vignettes/CytoNorm_channels_without_clustering.md
In saeyslab/CytoNorm: Normalisation of cytometry data measured across multiple batches
Use case 3: Normalize channels without including them in the clustering
step
================
In this vignette, you learn how CytoNorm can be used to normalize markers that are not used during the clustering step.
One of the strengths of CytoNorm and some other normalization algorithms
is the clustering step. By clustering first, cell subsets are separated,
which makes itis possible to apply different normalizations on the
different subsets. However,in cytometry, we often make the distinction
between phenotypic or cell type markers and functional or cell state
markers and clustering is typically done only with the phenotypic
markers, as functional markers typically do not contribute to the
clustering, or even reduce the clustering quality. Therefore, it is
important that the normalization algorithm allows making a
distinctionbetween the channels that need to be normalized, which might
include functional markers, and the channels that will be used for the
clustering.
By default, CytoNorm uses all the markers specified for normalization
also for clustering. It is however straightforward to specify different
sets of markers for the clustering step (in the FlowSOM parameters) and
for the actualnormalization procedure.
Prepare the CytoNorm normalization step
The phenotypic markers of our mesothelioma dataset were already
normalized with the two models in the previous use cases (Use case 1:
Run CytoNorm on a dataset without dedicated controls
vignette and Use case 2: Run CytoNorm to
normalize towards a distribution
vignette. Since we want to further
characterize the immune landscape, the next step was is analyze the
functional markers. A manual gating strategy of these markers was
designed and optimized by an expert for the first cohort of data (P1_C1
and P2_C1), which should be translated to the second cohort (P1_C2 and
P2_C2).
To do this, we train a CytoNorm model per panel where we set the goal to
the first cohort (P1_C1 or P2_C1), use the cell type markers for the
FlowSOMclustering and specify which (panel-specific) cell state markers
should be normalized.
library(CytoNorm)
library(flowCore)
library(FlowSOM)
library(ggpubr)
Train the CytoNorm model
List the files per batch that will be aggregated
dir_prepr <- "Data/Preprocessed"
# Organize preprocessed data in batches
files_P1_C2 <- list.files(path = "Data/Preprocessed/",
pattern = "ID[5-8]_Panel1_TP[1-3].fcs",
full.names = TRUE)
# List files (already normalized for cell type markers in use cases 1 and 2)
files_P1_C1_norm <- list.files(path = "Data/Normalized_cellType",
pattern = "ID[1-4]_Panel1_TP..fcs", full.names = TRUE)
files_P1_C2_norm <- list.files(path = "Data/Normalized_cellType",
pattern = "ID[5-8]_Panel1_TP..fcs", full.names = TRUE)
# Read in 1 file per panel to get channel information
ff_P1 <- read.FCS(files_P1_C1_norm[1])
# Retrieve channel information
cellTypeMarkers <- c("CD27", "CD38", "IgD", "IgM")
cellTypeChannels <- GetChannels(object = ff_P1, markers = cellTypeMarkers)
P1_cellStateMarkers <- c("CD5", "CD268", "CD24", "PDL1", "PD1", "CD86", "KI67")
P1_cellStateChannels <- GetChannels(object = ff_P1, markers = P1_cellStateMarkers)
# Read in manualLabels object that was generated in the CytoNorm_without_controls vignette
manualLabels <- readRDS("Data/Preprocessed/attachments/manualLabels.rds")
Train the CytoNorm model
model3_cellStateMarkersP1 <- CytoNorm.train(files = c(files_P1_C1_norm, files_P1_C2_norm),
labels = c(rep(x = "C1",
times = length(files_P1_C1_norm)),
rep(x = "C2",
times = length(files_P1_C2_norm))),
channels = P1_cellStateChannels,
transformList = NULL,
seed = 1,
plot = TRUE,
verbose = TRUE,
normParams = list("goal" = "C1"),
FlowSOM.params = list(nCells = 1e+06,
xdim = 7, ydim = 7,
nClus = 3,
scale = FALSE,
colsToUse = cellTypeChannels))
#> Warning in CytoNorm.train(files = c(files_P1_C1_norm, files_P1_C2_norm), : Reusing FlowSOM result previously saved at ./tmp/CytoNorm_FlowSOM.RDS.
#> If this was not intended, one can either specify another outputDir,
#> make use of the recompute parameter or move the FlowSOM object in the
#> file manager.
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP3.fcs
#> Warning in FlowSOM::NewData(fsom, ff): 566 cells (0.83%) seem far from their cluster centers.
#> Processing cluster 1
#> Computing Quantiles
#> C1 (FileID 1,2,3,4,5,6,7,8,9,10,11,12)
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom1.fcs
#> C2 (FileID 13,14,15,16,17,18,19,20,21,22,23,24)
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom1.fcs
#> Computing Splines
#> C1
#> C2
#> Processing cluster 2
#> Computing Quantiles
#> C1 (FileID 1,2,3,4,5,6,7,8,9,10,11,12)
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom2.fcs
#> C2 (FileID 13,14,15,16,17,18,19,20,21,22,23,24)
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom2.fcs
#> Computing Splines
#> C1
#> C2
#> Processing cluster 3
#> Computing Quantiles
#> C1 (FileID 1,2,3,4,5,6,7,8,9,10,11,12)
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom3.fcs
#> C2 (FileID 13,14,15,16,17,18,19,20,21,22,23,24)
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom3.fcs
#> Computing Splines
#> C1
#> C2
saveRDS(object = model3_cellStateMarkersP1, file = "RDS/model3_cellStateMarkersP1.rds")
By default, the FlowSOM clustering will be done with the channels
listed in the CytoNorm.train() call. However, it is possible to
provide other channels for the FlowSOM clustering by listing them at the
colsToUse argument within the FlowSOM.params. Note that in the
workflow of this paper, CytoNorm will again reuse the previous FlowSOM
model, as discussed before.
Apply the CytoNorm model
CytoNorm.normalize(model = model3_cellStateMarkersP1,
files = c(files_P1_C1_norm, files_P1_C2_norm),
labels = c(rep(x = "C1",times = length(files_P1_C1_norm)),
rep(x = "C2",times = length(files_P1_C2_norm))),
transformList = NULL,
verbose = TRUE,
prefix = "",
transformList.reverse = NULL,
outputDir = "Data/Normalized_cellState")
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP3.fcs
#> Warning in FlowSOM::NewData(fsom, ff): 566 cells (0.83%) seem far from their cluster centers.
#> Processing cluster 1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Processing cluster 2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Processing cluster 3
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Rebuilding Data/Normalized_cellType/ID1_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID1_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID1_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID2_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID2_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID2_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID3_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID3_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID3_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID4_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID4_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID4_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID5_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID5_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID5_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID6_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID6_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID6_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID7_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID7_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID7_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID8_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID8_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID8_Panel1_TP3.fcs
Evaluate the quality
Density plots
# List files
files_P1_C1_norm_norm <- list.files(path = "Data/Normalized_cellState",
pattern = "ID[1-4]_Panel1_TP..fcs", full.names = TRUE)
files_P1_C2_norm_norm <- list.files(path = "Data/Normalized_cellState",
pattern = "ID[5-8]_Panel1_TP..fcs", full.names = TRUE)
# Make plots
p <- plotDensities(input = list("P1_C1" = files_P1_C1_norm,
"P1_C2" = files_P1_C2_norm,
"P1_C1_norm" = files_P1_C1_norm_norm,
"P1_C2_norm" = files_P1_C2_norm_norm),
channels = P1_cellStateChannels,
colors = c("P1_C1" = "#900002","P1_C2" = "#FF3437"),
model = model3_cellStateMarkersP1)
#> Reading Data/Normalized_cellType/ID1_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID1_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID1_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID2_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID2_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID2_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID3_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID3_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID3_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID4_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID4_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID4_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID1_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID1_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID1_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID2_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID2_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID2_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID3_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID3_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID3_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID4_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID4_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID4_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID5_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID5_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID5_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID6_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID6_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID6_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID7_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID7_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID7_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID8_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID8_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID8_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID5_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID5_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID5_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID6_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID6_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID6_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID7_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID7_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID7_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID8_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID8_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID8_Panel1_TP3.fcs
#> [1] "original"
#> Warning in FlowSOM::NewData(model$fsom, as.matrix(dfs[[type]][model$fsom$map$colsUsed])): 1990 cells (1.16%) seem far from their cluster centers.
#> [1] "normalized"
# Arrange figure
p_ <- ggarrange(ggarrange(plotlist = p[1:length(p)-1],
ncol = (length(p)-1)/(2*length(P1_cellStateChannels)),
nrow = 2*length(P1_cellStateChannels)),
widths = c(1,3),
p$legend, nrow = 2, heights = c(10,1))
print(p_)
Spline plots
plotSplines(model = model3_cellStateMarkersP1, channels = P1_cellStateChannels, groupClusters = TRUE)
#> $C1
#>
#> $C2
#> Warning: Removed 65 rows containing missing values or values outside the scale range (`geom_line()`).
EMD and MAD
Make aggregates and get aggregate manual labels
dir.create("tmp/before")
#> Warning in dir.create("tmp/before"): 'tmp/before' already exists
dir.create("tmp/after")
#> Warning in dir.create("tmp/after"): 'tmp/after' already exists
aggregates <- list("agg_P1_C2" = files_P1_C2)
for (agg in names(aggregates)){
writeLines(agg)
files <- aggregates[[agg]]
set.seed(2023)
before <- AggregateFlowFrames(fileNames = files, silent = TRUE,
cTotal = length(files) * 10000)
write.FCS(before, paste0("tmp/before/", agg, ".fcs"))
after <- before
manual <- c()
for (i_file in unique(before@exprs[,"File"])){
ff <- read.FCS(paste0("Data/Normalized_cellType/", basename(aggregates[[agg]][i_file])))
i_IDs <- before@exprs[before@exprs[,"File"] == i_file,"Original_ID2"]
after@exprs[before@exprs[,"File"] == i_file,1:(ncol(after@exprs)-3)] <- ff@exprs[i_IDs, ]
labels <- as.character(manualLabels[[basename(aggregates[[agg]][i_file])]])
manual <- c(manual, labels[i_IDs])
}
manualLabels[[paste0(agg, ".fcs")]] <- factor(manual, levels = levels(manualLabels[[1]]))
write.FCS(after, paste0("tmp/after/", agg, ".fcs"))
}
#> agg_P1_C2
saveRDS(manualLabels, "Data/Preprocessed/attachments/manualLabels.rds")
Define cell types and files for model evaluation
cellTypes <- c("unlabeled",
"All B cells",
"Transitional B cells",
"Naive B cells",
"IgD+ memory B cells",
"IgM+ memory B cells",
"Class switched memory B cells",
"Double negative B cells")
models <- list("Model3a_before" = list("files" = c(files_P1_C1_norm, "tmp/before/agg_P1_C1.fcs",
"tmp/before/agg_P1_C2.fcs", files_P1_C2_norm),
"channels" = P1_cellStateChannels),
"Model3a_after" = list("files" = c(files_P1_C1_norm_norm, "tmp/after/agg_P1_C1.fcs",
"tmp/after/agg_P1_C2.fcs", files_P1_C2_norm_norm),
"channels" = P1_cellStateChannels))
Calculate EMD and MAD
EMDs <- list()
MADs <- list()
for (model in names(models)){
print(model)
files <- models[[model]][["files"]]
channels <- models[[model]][["channels"]]
EMDs[[model]] <- emdEvaluation(files = files,
channels = channels,
manual = manualLabels, return_all = TRUE,
manualThreshold = 20)
MADs[[model]] <- CytoNorm:::madEvaluation(files = files[!startsWith(files, "tmp")],
channels = channels, return_all = TRUE,
manual = manualLabels, transformList = NULL)
}
#> [1] "Model3a_before"
#> [1] "Model3a_after"
EMD_scores <- list()
for (subset in names(models)){
dist <- EMDs[[subset]]$distances
EMD_scores[[subset]] <- matrix(NA,
nrow = length(dist), ncol = length(dist[[1]]),
dimnames = list(names(dist),
names(dist[[1]])))
for (cellType in names(dist)){
for (marker in names(dist[[cellType]])){
EMD_scores[[subset]][cellType, marker] <- dist[[cellType]][[marker]][13, 14]
}
}
}
Make figures
markerlevels <- NULL
plotlist <- list()
# EMD plot
EMD_before <- EMD_scores[["Model3a_before"]][-2,]
EMD_after <- EMD_scores[["Model3a_after"]][-2,]
EMD_df <- data.frame("before" = c(EMD_before),
"after" = c(EMD_after),
"feature" = paste(rownames(EMD_before),
rep(colnames(EMD_before),
each = nrow(EMD_before)), sep = "_"))
if (is.null(markerlevels)){
markerlevels <- unique(unique(sub(".*_", "", EMD_df$feature)))
}
EMD_df$marker <- factor(sub(".*_", "", EMD_df$feature), levels = markerlevels)
EMD_df$celltype <- factor(sub("_.*", "", EMD_df$feature), levels = c("AllCells", cellTypes))
max <- max(EMD_df[,1:2], na.rm = T)
p_emd <- ggplot(EMD_df, aes(x = after, y = before)) +
xlim(0,max) + ylim(0,max) +
geom_point(aes(color = marker, shape = celltype)) +
geom_abline() +
scale_shape_manual(values = c(16, 17, 15, 3, 7, 8, 6)) +
ggtitle("EMD") +
theme_minimal()
leg_emd <- get_legend(p_emd)
p_emd <- p_emd +
theme(legend.position = "none")
plotlist[[length(plotlist)+1]] <- p_emd
names(markerlevels) <- sub(".*<(.*)>.*", "\\1", markerlevels)
# MAD plot
MAD_before <- MADs[["Model3a_before"]][["comparison"]][-2,]
MAD_after <- MADs[["Model3a_after"]][["comparison"]][-2,]
MAD_df <- data.frame("before" = c(MAD_before),
"after" = c(MAD_after),
"feature" = paste(rownames(MAD_before),
rep(colnames(MAD_before),
each = nrow(MAD_before)), sep = "_"))
MAD_df$marker <- factor(markerlevels[sub(".*_", "", MAD_df$feature)], levels = markerlevels)
MAD_df$celltype <- factor(sub("_.*", "", MAD_df$feature), c("AllCells", cellTypes))
max <- max(MAD_df[,1:2], na.rm = T)
p_mad <- ggplot(MAD_df, aes(x = after, y = before)) +
xlim(0,max) + ylim(0,max) +
geom_point(aes(color = marker, shape = celltype)) +
geom_abline() +
scale_shape_manual(values = c(16, 17, 15, 3, 7, 8, 6)) +
ggtitle("MAD") +
theme_minimal()
leg_mad <- get_legend(p_mad)
p_mad <- p_mad +
theme(legend.position = "none")
plotlist[[length(plotlist)+1]] <- p_mad
ggarrange(ggarrange(plotlist = plotlist),
as_ggplot(leg_emd), ncol=2, widths = c(4,1.5))
saeyslab/CytoNorm documentation built on Nov. 2, 2024, 12:39 p.m.
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Use case 3: Normalize channels without including them in the clustering step ================
In this vignette, you learn how CytoNorm can be used to normalize markers that are not used during the clustering step.
One of the strengths of CytoNorm and some other normalization algorithms is the clustering step. By clustering first, cell subsets are separated, which makes itis possible to apply different normalizations on the different subsets. However,in cytometry, we often make the distinction between phenotypic or cell type markers and functional or cell state markers and clustering is typically done only with the phenotypic markers, as functional markers typically do not contribute to the clustering, or even reduce the clustering quality. Therefore, it is important that the normalization algorithm allows making a distinctionbetween the channels that need to be normalized, which might include functional markers, and the channels that will be used for the clustering.
By default, CytoNorm uses all the markers specified for normalization also for clustering. It is however straightforward to specify different sets of markers for the clustering step (in the FlowSOM parameters) and for the actualnormalization procedure.
Prepare the CytoNorm normalization step
The phenotypic markers of our mesothelioma dataset were already normalized with the two models in the previous use cases (Use case 1: Run CytoNorm on a dataset without dedicated controls vignette and Use case 2: Run CytoNorm to normalize towards a distribution vignette. Since we want to further characterize the immune landscape, the next step was is analyze the functional markers. A manual gating strategy of these markers was designed and optimized by an expert for the first cohort of data (P1_C1 and P2_C1), which should be translated to the second cohort (P1_C2 and P2_C2).
To do this, we train a CytoNorm model per panel where we set the goal to the first cohort (P1_C1 or P2_C1), use the cell type markers for the FlowSOMclustering and specify which (panel-specific) cell state markers should be normalized.
library(CytoNorm)
library(flowCore)
library(FlowSOM)
library(ggpubr)
Train the CytoNorm model
List the files per batch that will be aggregated
dir_prepr <- "Data/Preprocessed"
# Organize preprocessed data in batches
files_P1_C2 <- list.files(path = "Data/Preprocessed/",
pattern = "ID[5-8]_Panel1_TP[1-3].fcs",
full.names = TRUE)
# List files (already normalized for cell type markers in use cases 1 and 2)
files_P1_C1_norm <- list.files(path = "Data/Normalized_cellType",
pattern = "ID[1-4]_Panel1_TP..fcs", full.names = TRUE)
files_P1_C2_norm <- list.files(path = "Data/Normalized_cellType",
pattern = "ID[5-8]_Panel1_TP..fcs", full.names = TRUE)
# Read in 1 file per panel to get channel information
ff_P1 <- read.FCS(files_P1_C1_norm[1])
# Retrieve channel information
cellTypeMarkers <- c("CD27", "CD38", "IgD", "IgM")
cellTypeChannels <- GetChannels(object = ff_P1, markers = cellTypeMarkers)
P1_cellStateMarkers <- c("CD5", "CD268", "CD24", "PDL1", "PD1", "CD86", "KI67")
P1_cellStateChannels <- GetChannels(object = ff_P1, markers = P1_cellStateMarkers)
# Read in manualLabels object that was generated in the CytoNorm_without_controls vignette
manualLabels <- readRDS("Data/Preprocessed/attachments/manualLabels.rds")
Train the CytoNorm model
model3_cellStateMarkersP1 <- CytoNorm.train(files = c(files_P1_C1_norm, files_P1_C2_norm),
labels = c(rep(x = "C1",
times = length(files_P1_C1_norm)),
rep(x = "C2",
times = length(files_P1_C2_norm))),
channels = P1_cellStateChannels,
transformList = NULL,
seed = 1,
plot = TRUE,
verbose = TRUE,
normParams = list("goal" = "C1"),
FlowSOM.params = list(nCells = 1e+06,
xdim = 7, ydim = 7,
nClus = 3,
scale = FALSE,
colsToUse = cellTypeChannels))
#> Warning in CytoNorm.train(files = c(files_P1_C1_norm, files_P1_C2_norm), : Reusing FlowSOM result previously saved at ./tmp/CytoNorm_FlowSOM.RDS.
#> If this was not intended, one can either specify another outputDir,
#> make use of the recompute parameter or move the FlowSOM object in the
#> file manager.
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP3.fcs
#> Warning in FlowSOM::NewData(fsom, ff): 566 cells (0.83%) seem far from their cluster centers.
#> Processing cluster 1
#> Computing Quantiles
#> C1 (FileID 1,2,3,4,5,6,7,8,9,10,11,12)
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom1.fcs
#> C2 (FileID 13,14,15,16,17,18,19,20,21,22,23,24)
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom1.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom1.fcs
#> Computing Splines
#> C1
#> C2
#> Processing cluster 2
#> Computing Quantiles
#> C1 (FileID 1,2,3,4,5,6,7,8,9,10,11,12)
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom2.fcs
#> C2 (FileID 13,14,15,16,17,18,19,20,21,22,23,24)
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom2.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom2.fcs
#> Computing Splines
#> C1
#> C2
#> Processing cluster 3
#> Computing Quantiles
#> C1 (FileID 1,2,3,4,5,6,7,8,9,10,11,12)
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom3.fcs
#> C2 (FileID 13,14,15,16,17,18,19,20,21,22,23,24)
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom3.fcs
#> Reading ./tmp/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom3.fcs
#> Computing Splines
#> C1
#> C2
saveRDS(object = model3_cellStateMarkersP1, file = "RDS/model3_cellStateMarkersP1.rds")
By default, the FlowSOM clustering will be done with the channels
listed in the CytoNorm.train() call. However, it is possible to
provide other channels for the FlowSOM clustering by listing them at the
colsToUse argument within the FlowSOM.params. Note that in the
workflow of this paper, CytoNorm will again reuse the previous FlowSOM
model, as discussed before.
Apply the CytoNorm model
CytoNorm.normalize(model = model3_cellStateMarkersP1,
files = c(files_P1_C1_norm, files_P1_C2_norm),
labels = c(rep(x = "C1",times = length(files_P1_C1_norm)),
rep(x = "C2",times = length(files_P1_C2_norm))),
transformList = NULL,
verbose = TRUE,
prefix = "",
transformList.reverse = NULL,
outputDir = "Data/Normalized_cellState")
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID1_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID2_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID3_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID4_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID5_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID6_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID7_Panel1_TP3.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP1.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP2.fcs
#> Splitting Data/Normalized_cellType/ID8_Panel1_TP3.fcs
#> Warning in FlowSOM::NewData(fsom, ff): 566 cells (0.83%) seem far from their cluster centers.
#> Processing cluster 1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom1.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom1.fcs (C2)
#> Normalizing C2
#> Processing cluster 2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom2.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom2.fcs (C2)
#> Normalizing C2
#> Processing cluster 3
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP1.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP2.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID1_Panel1_TP3.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP1.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP2.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID2_Panel1_TP3.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP1.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP2.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID3_Panel1_TP3.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP1.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP2.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID4_Panel1_TP3.fcs_fsom3.fcs (C1)
#> Normalizing C1
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP1.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP2.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID5_Panel1_TP3.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP1.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP2.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID6_Panel1_TP3.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP1.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP2.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID7_Panel1_TP3.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP1.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP2.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Data/Normalized_cellState/Data_Normalized_cellType_ID8_Panel1_TP3.fcs_fsom3.fcs (C2)
#> Normalizing C2
#> Rebuilding Data/Normalized_cellType/ID1_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID1_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID1_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID2_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID2_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID2_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID3_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID3_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID3_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID4_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID4_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID4_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID5_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID5_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID5_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID6_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID6_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID6_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID7_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID7_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID7_Panel1_TP3.fcs
#> Rebuilding Data/Normalized_cellType/ID8_Panel1_TP1.fcs
#> Rebuilding Data/Normalized_cellType/ID8_Panel1_TP2.fcs
#> Rebuilding Data/Normalized_cellType/ID8_Panel1_TP3.fcs
Evaluate the quality
Density plots
# List files
files_P1_C1_norm_norm <- list.files(path = "Data/Normalized_cellState",
pattern = "ID[1-4]_Panel1_TP..fcs", full.names = TRUE)
files_P1_C2_norm_norm <- list.files(path = "Data/Normalized_cellState",
pattern = "ID[5-8]_Panel1_TP..fcs", full.names = TRUE)
# Make plots
p <- plotDensities(input = list("P1_C1" = files_P1_C1_norm,
"P1_C2" = files_P1_C2_norm,
"P1_C1_norm" = files_P1_C1_norm_norm,
"P1_C2_norm" = files_P1_C2_norm_norm),
channels = P1_cellStateChannels,
colors = c("P1_C1" = "#900002","P1_C2" = "#FF3437"),
model = model3_cellStateMarkersP1)
#> Reading Data/Normalized_cellType/ID1_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID1_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID1_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID2_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID2_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID2_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID3_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID3_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID3_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID4_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID4_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID4_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID1_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID1_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID1_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID2_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID2_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID2_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID3_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID3_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID3_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID4_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID4_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID4_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID5_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID5_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID5_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID6_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID6_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID6_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID7_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID7_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID7_Panel1_TP3.fcs
#> Reading Data/Normalized_cellType/ID8_Panel1_TP1.fcs
#> Reading Data/Normalized_cellType/ID8_Panel1_TP2.fcs
#> Reading Data/Normalized_cellType/ID8_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID5_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID5_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID5_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID6_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID6_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID6_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID7_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID7_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID7_Panel1_TP3.fcs
#> Reading Data/Normalized_cellState/ID8_Panel1_TP1.fcs
#> Reading Data/Normalized_cellState/ID8_Panel1_TP2.fcs
#> Reading Data/Normalized_cellState/ID8_Panel1_TP3.fcs
#> [1] "original"
#> Warning in FlowSOM::NewData(model$fsom, as.matrix(dfs[[type]][model$fsom$map$colsUsed])): 1990 cells (1.16%) seem far from their cluster centers.
#> [1] "normalized"
# Arrange figure
p_ <- ggarrange(ggarrange(plotlist = p[1:length(p)-1],
ncol = (length(p)-1)/(2*length(P1_cellStateChannels)),
nrow = 2*length(P1_cellStateChannels)),
widths = c(1,3),
p$legend, nrow = 2, heights = c(10,1))
print(p_)
Spline plots
plotSplines(model = model3_cellStateMarkersP1, channels = P1_cellStateChannels, groupClusters = TRUE)
#> $C1
#>
#> $C2
#> Warning: Removed 65 rows containing missing values or values outside the scale range (`geom_line()`).
EMD and MAD
Make aggregates and get aggregate manual labels
dir.create("tmp/before")
#> Warning in dir.create("tmp/before"): 'tmp/before' already exists
dir.create("tmp/after")
#> Warning in dir.create("tmp/after"): 'tmp/after' already exists
aggregates <- list("agg_P1_C2" = files_P1_C2)
for (agg in names(aggregates)){
writeLines(agg)
files <- aggregates[[agg]]
set.seed(2023)
before <- AggregateFlowFrames(fileNames = files, silent = TRUE,
cTotal = length(files) * 10000)
write.FCS(before, paste0("tmp/before/", agg, ".fcs"))
after <- before
manual <- c()
for (i_file in unique(before@exprs[,"File"])){
ff <- read.FCS(paste0("Data/Normalized_cellType/", basename(aggregates[[agg]][i_file])))
i_IDs <- before@exprs[before@exprs[,"File"] == i_file,"Original_ID2"]
after@exprs[before@exprs[,"File"] == i_file,1:(ncol(after@exprs)-3)] <- ff@exprs[i_IDs, ]
labels <- as.character(manualLabels[[basename(aggregates[[agg]][i_file])]])
manual <- c(manual, labels[i_IDs])
}
manualLabels[[paste0(agg, ".fcs")]] <- factor(manual, levels = levels(manualLabels[[1]]))
write.FCS(after, paste0("tmp/after/", agg, ".fcs"))
}
#> agg_P1_C2
saveRDS(manualLabels, "Data/Preprocessed/attachments/manualLabels.rds")
Define cell types and files for model evaluation
cellTypes <- c("unlabeled",
"All B cells",
"Transitional B cells",
"Naive B cells",
"IgD+ memory B cells",
"IgM+ memory B cells",
"Class switched memory B cells",
"Double negative B cells")
models <- list("Model3a_before" = list("files" = c(files_P1_C1_norm, "tmp/before/agg_P1_C1.fcs",
"tmp/before/agg_P1_C2.fcs", files_P1_C2_norm),
"channels" = P1_cellStateChannels),
"Model3a_after" = list("files" = c(files_P1_C1_norm_norm, "tmp/after/agg_P1_C1.fcs",
"tmp/after/agg_P1_C2.fcs", files_P1_C2_norm_norm),
"channels" = P1_cellStateChannels))
Calculate EMD and MAD
EMDs <- list()
MADs <- list()
for (model in names(models)){
print(model)
files <- models[[model]][["files"]]
channels <- models[[model]][["channels"]]
EMDs[[model]] <- emdEvaluation(files = files,
channels = channels,
manual = manualLabels, return_all = TRUE,
manualThreshold = 20)
MADs[[model]] <- CytoNorm:::madEvaluation(files = files[!startsWith(files, "tmp")],
channels = channels, return_all = TRUE,
manual = manualLabels, transformList = NULL)
}
#> [1] "Model3a_before"
#> [1] "Model3a_after"
EMD_scores <- list()
for (subset in names(models)){
dist <- EMDs[[subset]]$distances
EMD_scores[[subset]] <- matrix(NA,
nrow = length(dist), ncol = length(dist[[1]]),
dimnames = list(names(dist),
names(dist[[1]])))
for (cellType in names(dist)){
for (marker in names(dist[[cellType]])){
EMD_scores[[subset]][cellType, marker] <- dist[[cellType]][[marker]][13, 14]
}
}
}
Make figures
markerlevels <- NULL
plotlist <- list()
# EMD plot
EMD_before <- EMD_scores[["Model3a_before"]][-2,]
EMD_after <- EMD_scores[["Model3a_after"]][-2,]
EMD_df <- data.frame("before" = c(EMD_before),
"after" = c(EMD_after),
"feature" = paste(rownames(EMD_before),
rep(colnames(EMD_before),
each = nrow(EMD_before)), sep = "_"))
if (is.null(markerlevels)){
markerlevels <- unique(unique(sub(".*_", "", EMD_df$feature)))
}
EMD_df$marker <- factor(sub(".*_", "", EMD_df$feature), levels = markerlevels)
EMD_df$celltype <- factor(sub("_.*", "", EMD_df$feature), levels = c("AllCells", cellTypes))
max <- max(EMD_df[,1:2], na.rm = T)
p_emd <- ggplot(EMD_df, aes(x = after, y = before)) +
xlim(0,max) + ylim(0,max) +
geom_point(aes(color = marker, shape = celltype)) +
geom_abline() +
scale_shape_manual(values = c(16, 17, 15, 3, 7, 8, 6)) +
ggtitle("EMD") +
theme_minimal()
leg_emd <- get_legend(p_emd)
p_emd <- p_emd +
theme(legend.position = "none")
plotlist[[length(plotlist)+1]] <- p_emd
names(markerlevels) <- sub(".*<(.*)>.*", "\\1", markerlevels)
# MAD plot
MAD_before <- MADs[["Model3a_before"]][["comparison"]][-2,]
MAD_after <- MADs[["Model3a_after"]][["comparison"]][-2,]
MAD_df <- data.frame("before" = c(MAD_before),
"after" = c(MAD_after),
"feature" = paste(rownames(MAD_before),
rep(colnames(MAD_before),
each = nrow(MAD_before)), sep = "_"))
MAD_df$marker <- factor(markerlevels[sub(".*_", "", MAD_df$feature)], levels = markerlevels)
MAD_df$celltype <- factor(sub("_.*", "", MAD_df$feature), c("AllCells", cellTypes))
max <- max(MAD_df[,1:2], na.rm = T)
p_mad <- ggplot(MAD_df, aes(x = after, y = before)) +
xlim(0,max) + ylim(0,max) +
geom_point(aes(color = marker, shape = celltype)) +
geom_abline() +
scale_shape_manual(values = c(16, 17, 15, 3, 7, 8, 6)) +
ggtitle("MAD") +
theme_minimal()
leg_mad <- get_legend(p_mad)
p_mad <- p_mad +
theme(legend.position = "none")
plotlist[[length(plotlist)+1]] <- p_mad
ggarrange(ggarrange(plotlist = plotlist),
as_ggplot(leg_emd), ncol=2, widths = c(4,1.5))
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