Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).
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no plots available for ADT data when using ADT + HTO #32
Hi all,
I hope you can help me here.
I've been following your tutorials to set up my data in the following order:
mat <- as.data.frame(as.matrix(mat.so@assays$RNA@data))
mat.adt <- as.data.frame(as.matrix(mat.so@assays$ADT@data))
mat.hto <- as.data.frame(as.matrix(mat.so@assays$HTO@data))
htos <- hto.anno(hto.data = mat.hto, cov.thr = 10, assignment.thr = 80)
htos <- subset(htos,htos$percent.match > 80)
# cell ids to hashtags
sample1 <- row.names(subset(htos,htos$assignment.annotation == "HTO1"))
sample1.rna <- mat[ , which(names(mat) %in% sample1)]
my.data <- data.aggregation(samples = c("sample1.rna","sample2.rna"),
condition.names = c("c","t"))
mat.icellr <- make.obj(my.data)
# Then add the adt data frame
mat.icellr <- add.adt(mat.icellr, adt.data = mat.adt)
# following with qc.stat and cell.filter
# normalize RNA
mat.icellr <- norm.data(mat.icellr, norm.method = "ranked.glsf", top.rank = 500)
# normalize ADT
mat.icellr <- norm.adt(mat.icellr)
# 2nd QC
mat.icellr <- qc.stats(mat.icellr,which.data = "main.data")
# gene stats
mat.icellr <- gene.stats(mat.icellr, which.data = "main.data")
# genes for PCA
# merge RNA + ADT
mat.icellr <- adt.rna.merge(mat.icellr, adt.data = "main")
# run PCA and so on.
Using this workflow I cannot plot anything using the "ADT_ab". However, when I use the ADT data alone, I'm able to do it.
I checked the dataframe from both objects and they seem the same, just that when I use HTO I got less cells as I removed duplicates and negatives
Object with ADT + HTO
###################################
,--. ,-----. ,--.,--.,------.
`--'' .--./ ,---. | || || .--. '
,--.| | | .-. :| || || '--'.'
| |' '--'\ --. | || || |
`--' `-----' `----'`--'`--'`--' '--'
###################################
An object of class iCellR version: 1.6.5
Raw/original data dimentions (rows,columns): 32285,11236
Data conditions in raw data: o,y (5822,5414)
Row names: 0610005C13Rik,0610006L08Rik,0610009B22Rik ...
Columns names: y_AAACCCAAGCAGGCAT,y_AAACCCACAGCTCTGG,y_AAACCCATCGCTGTCT ...
###################################
QC stats performed:TRUE, PCA performed:TRUE
Clustering performed:FALSE, Number of clusters:0
tSNE performed:FALSE, UMAP performed:TRUE, DiffMap performed:FALSE
Main data dimensions (rows,columns): 32310,9840
Data conditions in main data:o,y(4958,4882)
Normalization factors:0.689367228711468,...
Imputed data dimensions (rows,columns):0,0
############## scVDJ-seq ###########
VDJ data dimentions (rows,columns):0,0
############## CITE-seq ############
ADT raw data dimensions (rows,columns):25,13190
ADT main data dimensions (rows,columns):25,13190
ADT columns names:AAACCCAAGCAGGCAT...
ADT row names:ADT_B220...
############## scATAC-seq ############
ATAC raw data dimensions (rows,columns):0,0
ATAC main data dimensions (rows,columns):0,0
ATAC columns names:...
ATAC row names:...
############## Spatial ###########
Spatial data dimentions (rows,columns):0,0
########### iCellR object ##########
head(mat.icellr@adt.main)[1:3]
AAACCCAAGCAGGCAT AAACCCAAGTATGATG AAACCCAAGTCGAATA
ADT_B220 0.000000 0.00000 0.000000
ADT_CD115 9.870273 0.00000 3.290091
ADT_CD11b 0.000000 16.85752 101.145095
ADT_CD11c 0.000000 90.52983 0.000000
ADT_CD127 0.000000 0.00000 77.677947
ADT_Flt3 27.195452 45.32575 27.195452
Object with ADT only
###################################
,--. ,-----. ,--.,--.,------.
`--'' .--./ ,---. | || || .--. '
,--.| | | .-. :| || || '--'.'
| |' '--'\ --. | || || |
`--' `-----' `----'`--'`--'`--' '--'
###################################
An object of class iCellR version: 1.6.5
Raw/original data dimentions (rows,columns): 32285,13190
Data conditions: no conditions/single sample
Row names: Xkr4,Gm1992,Gm19938 ...
Columns names: AAACCCAAGCAGGCAT,AAACCCAAGTATGATG,AAACCCAAGTCGAATA ...
###################################
QC stats performed:TRUE, PCA performed:TRUE
Clustering performed:FALSE, Number of clusters:0
tSNE performed:FALSE, UMAP performed:TRUE, DiffMap performed:FALSE
Main data dimensions (rows,columns): 32310,11278
Normalization factors:0.640626466090974,...
Imputed data dimensions (rows,columns):0,0
############## scVDJ-seq ###########
VDJ data dimentions (rows,columns):0,0
############## CITE-seq ############
ADT raw data dimensions (rows,columns):25,13190
ADT main data dimensions (rows,columns):25,13190
ADT columns names:AAACCCAAGCAGGCAT...
ADT row names:ADT_B220...
############## scATAC-seq ############
ATAC raw data dimensions (rows,columns):0,0
ATAC main data dimensions (rows,columns):0,0
ATAC columns names:...
ATAC row names:...
############## Spatial ###########
Spatial data dimentions (rows,columns):0,0
########### iCellR object ##########
head(my.obj@adt.main)[1:3]
AAACCCAAGCAGGCAT AAACCCAAGTATGATG AAACCCAAGTCGAATA
ADT_B220 0.000000 0.00000 0.000000
ADT_CD115 9.870273 0.00000 3.290091
ADT_CD11b 0.000000 16.85752 101.145095
ADT_CD11c 0.000000 90.52983 0.000000
ADT_CD127 0.000000 0.00000 77.677947
ADT_Flt3 27.195452 45.32575 27.195452
Works as expected in the ADT only object, otherwise I get just a grey plot with no signal of expression.
I would appreciate if you can help me to point out the reason.
Hi all, I hope you can help me here. I've been following your tutorials to set up my data in the following order:
Using this workflow I cannot plot anything using the "ADT_ab". However, when I use the ADT data alone, I'm able to do it. I checked the dataframe from both objects and they seem the same, just that when I use HTO I got less cells as I removed duplicates and negatives
Object with ADT + HTO
Object with ADT only
If I do
Works as expected in the ADT only object, otherwise I get just a grey plot with no signal of expression. I would appreciate if you can help me to point out the reason.
Thanks!