Unmixing with Luciernaga

Introduction

Unmixing is a black box for many spectral flow cytometry users, you adjust gates on your single color controls, you provide full-stained samples, you unmix and then you evaluate the outputs with NxN plots. The golden rules of reference controls (1. Single Color Controls as Bright or Brighter than Full-Stain Sample; 2. Unmixing Single Color should be the same fluorophore (even better same manufacturer and lot); 3. Single Color Controls should have autofluorescence subtracted from a matching/equivalent unstained sample; 4. enough events) are useful guidepost that obviously work but few have mechanistic explanations behind why.

Building on examples from Jakob Theorell’s flowSpecs and Christopher Hall’s flowUnmix package, we implemented a way to take Luciernaga_QC() outputs of purified fluorophore signatures and unmix them using ordinal least squares (OLS) working from GatingSet objects, and returning FCS 3.0 standard files. In combination with functional programming principles, we have been leveraging this to understand how variations of fluorophore signature and brightness impact the unmixing of full-stained samples. We hope our addditive contribution enables users to push the limits of SFC and uncover new insights, write ways to handle issues arising from relative heterogeneity of individual immune cells unmixed with combination outputs, and spare future graduate students having to go write their own R package to answer space-wormhole questions.

Getting Started

This section uses the generated purified flourophore signatures generated by Luciernaga_QC() in the previous vignettes.

Let’s first load the required packages by calling them with library.

library(Luciernaga)
library(flowCore)
library(flowWorkspace)
library(openCyto)
library(ggcyto)  
library(data.table)
library(dplyr)
library(purrr) 
library(stringr)
library(ggplot2)
library(gt)
library(plotly)
library(htmltools)

Then we can find the .fcs files stored within the Luciernaga packages extdata folder and sort them by their respective type

File_Location <- system.file("extdata", package = "Luciernaga")
FCS_Pattern <- ".fcs$"
FCS_Files <- list.files(path = File_Location, pattern = FCS_Pattern,
                        full.names = TRUE, recursive = FALSE)
head(FCS_Files[10:30], 20)
#>  [1] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/21_Before.fcs"             
#>  [2] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/22_After.fcs"              
#>  [3] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/22_Before.fcs"             
#>  [4] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/23_After.fcs"              
#>  [5] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/23_Before.fcs"             
#>  [6] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/4BeadsUnstained(Beads).fcs"
#>  [7] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CCR4_BUV615(Beads).fcs"    
#>  [8] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CCR4_BUV615(Cells).fcs"    
#>  [9] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CCR6_BV786(Beads).fcs"     
#> [10] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CCR6_BV786(Cells).fcs"     
#> [11] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CCR7_BV650(Beads).fcs"     
#> [12] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CCR7_BV650(Cells).fcs"     
#> [13] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CD107a_APC-R700(Beads).fcs"
#> [14] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CD107a_APC-R700(Cells).fcs"
#> [15] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CD127_BV421(Beads).fcs"    
#> [16] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CD127_BV421(Cells).fcs"    
#> [17] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CD16_APC(Beads).fcs"       
#> [18] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CD16_APC(Cells).fcs"       
#> [19] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CD161_BV480(Beads).fcs"    
#> [20] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/CD161_BV480(Cells).fcs"

UnstainedFCSFiles <- FCS_Files[grep("Unstained", FCS_Files)]
UnstainedBeads <- UnstainedFCSFiles[grep("Beads", UnstainedFCSFiles)]
UnstainedCells <- UnstainedFCSFiles[-grep("Beads", UnstainedFCSFiles)]

BeadFCSFiles <- FCS_Files[grep("Beads", FCS_Files)]
BeadSingleColors <- BeadFCSFiles[-grep("Unstained", BeadFCSFiles)]

CellSingleColorFiles <- FCS_Files[grep("Cells", FCS_Files)]
CellSingleColors <- CellSingleColorFiles[!str_detect("Unstained", CellSingleColorFiles)]

Now lets create a GatingSet for our single-color cell unmixing controls

MyCytoSet <- load_cytoset_from_fcs(CellSingleColors, 
                                   truncate_max_range = FALSE, 
                                   transform = FALSE)
MyCytoSet
#> A cytoset with 30 samples.
#> 
#>   column names:
#>     Time, UV1-A, UV2-A, UV3-A, UV4-A, UV5-A, UV6-A, UV7-A, UV8-A, UV9-A, UV10-A, UV11-A, UV12-A, UV13-A, UV14-A, UV15-A, UV16-A, SSC-W, SSC-H, SSC-A, V1-A, V2-A, V3-A, V4-A, V5-A, V6-A, V7-A, V8-A, V9-A, V10-A, V11-A, V12-A, V13-A, V14-A, V15-A, V16-A, FSC-W, FSC-H, FSC-A, SSC-B-W, SSC-B-H, SSC-B-A, B1-A, B2-A, B3-A, B4-A, B5-A, B6-A, B7-A, B8-A, B9-A, B10-A, B11-A, B12-A, B13-A, B14-A, YG1-A, YG2-A, YG3-A, YG4-A, YG5-A, YG6-A, YG7-A, YG8-A, YG9-A, YG10-A, R1-A, R2-A, R3-A, R4-A, R5-A, R6-A, R7-A, R8-A
MyGatingSet <- GatingSet(MyCytoSet)
MyGatingSet
#> A GatingSet with 30 samples

FileLocation <- system.file("extdata", package = "Luciernaga")
MyGates <- fread(file.path(path = FileLocation, pattern = 'Gates.csv'))
gt(MyGates)

alias	pop	parent	dims	gating_method	gating_args	collapseDataForGating	groupBy	preprocessing_method	preprocessing_args
singletsFSC	+	root	FSC-A,FSC-H	singletGate		FALSE	NA	NA	NA
singletsSSC	+	singletsFSC	SSC-A,SSC-H	singletGate		FALSE	NA	NA	NA
singletsSSCB	+	singletsSSC	SSC-A,SSC-B-A	singletGate		FALSE	NA	NA	NA
nonDebris	+	singletsSSCB	FSC-A	gate_mindensity		FALSE	NA	NA	NA
lymphocytes	+	nonDebris	FSC-A, SSC-A	flowClust	K=2, target=c(1e5, 5e4)	FALSE	NA	NA	NA

MyGatingTemplate <- gatingTemplate(MyGates)
gt_gating(MyGatingTemplate, MyGatingSet)
MyGatingSet[[1]]
#> Sample:  CCR4_BUV615(Cells).fcs 
#> GatingHierarchy with  6  gates

Now lets create a GatingSet for our unstained cell unmixing controls

MyUnstainedCytoSet <- load_cytoset_from_fcs(UnstainedCells, 
                                   truncate_max_range = FALSE, 
                                   transform = FALSE)
MyUnstainedCytoSet
#> A cytoset with 18 samples.
#> 
#>   column names:
#>     Time, UV1-A, UV2-A, UV3-A, UV4-A, UV5-A, UV6-A, UV7-A, UV8-A, UV9-A, UV10-A, UV11-A, UV12-A, UV13-A, UV14-A, UV15-A, UV16-A, SSC-W, SSC-H, SSC-A, V1-A, V2-A, V3-A, V4-A, V5-A, V6-A, V7-A, V8-A, V9-A, V10-A, V11-A, V12-A, V13-A, V14-A, V15-A, V16-A, FSC-W, FSC-H, FSC-A, SSC-B-W, SSC-B-H, SSC-B-A, B1-A, B2-A, B3-A, B4-A, B5-A, B6-A, B7-A, B8-A, B9-A, B10-A, B11-A, B12-A, B13-A, B14-A, YG1-A, YG2-A, YG3-A, YG4-A, YG5-A, YG6-A, YG7-A, YG8-A, YG9-A, YG10-A, R1-A, R2-A, R3-A, R4-A, R5-A, R6-A, R7-A, R8-A
MyUnstainedGatingSet <- GatingSet(MyUnstainedCytoSet)
MyUnstainedGatingSet
#> A GatingSet with 18 samples

FileLocation <- system.file("extdata", package = "Luciernaga")
MyGates <- fread(file.path(path = FileLocation, pattern = 'Gates.csv'))
gt(MyGates)

alias	pop	parent	dims	gating_method	gating_args	collapseDataForGating	groupBy	preprocessing_method	preprocessing_args
singletsFSC	+	root	FSC-A,FSC-H	singletGate		FALSE	NA	NA	NA
singletsSSC	+	singletsFSC	SSC-A,SSC-H	singletGate		FALSE	NA	NA	NA
singletsSSCB	+	singletsSSC	SSC-A,SSC-B-A	singletGate		FALSE	NA	NA	NA
nonDebris	+	singletsSSCB	FSC-A	gate_mindensity		FALSE	NA	NA	NA
lymphocytes	+	nonDebris	FSC-A, SSC-A	flowClust	K=2, target=c(1e5, 5e4)	FALSE	NA	NA	NA

MyGatingTemplate <- gatingTemplate(MyGates)
gt_gating(MyGatingTemplate, MyUnstainedGatingSet)
MyUnstainedGatingSet[[1]]
#> Sample:  INF071_Ctrl_Unstained.fcs 
#> GatingHierarchy with  6  gates

Generate Luciernaga_QC Outputs

Now that the GatingSets are re-established, let’s continue where the last vignette left off by processing all the fcs files with Luciernaga_QC to characterize the fluorescent signatures within.

Let’s first provision the AFOverlap csv to handle conflicts.

FileLocation <- system.file("extdata", package = "Luciernaga")
pattern = "AutofluorescentOverlaps.csv"
AFOverlap <- list.files(path=FileLocation, pattern=pattern,
                        full.names = TRUE)
AFOverlap_CSV <- read.csv(AFOverlap, check.names = FALSE)
AFOverlap_CSV
#>   Fluorophore         MainDetector
#> 1   Unstained UV7-A,V7-A,V3-A,V5-A
#> 2      BUV496                UV7-A
#> 3       BV480                 V5-A
#> 4       BV510                 V7-A
#> 5       BV570                 V8-A

And next generate a CellAF unstained signature that can be used when these fluorophore-autofluorescence overlap files are encountered:

# pData(MyUnstainedGatingSet[1])
removestrings <- c(".fcs")

TheCellAF <- map(.x=MyUnstainedGatingSet[1], .f=Luciernaga_QC, subsets="lymphocytes",
                              removestrings=removestrings, sample.name="GUID",
                              unmixingcontroltype = "cells", Unstained = TRUE,
                              ratiopopcutoff = 0.001, Verbose = FALSE,
                              AFOverlap = AFOverlap, stats = "median",
                              ExportType = "data", SignatureReturnNow = TRUE,
                              outpath = TemporaryFolder, Increments=0.1,
                              SecondaryPeaks=2, experiment = "FirstExperiment",
                              condition = "ILTPanel", SCData="subtracted",
                              NegativeType="default")
#> Normalizing Data for Signature Comparison

TheCellAF <- TheCellAF[[1]] #Removes list caused by map

gt(TheCellAF)

UV1-A	UV2-A	UV3-A	UV4-A	UV5-A	UV6-A	UV7-A	UV8-A	UV9-A	UV10-A	UV11-A	UV12-A	UV13-A	UV14-A	UV15-A	UV16-A	V1-A	V2-A	V3-A	V4-A	V5-A	V6-A	V7-A	V8-A	V9-A	V10-A	V11-A	V12-A	V13-A	V14-A	V15-A	V16-A	B1-A	B2-A	B3-A	B4-A	B5-A	B6-A	B7-A	B8-A	B9-A	B10-A	B11-A	B12-A	B13-A	B14-A	YG1-A	YG2-A	YG3-A	YG4-A	YG5-A	YG6-A	YG7-A	YG8-A	YG9-A	YG10-A	R1-A	R2-A	R3-A	R4-A	R5-A	R6-A	R7-A	R8-A
359.38	612.9988	437.0275	559.4488	779.7476	1246.153	2423.138	1793.776	1682.139	625.345	402.5919	251.5363	198.8788	255.1806	175.5994	168.3106	379.9125	1167.719	1815.069	2080.031	3214.613	3112.45	4226.681	3096.637	2104.025	2316.6	1251.25	704.825	632.8438	574.3375	518.1688	295.5562	1192.289	1367.454	1904.727	1289.431	1090.77	870.5431	619.2231	459.5087	477.0831	316.0812	239.0244	221.5788	163.0619	210.1844	516.2194	394.8131	402.2363	388.5	308.8575	310.3838	380.73	196.4006	195.2213	131.2575	189.9812	239.6306	253.4731	285.1131	152.1969	142.1681	129.8794	89.97625

Now let’s use Luciernaga_QC() with ExportType = “fcs” to export the data as individual .fcs files, and set Brightness = TRUE to save .csv files that can be used for Luciernaga_Tree(). For this vignette, we will be saving the .fcs files to a temporary folder. On your own workstation, save the outputs to a folder where you can retrieve them later by providing a file.path to the outpath argument.

Let’s start by processing the single-color unmixing controls.

#pData(MyGatingSet)

StorageLocation <- file.path(tempdir(), "LuciernagaOutputs")

if (!dir.exists(StorageLocation)) {
  dir.create(StorageLocation)
}

SingleColor_Data  <- map(.x=MyGatingSet, .f=Luciernaga_QC, subsets="nonDebris",
                              removestrings=removestrings, sample.name="GUID",
                              unmixingcontroltype = "cells", Unstained = FALSE,
                              ratiopopcutoff = 0.001, Verbose = FALSE,
                              AFOverlap = AFOverlap, stats = "median",
                              ExportType = "fcs", Brightness=TRUE, SignatureReturnNow = FALSE,
                              outpath = StorageLocation, Increments=0.1,
                              SecondaryPeaks=2, experiment = "FirstExperiment",
                              condition = "ILTPanel", Subtraction = "Internal", 
                              CellAF=TheCellAF, SCData="subtracted",
                              NegativeType="default", minimalfcscutoff=0.01)
#> No second peak
#> No second peak
#> No second peak
#> No second peak
#> Only a single detector present. If this was not an autofluorescence overlap
#>               fluourophore, it would suggest there was no antibody staining, or everything
#>               was overstained. Please investigate further.
#> No second peak
#> No second peak
#> No second peak
#> No second peak
#> No second peak
#> No second peak

TheLuciernagaOutputs_FCS <- list.files(StorageLocation, pattern="fcs", full.names = TRUE)
head(TheLuciernagaOutputs_FCS, 4)
#> [1] "C:\\Users\\12692\\AppData\\Local\\Temp\\Rtmp0AUE9A/LuciernagaOutputs/CCR4_BUV615(Cells)_UV1010YG300V1000.fcs"
#> [2] "C:\\Users\\12692\\AppData\\Local\\Temp\\Rtmp0AUE9A/LuciernagaOutputs/CCR4_BUV615(Cells)_UV1010YG307V1000.fcs"
#> [3] "C:\\Users\\12692\\AppData\\Local\\Temp\\Rtmp0AUE9A/LuciernagaOutputs/CCR4_BUV615(Cells)_UV1010YG307V1003.fcs"
#> [4] "C:\\Users\\12692\\AppData\\Local\\Temp\\Rtmp0AUE9A/LuciernagaOutputs/CCR4_BUV615(Cells)_UV1010YG307V1004.fcs"

And let’s also process an unstained unmixing control specimen also to characterize autofluorescence that is present.

#pData(MyUnstainedGatingSet)
Unstained_Data  <- map(.x=MyUnstainedGatingSet[1], .f=Luciernaga_QC, subsets="nonDebris",
                              removestrings=removestrings, sample.name="GUID",
                              unmixingcontroltype = "cells", Unstained = TRUE,
                              ratiopopcutoff = 0.001, Verbose = FALSE,
                              AFOverlap = AFOverlap, stats = "median",
                              ExportType = "fcs", Brightness=TRUE, SignatureReturnNow = FALSE,
                              outpath = StorageLocation, Increments=0.1,
                              SecondaryPeaks=2, experiment = "FirstExperiment",
                              condition = "ILTPanel", Subtraction = "Internal", 
                              CellAF=TheCellAF, SCData="subtracted",
                              NegativeType="default", minimalfcscutoff=0.01)

Luciernaga_Tree

We generate a lot of clusters with Luciernaga_QC(). We can visualize them using the report and plotting functions described in the previous vignette, but selecting individual candidate output .fcs for use in unmixing can be tiresome and confusing. Luciernaga_Tree() is our initial attempt to reduce the burden, by instituting a decision tree to help filter the many outputs and return likely candidates that will work for unmixing. It relies on the Luciernaga_QC() Brightness=TRUE .csv outputs in making this decision.

We want to be upfront and say this is developmental. We have recently created the tools to allow us to query how fluorophore brightness, signature, and relative abundance impact the unmixing of full-stained samples. We have not had the time to delve into the outcomes at the depth we would like to come up with a grand unified theory of perfect unmixing. That is on my to-do-list as a postdoc/industry/whatever (hire me if this interest you or you want to avoid me working for your competitors). For the time, it works well enough, but with occasional bugs in the final unmixing. We highly encourage your feedback and tinkering with the decision trees step methodology in order to achieve more consistent results. For now, the process works as follows (detailed explanation logic).

With that background out of the way, let’s continue.

ReferencePath <- system.file("extdata", package = "Luciernaga")
PanelPath <- file.path(ReferencePath, "UnmixingPanel.csv")
UnmixingPanel <- read.csv(PanelPath, check.names=FALSE)

MoveThese <- Luciernaga_Tree(BrightnessFilePath = StorageLocation, PanelPath = PanelPath)
gt(head(MoveThese, 5))

sample	Cluster	Decision	Brightness	Detector1	Detector1Value	Detector2	Detector2Value	Detector3	Detector3Value	Detector1Raw	Detector2Raw	Detector3Raw	Count	Ratio	Time	UV1	UV2	UV3	UV4	UV5	UV6	UV7	UV8	UV9	UV10	UV11	UV12	UV13	UV14	UV15	UV16	SSC-W	SSC-H	SSC	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	V11	V12	V13	V14	V15	V16	FSC-W	FSC-H	FSC	SSC-B-W	SSC-B-H	SSC-B	B1	B2	B3	B4	B5	B6	B7	B8	B9	B10	B11	B12	B13	B14	YG1	YG2	YG3	YG4	YG5	YG6	YG7	YG8	YG9	YG10	R1	R2	R3	R4	R5	R6	R7	R8
CD62L_BUV395(Cells)	UV2_10-V7_04-B3_02	Fourth Level	16	UV2	10	V7	4	B3	2	16802.65	5682.600	2461.571	100	0.02515723	334183	4784.4324	16802.6517	8266.559	6514.1719	5815.1582	6467.6503	5803.6301	3065.7003	2504.0204	793.0607	527.2444	239.1900	220.3731	276.7122	225.5050	47.0050	753165.5	2305322	2857364.8	538.10629	1925.58435	2992.4813	3250.5686	4505.0500	4137.3751	5682.5996	4346.3407	2877.9094	3246.4780	1820.0875	1008.9063	815.13440	821.2188	570.693726	341.1375	702061.5	1752840	2062621	736991.8	1227946.5	1501699.9	1469.90649	1719.32745	2461.57123	1833.4966	1463.27600	1197.27835	851.6490	604.5134	619.3197	342.08875	400.73438	240.247498	160.808731	204.3262	856.78128	586.91249	619.9004	780.5728	554.4797	577.0959	670.6829	309.79408	175.13719	121.5797	736.05373	1107.39996	949.3766	842.2031	524.92029	430.989	352.98373	229.21343
CD8_BUV496(Cells)	UV7_10-V6_02	Second Level	12	UV7	10	V6	2	NA	NA	70514.98	9989.169	NA	356	0.19614325	215615	643.8644	2160.2963	1777.116	1547.4463	2121.7701	12550.8562	70514.9775	37302.5232	17475.8574	4163.3640	1116.9266	494.4078	309.6975	324.9072	165.4100	149.3450	714194.6	1264984	1482504.8	0.00000	0.00000	437.3188	4245.9658	13527.0786	9989.1694	9940.2881	3753.2686	1716.3438	1295.8689	199.3064	62.5968	70.64062	0.0000	9.831238	0.0000	687766.3	1457523	1650837	696512.3	610634.0	695478.2	4402.12341	2587.23120	1946.73193	382.9025	187.13818	35.05212	0.0000	0.0000	0.0000	0.00000	0.00000	0.000000	1.416245	0.0000	0.00000	0.00000	0.0000	0.0000	0.0000	0.0000	0.0000	0.00000	0.00000	0.0000	0.00000	0.00000	0.0000	0.0000	0.00000	0.000	0.00000	0.00000
CD69_BUV563(Cells)	UV9_10-YG1_05-B4_02	Fourth Level	17	UV9	10	YG1	5	B4	2	58291.45	24073.820	10734.081	2557	0.48483125	290718	450.6753	1821.7785	1427.554	1217.5560	1194.4254	1296.5423	1061.2197	19671.4825	58291.4473	14812.1540	3321.2531	1374.3013	721.0656	485.2597	286.5669	113.9425	676148.9	814268	918510.8	0.00000	0.00000	0.0000	0.0000	0.0000	0.0000	636.5564	3010.4248	1032.5907	732.8405	0.0000	0.0000	0.00000	0.0000	0.000000	0.0000	665055.9	1180244	1308017	664419.1	391099.0	433493.0	0.00000	0.00000	5128.04797	10734.0806	5463.21667	3337.10327	915.7665	527.0060	392.0116	110.88593	48.66751	9.205612	0.000000	0.0000	24073.81958	11266.91614	6576.4381	1813.6359	964.4513	675.1575	461.5172	62.57625	21.50624	0.0000	0.00000	0.00000	0.0000	0.0000	0.00000	0.000	0.00000	0.00000
CCR4_BUV615(Cells)	UV10_10-YG3_09-V10_03	Second Level	22	UV10	10	YG3	9	V10	3	11788.22	10077.725	2985.744	105	0.28301887	301599	163.4763	465.6991	296.905	335.6916	357.8181	443.3494	567.5186	448.5556	2767.7170	11788.2153	4934.8184	2550.1329	1411.9722	1318.7060	816.0426	357.2604	691204.0	942409	1078212.5	65.96564	171.42816	130.6594	138.1533	149.7719	145.0282	212.0938	285.2094	989.3125	2985.7437	947.3751	478.2250	262.24692	148.8438	110.893768	0.0000	670592.4	1333099	1482412	677919.3	432542.0	491643.7	0.00000	15.99713	93.40814	0.0000	402.24719	758.11221	357.5709	185.7218	245.3975	75.89815	21.72657	1.351883	13.100319	0.0000	847.51971	4367.43384	10077.7245	4452.3139	3342.9385	2586.9591	2417.1986	826.77660	584.55374	250.3744	63.52719	48.09562	102.7594	0.0000	16.03188	0.000	14.93718	12.46531
VD2_BUV661(Cells)	UV11_10-R2_09-YG5_04	Fourth Level	23	UV11	10	R2	9	YG5	4	86813.77	70225.652	27538.511	162	0.43665768	273109	419.2147	1979.3049	1661.798	1488.8387	1489.6198	2034.1936	2082.1655	1109.2660	896.0701	4066.1930	86813.7747	40145.4697	30780.3556	25282.2200	13378.0181	8205.7942	676304.0	1042726	1192782.1	0.00000	77.61871	102.3344	189.4062	265.0658	238.7688	360.1470	362.3813	290.2969	1423.9502	23883.7852	10718.9844	8158.66602	5113.2468	3031.771851	1253.5188	668752.4	1442295	1588795	666351.9	469419.5	528043.8	23.56128	21.75873	41.16785	163.3515	72.22876	184.69186	3418.9240	2455.4234	1950.8199	1238.99338	788.59373	503.219345	347.753754	289.6231	41.93719	99.20624	1108.7166	39621.1736	27538.5113	18824.3518	21832.0009	7933.17007	4608.65071	2325.3459	68384.88156	70225.65219	52687.7684	37440.7836	26223.80379	14398.319	11817.25746	5413.54745

By scrolling through the returned selections, we can screen based on our knowledge of the panel and decide if the outcomes seem reasonable. We will also verify that these decisions were correct before we reach the unmixing process by visualizing vs. reference signatures in Luciernaga_SingleColors(). In the case that the wrong file output was selected, it is much easier to remove and replace one .fcs file than 30.

Luciernaga_Move

Once Luciernaga_Tree() has identified the Luciernaga_QC() output .fcs files that will likely produce the best unmixing outcome, it would be absolutely brutal having to track down within a folder of hundreds of .fcs files with some repetition of CCR4BUV615_UV6_10-V7_08-B3_04 nomenclature (believe me, I did so initially). Luciernaga_Move() takes the Luciernaga_Tree() list of ideal candidates, and copies these .fcs files to a designated folder, saving you the hassle, and allowing you to simply point at that folder for use in the functions mentioned below.

Continuing from where we left off with Luciernaga_Tree() above, for this example, we will create a different temporary folder were we will store the selected .fcs files for later use in the unmixing.

SortedStorageLocation <- file.path(tempdir(), "LuciernagaSelected")

if (!dir.exists(SortedStorageLocation)) {
  dir.create(SortedStorageLocation)
}

UnmixingPanel <- read.csv(PanelPath, check.names=FALSE)
TheseFluorophores <- UnmixingPanel %>% pull(Fluorophore)

walk(.x=TheseFluorophores, .f=Luciernaga_Move, data=MoveThese, input=StorageLocation, output=SortedStorageLocation)

MovedFiles <- list.files(SortedStorageLocation, pattern="fcs", full.names=TRUE)
length(MovedFiles)
#> [1] 30

With these steps completed, we are now ready to proceed to the steps to validate our choice in unmixing controls.

Luciernaga_LinearSlices

We had previously showcased Luciernaga_LinearSlices() ability to take an .fcs file, and visualize variation in the signature based on the quantile splits for the MFI brightness. What we saw for APC CD16 is replicated below:

#pData(MyGatingSet)
APC_Example <- subset(MyGatingSet, str_detect(name, "CD16_"))

RawSlices <- Luciernaga_LinearSlices(x=APC_Example[1], subset="lymphocytes",
                                  sample.name="GUID", removestrings=removestrings,
                                  stats="median", returntype="raw",
                                  probsratio=0.1, output="plot", desiredAF="R1-A")

plotly::ggplotly(RawSlices)

#pData(MyGatingSet[6])

NormalizedSlices <- Luciernaga_LinearSlices(x=APC_Example[1], subset="lymphocytes",
                                  sample.name="GUID", removestrings=removestrings,
                                  stats="median", returntype="normalized",
                                  probsratio=0.1, output="plot", desiredAF="R1-A")

plotly::ggplotly(NormalizedSlices)

For today, we highlight Luciernaga_LinearSlices() ability to do this also with the Luciernaga_QC() outputs.

MovedFiles <- list.files(SortedStorageLocation, pattern="fcs", full.names=TRUE)
Selected_CS <- load_cytoset_from_fcs(MovedFiles, truncate_max_range = FALSE, transform = FALSE)
Selected_GS <- GatingSet(Selected_CS)

pData(Selected_GS)
#>                                                                                          name
#> CCR4_BUV615(Cells)_UV1010YG309V1003.fcs               CCR4_BUV615(Cells)_UV1010YG309V1003.fcs
#> CCR6_BV786(Cells)_V1510UV1502.fcs                           CCR6_BV786(Cells)_V1510UV1502.fcs
#> CCR7_BV650(Cells)_V1110YG502R202.fcs                     CCR7_BV650(Cells)_V1110YG502R202.fcs
#> CD107a_APC-R700(Cells)_R110R210YG504.fcs             CD107a_APC-R700(Cells)_R110R210YG504.fcs
#> CD127_BV421(Cells)_V110V210UV701.fcs                     CD127_BV421(Cells)_V110V210UV701.fcs
#> CD16_APC(Cells)_R110R209YG406.fcs                           CD16_APC(Cells)_R110R209YG406.fcs
#> CD161_BV480(Cells)_V510UV703.fcs                             CD161_BV480(Cells)_V510UV703.fcs
#> CD25_PE-Cy5(Cells)_YG510B807R203.fcs                     CD25_PE-Cy5(Cells)_YG510B807R203.fcs
#> CD26_PerCP-Cy5.5(Cells)_B910YG606V1306.fcs         CD26_PerCP-Cy5.5(Cells)_B910YG606V1306.fcs
#> CD27_APC-Fire750(Cells)_R710YG903V1502.fcs         CD27_APC-Fire750(Cells)_R710YG903V1502.fcs
#> CD3_AlexaFluor488(Cells)_B210.fcs                           CD3_AlexaFluor488(Cells)_B210.fcs
#> CD3_AlexaFluor647(Cells)_R210YG502.fcs                 CD3_AlexaFluor647(Cells)_R210YG502.fcs
#> CD3_SparkBlue550(Cells)_B310V703.fcs                     CD3_SparkBlue550(Cells)_B310V703.fcs
#> CD38_APC-Fire810(Cells)_R810YG1003.fcs                 CD38_APC-Fire810(Cells)_R810YG1003.fcs
#> CD4_BUV805(Cells)_UV1610R802.fcs                             CD4_BUV805(Cells)_UV1610R802.fcs
#> CD45RA_BV510(Cells)_V710UV804.fcs                           CD45RA_BV510(Cells)_V710UV804.fcs
#> CD56_BV605(Cells)_V1010YG304UV1003.fcs                 CD56_BV605(Cells)_V1010YG304UV1003.fcs
#> CD62L_BUV395(Cells)_UV210V704B302.fcs                   CD62L_BUV395(Cells)_UV210V704B302.fcs
#> CD69_BUV563(Cells)_UV910YG105B402.fcs                   CD69_BUV563(Cells)_UV910YG105B402.fcs
#> CD7_BV711(Cells)_V1310R403UV1402.fcs                     CD7_BV711(Cells)_V1310R403UV1402.fcs
#> CD8_BUV496(Cells)_UV710V602.fcs                               CD8_BUV496(Cells)_UV710V602.fcs
#> CXCR3_BUV737(Cells)_UV1410R506V704.fcs                 CXCR3_BUV737(Cells)_UV1410R506V704.fcs
#> Dump_CD19_PacificBlue(Cells)_V810V710UV806.fcs Dump_CD19_PacificBlue(Cells)_V810V710UV806.fcs
#> IFNg_BV750(Cells)_V1410V1509UV1502.fcs                 IFNg_BV750(Cells)_V1410V1509UV1502.fcs
#> INF071_Ctrl_Unstained_V710UV804B304.fcs               INF071_Ctrl_Unstained_V710UV804B304.fcs
#> NKG2D_PE(Cells)_YG110B407V802.fcs                           NKG2D_PE(Cells)_YG110B407V802.fcs
#> PD1_PE-Vio770(Cells)_YG910B1306V702.fcs               PD1_PE-Vio770(Cells)_YG910B1306V702.fcs
#> TNFa_PE-Dazzle594(Cells)_YG310B607V1002.fcs       TNFa_PE-Dazzle594(Cells)_YG310B607V1002.fcs
#> VD2_BUV661(Cells)_UV1110R209YG504.fcs                   VD2_BUV661(Cells)_UV1110R209YG504.fcs
#> Viability_ZombieNIR(Cells)_R710R610B1303.fcs     Viability_ZombieNIR(Cells)_R710R610B1303.fcs

ThePlots <- map(.x=Selected_GS, .f=Luciernaga_LinearSlices, subset="root", removestrings=".fcs",
                sample.name="GUID", stats="median", returntype="normalized", output="plot")

plotly::ggplotly(ThePlots[[1]])

As we can see, with the exception of cells below 30 percentile in brightness, most of the variation in signature we saw in the original file, and that the sorting within Luciernaga_QC() appears to have sorted cells of similar signature regardless of brightness.

By passing the generated plots to Utility_Patchwork(), we can set returntype=“pdf” or “patchwork” to generate a report for all fluorophores.

PatchworkObjects <- Utility_Patchwork(ThePlots, filename = "LinearSlices", outfolder=ReferencePath, returntype = "patchwork")
PatchworkObjects[1]
#> $`1`

Luciernaga_SingleColors

The main purpose of Luciernaga_SingleColors() is to generate a reference matrix for use in unmixing full-stained samples. It additionally provides a mechanism by which we can visualize the signatures we plan on using and compare them to the reference signatures stored within Luciernaga. This allows us to screen out potential issues in the single-color reference matrix before we proceed to unmix the full-stained specimens.

To begin, we need to generate either a .csv file or a data.frame for each of the fluorophores present, and specify a cutoff point for each, dictated by our observations from what we saw with Luciernaga_LinearSlices(). For this example, we will do this in R and set the intervals from 0.4 to 1.0 across the board. If you want to fine-tune things further, it would be easier to save the output as a .csv file, modify it, and then return it to R.

UnmixingPanel <- read.csv(PanelPath, check.names=FALSE)
ThePanelCuts <- UnmixingPanel %>% select(-Detector) %>% mutate(From=0.3) %>% mutate(To=1)
head(ThePanelCuts, 5)
#>   Fluorophore From To
#> 1    BUV395-A  0.3  1
#> 2    BUV496-A  0.3  1
#> 3    BUV563-A  0.3  1
#> 4    BUV615-A  0.3  1
#> 5    BUV661-A  0.3  1
#write.csv(ThePanelCuts, path="SaveHere.csv", row.names=FALSE)

An important thing to ensure is that we correctly identify each fluorophore and ligand at this step, to ensure names are correct in the unmixed full-stained .fcs file. In this case, we are using the keyword “TUBENAME” to identify between the .fcs files. This is what the original name looks like:

keyword(Selected_GS[1], "TUBENAME")
#>                 TUBENAME
#> 1 DR_CCR4 BUV615 (Cells)

Luciernaga_SingleColors() will automatically clean up any portion of the name that has “(Cells)” or “(Beads)” present in it. However, that would leave the final name as “DR_CCR4 BUV615” which would be converted into Fluorophore = BUV615, Ligand = DR_CCR4. To clean this up, we will remove the authors initials (“DR_”) by providing them as part of a list to the remove strings argument.

#pData(Selected_GS)
removestrings=c("DR_", ".fcs")
SCs <- map(.x=Selected_GS[1], .f=Luciernaga_SingleColors, sample.name="TUBENAME",
           removestrings=removestrings, subset="root", PanelCuts=ThePanelCuts,
           stats="median", Verbose=TRUE, SignatureView=TRUE, returntype = "plots")
#> After removestrings cleanup the name is CCR4 BUV615
#> The Fluorophore is BUV615 and the ligand is CCR4

In the case above, we set the returntype = “plot” to visualize the outcome compared to the stored Reference Signatures within Luciernaga. Let’s see what these look like:

plotly::ggplotly(SCs[[1]])

As you can tell, the provided signature originating from our output .fcs file closely resembles that of the reference signature. We can repeat this for all the fluorophores and using Utility_Patchwork(), set returntype = “pdf” and examine all fluorophores to spot any issues. For this example, I will set it the argument to “patchwork” to visualize.

SCs <- map(.x=Selected_GS, .f=Luciernaga_SingleColors, sample.name="TUBENAME",
           removestrings=removestrings, subset="root", PanelCuts=ThePanelCuts,
           stats="median", Verbose=TRUE, SignatureView=TRUE, returntype = "plots")

TheView <- Utility_Patchwork(x=SCs, filename="ReferenceMatches",
                             outfolder = ReferencePath, returntype = "patchwork")

TheView[3]
#> $`3`

In this particular case, we can see that while most generated signatures aligned with their references, the .fcs files we are using for BUV737 and PacificBlue deviate substantially and are likely to impact the final unmixing. Within our workflow, we would follow up by removing the current output .fcs from the Selected Folder, and replace it with another variant.

While this highlights the visualizing portion of Luciernaga_SingleColors() lets go ahead and generate the reference matrix by changing returntype = “data”

SC_Reference <- map(.x=Selected_GS, .f=Luciernaga_SingleColors, sample.name="TUBENAME",
                    removestrings=removestrings, subset="root", PanelCuts=ThePanelCuts,
                    stats="median", Verbose=FALSE, SignatureView=FALSE, returntype = "data") %>%
                    bind_rows()
head(SC_Reference, 4)
#>   Fluorophore Ligand     UV1-A       UV2-A      UV3-A     UV4-A     UV5-A
#> 1      BUV615   CCR4  186.1606  641.521606  390.39441  452.4603  452.2001
#> 2       BV786   CCR6   53.8475    7.325958   59.87189    0.0000    0.0000
#> 3       BV650   CCR7  593.7357 1046.530701  908.34186 1106.6628 1231.2781
#> 4    APC-R700 CD107a 1279.4733 3029.070801 3013.52637 3407.7139 4471.2764
#>       UV6-A      UV7-A     UV8-A    UV9-A    UV10-A    UV11-A   UV12-A
#> 1  557.8869   802.7666  660.1525 3567.620 14820.930  6095.664 3081.617
#> 2    0.0000     0.0000    0.0000    0.000     0.000     0.000    0.000
#> 3 1933.8989  3245.7625 2545.9304 2495.542  2563.000 10016.305 4557.254
#> 4 7032.0078 11404.7373 7828.4893 6751.242  2659.576 12536.651 5248.198
#>       UV13-A   UV14-A    UV15-A   UV16-A       V1-A      V2-A       V3-A
#> 1 1898.16150 1681.135  984.2044  508.316   19.35312  193.6343   141.0407
#> 2   10.70999 1352.324 5023.2507 4109.182 1641.57812 1643.1250  1479.6719
#> 3 3401.24329 2766.304 1604.9755 1068.918 4004.13745 5897.5815  6977.1631
#> 4 6263.04443 6465.568 3412.2507 2374.199 2380.12524 7611.5874 11858.7568
#>         V4-A       V5-A       V6-A       V7-A       V8-A     V9-A     V10-A
#> 1   138.1533   203.8094   145.0282   238.7686   382.6969 1327.975  3955.188
#> 2   641.8844   243.9250   106.8031     0.0000     0.0000    0.000     0.000
#> 3  5040.4067  5719.1064  5486.0781  7418.2969  5893.8003 5118.094 20654.357
#> 4 12383.5254 17391.3438 16034.9072 21225.4629 15140.5371 9949.020 11313.088
#>       V11-A      V12-A      V13-A     V14-A      V15-A     V16-A        B1-A
#> 1  1349.906   634.4249   358.0844   217.250   161.4937     0.000    2.542908
#> 2     0.000     0.0000   395.0031  7721.519 32902.4453 17867.919    0.000000
#> 3 58440.148 26664.5850 19260.8643 11865.803  7511.5566  3044.388 1887.603638
#> 4 36744.539 15177.9385 18939.9375 13898.775  8024.9131  3463.350 5915.804688
#>         B2-A       B3-A     B4-A      B5-A      B6-A       B7-A      B8-A
#> 1   17.47784   83.17242    0.000  526.4265  960.6359   424.8428  264.1306
#> 2    0.00000    0.00000    0.000    0.0000    0.0000     0.0000    0.0000
#> 3 2249.52002 3330.79468 2431.186 1948.3414 1809.3558  1888.2152 1424.3934
#> 4 6264.07324 8485.97656 5455.523 4460.5435 3486.8074 11263.1777 7628.5659
#>        B9-A      B10-A      B11-A      B12-A       B13-A       B14-A    YG1-A
#> 1  331.4025   82.72188   51.91843   24.26936    1.062187    7.885941 1144.792
#> 2    0.0000    0.00000   59.73999  231.42810  592.089050  648.224030    0.000
#> 3 1301.8878  809.12939  600.07156  424.45654  350.554016  341.123138 1052.800
#> 4 6682.5752 5642.40430 4194.80371 2879.23633 1801.791870 1890.049805 1597.429
#>          YG2-A     YG3-A     YG4-A     YG5-A     YG6-A     YG7-A      YG8-A
#> 1 5.777896e+03 12523.749  5701.758  4401.185  3108.451  2949.235  1086.6554
#> 2 3.468323e-02     0.000     0.000     0.000     0.000     0.000   165.0431
#> 3 9.510966e+02  2228.429 11140.862  7616.473  5853.169  6489.719  2278.3098
#> 4 1.466449e+03  2041.359 70883.148 47194.148 30333.732 57502.301 24562.9121
#>        YG9-A    YG10-A        R1-A        R2-A         R3-A         R4-A
#> 1   699.6469  335.9831    151.7378     41.5275 1.197094e+02     22.91782
#> 2   736.0688  612.6853      0.0000      0.0000 1.765671e-01     22.84718
#> 3  1590.9769  789.6956   6911.8921   6685.9980 5.755832e+03   3799.20117
#> 4 13512.3770 7128.1426 142743.1562 131535.3906 9.798209e+04 108904.66406
#>          R5-A       R6-A        R7-A         R8-A
#> 1    52.05063     0.0000    66.28156     2.224689
#> 2   197.25562   954.9206  2531.72974  1467.375549
#> 3  2560.01501  1486.2678  1330.36316   642.263794
#> 4 91088.17188 48160.9531 39263.82812 18772.619141

While we are at it, we might as well identify how far the cosine difference for the trouble Pacific Blue Fluorophore is before we go and correct it:

PacificBlue <- SC_Reference %>% select(-Ligand) %>% rename(Sample=Fluorophore)

Results <- QC_WhatsThis(x="Pacific Blue", data=PacificBlue, NumberHits=10,
                        returnPlots = TRUE)
#> Normalizing Data for Signature Comparison

Results[[1]]
#>         Fluorophore ID_Pacific Blue
#> 1             BV510            0.88
#> 2    LIVE DEAD Aqua            0.84
#> 3          VioGreen            0.82
#> 4      Krome Orange            0.80
#> 5             OC515            0.80
#> 6       Zombie Aqua            0.80
#> 7  Monochlorobimane            0.79
#> 8   Amethyst Orange            0.78
#> 9             BV480            0.78
#> 10      cFluor V547            0.78

plotly::ggplotly(Results[[2]])

As you can tell, the fluorophore more closely resembles BV510 with no Pacific Blue appearing in the list of hits. This suggest that the returned fluorescent signature in the .fcs file is mainly autofluorescence, and should not be used.

Back on topic, once we have corrected the Selected Folder, rerun Luciernaga_SingleColors() and are satisfied with out results, we should go ahead and save the results data.frame elsewhere for further reference, or that they can be so edited to correct for any typos or format issues that may have been missed before creating unmixed .fcs files.

SC_Reference <- SC_Reference %>%
  mutate(Fluorophore = case_when(Fluorophore == "Unstained" ~ "AF", TRUE ~ Fluorophore))

TheSCReferences <- file.path(ReferencePath, "UnmixingSCs.csv")

write.csv(SC_Reference, TheSCReferences, row.names=FALSE)

Luciernaga_Unmix

Luciernaga_Unmix() is the unmixing function implemented within the Luciernaga package. What mainly distinguishes it from other R package implementations of ordinary least squares unmixing is it works at the GatingSet level in terms of infrastructure (reducing active memory use) and is set up in such a way to allow us to rapidly iterate/modify/change the inputs to subsequently evaluate the unmixed full-stained .fcs files for the impacts that those decisions had on the unmixing. We have put some effort into ensuring that the subsequent unmixed files are compatible with various software typically used by those who prefer to use GUI for their flow data. This involved changes done within the newly produced .fcs files exprs, parameters and description folder, it’s possible we may have missed something, so if you encounter a bug, please reach out.

As previously stated, this remains experimental, and at the moment is just intended as a tool to allow me to querry how brightness/signature/abundance of individual single colors impacts the full-unmixing. As I improve on my existing knowledge, the quality of inputs/outputs is likely to also improve as I figure out the things that I don’t yet know and correct for them. So consider this a work in progress for now, feel free to reach out if you know something I don’t, and want to collaborate on getting it implemented here.

For now, let’s identify the raw full-stained files and load them into a GatingSet:

File_Location <- system.file("extdata", package = "Luciernaga")
FCS_Pattern <- ".fcs$"
FCS_Files <- list.files(path = File_Location, pattern = FCS_Pattern,
                        full.names = TRUE, recursive = FALSE)
RawFullStainedFCSFiles <- FCS_Files[grep("Tetramer", FCS_Files)]
RawFullStainedFCSFiles <- RawFullStainedFCSFiles[-grep("Unmixed", RawFullStainedFCSFiles)]

UnmixCytoSet <- load_cytoset_from_fcs(RawFullStainedFCSFiles, truncate_max_range = FALSE, transform = FALSE)
UnmixGatingSet <- GatingSet(UnmixCytoSet)

Let’s identify the Single Color Reference Data output from Luciernaga_SingleColors() that we have validated (correcting from any issues)

ReferencePath <- system.file("extdata", package = "Luciernaga")
ValidatedSCReferenceData <- file.path(ReferencePath, "ValidatedSCReferenceData.csv")
SingleColorReference <- read.csv(ValidatedSCReferenceData, check.names = FALSE)

And finally, lets provide a file.path to the panel (to establish correct ordering of fluorophores in the final file)

PanelPath <- file.path(ReferencePath, "UnmixingPanel.csv")
PanelNames <- read.csv(PanelPath, check.names=FALSE)

With these pre-requisites prepared we can go ahead. For this example, we will merge the “GROUPNAME” and “TUBENAME” to form the final name. For the final file, we will use the addon argument to append “_Unmixed” at the end. As Ordinary Least Squares (OLS) returns values close to 0, the multiplier increases all values across the board, which allows the data to resemble that of other softwares when the bi-exponential transform is applied.

TheSampleName <- c("GROUPNAME", "TUBENAME")

UnmixSuccess <- map(.x=UnmixGatingSet, .f=Luciernaga_Unmix, controlData=SingleColorReference,
                    sample.name=TheSampleName, addon="_Unmixed", subset="root", removestrings=".fcs",
                    multiplier=50000, outpath=UnmixedOutpath, PanelPath=PanelPath, Verbose=FALSE)

Luciernaga_IterativeUnmix

This function is an extension of Luciernaga_Unmix() using the same inputs, with the added provision that you provide it a folder of variant of Luciernaga_QC() .fcs files for a single fluorophore of interest. Luciernaga_IterativeUnmix() will then proceed one by one through the files in that folder, process them individually and swap them in to the Reference Matrix, unmix the full-stain samples, and return the variant unmixed full-stain files to the outfolder. It will repeat this until everything is complete. What we will do subsequently, is use Utility_UnityPlots() and Utility_NxNPlots() and the workflow described in Vignette 1 to consolidate all the variant unmixed files and evaluate how the variation in that iterated single-color impacted the final unmixing.

IterativePath <- file.path(ReferencePath, "DifferentialPerCP")
removestrings <- c("DR_", ".fcs")
iterate_removestrings <- c("DR_", "(Cells)", ".fcs", " ", "PerCP-Cy5.5", "CD26", "_")
TheSampleName <- c("GROUPNAME", "TUBENAME") 


Luciernaga_IterativeUnmixing(IterativePath=IterativePath, iterate_removestrings=iterate_removestrings,
                               removestrings=removestrings, sample.name=TheSampleName, subset="root",
                               PanelCuts=ThePanelCuts, stats="median", Verbose=FALSE, SignatureView=FALSE,
                               FullStainedGS=UnmixGatingSet, controlData=SingleColorReference, multiplier=50000,
                               outpath=UnmixedOutpath, PanelPath=PanelPath)

Conclusion

And with that, we conclude our tour of the current state of the unmixing functions within the Luciernaga package. They remain a work in progress, and we welcome any contributions/insights/bug-reports to continue improving on them. This entire project arose when curious of how placing a positive gate on a single-color unmixing control would alter the unmixing of that file, and the sum of Luciernaga’s functions have been geared to allowing me to answer these questions so that no other graduate student will have to go through the horror of “it unmixed weird, no idea why” 20 years from now.

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David Rach

30 October 2024