Quality Control

David Rach

University of Maryland, Baltimore
drach@som.umaryland.edu

30 October 2024

Instrument QC

This vignette covers the Luciernaga functions that are involved in assessing instrumental quality control. Currently, this section is tailored for Cytek Aurora and Northern Light (TM) spectral cytometers that utilize SpectroFlo as their acquisition and unmixing software. The reason for this platform bias is that these were the only spectral cytometers at the authors home institution when designing the package. If you would like to help us remedy this historical bias and expand the functions to other instruments, feel free to reach out via GitHub! :)

The following functions can work from either the Levy-Jennings Tracking Reports that are generated following automated instrument QC (exported as .csv files) or by retrieving the equivalent data stored as keywords within an .fcs files description for a collection of .fcs files across the timespan of interest.

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)

Cytek Aurora instruments use fluorescent beads as part of their daily quality control checks. These beads have complex signatures, with most beads having peak detectors in either the Ultraviolet or Yellow-Green Laser as can be seen below.

File_Location <- system.file("extdata", package = "Luciernaga")
fcs_files <- list.files(File_Location, pattern=".fcs", full.names = TRUE)
QCBeadFile <- fcs_files[grep("After", fcs_files)][[1]]

QCBead_CS <- load_cytoset_from_fcs(QCBeadFile, transformation = FALSE, truncate_max_range = FALSE)
QCBead_GS <- GatingSet(QCBead_CS)

Data <- Luciernaga_QC(x=QCBead_GS[1], subsets="root", sample.name="GUID", SignatureReturnNow = TRUE, Verbose=TRUE,
              unmixingcontroltype="beads", Unstained=TRUE, removestrings=".fcs", stats="median")
#> 0 of all events were negative and will be rounded to 0
#> Returning Peak Bead Detector Medians
#>   TheDetector TheMedian
#> 1         YG3   2170350
#> 2        UV16   2162536
#>   Fluors Counts
#> 1    YG3   3085
#> Normalizing Data for Signature Comparison
#> Returning designated stats values for Beads_Unstained, please return
#>     to LuciernagaQC as BeadsAF for subtraction for single color unmixing controls

Data <- Data %>% mutate(Sample="QCBeads") %>% relocate(Sample, .before=1)

QCBeads <- QC_WhatsThis(x="QCBeads", data=Data, NumberHits=0, returnPlots=TRUE)
#> Normalizing Data for Signature Comparison

QCBeads[[2]]

Using the QC beads, the gains for instrument detectors and power to individual lasers are adjusted to maintain a stable MFI across time. These adjustments are available to individual users as part of a QC report, with the underlying data available under the Levy-Jennings tracking tab, with the option to download as a .csv file. Unfortunately, it is not in a “tidy” data format, limiting our ability for easy exploratory data analysis. We provide a couple functions to convert the .csv output into a “tidy” format and visualize the data contained within.

The first step is to indicate where the saved .csv file containing the Levy-Jennings tracking data from the cytometer is stored. On a local computer, the code to do this would look similar to the following:

FileLocation <- file.path("C:", "Users", "JohnDoe", "Desktop",
                          "LevyJenningsTracking.csv")

For this vignette, we will use one of these Levy-Jennings Tracking .csvs from a 5 Laser Cytek Aurora (UV16-V16-B14-YG10-R8), that can be found within Luciernaga’s extdata folder.

File_Location <- system.file("extdata", package = "Luciernaga")
CSV_Pattern <- ".CSV$"
CSV_Files <- list.files(path=File_Location, pattern=CSV_Pattern,
                        full.names=TRUE)
CSV_Files
#> [1] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/LJTrackingData.CSV"

QC_FilePrep

Since this .CSV file is not in a tidy format, read.csv() or similar functions will struggle to import correctly. Our function, QC_FilePrep() reads in each row individually as text, and then follows up on the pre-processing into a “tidy” format.

TidyData <- QC_FilePrep(CSV_Files, TrackChange = FALSE)
#> Lines extracted from 2 to 227 written to LineChunk_1.txt 
#> Lines extracted from 230 to 455 written to LineChunk_2.txt 
#> Lines extracted from 458 to 683 written to LineChunk_3.txt 
#> Lines extracted from 686 to 911 written to LineChunk_4.txt 
#> Lines extracted from 914 to 1139 written to LineChunk_5.txt
gt(head(TidyData, 5))

DateTime	FSC-Gain	Flag-FSC-Gain	SSC-Gain	Flag-SSC-Gain	SSC-B-Gain	Flag-SSC-B-Gain	UV1-Gain	Flag-UV1-Gain	UV2-Gain	Flag-UV2-Gain	UV3-Gain	Flag-UV3-Gain	UV4-Gain	Flag-UV4-Gain	UV5-Gain	Flag-UV5-Gain	UV6-Gain	Flag-UV6-Gain	UV7-Gain	Flag-UV7-Gain	UV8-Gain	Flag-UV8-Gain	UV9-Gain	Flag-UV9-Gain	UV10-Gain	Flag-UV10-Gain	UV11-Gain	Flag-UV11-Gain	UV12-Gain	Flag-UV12-Gain	UV13-Gain	Flag-UV13-Gain	UV14-Gain	Flag-UV14-Gain	UV15-Gain	Flag-UV15-Gain	UV16-Gain	Flag-UV16-Gain	V1-Gain	Flag-V1-Gain	V2-Gain	Flag-V2-Gain	V3-Gain	Flag-V3-Gain	V4-Gain	Flag-V4-Gain	V5-Gain	Flag-V5-Gain	V6-Gain	Flag-V6-Gain	V7-Gain	Flag-V7-Gain	V8-Gain	Flag-V8-Gain	V9-Gain	Flag-V9-Gain	V10-Gain	Flag-V10-Gain	V11-Gain	Flag-V11-Gain	V12-Gain	Flag-V12-Gain	V13-Gain	Flag-V13-Gain	V14-Gain	Flag-V14-Gain	V15-Gain	Flag-V15-Gain	V16-Gain	Flag-V16-Gain	B1-Gain	Flag-B1-Gain	B2-Gain	Flag-B2-Gain	B3-Gain	Flag-B3-Gain	B4-Gain	Flag-B4-Gain	B5-Gain	Flag-B5-Gain	B6-Gain	Flag-B6-Gain	B7-Gain	Flag-B7-Gain	B8-Gain	Flag-B8-Gain	B9-Gain	Flag-B9-Gain	B10-Gain	Flag-B10-Gain	B11-Gain	Flag-B11-Gain	B12-Gain	Flag-B12-Gain	B13-Gain	Flag-B13-Gain	B14-Gain	Flag-B14-Gain	YG1-Gain	Flag-YG1-Gain	YG2-Gain	Flag-YG2-Gain	YG3-Gain	Flag-YG3-Gain	YG4-Gain	Flag-YG4-Gain	YG5-Gain	Flag-YG5-Gain	YG6-Gain	Flag-YG6-Gain	YG7-Gain	Flag-YG7-Gain	YG8-Gain	Flag-YG8-Gain	YG9-Gain	Flag-YG9-Gain	YG10-Gain	Flag-YG10-Gain	R1-Gain	Flag-R1-Gain	R2-Gain	Flag-R2-Gain	R3-Gain	Flag-R3-Gain	R4-Gain	Flag-R4-Gain	R5-Gain	Flag-R5-Gain	R6-Gain	Flag-R6-Gain	R7-Gain	Flag-R7-Gain	R8-Gain	Flag-R8-Gain	FSC-% rCV	Flag-FSC-% rCV	SSC-% rCV	Flag-SSC-% rCV	SSC-B-% rCV	Flag-SSC-B-% rCV	UV1-% rCV	Flag-UV1-% rCV	UV2-% rCV	Flag-UV2-% rCV	UV3-% rCV	Flag-UV3-% rCV	UV4-% rCV	Flag-UV4-% rCV	UV5-% rCV	Flag-UV5-% rCV	UV6-% rCV	Flag-UV6-% rCV	UV7-% rCV	Flag-UV7-% rCV	UV8-% rCV	Flag-UV8-% rCV	UV9-% rCV	Flag-UV9-% rCV	UV10-% rCV	Flag-UV10-% rCV	UV11-% rCV	Flag-UV11-% rCV	UV12-% rCV	Flag-UV12-% rCV	UV13-% rCV	Flag-UV13-% rCV	UV14-% rCV	Flag-UV14-% rCV	UV15-% rCV	Flag-UV15-% rCV	UV16-% rCV	Flag-UV16-% rCV	V1-% rCV	Flag-V1-% rCV	V2-% rCV	Flag-V2-% rCV	V3-% rCV	Flag-V3-% rCV	V4-% rCV	Flag-V4-% rCV	V5-% rCV	Flag-V5-% rCV	V6-% rCV	Flag-V6-% rCV	V7-% rCV	Flag-V7-% rCV	V8-% rCV	Flag-V8-% rCV	V9-% rCV	Flag-V9-% rCV	V10-% rCV	Flag-V10-% rCV	V11-% rCV	Flag-V11-% rCV	V12-% rCV	Flag-V12-% rCV	V13-% rCV	Flag-V13-% rCV	V14-% rCV	Flag-V14-% rCV	V15-% rCV	Flag-V15-% rCV	V16-% rCV	Flag-V16-% rCV	B1-% rCV	Flag-B1-% rCV	B2-% rCV	Flag-B2-% rCV	B3-% rCV	Flag-B3-% rCV	B4-% rCV	Flag-B4-% rCV	B5-% rCV	Flag-B5-% rCV	B6-% rCV	Flag-B6-% rCV	B7-% rCV	Flag-B7-% rCV	B8-% rCV	Flag-B8-% rCV	B9-% rCV	Flag-B9-% rCV	B10-% rCV	Flag-B10-% rCV	B11-% rCV	Flag-B11-% rCV	B12-% rCV	Flag-B12-% rCV	B13-% rCV	Flag-B13-% rCV	B14-% rCV	Flag-B14-% rCV	YG1-% rCV	Flag-YG1-% rCV	YG2-% rCV	Flag-YG2-% rCV	YG3-% rCV	Flag-YG3-% rCV	YG4-% rCV	Flag-YG4-% rCV	YG5-% rCV	Flag-YG5-% rCV	YG6-% rCV	Flag-YG6-% rCV	YG7-% rCV	Flag-YG7-% rCV	YG8-% rCV	Flag-YG8-% rCV	YG9-% rCV	Flag-YG9-% rCV	YG10-% rCV	Flag-YG10-% rCV	R1-% rCV	Flag-R1-% rCV	R2-% rCV	Flag-R2-% rCV	R3-% rCV	Flag-R3-% rCV	R4-% rCV	Flag-R4-% rCV	R5-% rCV	Flag-R5-% rCV	R6-% rCV	Flag-R6-% rCV	R7-% rCV	Flag-R7-% rCV	R8-% rCV	Flag-R8-% rCV	YellowGreen-Laser Delay	Flag-YellowGreen-Laser Delay	Violet-Laser Delay	Flag-Violet-Laser Delay	Red-Laser Delay	Flag-Red-Laser Delay	UV-Laser Delay	Flag-UV-Laser Delay	YellowGreen-Area Scaling Factor	Flag-YellowGreen-Area Scaling Factor	Violet-Area Scaling Factor	Flag-Violet-Area Scaling Factor	Blue-Area Scaling Factor	Flag-Blue-Area Scaling Factor	Red-Area Scaling Factor	Flag-Red-Area Scaling Factor	UV-Area Scaling Factor	Flag-UV-Area Scaling Factor	FSC	Flag-FSC
2024-09-06 09:55:00	952	FALSE	175	FALSE	360	FALSE	1034	FALSE	262	FALSE	204	FALSE	151	FALSE	192	FALSE	231	FALSE	367	FALSE	294	FALSE	355	FALSE	235	FALSE	301	FALSE	281	FALSE	410	FALSE	675	FALSE	538	FALSE	676	FALSE	124	FALSE	235	FALSE	216	FALSE	143	FALSE	171	FALSE	179	FALSE	235	FALSE	298	FALSE	248	FALSE	287	FALSE	231	FALSE	181	FALSE	172	FALSE	239	FALSE	410	FALSE	276	FALSE	1055	FALSE	645	FALSE	594	FALSE	536	FALSE	392	FALSE	374	FALSE	789	FALSE	555	FALSE	765	FALSE	690	FALSE	473	FALSE	453	FALSE	658	FALSE	733	FALSE	381	FALSE	375	FALSE	242	FALSE	465	FALSE	365	FALSE	411	FALSE	291	FALSE	265	FALSE	358	FALSE	327	FALSE	213	FALSE	199	FALSE	281	FALSE	281	FALSE	229	FALSE	212	FALSE	357	FALSE	208	FALSE	1.1432090	FALSE	1.656716	FALSE	3.391170	FALSE	6.264603	FALSE	2.429039	FALSE	1.863501	FALSE	1.916417	FALSE	1.886120	FALSE	2.022500	FALSE	2.037093	FALSE	1.997900	FALSE	1.908385	FALSE	2.030460	FALSE	1.977809	FALSE	1.986099	FALSE	2.038826	FALSE	2.067160	FALSE	2.334379	FALSE	2.715388	FALSE	2.129838	FALSE	1.995929	FALSE	1.946202	FALSE	1.767177	FALSE	1.689073	FALSE	1.713192	FALSE	1.596646	FALSE	1.506381	FALSE	1.484919	FALSE	1.554641	FALSE	1.464889	FALSE	1.546612	FALSE	1.828770	FALSE	2.036353	FALSE	2.232001	FALSE	2.520183	FALSE	2.261960	FALSE	2.048054	FALSE	1.910570	FALSE	1.908745	FALSE	1.945035	FALSE	2.088102	FALSE	1.905307	FALSE	1.635550	FALSE	1.495144	FALSE	1.341895	FALSE	1.161039	FALSE	0.9503762	FALSE	1.245947	FALSE	1.722119	FALSE	1.720193	FALSE	1.755334	FALSE	1.767388	FALSE	1.542750	FALSE	1.430754	FALSE	1.466286	FALSE	1.245032	FALSE	0.9637733	FALSE	1.244421	FALSE	1.991794	FALSE	2.455691	FALSE	2.457680	FALSE	2.258698	FALSE	2.106153	FALSE	2.386690	FALSE	2.477646	FALSE	2.586058	FALSE	2.811563	FALSE	-40.500	FALSE	-21.075	FALSE	18.325	FALSE	39.05000	FALSE	1.117378	FALSE	1.100811	FALSE	1.055206	FALSE	1.124783	FALSE	1.153174	FALSE	1.047562	FALSE
2024-09-05 09:47:00	952	FALSE	175	FALSE	360	FALSE	1034	FALSE	262	FALSE	204	FALSE	151	FALSE	192	FALSE	231	FALSE	367	FALSE	294	FALSE	355	FALSE	235	FALSE	301	FALSE	281	FALSE	410	FALSE	675	FALSE	538	FALSE	676	FALSE	124	FALSE	235	FALSE	216	FALSE	143	FALSE	171	FALSE	179	FALSE	235	FALSE	298	FALSE	248	FALSE	287	FALSE	231	FALSE	181	FALSE	172	FALSE	239	FALSE	410	FALSE	276	FALSE	1055	FALSE	645	FALSE	594	FALSE	536	FALSE	392	FALSE	374	FALSE	789	FALSE	555	FALSE	765	FALSE	690	FALSE	473	FALSE	453	FALSE	658	FALSE	733	FALSE	381	FALSE	375	FALSE	242	FALSE	465	FALSE	365	FALSE	411	FALSE	291	FALSE	265	FALSE	358	FALSE	327	FALSE	213	FALSE	199	FALSE	281	FALSE	281	FALSE	229	FALSE	212	FALSE	357	FALSE	208	FALSE	0.9828051	FALSE	1.486589	FALSE	3.168070	FALSE	6.778501	FALSE	2.789469	FALSE	2.000621	FALSE	1.961279	FALSE	1.899708	FALSE	2.028608	FALSE	2.011621	FALSE	2.020543	FALSE	2.030485	FALSE	2.163587	FALSE	2.207438	FALSE	2.330349	FALSE	2.372933	FALSE	2.557147	FALSE	2.845392	FALSE	3.373650	FALSE	2.056399	FALSE	1.931538	FALSE	1.898221	FALSE	1.733819	FALSE	1.657402	FALSE	1.684843	FALSE	1.573963	FALSE	1.529423	FALSE	1.479667	FALSE	1.540358	FALSE	1.529897	FALSE	1.657230	FALSE	1.853214	FALSE	2.166572	FALSE	2.352311	FALSE	2.849532	FALSE	2.164973	FALSE	2.002620	FALSE	1.837303	FALSE	1.911555	FALSE	1.927605	FALSE	2.030733	FALSE	1.868655	FALSE	1.632163	FALSE	1.518442	FALSE	1.324966	FALSE	1.138469	FALSE	0.9969820	FALSE	1.332663	FALSE	2.020584	FALSE	2.099529	FALSE	2.094043	FALSE	2.079396	FALSE	1.805496	FALSE	1.658305	FALSE	1.628693	FALSE	1.255993	FALSE	1.1332900	FALSE	1.370803	FALSE	2.119825	FALSE	2.008878	FALSE	1.918429	FALSE	1.815691	FALSE	1.522780	FALSE	1.615622	FALSE	1.838615	FALSE	2.088522	FALSE	2.575110	FALSE	-40.475	FALSE	-21.050	FALSE	18.350	FALSE	39.25000	FALSE	1.115326	FALSE	1.101323	FALSE	1.049863	FALSE	1.131018	FALSE	1.147614	FALSE	1.049160	FALSE
2024-09-04 11:54:00	952	FALSE	175	FALSE	374	FALSE	1005	FALSE	261	FALSE	201	FALSE	150	FALSE	189	FALSE	228	FALSE	360	FALSE	287	FALSE	346	FALSE	230	FALSE	295	FALSE	273	FALSE	407	FALSE	694	FALSE	562	FALSE	731	FALSE	124	FALSE	236	FALSE	216	FALSE	143	FALSE	171	FALSE	180	FALSE	235	FALSE	295	FALSE	247	FALSE	284	FALSE	230	FALSE	178	FALSE	173	FALSE	248	FALSE	429	FALSE	301	FALSE	1053	FALSE	633	FALSE	587	FALSE	530	FALSE	377	FALSE	366	FALSE	766	FALSE	542	FALSE	740	FALSE	670	FALSE	480	FALSE	466	FALSE	683	FALSE	788	FALSE	373	FALSE	365	FALSE	239	FALSE	462	FALSE	361	FALSE	409	FALSE	291	FALSE	274	FALSE	379	FALSE	360	FALSE	211	FALSE	194	FALSE	274	FALSE	282	FALSE	236	FALSE	222	FALSE	379	FALSE	228	FALSE	1.1427850	FALSE	1.650216	FALSE	3.404454	FALSE	6.249073	FALSE	2.491864	FALSE	1.862134	FALSE	1.909469	FALSE	1.861105	FALSE	2.000672	FALSE	1.986089	FALSE	1.930265	FALSE	1.889304	FALSE	2.061495	FALSE	2.053966	FALSE	2.092777	FALSE	2.112250	FALSE	2.509193	FALSE	3.136347	FALSE	4.412608	FALSE	2.100889	FALSE	2.002945	FALSE	1.932808	FALSE	1.740571	FALSE	1.657038	FALSE	1.683967	FALSE	1.580156	FALSE	1.528652	FALSE	1.531892	FALSE	1.582444	FALSE	1.515454	FALSE	1.728714	FALSE	1.924146	FALSE	2.416316	FALSE	2.948268	FALSE	4.212700	FALSE	2.240129	FALSE	2.048242	FALSE	1.964174	FALSE	1.970928	FALSE	1.998484	FALSE	2.127932	FALSE	2.011554	FALSE	1.847309	FALSE	1.686030	FALSE	1.344182	FALSE	1.440430	FALSE	1.7554460	FALSE	2.472930	FALSE	3.815283	FALSE	1.891177	FALSE	1.851248	FALSE	1.834599	FALSE	1.612341	FALSE	1.529321	FALSE	1.573667	FALSE	1.207768	FALSE	1.6748610	FALSE	2.417189	FALSE	4.006108	FALSE	2.699635	FALSE	2.870104	FALSE	2.714585	FALSE	2.035859	FALSE	2.236473	FALSE	2.641256	FALSE	3.192090	FALSE	4.178363	FALSE	-40.450	FALSE	-21.025	FALSE	18.325	FALSE	39.15001	FALSE	1.117487	FALSE	1.100363	FALSE	1.060736	FALSE	1.121434	FALSE	1.159118	FALSE	1.049232	FALSE
2024-09-03 10:27:00	952	FALSE	175	FALSE	366	FALSE	1015	FALSE	261	FALSE	205	FALSE	149	FALSE	191	FALSE	230	FALSE	365	FALSE	291	FALSE	356	FALSE	237	FALSE	302	FALSE	278	FALSE	409	FALSE	675	FALSE	538	FALSE	673	FALSE	125	FALSE	235	FALSE	217	FALSE	143	FALSE	171	FALSE	180	FALSE	236	FALSE	298	FALSE	251	FALSE	287	FALSE	231	FALSE	179	FALSE	172	FALSE	239	FALSE	410	FALSE	278	FALSE	1039	FALSE	629	FALSE	590	FALSE	533	FALSE	387	FALSE	372	FALSE	775	FALSE	542	FALSE	740	FALSE	675	FALSE	467	FALSE	447	FALSE	649	FALSE	726	FALSE	385	FALSE	384	FALSE	246	FALSE	475	FALSE	371	FALSE	417	FALSE	292	FALSE	266	FALSE	361	FALSE	331	FALSE	210	FALSE	198	FALSE	277	FALSE	278	FALSE	225	FALSE	209	FALSE	352	FALSE	206	FALSE	1.0940750	FALSE	1.506786	FALSE	3.360724	FALSE	6.462072	FALSE	2.763116	FALSE	2.037099	FALSE	2.050432	FALSE	2.001332	FALSE	2.125264	FALSE	2.105296	FALSE	2.099908	FALSE	2.050431	FALSE	2.211665	FALSE	2.233405	FALSE	2.301775	FALSE	2.414908	FALSE	2.465203	FALSE	2.715020	FALSE	3.019860	FALSE	2.132218	FALSE	1.987542	FALSE	1.943148	FALSE	1.745199	FALSE	1.642447	FALSE	1.655562	FALSE	1.563381	FALSE	1.463618	FALSE	1.420285	FALSE	1.474916	FALSE	1.404552	FALSE	1.551269	FALSE	1.904409	FALSE	2.144289	FALSE	2.302552	FALSE	2.543879	FALSE	2.202363	FALSE	2.016690	FALSE	1.841154	FALSE	1.874081	FALSE	1.903103	FALSE	2.033110	FALSE	1.856272	FALSE	1.582899	FALSE	1.409550	FALSE	1.280894	FALSE	1.108260	FALSE	0.9129870	FALSE	1.195870	FALSE	1.648019	FALSE	1.664794	FALSE	1.681845	FALSE	1.697725	FALSE	1.483731	FALSE	1.341364	FALSE	1.411474	FALSE	1.238907	FALSE	0.9590386	FALSE	1.203480	FALSE	1.943065	FALSE	2.228366	FALSE	2.160040	FALSE	1.991672	FALSE	1.831947	FALSE	2.012692	FALSE	2.129553	FALSE	2.270175	FALSE	2.494766	FALSE	-40.375	FALSE	-21.025	FALSE	18.350	FALSE	39.17500	FALSE	1.117271	FALSE	1.099861	FALSE	1.060087	FALSE	1.122895	FALSE	1.158153	FALSE	1.049512	FALSE
2024-08-30 09:10:00	952	FALSE	174	FALSE	362	FALSE	1006	FALSE	255	FALSE	199	FALSE	146	FALSE	187	FALSE	226	FALSE	359	FALSE	286	FALSE	346	FALSE	229	FALSE	299	FALSE	277	FALSE	405	FALSE	664	FALSE	528	FALSE	649	FALSE	124	FALSE	235	FALSE	216	FALSE	143	FALSE	171	FALSE	180	FALSE	236	FALSE	298	FALSE	248	FALSE	285	FALSE	234	FALSE	183	FALSE	174	FALSE	239	FALSE	409	FALSE	272	FALSE	1061	FALSE	650	FALSE	601	FALSE	539	FALSE	386	FALSE	372	FALSE	798	FALSE	566	FALSE	766	FALSE	698	FALSE	477	FALSE	456	FALSE	652	FALSE	727	FALSE	381	FALSE	366	FALSE	240	FALSE	472	FALSE	373	FALSE	417	FALSE	295	FALSE	264	FALSE	356	FALSE	325	FALSE	227	FALSE	210	FALSE	296	FALSE	292	FALSE	233	FALSE	213	FALSE	356	FALSE	206	FALSE	1.1230390	FALSE	1.587396	FALSE	3.144476	FALSE	6.039213	FALSE	2.320482	FALSE	1.712948	FALSE	1.761460	FALSE	1.747827	FALSE	1.890462	FALSE	1.882053	FALSE	1.854762	FALSE	1.793789	FALSE	1.957978	FALSE	1.850497	FALSE	1.870026	FALSE	1.888335	FALSE	1.900911	FALSE	2.128706	FALSE	2.481989	FALSE	1.938528	FALSE	1.831381	FALSE	1.784025	FALSE	1.625682	FALSE	1.564482	FALSE	1.597764	FALSE	1.509278	FALSE	1.443830	FALSE	1.445605	FALSE	1.520399	FALSE	1.377021	FALSE	1.460671	FALSE	1.722973	FALSE	1.864548	FALSE	2.054577	FALSE	2.370376	FALSE	2.130341	FALSE	1.936619	FALSE	1.764786	FALSE	1.791266	FALSE	1.849550	FALSE	1.972338	FALSE	1.781428	FALSE	1.546424	FALSE	1.421219	FALSE	1.329690	FALSE	1.157314	FALSE	0.9199556	FALSE	1.213791	FALSE	1.687987	FALSE	1.699381	FALSE	1.741229	FALSE	1.758894	FALSE	1.519310	FALSE	1.369852	FALSE	1.410669	FALSE	1.184459	FALSE	0.8889793	FALSE	1.166232	FALSE	1.898138	FALSE	2.260776	FALSE	2.203071	FALSE	2.038892	FALSE	1.921937	FALSE	2.134946	FALSE	2.198344	FALSE	2.371886	FALSE	2.616197	FALSE	-40.525	FALSE	-21.075	FALSE	18.400	FALSE	39.22500	FALSE	1.116796	FALSE	1.101068	FALSE	1.053692	FALSE	1.127847	FALSE	1.153375	FALSE	1.048213	FALSE

Individual rows are the individual QC reports, with the columns being the different parameters. Additionally, if the individual parameter exceeded the variance allowed resulting in a QC failure, it is recorded under the corresponding Flag- column. With the data now “tidy”, it can be more readily used in R to plot individual detector(s) that might be of interest.

Additionally, setting QC_FilePrep() TrackChange to TRUE will generate a similar output to that of the QC report showing Change from the previous QC report which can provide additional information for instrument monitoring.

TidyData_Track <- QC_FilePrep(CSV_Files, TrackChange = TRUE)
#> Lines extracted from 2 to 227 written to LineChunk_1.txt 
#> Lines extracted from 230 to 455 written to LineChunk_2.txt 
#> Lines extracted from 458 to 683 written to LineChunk_3.txt 
#> Lines extracted from 686 to 911 written to LineChunk_4.txt 
#> Lines extracted from 914 to 1139 written to LineChunk_5.txt
gt(head(TidyData_Track, 3))

DateTime	FSC-Gain	Change_FSC-Gain	Flag-FSC-Gain	Flag-Change_FSC-Gain	SSC-Gain	Change_SSC-Gain	Flag-SSC-Gain	Flag-Change_SSC-Gain	SSC-B-Gain	Change_SSC-B-Gain	Flag-SSC-B-Gain	Flag-Change_SSC-B-Gain	UV1-Gain	Change_UV1-Gain	Flag-UV1-Gain	Flag-Change_UV1-Gain	UV2-Gain	Change_UV2-Gain	Flag-UV2-Gain	Flag-Change_UV2-Gain	UV3-Gain	Change_UV3-Gain	Flag-UV3-Gain	Flag-Change_UV3-Gain	UV4-Gain	Change_UV4-Gain	Flag-UV4-Gain	Flag-Change_UV4-Gain	UV5-Gain	Change_UV5-Gain	Flag-UV5-Gain	Flag-Change_UV5-Gain	UV6-Gain	Change_UV6-Gain	Flag-UV6-Gain	Flag-Change_UV6-Gain	UV7-Gain	Change_UV7-Gain	Flag-UV7-Gain	Flag-Change_UV7-Gain	UV8-Gain	Change_UV8-Gain	Flag-UV8-Gain	Flag-Change_UV8-Gain	UV9-Gain	Change_UV9-Gain	Flag-UV9-Gain	Flag-Change_UV9-Gain	UV10-Gain	Change_UV10-Gain	Flag-UV10-Gain	Flag-Change_UV10-Gain	UV11-Gain	Change_UV11-Gain	Flag-UV11-Gain	Flag-Change_UV11-Gain	UV12-Gain	Change_UV12-Gain	Flag-UV12-Gain	Flag-Change_UV12-Gain	UV13-Gain	Change_UV13-Gain	Flag-UV13-Gain	Flag-Change_UV13-Gain	UV14-Gain	Change_UV14-Gain	Flag-UV14-Gain	Flag-Change_UV14-Gain	UV15-Gain	Change_UV15-Gain	Flag-UV15-Gain	Flag-Change_UV15-Gain	UV16-Gain	Change_UV16-Gain	Flag-UV16-Gain	Flag-Change_UV16-Gain	V1-Gain	Change_V1-Gain	Flag-V1-Gain	Flag-Change_V1-Gain	V2-Gain	Change_V2-Gain	Flag-V2-Gain	Flag-Change_V2-Gain	V3-Gain	Change_V3-Gain	Flag-V3-Gain	Flag-Change_V3-Gain	V4-Gain	Change_V4-Gain	Flag-V4-Gain	Flag-Change_V4-Gain	V5-Gain	Change_V5-Gain	Flag-V5-Gain	Flag-Change_V5-Gain	V6-Gain	Change_V6-Gain	Flag-V6-Gain	Flag-Change_V6-Gain	V7-Gain	Change_V7-Gain	Flag-V7-Gain	Flag-Change_V7-Gain	V8-Gain	Change_V8-Gain	Flag-V8-Gain	Flag-Change_V8-Gain	V9-Gain	Change_V9-Gain	Flag-V9-Gain	Flag-Change_V9-Gain	V10-Gain	Change_V10-Gain	Flag-V10-Gain	Flag-Change_V10-Gain	V11-Gain	Change_V11-Gain	Flag-V11-Gain	Flag-Change_V11-Gain	V12-Gain	Change_V12-Gain	Flag-V12-Gain	Flag-Change_V12-Gain	V13-Gain	Change_V13-Gain	Flag-V13-Gain	Flag-Change_V13-Gain	V14-Gain	Change_V14-Gain	Flag-V14-Gain	Flag-Change_V14-Gain	V15-Gain	Change_V15-Gain	Flag-V15-Gain	Flag-Change_V15-Gain	V16-Gain	Change_V16-Gain	Flag-V16-Gain	Flag-Change_V16-Gain	B1-Gain	Change_B1-Gain	Flag-B1-Gain	Flag-Change_B1-Gain	B2-Gain	Change_B2-Gain	Flag-B2-Gain	Flag-Change_B2-Gain	B3-Gain	Change_B3-Gain	Flag-B3-Gain	Flag-Change_B3-Gain	B4-Gain	Change_B4-Gain	Flag-B4-Gain	Flag-Change_B4-Gain	B5-Gain	Change_B5-Gain	Flag-B5-Gain	Flag-Change_B5-Gain	B6-Gain	Change_B6-Gain	Flag-B6-Gain	Flag-Change_B6-Gain	B7-Gain	Change_B7-Gain	Flag-B7-Gain	Flag-Change_B7-Gain	B8-Gain	Change_B8-Gain	Flag-B8-Gain	Flag-Change_B8-Gain	B9-Gain	Change_B9-Gain	Flag-B9-Gain	Flag-Change_B9-Gain	B10-Gain	Change_B10-Gain	Flag-B10-Gain	Flag-Change_B10-Gain	B11-Gain	Change_B11-Gain	Flag-B11-Gain	Flag-Change_B11-Gain	B12-Gain	Change_B12-Gain	Flag-B12-Gain	Flag-Change_B12-Gain	B13-Gain	Change_B13-Gain	Flag-B13-Gain	Flag-Change_B13-Gain	B14-Gain	Change_B14-Gain	Flag-B14-Gain	Flag-Change_B14-Gain	YG1-Gain	Change_YG1-Gain	Flag-YG1-Gain	Flag-Change_YG1-Gain	YG2-Gain	Change_YG2-Gain	Flag-YG2-Gain	Flag-Change_YG2-Gain	YG3-Gain	Change_YG3-Gain	Flag-YG3-Gain	Flag-Change_YG3-Gain	YG4-Gain	Change_YG4-Gain	Flag-YG4-Gain	Flag-Change_YG4-Gain	YG5-Gain	Change_YG5-Gain	Flag-YG5-Gain	Flag-Change_YG5-Gain	YG6-Gain	Change_YG6-Gain	Flag-YG6-Gain	Flag-Change_YG6-Gain	YG7-Gain	Change_YG7-Gain	Flag-YG7-Gain	Flag-Change_YG7-Gain	YG8-Gain	Change_YG8-Gain	Flag-YG8-Gain	Flag-Change_YG8-Gain	YG9-Gain	Change_YG9-Gain	Flag-YG9-Gain	Flag-Change_YG9-Gain	YG10-Gain	Change_YG10-Gain	Flag-YG10-Gain	Flag-Change_YG10-Gain	R1-Gain	Change_R1-Gain	Flag-R1-Gain	Flag-Change_R1-Gain	R2-Gain	Change_R2-Gain	Flag-R2-Gain	Flag-Change_R2-Gain	R3-Gain	Change_R3-Gain	Flag-R3-Gain	Flag-Change_R3-Gain	R4-Gain	Change_R4-Gain	Flag-R4-Gain	Flag-Change_R4-Gain	R5-Gain	Change_R5-Gain	Flag-R5-Gain	Flag-Change_R5-Gain	R6-Gain	Change_R6-Gain	Flag-R6-Gain	Flag-Change_R6-Gain	R7-Gain	Change_R7-Gain	Flag-R7-Gain	Flag-Change_R7-Gain	R8-Gain	Change_R8-Gain	Flag-R8-Gain	Flag-Change_R8-Gain	FSC-% rCV	Change_FSC-% rCV	Flag-FSC-% rCV	Flag-Change_FSC-% rCV	SSC-% rCV	Change_SSC-% rCV	Flag-SSC-% rCV	Flag-Change_SSC-% rCV	SSC-B-% rCV	Change_SSC-B-% rCV	Flag-SSC-B-% rCV	Flag-Change_SSC-B-% rCV	UV1-% rCV	Change_UV1-% rCV	Flag-UV1-% rCV	Flag-Change_UV1-% rCV	UV2-% rCV	Change_UV2-% rCV	Flag-UV2-% rCV	Flag-Change_UV2-% rCV	UV3-% rCV	Change_UV3-% rCV	Flag-UV3-% rCV	Flag-Change_UV3-% rCV	UV4-% rCV	Change_UV4-% rCV	Flag-UV4-% rCV	Flag-Change_UV4-% rCV	UV5-% rCV	Change_UV5-% rCV	Flag-UV5-% rCV	Flag-Change_UV5-% rCV	UV6-% rCV	Change_UV6-% rCV	Flag-UV6-% rCV	Flag-Change_UV6-% rCV	UV7-% rCV	Change_UV7-% rCV	Flag-UV7-% rCV	Flag-Change_UV7-% rCV	UV8-% rCV	Change_UV8-% rCV	Flag-UV8-% rCV	Flag-Change_UV8-% rCV	UV9-% rCV	Change_UV9-% rCV	Flag-UV9-% rCV	Flag-Change_UV9-% rCV	UV10-% rCV	Change_UV10-% rCV	Flag-UV10-% rCV	Flag-Change_UV10-% rCV	UV11-% rCV	Change_UV11-% rCV	Flag-UV11-% rCV	Flag-Change_UV11-% rCV	UV12-% rCV	Change_UV12-% rCV	Flag-UV12-% rCV	Flag-Change_UV12-% rCV	UV13-% rCV	Change_UV13-% rCV	Flag-UV13-% rCV	Flag-Change_UV13-% rCV	UV14-% rCV	Change_UV14-% rCV	Flag-UV14-% rCV	Flag-Change_UV14-% rCV	UV15-% rCV	Change_UV15-% rCV	Flag-UV15-% rCV	Flag-Change_UV15-% rCV	UV16-% rCV	Change_UV16-% rCV	Flag-UV16-% rCV	Flag-Change_UV16-% rCV	V1-% rCV	Change_V1-% rCV	Flag-V1-% rCV	Flag-Change_V1-% rCV	V2-% rCV	Change_V2-% rCV	Flag-V2-% rCV	Flag-Change_V2-% rCV	V3-% rCV	Change_V3-% rCV	Flag-V3-% rCV	Flag-Change_V3-% rCV	V4-% rCV	Change_V4-% rCV	Flag-V4-% rCV	Flag-Change_V4-% rCV	V5-% rCV	Change_V5-% rCV	Flag-V5-% rCV	Flag-Change_V5-% rCV	V6-% rCV	Change_V6-% rCV	Flag-V6-% rCV	Flag-Change_V6-% rCV	V7-% rCV	Change_V7-% rCV	Flag-V7-% rCV	Flag-Change_V7-% rCV	V8-% rCV	Change_V8-% rCV	Flag-V8-% rCV	Flag-Change_V8-% rCV	V9-% rCV	Change_V9-% rCV	Flag-V9-% rCV	Flag-Change_V9-% rCV	V10-% rCV	Change_V10-% rCV	Flag-V10-% rCV	Flag-Change_V10-% rCV	V11-% rCV	Change_V11-% rCV	Flag-V11-% rCV	Flag-Change_V11-% rCV	V12-% rCV	Change_V12-% rCV	Flag-V12-% rCV	Flag-Change_V12-% rCV	V13-% rCV	Change_V13-% rCV	Flag-V13-% rCV	Flag-Change_V13-% rCV	V14-% rCV	Change_V14-% rCV	Flag-V14-% rCV	Flag-Change_V14-% rCV	V15-% rCV	Change_V15-% rCV	Flag-V15-% rCV	Flag-Change_V15-% rCV	V16-% rCV	Change_V16-% rCV	Flag-V16-% rCV	Flag-Change_V16-% rCV	B1-% rCV	Change_B1-% rCV	Flag-B1-% rCV	Flag-Change_B1-% rCV	B2-% rCV	Change_B2-% rCV	Flag-B2-% rCV	Flag-Change_B2-% rCV	B3-% rCV	Change_B3-% rCV	Flag-B3-% rCV	Flag-Change_B3-% rCV	B4-% rCV	Change_B4-% rCV	Flag-B4-% rCV	Flag-Change_B4-% rCV	B5-% rCV	Change_B5-% rCV	Flag-B5-% rCV	Flag-Change_B5-% rCV	B6-% rCV	Change_B6-% rCV	Flag-B6-% rCV	Flag-Change_B6-% rCV	B7-% rCV	Change_B7-% rCV	Flag-B7-% rCV	Flag-Change_B7-% rCV	B8-% rCV	Change_B8-% rCV	Flag-B8-% rCV	Flag-Change_B8-% rCV	B9-% rCV	Change_B9-% rCV	Flag-B9-% rCV	Flag-Change_B9-% rCV	B10-% rCV	Change_B10-% rCV	Flag-B10-% rCV	Flag-Change_B10-% rCV	B11-% rCV	Change_B11-% rCV	Flag-B11-% rCV	Flag-Change_B11-% rCV	B12-% rCV	Change_B12-% rCV	Flag-B12-% rCV	Flag-Change_B12-% rCV	B13-% rCV	Change_B13-% rCV	Flag-B13-% rCV	Flag-Change_B13-% rCV	B14-% rCV	Change_B14-% rCV	Flag-B14-% rCV	Flag-Change_B14-% rCV	YG1-% rCV	Change_YG1-% rCV	Flag-YG1-% rCV	Flag-Change_YG1-% rCV	YG2-% rCV	Change_YG2-% rCV	Flag-YG2-% rCV	Flag-Change_YG2-% rCV	YG3-% rCV	Change_YG3-% rCV	Flag-YG3-% rCV	Flag-Change_YG3-% rCV	YG4-% rCV	Change_YG4-% rCV	Flag-YG4-% rCV	Flag-Change_YG4-% rCV	YG5-% rCV	Change_YG5-% rCV	Flag-YG5-% rCV	Flag-Change_YG5-% rCV	YG6-% rCV	Change_YG6-% rCV	Flag-YG6-% rCV	Flag-Change_YG6-% rCV	YG7-% rCV	Change_YG7-% rCV	Flag-YG7-% rCV	Flag-Change_YG7-% rCV	YG8-% rCV	Change_YG8-% rCV	Flag-YG8-% rCV	Flag-Change_YG8-% rCV	YG9-% rCV	Change_YG9-% rCV	Flag-YG9-% rCV	Flag-Change_YG9-% rCV	YG10-% rCV	Change_YG10-% rCV	Flag-YG10-% rCV	Flag-Change_YG10-% rCV	R1-% rCV	Change_R1-% rCV	Flag-R1-% rCV	Flag-Change_R1-% rCV	R2-% rCV	Change_R2-% rCV	Flag-R2-% rCV	Flag-Change_R2-% rCV	R3-% rCV	Change_R3-% rCV	Flag-R3-% rCV	Flag-Change_R3-% rCV	R4-% rCV	Change_R4-% rCV	Flag-R4-% rCV	Flag-Change_R4-% rCV	R5-% rCV	Change_R5-% rCV	Flag-R5-% rCV	Flag-Change_R5-% rCV	R6-% rCV	Change_R6-% rCV	Flag-R6-% rCV	Flag-Change_R6-% rCV	R7-% rCV	Change_R7-% rCV	Flag-R7-% rCV	Flag-Change_R7-% rCV	R8-% rCV	Change_R8-% rCV	Flag-R8-% rCV	Flag-Change_R8-% rCV	YellowGreen-Laser Delay	Change_YellowGreen-Laser Delay	Flag-YellowGreen-Laser Delay	Flag-Change_YellowGreen-Laser Delay	Violet-Laser Delay	Change_Violet-Laser Delay	Flag-Violet-Laser Delay	Flag-Change_Violet-Laser Delay	Red-Laser Delay	Change_Red-Laser Delay	Flag-Red-Laser Delay	Flag-Change_Red-Laser Delay	UV-Laser Delay	Change_UV-Laser Delay	Flag-UV-Laser Delay	Flag-Change_UV-Laser Delay	YellowGreen-Area Scaling Factor	Change_YellowGreen-Area Scaling Factor	Flag-YellowGreen-Area Scaling Factor	Flag-Change_YellowGreen-Area Scaling Factor	Violet-Area Scaling Factor	Change_Violet-Area Scaling Factor	Flag-Violet-Area Scaling Factor	Flag-Change_Violet-Area Scaling Factor	Blue-Area Scaling Factor	Change_Blue-Area Scaling Factor	Flag-Blue-Area Scaling Factor	Flag-Change_Blue-Area Scaling Factor	Red-Area Scaling Factor	Change_Red-Area Scaling Factor	Flag-Red-Area Scaling Factor	Flag-Change_Red-Area Scaling Factor	UV-Area Scaling Factor	Change_UV-Area Scaling Factor	Flag-UV-Area Scaling Factor	Flag-Change_UV-Area Scaling Factor	FSC	Change_FSC	Flag-FSC	Flag-Change_FSC
2024-09-06 09:55:00	952	0	FALSE	FALSE	175	0	FALSE	FALSE	360	0	FALSE	FALSE	1034	0	FALSE	FALSE	262	0	FALSE	FALSE	204	0	FALSE	FALSE	151	0	FALSE	FALSE	192	0	FALSE	FALSE	231	0	FALSE	FALSE	367	0	FALSE	FALSE	294	0	FALSE	FALSE	355	0	FALSE	FALSE	235	0	FALSE	FALSE	301	0	FALSE	FALSE	281	0	FALSE	FALSE	410	0	FALSE	FALSE	675	0	FALSE	FALSE	538	0	FALSE	FALSE	676	0	FALSE	FALSE	124	0	FALSE	FALSE	235	0	FALSE	FALSE	216	0	FALSE	FALSE	143	0	FALSE	FALSE	171	0	FALSE	FALSE	179	0	FALSE	FALSE	235	0	FALSE	FALSE	298	0	FALSE	FALSE	248	0	FALSE	FALSE	287	0	FALSE	FALSE	231	0	FALSE	FALSE	181	0	FALSE	FALSE	172	0	FALSE	FALSE	239	0	FALSE	FALSE	410	0	FALSE	FALSE	276	0	FALSE	FALSE	1055	0	FALSE	FALSE	645	0	FALSE	FALSE	594	0	FALSE	FALSE	536	0	FALSE	FALSE	392	0	FALSE	FALSE	374	0	FALSE	FALSE	789	0	FALSE	FALSE	555	0	FALSE	FALSE	765	0	FALSE	FALSE	690	0	FALSE	FALSE	473	0	FALSE	FALSE	453	0	FALSE	FALSE	658	0	FALSE	FALSE	733	0	FALSE	FALSE	381	0	FALSE	FALSE	375	0	FALSE	FALSE	242	0	FALSE	FALSE	465	0	FALSE	FALSE	365	0	FALSE	FALSE	411	0	FALSE	FALSE	291	0	FALSE	FALSE	265	0	FALSE	FALSE	358	0	FALSE	FALSE	327	0	FALSE	FALSE	213	0	FALSE	FALSE	199	0	FALSE	FALSE	281	0	FALSE	FALSE	281	0	FALSE	FALSE	229	0	FALSE	FALSE	212	0	FALSE	FALSE	357	0	FALSE	FALSE	208	0	FALSE	FALSE	1.1432090	0.1604039	FALSE	FALSE	1.656716	0.170127	FALSE	FALSE	3.391170	0.223100	FALSE	FALSE	6.264603	-0.513898	FALSE	FALSE	2.429039	-0.360430	FALSE	FALSE	1.863501	-0.137120	FALSE	FALSE	1.916417	-0.044862	FALSE	FALSE	1.886120	-0.013588	FALSE	FALSE	2.022500	-0.006108	FALSE	FALSE	2.037093	0.025472	FALSE	FALSE	1.997900	-0.022643	FALSE	FALSE	1.908385	-0.122100	FALSE	FALSE	2.030460	-0.133127	FALSE	FALSE	1.977809	-0.229629	FALSE	FALSE	1.986099	-0.344250	FALSE	FALSE	2.038826	-0.334107	FALSE	FALSE	2.067160	-0.489987	FALSE	FALSE	2.334379	-0.511013	FALSE	FALSE	2.715388	-0.658262	FALSE	FALSE	2.129838	0.073439	FALSE	FALSE	1.995929	0.064391	FALSE	FALSE	1.946202	0.047981	FALSE	FALSE	1.767177	0.033358	FALSE	FALSE	1.689073	0.031671	FALSE	FALSE	1.713192	0.028349	FALSE	FALSE	1.596646	0.022683	FALSE	FALSE	1.506381	-0.023042	FALSE	FALSE	1.484919	0.005252	FALSE	FALSE	1.554641	0.014283	FALSE	FALSE	1.464889	-0.065008	FALSE	FALSE	1.546612	-0.110618	FALSE	FALSE	1.828770	-0.024444	FALSE	FALSE	2.036353	-0.130219	FALSE	FALSE	2.232001	-0.120310	FALSE	FALSE	2.520183	-0.329349	FALSE	FALSE	2.261960	0.096987	FALSE	FALSE	2.048054	0.045434	FALSE	FALSE	1.910570	0.073267	FALSE	FALSE	1.908745	-0.002810	FALSE	FALSE	1.945035	0.017430	FALSE	FALSE	2.088102	0.057369	FALSE	FALSE	1.905307	0.036652	FALSE	FALSE	1.635550	0.003387	FALSE	FALSE	1.495144	-0.023298	FALSE	FALSE	1.341895	0.016929	FALSE	FALSE	1.161039	0.022570	FALSE	FALSE	0.9503762	-0.0466058	FALSE	FALSE	1.245947	-0.086716	FALSE	FALSE	1.722119	-0.298465	FALSE	FALSE	1.720193	-0.379336	FALSE	FALSE	1.755334	-0.338709	FALSE	FALSE	1.767388	-0.312008	FALSE	FALSE	1.542750	-0.262746	FALSE	FALSE	1.430754	-0.227551	FALSE	FALSE	1.466286	-0.162407	FALSE	FALSE	1.245032	-0.010961	FALSE	FALSE	0.9637733	-0.1695167	FALSE	FALSE	1.244421	-0.126382	FALSE	FALSE	1.991794	-0.128031	FALSE	FALSE	2.455691	0.446813	FALSE	FALSE	2.457680	0.539251	FALSE	FALSE	2.258698	0.443007	FALSE	FALSE	2.106153	0.583373	FALSE	FALSE	2.386690	0.771068	FALSE	FALSE	2.477646	0.639031	FALSE	FALSE	2.586058	0.497536	FALSE	FALSE	2.811563	0.236453	FALSE	FALSE	-40.500	-0.025	FALSE	FALSE	-21.075	-0.025	FALSE	FALSE	18.325	-0.025	FALSE	FALSE	39.05000	-0.20000	FALSE	FALSE	1.117378	0.002052	FALSE	FALSE	1.100811	-0.000512	FALSE	FALSE	1.055206	0.005343	FALSE	FALSE	1.124783	-0.006235	FALSE	FALSE	1.153174	0.005560	FALSE	FALSE	1.047562	-0.001598	FALSE	FALSE
2024-09-05 09:47:00	952	0	FALSE	FALSE	175	0	FALSE	FALSE	360	-14	FALSE	FALSE	1034	29	FALSE	FALSE	262	1	FALSE	FALSE	204	3	FALSE	FALSE	151	1	FALSE	FALSE	192	3	FALSE	FALSE	231	3	FALSE	FALSE	367	7	FALSE	FALSE	294	7	FALSE	FALSE	355	9	FALSE	FALSE	235	5	FALSE	FALSE	301	6	FALSE	FALSE	281	8	FALSE	FALSE	410	3	FALSE	FALSE	675	-19	FALSE	FALSE	538	-24	FALSE	FALSE	676	-55	FALSE	FALSE	124	0	FALSE	FALSE	235	-1	FALSE	FALSE	216	0	FALSE	FALSE	143	0	FALSE	FALSE	171	0	FALSE	FALSE	179	-1	FALSE	FALSE	235	0	FALSE	FALSE	298	3	FALSE	FALSE	248	1	FALSE	FALSE	287	3	FALSE	FALSE	231	1	FALSE	FALSE	181	3	FALSE	FALSE	172	-1	FALSE	FALSE	239	-9	FALSE	FALSE	410	-19	FALSE	FALSE	276	-25	FALSE	FALSE	1055	2	FALSE	FALSE	645	12	FALSE	FALSE	594	7	FALSE	FALSE	536	6	FALSE	FALSE	392	15	FALSE	FALSE	374	8	FALSE	FALSE	789	23	FALSE	FALSE	555	13	FALSE	FALSE	765	25	FALSE	FALSE	690	20	FALSE	FALSE	473	-7	FALSE	FALSE	453	-13	FALSE	FALSE	658	-25	FALSE	FALSE	733	-55	FALSE	FALSE	381	8	FALSE	FALSE	375	10	FALSE	FALSE	242	3	FALSE	FALSE	465	3	FALSE	FALSE	365	4	FALSE	FALSE	411	2	FALSE	FALSE	291	0	FALSE	FALSE	265	-9	FALSE	FALSE	358	-21	FALSE	FALSE	327	-33	FALSE	FALSE	213	2	FALSE	FALSE	199	5	FALSE	FALSE	281	7	FALSE	FALSE	281	-1	FALSE	FALSE	229	-7	FALSE	FALSE	212	-10	FALSE	FALSE	357	-22	FALSE	FALSE	208	-20	FALSE	FALSE	0.9828051	-0.1599799	FALSE	FALSE	1.486589	-0.163627	FALSE	FALSE	3.168070	-0.236384	FALSE	FALSE	6.778501	0.529428	FALSE	FALSE	2.789469	0.297605	FALSE	FALSE	2.000621	0.138487	FALSE	FALSE	1.961279	0.051810	FALSE	FALSE	1.899708	0.038603	FALSE	FALSE	2.028608	0.027936	FALSE	FALSE	2.011621	0.025532	FALSE	FALSE	2.020543	0.090278	FALSE	FALSE	2.030485	0.141181	FALSE	FALSE	2.163587	0.102092	FALSE	FALSE	2.207438	0.153472	FALSE	FALSE	2.330349	0.237572	FALSE	FALSE	2.372933	0.260683	FALSE	FALSE	2.557147	0.047954	FALSE	FALSE	2.845392	-0.290955	FALSE	FALSE	3.373650	-1.038958	FALSE	FALSE	2.056399	-0.044490	FALSE	FALSE	1.931538	-0.071407	FALSE	FALSE	1.898221	-0.034587	FALSE	FALSE	1.733819	-0.006752	FALSE	FALSE	1.657402	0.000364	FALSE	FALSE	1.684843	0.000876	FALSE	FALSE	1.573963	-0.006193	FALSE	FALSE	1.529423	0.000771	FALSE	FALSE	1.479667	-0.052225	FALSE	FALSE	1.540358	-0.042086	FALSE	FALSE	1.529897	0.014443	FALSE	FALSE	1.657230	-0.071484	FALSE	FALSE	1.853214	-0.070932	FALSE	FALSE	2.166572	-0.249744	FALSE	FALSE	2.352311	-0.595957	FALSE	FALSE	2.849532	-1.363168	FALSE	FALSE	2.164973	-0.075156	FALSE	FALSE	2.002620	-0.045622	FALSE	FALSE	1.837303	-0.126871	FALSE	FALSE	1.911555	-0.059373	FALSE	FALSE	1.927605	-0.070879	FALSE	FALSE	2.030733	-0.097199	FALSE	FALSE	1.868655	-0.142899	FALSE	FALSE	1.632163	-0.215146	FALSE	FALSE	1.518442	-0.167588	FALSE	FALSE	1.324966	-0.019216	FALSE	FALSE	1.138469	-0.301961	FALSE	FALSE	0.9969820	-0.7584640	FALSE	FALSE	1.332663	-1.140267	FALSE	FALSE	2.020584	-1.794699	FALSE	FALSE	2.099529	0.208352	FALSE	FALSE	2.094043	0.242795	FALSE	FALSE	2.079396	0.244797	FALSE	FALSE	1.805496	0.193155	FALSE	FALSE	1.658305	0.128984	FALSE	FALSE	1.628693	0.055026	FALSE	FALSE	1.255993	0.048225	FALSE	FALSE	1.1332900	-0.5415710	FALSE	FALSE	1.370803	-1.046386	FALSE	FALSE	2.119825	-1.886283	FALSE	FALSE	2.008878	-0.690757	FALSE	FALSE	1.918429	-0.951675	FALSE	FALSE	1.815691	-0.898894	FALSE	FALSE	1.522780	-0.513079	FALSE	FALSE	1.615622	-0.620851	FALSE	FALSE	1.838615	-0.802641	FALSE	FALSE	2.088522	-1.103568	FALSE	FALSE	2.575110	-1.603253	FALSE	FALSE	-40.475	-0.025	FALSE	FALSE	-21.050	-0.025	FALSE	FALSE	18.350	0.025	FALSE	FALSE	39.25000	0.09999	FALSE	FALSE	1.115326	-0.002161	FALSE	FALSE	1.101323	0.000960	FALSE	FALSE	1.049863	-0.010873	FALSE	FALSE	1.131018	0.009584	FALSE	FALSE	1.147614	-0.011504	FALSE	FALSE	1.049160	-0.000072	FALSE	FALSE
2024-09-04 11:54:00	952	0	FALSE	FALSE	175	0	FALSE	FALSE	374	8	FALSE	FALSE	1005	-10	FALSE	FALSE	261	0	FALSE	FALSE	201	-4	FALSE	FALSE	150	1	FALSE	FALSE	189	-2	FALSE	FALSE	228	-2	FALSE	FALSE	360	-5	FALSE	FALSE	287	-4	FALSE	FALSE	346	-10	FALSE	FALSE	230	-7	FALSE	FALSE	295	-7	FALSE	FALSE	273	-5	FALSE	FALSE	407	-2	FALSE	FALSE	694	19	FALSE	FALSE	562	24	FALSE	FALSE	731	58	FALSE	FALSE	124	-1	FALSE	FALSE	236	1	FALSE	FALSE	216	-1	FALSE	FALSE	143	0	FALSE	FALSE	171	0	FALSE	FALSE	180	0	FALSE	FALSE	235	-1	FALSE	FALSE	295	-3	FALSE	FALSE	247	-4	FALSE	FALSE	284	-3	FALSE	FALSE	230	-1	FALSE	FALSE	178	-1	FALSE	FALSE	173	1	FALSE	FALSE	248	9	FALSE	FALSE	429	19	FALSE	FALSE	301	23	FALSE	FALSE	1053	14	FALSE	FALSE	633	4	FALSE	FALSE	587	-3	FALSE	FALSE	530	-3	FALSE	FALSE	377	-10	FALSE	FALSE	366	-6	FALSE	FALSE	766	-9	FALSE	FALSE	542	0	FALSE	FALSE	740	0	FALSE	FALSE	670	-5	FALSE	FALSE	480	13	FALSE	FALSE	466	19	FALSE	FALSE	683	34	FALSE	FALSE	788	62	FALSE	FALSE	373	-12	FALSE	FALSE	365	-19	FALSE	FALSE	239	-7	FALSE	FALSE	462	-13	FALSE	FALSE	361	-10	FALSE	FALSE	409	-8	FALSE	FALSE	291	-1	FALSE	FALSE	274	8	FALSE	FALSE	379	18	FALSE	FALSE	360	29	FALSE	FALSE	211	1	FALSE	FALSE	194	-4	FALSE	FALSE	274	-3	FALSE	FALSE	282	4	FALSE	FALSE	236	11	FALSE	FALSE	222	13	FALSE	FALSE	379	27	FALSE	FALSE	228	22	FALSE	FALSE	1.1427850	0.0487100	FALSE	FALSE	1.650216	0.143430	FALSE	FALSE	3.404454	0.043730	FALSE	FALSE	6.249073	-0.212999	FALSE	FALSE	2.491864	-0.271252	FALSE	FALSE	1.862134	-0.174965	FALSE	FALSE	1.909469	-0.140963	FALSE	FALSE	1.861105	-0.140227	FALSE	FALSE	2.000672	-0.124592	FALSE	FALSE	1.986089	-0.119207	FALSE	FALSE	1.930265	-0.169643	FALSE	FALSE	1.889304	-0.161127	FALSE	FALSE	2.061495	-0.150170	FALSE	FALSE	2.053966	-0.179439	FALSE	FALSE	2.092777	-0.208998	FALSE	FALSE	2.112250	-0.302658	FALSE	FALSE	2.509193	0.043990	FALSE	FALSE	3.136347	0.421327	FALSE	FALSE	4.412608	1.392748	FALSE	FALSE	2.100889	-0.031329	FALSE	FALSE	2.002945	0.015403	FALSE	FALSE	1.932808	-0.010340	FALSE	FALSE	1.740571	-0.004628	FALSE	FALSE	1.657038	0.014591	FALSE	FALSE	1.683967	0.028405	FALSE	FALSE	1.580156	0.016775	FALSE	FALSE	1.528652	0.065034	FALSE	FALSE	1.531892	0.111607	FALSE	FALSE	1.582444	0.107528	FALSE	FALSE	1.515454	0.110902	FALSE	FALSE	1.728714	0.177445	FALSE	FALSE	1.924146	0.019737	FALSE	FALSE	2.416316	0.272027	FALSE	FALSE	2.948268	0.645716	FALSE	FALSE	4.212700	1.668821	FALSE	FALSE	2.240129	0.037766	FALSE	FALSE	2.048242	0.031552	FALSE	FALSE	1.964174	0.123020	FALSE	FALSE	1.970928	0.096847	FALSE	FALSE	1.998484	0.095381	FALSE	FALSE	2.127932	0.094822	FALSE	FALSE	2.011554	0.155282	FALSE	FALSE	1.847309	0.264410	FALSE	FALSE	1.686030	0.276480	FALSE	FALSE	1.344182	0.063288	FALSE	FALSE	1.440430	0.332170	FALSE	FALSE	1.7554460	0.8424590	FALSE	FALSE	2.472930	1.277060	FALSE	FALSE	3.815283	2.167264	FALSE	FALSE	1.891177	0.226383	FALSE	FALSE	1.851248	0.169403	FALSE	FALSE	1.834599	0.136874	FALSE	FALSE	1.612341	0.128610	FALSE	FALSE	1.529321	0.187957	FALSE	FALSE	1.573667	0.162193	FALSE	FALSE	1.207768	-0.031139	FALSE	FALSE	1.6748610	0.7158224	FALSE	FALSE	2.417189	1.213709	FALSE	FALSE	4.006108	2.063043	FALSE	FALSE	2.699635	0.471269	FALSE	FALSE	2.870104	0.710064	FALSE	FALSE	2.714585	0.722913	FALSE	FALSE	2.035859	0.203912	FALSE	FALSE	2.236473	0.223781	FALSE	FALSE	2.641256	0.511703	FALSE	FALSE	3.192090	0.921915	FALSE	FALSE	4.178363	1.683597	FALSE	FALSE	-40.450	-0.075	FALSE	FALSE	-21.025	0.000	FALSE	FALSE	18.325	-0.025	FALSE	FALSE	39.15001	-0.02499	FALSE	FALSE	1.117487	0.000216	FALSE	FALSE	1.100363	0.000502	FALSE	FALSE	1.060736	0.000649	FALSE	FALSE	1.121434	-0.001461	FALSE	FALSE	1.159118	0.000965	FALSE	FALSE	1.049232	-0.000280	FALSE	FALSE

QC_Plots

Once we have our data, we can visualize it. This can be done with QC_Plots() for individual parameters of interest, or iterarting over the different columns to generate all the plots. Since there are a large number of parameters contained as individual columns, the argument “MeasurementType” will filter the columns for matching character values in the column names. These will be selected from the rest and their data used for the downstream plotting.

head(colnames(TidyData), 20)
#>  [1] "DateTime"        "FSC-Gain"        "Flag-FSC-Gain"   "SSC-Gain"       
#>  [5] "Flag-SSC-Gain"   "SSC-B-Gain"      "Flag-SSC-B-Gain" "UV1-Gain"       
#>  [9] "Flag-UV1-Gain"   "UV2-Gain"        "Flag-UV2-Gain"   "UV3-Gain"       
#> [13] "Flag-UV3-Gain"   "UV4-Gain"        "Flag-UV4-Gain"   "UV5-Gain"       
#> [17] "Flag-UV5-Gain"   "UV6-Gain"        "Flag-UV6-Gain"   "UV7-Gain"

StorageLocation <- file.path("C:", "Users", "JohnDoe", "Desktop")

AvailableBroadMeasurementTypes <- c("Gain", "rCV", "Laser Delay",
                                    "Area Scaling Factor")
SinglePlot <- "UV7-Gain"

TheSinglePlot <- QC_Plots(x = TidyData, MeasurementType = SinglePlot,
                          FailedFlag = TRUE, returntype="patchwork",
                          path=StorageLocation, filename="CytekAurora5L_QC")

TheSinglePlot
#> $`1`

TodaysExample <- c("Gain")

Plots <- QC_Plots(x = TidyData, MeasurementType = TodaysExample,
                  FailedFlag = TRUE, returntype="patchwork",
                  path=StorageLocation, filename="CytekAurora5L_QC")
Plots[1]
#> $`1`

Setting FailedFlag = TRUE will add a red box on the graph to showcase the date when a particular detector failed the automated QC check by exceeding the allowed variance set for the instrument.

As with most functions in Luciernaga, CytekQCPlots() uses Utility_Patchwork() for the .pdf generation. The layout arrangement can be edited by providing additional arguments to generate a desired layout for desired page size.

QC Retrieval

In the absence of QC reports from an instrument, we can use .fcs files acquired on the instrument over a desired period of time to retrieve the same information. This is thanks to a lot of this data being stored as description parameters within an .fcs file.

To do this, we will use .fcs files consisting of 3000 events acquired before the DailyQC as part of monitoring. We will first identify the files of interest, 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)
QCBeads <- FCS_Files[grep("Before", FCS_Files)]
head(QCBeads)
#> [1] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/13_Before.fcs"
#> [2] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/14_Before.fcs"
#> [3] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/15_Before.fcs"
#> [4] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/20_Before.fcs"
#> [5] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/21_Before.fcs"
#> [6] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/22_Before.fcs"

MyCytoSet <- load_cytoset_from_fcs(QCBeads, truncate_max_range = FALSE,
                                   transform = FALSE)
MyGatingSet <- GatingSet(MyCytoSet)
MyGatingSet
#> A GatingSet with 7 samples

Having the files now loaded into a GatingSet, we can retrieve the information using QC_Retrieval function.

SingleSpecimen <- QC_Retrieval(x=MyGatingSet[[1]], sample.name="TUBENAME")

gt(SingleSpecimen)

SAMPLE	DATE	TIME	CYT	CYTSN	OP	UV1-A_Gain	UV2-A_Gain	UV3-A_Gain	UV4-A_Gain	UV5-A_Gain	UV6-A_Gain	UV7-A_Gain	UV8-A_Gain	UV9-A_Gain	UV10-A_Gain	UV11-A_Gain	UV12-A_Gain	UV13-A_Gain	UV14-A_Gain	UV15-A_Gain	UV16-A_Gain	SSC-H_Gain	SSC-A_Gain	V1-A_Gain	V2-A_Gain	V3-A_Gain	V4-A_Gain	V5-A_Gain	V6-A_Gain	V7-A_Gain	V8-A_Gain	V9-A_Gain	V10-A_Gain	V11-A_Gain	V12-A_Gain	V13-A_Gain	V14-A_Gain	V15-A_Gain	V16-A_Gain	FSC-H_Gain	FSC-A_Gain	SSC-B-H_Gain	SSC-B-A_Gain	B1-A_Gain	B2-A_Gain	B3-A_Gain	B4-A_Gain	B5-A_Gain	B6-A_Gain	B7-A_Gain	B8-A_Gain	B9-A_Gain	B10-A_Gain	B11-A_Gain	B12-A_Gain	B13-A_Gain	B14-A_Gain	YG1-A_Gain	YG2-A_Gain	YG3-A_Gain	YG4-A_Gain	YG5-A_Gain	YG6-A_Gain	YG7-A_Gain	YG8-A_Gain	YG9-A_Gain	YG10-A_Gain	R1-A_Gain	R2-A_Gain	R3-A_Gain	R4-A_Gain	R5-A_Gain	R6-A_Gain	R7-A_Gain	R8-A_Gain	YellowGreen_LaserDelay	Violet_LaserDelay	Blue_LaserDelay	Red_LaserDelay	UV_LaserDelay	YellowGreen_AreaScalingFactor	Violet_AreaScalingFactor	Blue_AreaScalingFactor	Red_AreaScalingFactor	UV_AreaScalingFactor
13 Before	2024-08-13	8H 44M 54.74S	Aurora	U1368	Admin	1007	256	200	147	187	225	356	288	345	228	300	279	405	666	527	648	174	174	124	235	216	144	171	180	235	297	248	285	234	182	174	240	410	274	975	975	359	359	1049	635	593	537	384	369	796	557	754	688	472	452	650	718	381	366	236	473	368	420	293	263	354	321	226	208	294	288	231	211	353	204	-40.4	-20.975	0	18.3	38.925	1.12	1.1	1.05	1.13	1.15

AllSpecimens <- map(.x=MyGatingSet, .f=QC_Retrieval, sample.name="TUBENAME") %>%
  bind_rows()

gt(AllSpecimens)

SAMPLE	DATE	TIME	CYT	CYTSN	OP	UV1-A_Gain	UV2-A_Gain	UV3-A_Gain	UV4-A_Gain	UV5-A_Gain	UV6-A_Gain	UV7-A_Gain	UV8-A_Gain	UV9-A_Gain	UV10-A_Gain	UV11-A_Gain	UV12-A_Gain	UV13-A_Gain	UV14-A_Gain	UV15-A_Gain	UV16-A_Gain	SSC-H_Gain	SSC-A_Gain	V1-A_Gain	V2-A_Gain	V3-A_Gain	V4-A_Gain	V5-A_Gain	V6-A_Gain	V7-A_Gain	V8-A_Gain	V9-A_Gain	V10-A_Gain	V11-A_Gain	V12-A_Gain	V13-A_Gain	V14-A_Gain	V15-A_Gain	V16-A_Gain	FSC-H_Gain	FSC-A_Gain	SSC-B-H_Gain	SSC-B-A_Gain	B1-A_Gain	B2-A_Gain	B3-A_Gain	B4-A_Gain	B5-A_Gain	B6-A_Gain	B7-A_Gain	B8-A_Gain	B9-A_Gain	B10-A_Gain	B11-A_Gain	B12-A_Gain	B13-A_Gain	B14-A_Gain	YG1-A_Gain	YG2-A_Gain	YG3-A_Gain	YG4-A_Gain	YG5-A_Gain	YG6-A_Gain	YG7-A_Gain	YG8-A_Gain	YG9-A_Gain	YG10-A_Gain	R1-A_Gain	R2-A_Gain	R3-A_Gain	R4-A_Gain	R5-A_Gain	R6-A_Gain	R7-A_Gain	R8-A_Gain	YellowGreen_LaserDelay	Violet_LaserDelay	Blue_LaserDelay	Red_LaserDelay	UV_LaserDelay	YellowGreen_AreaScalingFactor	Violet_AreaScalingFactor	Blue_AreaScalingFactor	Red_AreaScalingFactor	UV_AreaScalingFactor
13 Before	2024-08-13	8H 44M 54.74S	Aurora	U1368	Admin	1007	256	200	147	187	225	356	288	345	228	300	279	405	666	527	648	174	174	124	235	216	144	171	180	235	297	248	285	234	182	174	240	410	274	975	975	359	359	1049	635	593	537	384	369	796	557	754	688	472	452	650	718	381	366	236	473	368	420	293	263	354	321	226	208	294	288	231	211	353	204	-40.400	-20.975	0	18.300	38.925	1.12	1.1	1.05	1.13	1.15
14 Before	2024-08-14	10H 7M 1.7S	Aurora	U1368	Admin	1001	257	200	146	187	225	356	287	346	231	299	278	404	665	527	651	174	174	124	235	216	144	171	180	235	297	251	288	234	182	174	240	411	275	976	976	363	363	1047	635	593	539	388	373	796	555	756	686	471	452	651	720	385	376	242	474	369	420	293	264	357	324	218	204	286	282	228	210	351	203	-40.475	-21.025	0	18.300	39.050	1.12	1.1	1.05	1.13	1.16
15 Before	2024-08-15	10H 53M 57.27S	Aurora	U1368	Admin	979	253	198	143	184	222	351	280	339	227	295	272	399	661	529	663	174	174	124	235	217	143	171	180	235	298	250	286	233	182	174	242	415	282	977	977	238	238	1046	634	600	537	386	372	790	560	764	695	476	457	661	743	388	377	244	479	376	420	299	272	369	342	220	204	287	285	232	215	361	212	-40.400	-20.950	0	18.375	39.075	1.12	1.1	1.06	1.12	1.16
20 Before	2024-08-20	12H 50M 5.41S	Aurora	U1368	Admin	1000	256	201	147	187	226	358	286	349	231	295	274	402	660	528	658	174	174	124	236	217	143	171	180	236	296	251	285	231	180	172	239	409	276	977	977	240	240	1043	634	591	536	387	372	790	546	754	672	470	451	650	731	380	372	242	468	363	410	291	262	357	327	209	198	277	279	227	209	352	206	-40.475	-21.025	0	18.325	39.100	1.11	1.1	1.05	1.13	1.15
21 Before	2024-08-21	9H 37M 51.18S	Aurora	U1368	Admin	964	251	197	143	184	222	347	276	335	225	283	259	383	642	531	698	174	174	125	236	216	143	170	178	234	293	249	282	223	172	166	235	417	299	977	977	369	369	1042	622	583	520	381	362	755	529	707	651	455	442	662	784	384	378	244	463	355	399	285	264	371	360	193	181	254	258	218	207	363	222	-40.375	-21.000	0	18.300	39.050	1.12	1.1	1.06	1.12	1.16
22 Before	2024-08-22	8H 55M 15.81S	Aurora	U1368	Admin	969	249	196	144	184	221	349	281	339	227	293	273	398	652	518	640	175	175	124	234	216	143	171	180	236	297	247	284	235	182	174	239	407	273	977	977	361	361	1069	639	601	538	388	368	792	562	756	697	472	452	647	720	380	369	239	475	373	422	294	264	356	323	225	210	292	290	230	210	352	203	-40.425	-20.975	0	18.450	39.250	1.12	1.1	1.05	1.13	1.16
23 Before	2024-08-23	9H 18M 47.31S	Aurora	U1368	Admin	986	253	199	146	186	224	354	285	343	229	296	275	402	660	525	647	175	175	124	235	217	143	171	181	237	297	247	284	235	183	174	240	408	274	978	978	362	362	1067	639	599	537	387	366	791	563	754	696	472	451	646	719	378	367	238	473	372	421	293	264	355	322	225	209	293	289	230	211	352	203	-40.475	-21.000	0	18.350	39.100	1.12	1.1	1.05	1.13	1.15

Once we have generated these data outputs, we can now send them to QC_Plots for visualization of the instrument gain, laser delay and area scaling factors similar to as if we had retrieved the data from the Levy-Jennings Tracking .csv.

YellowExample <- c("YG")

Plots <- QC_Plots(x = AllSpecimens, MeasurementType = YellowExample,
                  FailedFlag = FALSE, returntype="patchwork",
                  path=StorageLocation, filename="CytekAurora5L_QC",
                  therows=3, thecolumns=1, width=7, height=9)

Plots[[1]]

QC_GainMonitoring

While QC_FilePrep() and QC_Retrieval() provide two ways of retrieving information about the individual detectors gains, they only tell part of the instrument story. Gains and Lasers are dynamically changed to maintain a stable MFI over time. One way that our cytometry core has set out to monitor this is acquiring 3000 QC beads immediately before and after running the daily QC, then extracting and visualizing the MFI values in combination with the gain parameters. These before/after .fcs files are processed by the QC_GainMonitoring() function.

QCBeads <- FCS_Files[grep("Before|After", FCS_Files)]
head(QCBeads)
#> [1] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/13_After.fcs" 
#> [2] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/13_Before.fcs"
#> [3] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/14_After.fcs" 
#> [4] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/14_Before.fcs"
#> [5] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/15_After.fcs" 
#> [6] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/15_Before.fcs"

BeforeAfter_CS <- load_cytoset_from_fcs(files=QCBeads, transform=FALSE, truncate_max_range = FALSE)

BeforeAfter <- map(.x=BeforeAfter_CS, .f=QC_GainMonitoring, sample.name = "TUBENAME", stats="median") %>% bind_rows()

gt(head(BeforeAfter, 4))

SAMPLE	DATE	TIME	Timepoint	CYT	CYTSN	OP	UV1-A_Gain	UV2-A_Gain	UV3-A_Gain	UV4-A_Gain	UV5-A_Gain	UV6-A_Gain	UV7-A_Gain	UV8-A_Gain	UV9-A_Gain	UV10-A_Gain	UV11-A_Gain	UV12-A_Gain	UV13-A_Gain	UV14-A_Gain	UV15-A_Gain	UV16-A_Gain	SSC-H_Gain	SSC-A_Gain	V1-A_Gain	V2-A_Gain	V3-A_Gain	V4-A_Gain	V5-A_Gain	V6-A_Gain	V7-A_Gain	V8-A_Gain	V9-A_Gain	V10-A_Gain	V11-A_Gain	V12-A_Gain	V13-A_Gain	V14-A_Gain	V15-A_Gain	V16-A_Gain	FSC-H_Gain	FSC-A_Gain	SSC-B-H_Gain	SSC-B-A_Gain	B1-A_Gain	B2-A_Gain	B3-A_Gain	B4-A_Gain	B5-A_Gain	B6-A_Gain	B7-A_Gain	B8-A_Gain	B9-A_Gain	B10-A_Gain	B11-A_Gain	B12-A_Gain	B13-A_Gain	B14-A_Gain	YG1-A_Gain	YG2-A_Gain	YG3-A_Gain	YG4-A_Gain	YG5-A_Gain	YG6-A_Gain	YG7-A_Gain	YG8-A_Gain	YG9-A_Gain	YG10-A_Gain	R1-A_Gain	R2-A_Gain	R3-A_Gain	R4-A_Gain	R5-A_Gain	R6-A_Gain	R7-A_Gain	R8-A_Gain	YellowGreen_LaserDelay	Violet_LaserDelay	Blue_LaserDelay	Red_LaserDelay	UV_LaserDelay	YellowGreen_AreaScalingFactor	Violet_AreaScalingFactor	Blue_AreaScalingFactor	Red_AreaScalingFactor	UV_AreaScalingFactor	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-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-H	FSC-A	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
13 After	2024-08-13	8H 48M 21.21S	After	Aurora	U1368	Admin	1001	257	200	146	187	225	356	287	346	231	299	278	404	665	527	651	174	174	124	235	216	144	171	180	235	297	251	288	234	182	174	240	411	275	976	976	363	363	1047	635	593	539	388	373	796	555	756	686	471	452	651	720	385	376	242	474	369	420	293	264	357	324	218	204	286	282	228	210	351	203	-40.475	-21.025	0	18.300	39.050	1.12	1.1	1.05	1.13	1.16	117904.6	762445.3	661904.7	586761.6	634296.6	976572.1	1382279	842342.2	1321169	1545869	854973.1	437813.3	408506.5	1393654	1852736	2102292	1984859	1860744	445857.5	1022547	1164956	1190935	1120486	817163.0	943516.8	969111.9	1214867	2030828	1006398.5	489580.0	489075.8	1213638	1772899	1347475	1970879	1989145	1978225	1884179	143378.9	130824.3	272576.4	485604.8	686618.6	876702.0	619935.5	452341.9	386115.9	250190.9	350469.3	592305.8	637185.7	1030562.9	584627.6	1388661	2164008	1243471	871903.0	593033.9	713037.1	839604.6	1101498	951768.6	389142.4	526722.2	420617.4	358073.3	669359.1	958557.5	1331739	957759.2
13 Before	2024-08-13	8H 44M 54.74S	Before	Aurora	U1368	Admin	1007	256	200	147	187	225	356	288	345	228	300	279	405	666	527	648	174	174	124	235	216	144	171	180	235	297	248	285	234	182	174	240	410	274	975	975	359	359	1049	635	593	537	384	369	796	557	754	688	472	452	650	718	381	366	236	473	368	420	293	263	354	321	226	208	294	288	231	211	353	204	-40.400	-20.975	0	18.300	38.925	1.12	1.1	1.05	1.13	1.15	112204.6	735725.3	652271.8	587895.8	638151.8	981050.1	1380002	849673.8	1320298	1539217	859490.5	436523.0	408846.3	1386419	1792307	2058909	1984907	1981675	466798.5	1078165	1226517	1205442	1128992	828394.5	954202.0	1013775.3	1210774	2093218	1012397.7	504710.5	500134.1	1247245	1817983	1368498	1968714	1991604	1986582	1978010	142483.9	130807.2	271396.0	483473.4	679594.0	863634.8	623386.2	463262.8	385468.5	249644.7	340693.8	578856.6	614784.8	996369.5	589754.6	1343417	2090647	1241923	871435.4	585505.1	691881.6	812174.9	1051217	913776.2	402288.4	536626.8	428714.6	362460.0	662592.4	949234.7	1301267	939539.1
14 After	2024-08-14	10H 10M 31.84S	After	Aurora	U1368	Admin	979	253	198	143	184	222	351	280	339	227	295	272	399	661	529	663	174	174	124	235	217	143	171	180	235	298	250	286	233	182	174	242	415	282	977	977	238	238	1046	634	600	537	386	372	790	560	764	695	476	457	661	743	388	377	244	479	376	420	299	272	369	342	220	204	287	285	232	215	361	212	-40.400	-20.950	0	18.375	39.075	1.12	1.1	1.06	1.12	1.16	115612.5	755833.6	657551.8	580374.8	625504.6	970113.2	1369624	827943.8	1305119	1517024	840955.1	426343.6	403352.2	1389917	1863255	2150147	1982400	1857755	447282.3	1025331	1180004	1184709	1122221	818680.5	944839.4	967105.1	1198763	2005086	991654.4	486143.0	486754.7	1221019	1788007	1389493	1970336	1990095	1995577	1915996	144162.2	131639.4	278397.8	487093.6	681547.3	868017.3	620416.5	459887.5	394848.6	255590.6	354839.2	603233.5	648428.4	1070436.4	572638.0	1361581	2119910	1209174	874900.7	579265.7	710038.9	846634.2	1115488	986448.6	382909.6	514088.1	411015.1	350039.6	669685.8	967028.5	1353727	993218.7
14 Before	2024-08-14	10H 7M 1.7S	Before	Aurora	U1368	Admin	1001	257	200	146	187	225	356	287	346	231	299	278	404	665	527	651	174	174	124	235	216	144	171	180	235	297	251	288	234	182	174	240	411	275	976	976	363	363	1047	635	593	539	388	373	796	555	756	686	471	452	651	720	385	376	242	474	369	420	293	264	357	324	218	204	286	282	228	210	351	203	-40.475	-21.025	0	18.300	39.050	1.12	1.1	1.05	1.13	1.16	113293.1	747641.2	663131.9	594633.8	650139.9	998866.3	1405886	858603.1	1354592	1611097	866457.7	445029.3	412665.3	1381891	1786468	2028115	1986073	1985836	465074.8	1074143	1224534	1202835	1127028	827767.6	954683.2	1020587.2	1246120	2156764	1013065.4	505094.7	497750.6	1230448	1783860	1322181	1973694	1994224	3130029	3126592	143127.2	131474.6	273186.8	487212.2	702470.8	890981.0	624867.0	458311.4	386742.5	249454.1	338474.7	572389.6	602888.3	970089.7	604001.5	1438799	2207057	1250088	870417.6	581339.7	682257.1	798407.7	1028559	878039.5	372609.1	523663.0	412355.6	350646.0	641142.5	919490.8	1248841	889689.6

The setup for these plots requires a couple additional arguments. The first Metadata designates the column name under which the comparison categories are stored (in this case, Timepoint column, with Before and After designations). The second is we change plotType from the default “individual” to the “comparison” category.

MFIExample <- c("R1")

Plots <- QC_Plots(x = BeforeAfter, MeasurementType = MFIExample,
                  Metadata = "Timepoint", plotType = "comparison",
                  FailedFlag = FALSE, returntype="patchwork",
                  path=StorageLocation, filename="CytekAurora5L_QC")
Plots[[1]]

As we can see from the example, looking at the MFI we can see that after daily QC, the recorded MFI across bead samples is being recorded is similar across time, thanks in part to the changes in the individual gain depicted above.

Library Reference Controls

For Cytek instruments that use SpectroFlo, you are able to store unmixing controls in the library for re-use across future experiments. These can be exported out as .XML files, and re-imported to other instruments. Unfortunately, after original acquisition, there is no good way to visualize what the signature of the stored library control is. Whether the library reference control is accurate, contaminated with autofluorescence, or somewhere in between, we are left to parse out from effect on full-stained samples after unmixing. The following functions are designed to extract the signature information from the .XML files to allow for visualization of the underlying normalized signatures. These additionally can be plotted vs. reference signatures to compare how closely they match.

For an individual user, we would first load the required libraries, and then provide the location where the exported .XML files are stored. An example is provided below.

library(Luciernaga)
library(xml2)
library(tidyr)
library(purrr)
library(dplyr)
library(ggplot2)

FolderLocation <- file.path("C:", "Users", "JohnDoe", "Desktop",
                            "LibraryControls5L")
StorageLocation <- file.path(FolderLocation, "Visualized")

In our example today, we will be accessing the .XML files stored within the Luciernaga packages extdata folder.

Folder_Location <- system.file("extdata", package = "Luciernaga")
XML_Pattern <- ".XML$"
XML_Files <- list.files(path = Folder_Location, pattern = XML_Pattern,
                        full.names = TRUE, recursive = FALSE)
XML_Files
#> [1] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/7-AAD(Cells)-BISCC24.XML"       
#> [2] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/APC-Cy7(Cells)-BISCC24.XML"     
#> [3] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/BV510(Beads)-BISCC24.XML"       
#> [4] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/BV750(Beads)-BISCC24.XML"       
#> [5] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/BV750(Cells)-BISCC24.XML"       
#> [6] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/FITC(Beads)-BISCC24.XML"        
#> [7] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/PE-Fire700(Beads)-BISCC24.XML"  
#> [8] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/PE-Fire700(Cells)-BISCC24.XML"  
#> [9] "C:/Users/12692/AppData/Local/R/win-library/4.4/Luciernaga/extdata/SparkBlue550(Beads)-BISCC24.XML"

QC_LibraryParse

QC_LibraryParse() is the function that will convert the .XML files into “tidy” data, as either plots or a dataframe. This can either be done individually, or in combination with map() for all the files.

By setting the returntype to “plots”, we can return each file as a ggplot object. In combination with Utility_Patchwork() we can generate a .pdf file or an assembled patchwork plot to our desired dimensions.

SinglePlot <- QC_LibraryParse(XML_Files[2], returntype="plots", references=FALSE)

ThePlots <- map(.x=XML_Files, .f=QC_LibraryParse, returntype="plots",
                references=FALSE)

AssembledPlot <- Utility_Patchwork(x=ThePlots, filename="LibraryControls5L",
                  outfolder=NULL, returntype="patchwork", thecolumns=3,
                  therows = 3, width = 7, height = 9)

AssembledPlot[1]
#> $`1`

The Library Reference Controls featured above where acquired during a training workshop. As you may notice, some library reference controls appear normal, while others have some quality control issues. Profiling the reference controls acquired on our institutions instruments, we noticed that the location the detector gate is placed on the brightness detector plot ties in to whether the y-axis and peak max-value is at 1. When this is misplaced, the stored reference control has a peak greater than 1.

By setting the argument “references=TRUE”, we can append the reference signatures in red to our existing library controls.

ThePlots <- map(.x=XML_Files, .f=QC_LibraryParse, returntype="plots",
                references=TRUE)

AssembledPlot <- Utility_Patchwork(x=ThePlots, filename="LibraryControls5L",
                  outfolder=NULL, returntype="patchwork", thecolumns=3,
                  therows = 3, width = 7, height = 9)

AssembledPlot[1]
#> $`1`

We have attempted to provide coverage for most fluorophores on all Cytek Aurora and Northern Light Instruments. Some fluorophores not present in the internal .csv, a mismatch in the instrument specific fluorophore name can lead to the absence of a reference signature being appended to the individual plot. You can investigate which of these is the case using the QC_ReferenceLibrary() function.

QC_ReferenceLibrary(FluorNameContains = "FITC", NumberDetectors=64)
#>       Fluorophore
#> 1            FITC
#> 2 Vio Bright FITC

Alternatively for QC_LibraryParse, we can set the returntype to “dataframe” and return in a “tidy” format.

LibraryData_Single <- QC_LibraryParse(XML_Files[2], returntype="dataframe", references=FALSE)

LibraryData <- map(.x=XML_Files, .f=QC_LibraryParse, returntype="dataframe", references=FALSE) %>% bind_rows()
gt(head(LibraryData, 4))

Fluorochrome	Sample	Creator	Date	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54
7-AAD	BISCC_Aurora 4	BISCC24	2024-04-22	0.000000e+00	0.0000000000	0.0000000000	0.0000000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	3.357841e-03	0.0200933330	0.0269295648	0.0351323448	0.031176023	0.02734881	0.02701301	0.0008614465	2.197239e-05	0.0003561859	0.0002874869	0.0000000000	0.0000000000	0.0000000000	0.0071704444	0.0384922177	0.2119562000	0.9707101000	1.1845543400	1.65461421	1.7723749	2.1561450	2.535599	0.000000e+00	0.000000e+00	0.000000e+00	0.0130858915	4.635381e-02	1.709323e-01	1.000000e+00	1.187494e+00	1.5193587500	1.976335880	2.2245504900	2.30240600	2.551642660	2.94315457	2.104682e-02	1.389085e-02	1.771384e-02	0.024102336	2.454833e-02	0.0234715976	0.03078522	4.115970e-02
APC-Cy7	BISCC_CD19_Aurora 4	BISCC24	2024-04-22	0.000000e+00	0.0000000000	0.0000000000	0.0000000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.0001239171	0.0001191841	0.0001597688	0.005176608	0.03450280	0.03727421	0.0000000000	0.000000e+00	0.0000000000	0.0000000000	0.0000000000	0.0000000000	0.0000000000	0.0000000000	0.0000000000	0.0000000000	0.0045011616	0.0009675164	0.00678298	0.1113393	0.9478439	1.296947	0.000000e+00	0.000000e+00	0.000000e+00	0.0000000000	0.000000e+00	0.000000e+00	4.444736e-04	4.964399e-04	0.0002054263	0.000000000	0.0008714843	0.02904116	0.087956876	0.09239815	7.936542e-03	3.202018e-03	2.718762e-03	0.004644698	3.135787e-02	0.2705970000	1.00000000	1.278071e+00
BV510	BISCC_CD5_Day 2	BISCC24	2024-04-23	2.579751e-02	0.0003314757	0.0001892647	0.0040828455	1.739238e-02	5.436631e-02	1.877477e-01	3.018546e-01	2.225809e-01	7.746916e-02	0.0569705330	0.0550897121	0.0565819144	0.041321658	0.03384400	0.02612195	0.0028797654	1.728448e-02	0.0767551700	0.1833572090	0.5149905680	0.7400627000	1.0000000000	1.1565735300	1.0466210000	1.0731535000	0.8709386590	0.7513141630	0.83628800	0.7499129	0.8432913	0.886213	1.359256e-02	1.316562e-02	5.320899e-03	0.0019610294	1.647135e-03	1.483940e-03	9.997307e-04	1.617119e-03	0.0017682174	0.000943677	0.0013562749	0.00000000	0.004724612	0.00000000	0.000000e+00	0.000000e+00	3.441415e-05	0.000216865	8.330735e-05	0.0002015475	0.00000000	2.408894e-05
BV750	BISCC_CD4_Day 2	BISCC24	2024-04-23	1.833979e-05	0.0000000000	0.0001768138	0.0001059261	9.296519e-05	4.824572e-05	2.452453e-05	2.213248e-05	2.117732e-05	8.294084e-06	0.0000106847	0.0001562465	0.0044613980	0.024650610	0.02805302	0.01853093	0.0059054834	2.287031e-03	0.0018268854	0.0006855681	0.0003058358	0.0002672605	0.0002272015	0.0002505331	0.0002086246	0.0002300744	0.0003114131	0.0047987495	0.14692925	1.0000000	1.4655287	1.379369	3.193051e-05	6.657506e-05	4.044967e-05	0.0000229305	1.932283e-05	1.919852e-05	1.346646e-05	4.830633e-05	0.0002198810	0.002983311	0.0247765724	0.05669076	0.050219400	0.05182420	2.189264e-06	1.145108e-05	8.538878e-05	0.001145449	8.734865e-03	0.0167495348	0.01845885	2.085280e-02

The data from the extracted signatures can then be saved as a .csv file for future reference.

StorageLocation <- file.path("C:", "Users", "JohnDoe", "Desktop",
                             "LibraryControls5L")
FileName <- "ReferenceData.csv"
StorageName <- file.path(StorageLocation, FileName)
# write.csv(TheData, file=StorageName, row.names=FALSE)

Please be aware, if the .XML library controls imported originated from different instruments (consequently different laser and detector configurations), the map() followed with bind_rows() can occasionally fail to produce the desired output. A workaround is to take the map() outputted rows, sort them based on number of columns, and then bind them into their own data.frames on the basis of shared instruments laser and detector configurations. An example of how we did this is provided below and can be adapted for your individual situation.

#Setting up example
LibraryData <- map(.x=XML_Files, .f=QC_LibraryParse, returntype="dataframe", references=FALSE)

LibraryData[[3]] <- LibraryData[[3]][, -ncol(LibraryData[[3]])]

ncol(LibraryData[[3]]) == ncol(LibraryData[[1]])
#> [1] FALSE

FirstItem <- ncol(LibraryData[[1]])

MisbehavingRows <- which(sapply(LibraryData, function(x) ncol(x) != FirstItem))

if(length(MisbehavingRows) != 0) {
  TheData_filtered <- LibraryData[-MisbehavingRows]
} else {TheData_filtered <- LibraryData}

TheData <- bind_rows(TheData_filtered)

#write.csv(TheData, file=StorageName, row.names=FALSE)

QC_UserLibraries

When working with XML files for multiple users, the assembled dataframe output of QC_LibraryParse() can be passed to the QC_UserLibraries function, that will filter the individual library controls by user, and send them off to Utility_Patchwork() to create a .pdf output to the desired specifications. This can be useful tool for core staff to quickly generate outputs for each instrument user, that can then be sent to them to decide if they need to change any of their existing library reference controls. The saveCSV = TRUE argument would additionally return a .csv of the corresponding dataframe.

TemporaryLocation <- file.path(tempdir(), "LuciernagaTemporaryExamples")
if (!dir.exists(TemporaryLocation)) {dir.create(TemporaryLocation)}

TheIndividuals <- TheData %>% pull(Creator) %>% unique()
TheIndividuals
#> [1] "BISCC24"

IndividualUser <- QC_UserLibraries(x=TheIndividuals[[1]], Data=TheData,
                                   NameAppend="_LibraryQC", outpath=TemporaryLocation,
                                   references = TRUE, thecolumns = 3, therows=4,
                                   width=7, height=9, saveCSV=FALSE)
#> $`1`

AllUsers <- map(.x=TheIndividuals[1:2], .f=QC_UserLibraries, Data=TheData,
                 NameAppend="_LibraryQC", outpath=TemporaryLocation,references = TRUE,
                 thecolumns = 3, therows=4, width=7, height=9, saveCSV=FALSE)
#> $`1`
#> 
#> named list()

ThePDF <- list.files(TemporaryLocation, pattern="_LibraryQC.pdf")
ThePDF
#> [1] "__LibraryQC.pdf"        "BISCC24__LibraryQC.pdf"

Comparing Normalized Signatures

We previously highlighted the ability within QC_LibraryParse to set reference=TRUE and compare our acquired library signatures to that of a reference signature. The following functions provide additionally functionality, extending this to signatures derrived from additional sources (including those derrived from .fcs files, covered extensively in Vignette 04_Fluorescent Signatures).

Let’s for now continue by using a signature derrived from the Library controls highlighted above:

LibraryData_Single <- QC_LibraryParse(XML_Files[2], returntype="dataframe", references=FALSE)
gt(LibraryData_Single)

Fluorochrome	Sample	Creator	Date	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54
APC-Cy7	BISCC_CD19_Aurora 4	BISCC24	2024-04-22	0	0	0	0	0	0	0	0	0	0	0.0001239171	0.0001191841	0.0001597688	0.005176608	0.0345028	0.03727421	0	0	0	0	0	0	0	0	0	0	0.004501162	0.0009675164	0.00678298	0.1113393	0.9478439	1.296947	0	0	0	0	0	0	0.0004444736	0.0004964399	0.0002054263	0	0.0008714843	0.02904116	0.08795688	0.09239815	0.007936542	0.003202018	0.002718762	0.004644698	0.03135787	0.270597	1	1.278071

As it currently exist, there are a few additional columns that are not required for the following functions, which we will remove.

Data <- LibraryData_Single %>% select(-Sample, -Creator, -Date) %>% rename(Sample=Fluorochrome)
gt(Data)

Sample	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54
APC-Cy7	0	0	0	0	0	0	0	0	0	0	0.0001239171	0.0001191841	0.0001597688	0.005176608	0.0345028	0.03727421	0	0	0	0	0	0	0	0	0	0	0.004501162	0.0009675164	0.00678298	0.1113393	0.9478439	1.296947	0	0	0	0	0	0	0.0004444736	0.0004964399	0.0002054263	0	0.0008714843	0.02904116	0.08795688	0.09239815	0.007936542	0.003202018	0.002718762	0.004644698	0.03135787	0.270597	1	1.278071

QC_WhatsThis

The QC_WhatsThis() functions takes the above output, and will search the reference data for fluorophores with the closest resembling signature

TheFluorophore <- Data %>% pull(Sample)

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

Results[[1]]
#>                  Fluorophore ID_APC-Cy7
#> 1            Alexa Fluor 790       0.97
#> 2                      CF770       0.89
#> 3                DyLight 800       0.88
#> 4  Fixable Viability Dye 780       0.81
#> 5          Ghost Dye Red 780       0.81
#> 6                 eFluor 780       0.79
#> 7              LIVE DEAD NIR       0.79
#> 8               APC-Fire 810       0.78
#> 9                cFluor R780       0.78
#> 10               cFluor R840       0.78

Setting returnPlots = TRUE, will additionally provide the plotted signatures. In combination with ggplotly(), this can be quite useful.

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

As we can see, the acquired library signature differs from the reference signature.

QC_SimilarFluorophores

Additionally, we don’t even need to provide a signature to compare to, if the fluorophore is present within the reference dataset, we can compare it to the other fluorophores present and similarly visualize it using the QC_SimilarFluorophores() option.

Results <- QC_SimilarFluorophores(TheFluorophore="Spark Blue 550", NumberDetectors=64, NumberHits = 10, returnPlots=TRUE)

Results[[1]]
#>           Fluorophore Spark Blue 550
#> 1               CF514           1.00
#> 2         cFluor B548           1.00
#> 3               RB545           0.96
#> 4                 YFP           0.94
#> 5         NucView 488           0.91
#> 6     Alexa Fluor 532           0.90
#> 7               CF532           0.89
#> 8  NovaFluor Blue 555           0.89
#> 9          eFluor 520           0.88
#> 10               EYFP           0.87

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

QC_ReferenceLibrary

If there is a mismatch in how the fluorophore is named within the reference data (or is absent), using the QC_ReferenceLibrary() function would be useful in correcting the inputed name for QC_SimilarFluorophores() for the output listed above.

QC_ReferenceLibrary(FluorNameContains = "Spark", NumberDetectors=64)
#>          Fluorophore
#> 1     Spark Blue 515
#> 2     Spark Blue 550
#> 3     Spark Blue 574
#> 4      Spark NIR 685
#> 5  Spark PLUS UV 395
#> 6      Spark Red 718
#> 7       Spark UV 387
#> 8   Spark Violet 423
#> 9   Spark Violet 500
#> 10  Spark Violet 538
#> 11      Spark YG 570
#> 12      Spark YG 581
#> 13      Spark YG 593

QC_ProspectiveFluorophores

And finally, we have an experimental function QC_ProspectiveFluorophores() that will leverage information about an existing panel (and the peak detectors its fluorophore currently occupy) and contrast that to the reference library fluorophores to find candidate fluorophores that might potentially fit in the gaps.

OutPath <- file.path("C:", "Users", "JohnDoe", "Desktop")

Folder_Location <- system.file("extdata", package = "Luciernaga")
ThePanelLocation <- list.files(Folder_Location, pattern="^Panel.csv", full.names=TRUE)
ThePanel <- read.csv(ThePanelLocation, check.names=FALSE) %>% select(Fluorophore)

The arguments to provide to the function are a filepath to the panel.csv, NumberDetectors that your instrument has (64 for a 5L Aurora). TheCutoff corresponds to the cosine value when two fluorophores are compared to each other, I set the cutoff at 0.9 similarity. Rank Value comes from base R’s kappa() how error introduced will spill over factoring across all fluorophore references in the matrix. Rough estimate for relative complexity in our case.

ProspectiveAdditions <- QC_ProspectiveAdditions(path=ThePanelLocation, NumberDetectors=64,
                                                TheCutoff=0.9, returnAll=FALSE, returnCSV=FALSE,
                                                filename="ProspectiveAdditions", outpath=OutPath)
gt(ProspectiveAdditions)

Fluorophore	TheDetector	HighOverlaps	RankValue
PerCP-eFluor 710	42	1	182.58
PerCP	40	1	203.19
StarBright Y720	53	1	208.46
CellVue Claret Far Red	59	1	223.8
BB660	39	1	224.02
Spark Blue 515	33	1	236.71
Vio Bright V423	18	1	238.45
CellTrace BODIPY	49	1	244.59
NovaFluor Yellow 690	52	1	244.67
PE-Fire 640	50	1	247.53
Zombie UV	6	1	249.14
VioBlue	19	1	250.12
Spark UV 387	1	1	255.73
BB755	44	1	255.84
Qdot 585	24	1	260.27
SYTOX Blue	20	1	260.9
Alexa Fluor 790	64	1	260.9
CF583	48	1	262.17
PE-Fire 744	54	1	262.49
PerCP-Fire 806	46	1	263.31
ViaKrome 808	61	1	263.46
NovaFluor Yellow 755	55	1	263.76
PE-Fire 810	56	1	269.24
NovaFluor Blue 760	45	1	270.41
BB630	38	1	283.69
Sapphire	22	1	319.11
Spark Blue 574	36	1	421.65
NovaFluor Blue 585	37	1	431.67

The results indicate potential fluorophores for Detectors not currently occupied by our panel, with cosine overlaps of less than TheCutoff (0.9). The RankValue is for the existing panel plus the given fluorophore. For panel design/exploratory purposes in our own lab, we would search for the Fluorophore name using QC_SimilarFluorophores() and identify other similar fluorophores by other commercial vendors to further investigate.

For example from the above list:

Results <- QC_SimilarFluorophores(TheFluorophore="PE-Fire 744", NumberDetectors=64, NumberHits = 10, returnPlots=TRUE)
plotly::ggplotly(Results[[2]])

Concluding Thoughts

We hope that this vignette walkthrough was of interest, and that you can think of ways to implement it within your own workflow. If you have thought on how to improve things, please reach out! This was a particular fun section of the package to work on and I would love to continue to improve on the existing functions in the future.

#> R version 4.4.1 (2024-06-14 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 22631)
#> 
#> Matrix products: default
#> 
#> 
#> locale:
#> [1] LC_COLLATE=English_United States.utf8 
#> [2] LC_CTYPE=English_United States.utf8   
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C                          
#> [5] LC_TIME=English_United States.utf8    
#> 
#> time zone: America/New_York
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] tidyr_1.3.1          xml2_1.3.6           htmltools_0.5.8.1   
#>  [4] plotly_4.10.4        gt_0.11.1            stringr_1.5.1       
#>  [7] purrr_1.0.2          dplyr_1.1.4          data.table_1.16.2   
#> [10] ggcyto_1.32.0        ncdfFlow_2.50.0      BH_1.84.0-0         
#> [13] ggplot2_3.5.1        openCyto_2.16.1      flowWorkspace_4.16.0
#> [16] flowCore_2.16.0      Luciernaga_0.99.1    BiocStyle_2.32.1    
#> 
#> loaded via a namespace (and not attached):
#>  [1] RBGL_1.80.0           gridExtra_2.3         rlang_1.1.4          
#>  [4] magrittr_2.0.3        matrixStats_1.4.1     ggridges_0.5.6       
#>  [7] compiler_4.4.1        dir.expiry_1.12.0     png_0.1-8            
#> [10] systemfonts_1.1.0     vctrs_0.6.5           reshape2_1.4.4       
#> [13] pkgconfig_2.0.3       fastmap_1.2.0         labeling_0.4.3       
#> [16] utf8_1.2.4            rmarkdown_2.28        graph_1.82.0         
#> [19] ragg_1.3.3            xfun_0.48             zlibbioc_1.50.0      
#> [22] cachem_1.1.0          jsonlite_1.8.9        highr_0.11           
#> [25] SnowballC_0.7.1       parallel_4.4.1        R6_2.5.1             
#> [28] bslib_0.8.0           stringi_1.8.4         RColorBrewer_1.1-3   
#> [31] reticulate_1.39.0     lubridate_1.9.3       jquerylib_0.1.4      
#> [34] figpatch_0.2          Rcpp_1.0.13           bookdown_0.41        
#> [37] knitr_1.48            zoo_1.8-12            Matrix_1.7-0         
#> [40] timechange_0.3.0      tidyselect_1.2.1      rstudioapi_0.17.0    
#> [43] yaml_2.3.10           viridis_0.6.5         lattice_0.22-6       
#> [46] tibble_3.2.1          plyr_1.8.9            Biobase_2.64.0       
#> [49] basilisk.utils_1.16.0 withr_3.0.1           evaluate_1.0.1       
#> [52] Rtsne_0.17            desc_1.4.3            pillar_1.9.0         
#> [55] lsa_0.73.3            BiocManager_1.30.25   filelock_1.0.3       
#> [58] stats4_4.4.1          generics_0.1.3        S4Vectors_0.42.1     
#> [61] munsell_0.5.1         scales_1.3.0          glue_1.8.0           
#> [64] lazyeval_0.2.2        tools_4.4.1           hexbin_1.28.4        
#> [67] fs_1.6.4              XML_3.99-0.17         grid_4.4.1           
#> [70] flowClust_3.42.0      RProtoBufLib_2.16.0   crosstalk_1.2.1      
#> [73] colorspace_2.1-1      patchwork_1.3.0       basilisk_1.16.0      
#> [76] cli_3.6.3             textshaping_0.4.0     fansi_1.0.6          
#> [79] cytolib_2.16.0        viridisLite_0.4.2     uwot_0.2.2           
#> [82] Rgraphviz_2.48.0      gtable_0.3.5          sass_0.4.9           
#> [85] digest_0.6.37         BiocGenerics_0.50.0   htmlwidgets_1.6.4    
#> [88] farver_2.1.2          pkgdown_2.1.1         lifecycle_1.0.4      
#> [91] httr_1.4.7