/CytometryInR/discussions/179)
+
+- May 05, 2026 [Cytometry in R - Week # 11 - Data for Statistics](https://github.com/UMGCCCFCSR/CytometryInR/discussions/186)
+
 ![](images/YouTubeHex.png)

ExistingResources.qmd

@@ -13,19 +13,29 @@ sidebar: false
 [![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa]
 </div>
 
+<<<<<<< HEAD
+We are not the first "Cytometry in R" course, nor will we be the last. This page is linking to the already existant online Cytometry in R resources that we have encountered and benefited from during our own learning journey. May they prove useful to you as you progress your way through yours! 
+=======
 We are not the first "Cytometry in R" course, nor will we be the last. If this is your first R course, now that we are about a third of the way through the course (and you have some general R knowledge, context, and troubleshooting skills), exploring how others approached some of these same initial subjects may be useful if you want to dive deeper. 
 
 This page links to existing online Cytometry in R resources that I have encountered and benefited from during my own learning journey. Not all the code as presented may still work, as R packages evolve over time, so some troubleshooting may be necessary on your end to replicate what they show. Good luck, and may you find some useful concepts to further help you along on your own journeys! 
 
 ![](images/ExistingResources.png)
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 
 <br>
 
 ---
 
 # Christopher Hall - Flow Cytometry Data Analysis in R
 
+<<<<<<< HEAD
+[Cytometry-R-Scripts: R scripts to help with your flow cytometry analysis](https://github.com/hally166/Cytometry-R-scripts)
+
+[R_flowcytometry_course: The files and presentation from the Cytometry Core Facility flow cytometry data analysis course in R](https://github.com/hally166/R_flowcytometry_course)
+=======
 [Christopher Hall](https://uk.linkedin.com/in/christopher1hall) (currently at University of Edinburgh) created a series of YouTube videos on how to implement flow cytometry analysis in R. In the course of an hour, he provides an overview of many of the same topics we looked at over the first 8 weeks. I first encountered these videos late 2021/early 2022, and they were immensely helpful in getting started. 
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 
 ### Installation and Loading Data
 
@@ -43,6 +53,10 @@ This page links to existing online Cytometry in R resources that I have encounte
 
 [(3) Flow Cytometry Data Analysis in R: gating with flowWorkspace](https://youtu.be/ijHOGHP82EY?si=87OB8t8wynJNnTf3)
 
+<<<<<<< HEAD
+
+=======
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 <iframe width="560" height="315" src="https://www.youtube.com/embed/ijHOGHP82EY?si=-kAM2ExNW4Og0y3U" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
 ### Visualization
@@ -58,7 +72,11 @@ This page links to existing online Cytometry in R resources that I have encounte
 
 # Ozette Technologies - BioC 2023 Workshop
 
+<<<<<<< HEAD
+Workshop given at the Bioc2023 conference, authored by Arpan Neupane and Andrew McDavid. 
+=======
 During the [Bioc2023](https://bioc2023.bioconductor.org/) conference in Boston, Arpan Neupane and Andrew McDavid, who were working at [Ozette](https://ozette.com/) gave a workshop on how to use flowWorkspace and the other R packages that they maintain. I didn't make it to the workshop (misread the room number and ended up in an interesting parallel computing talk), but went through the material on the train ride back to Baltimore. In the process, I found out about the memory benefits of using "cytosets" and how to implement `openCyto` gates, which fast forward a few years has led to much of the general workflow showcased throughout the Cytometry in R course. 
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 
 [Workshop: Reproducible and programmatic analysis of flow cytometry experiments with the cytoverse](https://youtu.be/_8x-prIxJgw?si=Tm-QoBQ3qc568xXD)
 
@@ -68,10 +86,32 @@ During the [Bioc2023](https://bioc2023.bioconductor.org/) conference in Boston,
 
 ---
 
+<<<<<<< HEAD
+# Pritam Kumar Panda - Flow Cytometry Data Analysis & Visualization in R using CytoExploreR
+
+[Flow-Cytometry-analysis-in-R](https://github.com/pritampanda15/Proteomics/tree/main/Flow-Cytometry-analysis-in-R-main)
+
+[CytoExploreR-Interactive-visualization](https://github.com/pritampanda15/Proteomics/tree/main/CytoExploreR-Interactive-visualization-main)
+
+### Complete Guide
+
+[Flow Cytometry Data Analysis & Visualization in R using CytoExploreR: Complete Guide](https://youtu.be/AvIvRorrh8c?si=MVViua5fSBrahtzv)
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/AvIvRorrh8c?si=QektorbguSVJ_lP-" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+<br>
+
+---
+
+
+# Bioinformatics DotCa - Introduction to Flow Cytometry in R
+
+=======
 # Bioinformatics DotCa - Introduction to Flow Cytometry in R
 
 One of the older Cytometry in R series on YouTube compromising a workshop series by [Ryan Brinkman](https://www.bccrc.ca/dept/tfl/people/ryan-brinkman)'s group. While I didn't encounter them until much later, they influenced many of the other video series you can see on this page. Some of the code is starting to show its age, as quite a few of the R packages have been superseeded (especially when it comes to gating and visualization), but still worthwhile watching. 
 
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 ### Introduction to Flow Cytometry in R
 
 [Introduction to Flow Cytometry in R](https://youtu.be/0_dN8VKhOJ0?si=Mn7UB0Gps5twqAqK)
@@ -102,6 +142,10 @@ One of the older Cytometry in R series on YouTube compromising a workshop series
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/xnvNCsu56Vo?si=botz0swIm31W5340" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
+<<<<<<< HEAD
+# Tulika Rai - Learn Innovatively With Me
+
+=======
 
 # Givanna Putri and Thomas Ashhurst - Introduction to Cytometry Data Analysis in R workshop
 
@@ -151,6 +195,7 @@ One of the more recent tutorial series I encountered in the last year, while rel
 
 One of the more recent tutorial series I encountered in the last year, while a bit more focused on a particular application than the other video series, once you have some additional R context it is worth checking out. 
 
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 ### flowAI Flow Cytometry Data Cleaning using R
 
 [flowAI Flow Cytometry Data Cleaning using R: A Step-by-step Tutorial](https://youtu.be/PvB37SEe7lI?si=X3iYcGT7A3G603oP)
@@ -167,12 +212,26 @@ One of the more recent tutorial series I encountered in the last year, while a b
 
 ---
 
+<<<<<<< HEAD
+# Givanna Putri - Introduction to Cytometry Data Analysis in R workshop
+
+[ACS 2021 Workshops - Introduction to Cytometry Data Analysis in R workshop](https://youtu.be/-0QrXgk_NRw?si=GqcjU-_MQ3UvYwvI)
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/-0QrXgk_NRw?si=GqcjU-_MQ3UvYwvI" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+<br>
+
+---
+
+# Timothy Keyes - 
+=======
 # Others
 
 The additional resources below are ones that I encountered while building this Existing Resources page, but have not had time to fully watch yet. 
 
 
 ### Timothy Keyes - 
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 
 [{tidytof}: Predicting Patient Outcomes from Single-cell Data using Tidy Data Principles](https://youtu.be/5NhpC836aSc?si=QqoeW31cQcHFzu5W)
 
@@ -182,15 +241,44 @@ The additional resources below are ones that I encountered while building this E
 
 ---
 
+<<<<<<< HEAD
+# Ryan Duggan - Cytometry on Air
+
+[Cytometry on Air: Analyzing Flow Cytometry Data in R](https://www.youtube.com/live/_B7mo6dB3BU?si=eW20X4YWgaCoYnfw) Presentation by TJ Chen and Greg Finak,
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/_B7mo6dB3BU?si=eW20X4YWgaCoYnfw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+<br>
+
+---
+
+
+# Guillaume Beyrend - Learn Cytometry
+
+[Learn Cytometry](https://learncytometry.com/videos/) Originally appeared to have been paywalled, doesn't currently appear to be the case. 
+
+<br>
+
+---
+
+# Hong Qin - flow analysis in R
+
+### Flow Analysis in R
+=======
 ### Hong Qin - flow analysis in R
 
 #### Flow Analysis in R
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 
 [flow analysis in R, bio125, Spring 2015](https://youtu.be/r7Mf6joCNzM?si=5j9Fy8fn6d7o0Z-u)
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/r7Mf6joCNzM?si=5j9Fy8fn6d7o0Z-u" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
+<<<<<<< HEAD
+###  Flow Cytometer Data Analysis
+=======
 ####  Flow Cytometer Data Analysis
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 
 [BIO233 demo, flow cytometer data analysis, simple example](https://youtu.be/BN5Ldu1AFgk?si=FV2K7jJGe9H6na08)
 
@@ -201,7 +289,11 @@ The additional resources below are ones that I encountered while building this E
 
 ---
 
+<<<<<<< HEAD
+# Swayam Prabha - Flow cytometry data analysis in R/Bioconductor
+=======
 ### Swayam Prabha - Flow cytometry data analysis in R/Bioconductor
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 
 [Lecture 15 : Flow cytometry data analysis in R/Bioconductor](https://youtu.be/g-VUT3riwOM?si=SlX88J3HpsjYaqFI)
 
@@ -211,6 +303,8 @@ The additional resources below are ones that I encountered while building this E
 
 ---
 
+<<<<<<< HEAD
+=======
 ### Guillaume Beyrend - Learn Cytometry
 
 [Learn Cytometry](https://learncytometry.com/videos/) Originally appeared to have been pay-walled, doesn't currently appear to be the case. Has a very different teaching approach from my own.
@@ -219,6 +313,7 @@ The additional resources below are ones that I encountered while building this E
 
 ---
 
+>>>>>>> e277891a345df446c1e406e18bbff78126d26c90
 
 <div style="text-align: right;">
 [![AGPL-3.0][agpl3-image]][agpl3]

Homeworks.qmd

@@ -266,4 +266,49 @@ Click [here](https://github.com/UMGCCCFCSR/CytometryInR/pulls?q=label%3A%22Week+
 
 Stay tuned for our [walk-through]() answers on Course section completion. 
 
+# Week 10 - Downsampling and Concatenation
+
+![](/course/10_Downsampling/images/TakeAway.jpg)
+
+Redirect to [Week 10](course/10_Downsampling/index.qmd) content. 
+
+
+:::{.callout-tip title="Problem 1"}
+Load a dataset into R, gate it however you like, and then export out a population of interest as their own .fcs files. Open them in either Floreada.io or the commercial software of your choice, and take a screenshot of how they look by two markers of interest. 
+:::
+
+:::{.callout-tip title="Problem 2"}
+In the example for `Downsampling()` we only changed one keyword (GUID), after substituting in our desired addon right before the .fcs. Since keyword use might vary by manufacturer, create a couple additional arguments for `Downsampling()` that allow you to change out the values for some additional keywords.
+:::
+
+:::{.callout-tip title="Problem 3"}
+Trickier - After concatenating out an .fcs file for a cell subset of your choice, reload it back into R, extract out both the exprs matrix, and the description list. Using the keywords that got added, figure out a way using dplyr to revert the numeric keys (denoted by "_key") in the exprs matrix back to their original character values as recorded in the keywords. 
+:::
+
+Click [here](https://github.com/UMGCCCFCSR/CytometryInR/pulls?q=label%3A%22Week+10%22) to see previous community attempts at answering these optional take-home problems.
+
+Stay tuned for our [walk-through]() answers on Course section completion. 
+
+# Week 11 - Data for Statistics
+
+![](/course/11_DataForStats/images/TakeAway.jpg)
+
+Redirect to [Week 11](course/11_DataForStats/index.qmd) content. 
+
+:::{.callout-tip title="Problem 1"}
+Implement additional `openCyto` gates, and validate that they are being placed correctly using `Utility_GatingPlot()`. Update the gate_range arguments until satisfied. Then, add new gates, and run the resulting dataset through `CombinedFlow()`, screenshotting any values that return with p-values that upon visualizing the data also look reasonable (not driven by a single-outlier). 
+:::
+
+:::{.callout-tip title="Problem 2"}
+In our ggbeeswarm boxplot, currently we are seeing the proportion on the y-axis. Figure out how to modify the `StatPlotsForFlow()` function to instead display the axis as a percentage, and then using additional function arguments and conditions, set the function up to allow you to switch between as desired. Also, externalize size and cex to modify your beeswarm boxplots in an easier fashion!
+:::
+
+:::{.callout-tip title="Problem 3"}
+Some "typical" immunology workflows run a "normality" test before implementing a corresponding downstream test based on the result (Your friendly-neighbourhood statistician may have some strong thoughts on this field practice).  Regardless of your position on this approach, see if you can modify our `StatsFromFlow()` function, using additional arguments conditional statements, to incorporate in a shapiro-wilks or `fBasics` package Omnibus K2 test first, and based on the outputs proceed downstream to either the parametric or non-parametric options
+:::
+
+Click [here](https://github.com/UMGCCCFCSR/CytometryInR/pulls?q=label%3A%22Week+11%22) to see previous community attempts at answering these optional take-home problems.
+
+Stay tuned for our [walk-through]() answers on Course section completion. 
+
 

README.md

@@ -4,7 +4,7 @@ Cytometry in R is a free virtual mini-course being organized by the [Flow Cytome
 
 We are excited that so many individuals worldwide have chosen to take part, and we look forward to helping you get started on your own learning journeys. 
 
-![](/images/WorldwideSignups.png){width=100%}
+![](/images/WorldwideSignups.png){width="100%"}
 
 Click here to go to our [Course Website](https://umgcccfcsr.github.io/CytometryInR)