addGeneExpressionMatrix throws error for multiome analysis - Error in as.vector(data) : no method for coercing this S4 class to a vector

[hira-anis]

Hey everyone,

I came across this error in my workflow. I understand the warnings and intend to work on them later. However, i need help sorting out the error please. I installed the "dev" branch of ArchR. Thank you for all the help in advance!

class(seRNA2)
[1] "RangedSummarizedExperiment"
attr(,"package")
[1] "SummarizedExperiment"
class(multiome_bm)
[1] "ArchRProject"
attr(,"package")
[1] "ArchR"
multiome_bm <- addGeneExpressionMatrix(input=multiome_bm, seRNA=seRNA2, force = TRUE)
ArchR logging to : ArchRLogs/ArchR-addGeneExpressionMatrix-3ae8a02639f-Date-2024-02-23_Time-21-43-44.log
If there is an issue, please report to github with logFile!
2024-02-23 21:43:44 : Overlap w/ scATAC = 0.851
2024-02-23 21:43:44 : Warning! Not all cells in input exist in seRNA! This may cause downstream issues with functions that require information from all cells. For example, addIterativeLSI() will not work on this GeneExpressionMatrix! To remove these mis-matched cells, subset your ArchRProject using the subsetArchRProject() function to contain only cells present in seRNA and set 'strictMatch = TRUE'
2024-02-23 21:43:44 : Overlap Per Sample w/ scATAC : 2367_popA=1887,2367_popB=1135,2367_popD=5,2374_popA=567,2374_popB=1733,2374_popD=1060,7041_popA=121,7041_popB=1370,7099_popB=628,7099_popD=287,7121_popA=213,7121_popB=3666,7121_popD=9
Error in as.vector(data) :
no method for coercing this S4 class to a vector

─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.3 (2023-03-15)
os macOS Big Sur 11.0.1
system x86_64, darwin17.0
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2024-02-23
rstudio 2023.03.0+386 Cherry Blossom (desktop)
pandoc 2.11.3.2 @ /usr/local/bin/pandoc

─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
ArchR * 1.0.3 2024-02-24 [1] Github (65968a4)
Biobase * 2.58.0 2022-11-01 [1] Bioconductor
BiocGenerics * 0.44.0 2022-11-01 [1] Bioconductor
bitops 1.0-7 2021-04-24 [1] CRAN (R 4.2.0)
cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
Cairo 1.6-2 2023-11-28 [1] CRAN (R 4.2.3)
cli 3.6.2 2023-12-11 [1] CRAN (R 4.2.3)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0)
data.table * 1.15.0 2024-01-30 [1] CRAN (R 4.2.3)
DelayedArray 0.24.0 2022-11-01 [1] Bioconductor
devtools * 2.4.5 2022-10-11 [1] CRAN (R 4.2.0)
digest 0.6.34 2024-01-11 [1] CRAN (R 4.2.3)
dplyr 1.1.4 2023-11-17 [1] CRAN (R 4.2.3)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.2.3)
fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0)
fs 1.6.3 2023-07-20 [1] CRAN (R 4.2.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
GenomeInfoDb * 1.34.9 2023-02-02 [1] Bioconductor
GenomeInfoDbData 1.2.9 2023-03-26 [1] Bioconductor
GenomicRanges * 1.50.2 2022-12-16 [1] Bioconductor
ggplot2 * 3.4.4 2023-10-12 [1] CRAN (R 4.2.0)
glue 1.7.0 2024-01-09 [1] CRAN (R 4.2.3)
gridExtra * 2.3 2017-09-09 [1] CRAN (R 4.2.0)
gtable * 0.3.4 2023-08-21 [1] CRAN (R 4.2.0)
gtools * 3.9.5 2023-11-20 [1] CRAN (R 4.2.3)
htmltools 0.5.7 2023-11-03 [1] CRAN (R 4.2.3)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.2.3)
httpuv 1.6.14 2024-01-26 [1] CRAN (R 4.2.3)
IRanges * 2.32.0 2022-11-01 [1] Bioconductor
later 1.3.2 2023-12-06 [1] CRAN (R 4.2.3)
lattice 0.22-5 2023-10-24 [1] CRAN (R 4.2.3)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.2.3)
magrittr * 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
Matrix * 1.6-4 2023-11-30 [1] CRAN (R 4.2.0)
MatrixGenerics * 1.10.0 2022-11-01 [1] Bioconductor
matrixStats * 1.2.0 2023-12-11 [1] CRAN (R 4.2.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0)
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0)
pkgbuild 1.4.3 2023-12-10 [1] CRAN (R 4.2.3)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
pkgload 1.3.4 2024-01-16 [1] CRAN (R 4.2.3)
plyr * 1.8.9 2023-10-02 [1] CRAN (R 4.2.0)
profvis 0.3.8 2023-05-02 [1] CRAN (R 4.2.0)
promises 1.2.1 2023-08-10 [1] CRAN (R 4.2.0)
purrr 1.0.2 2023-08-10 [1] CRAN (R 4.2.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
Rcpp * 1.0.12 2024-01-09 [1] CRAN (R 4.2.3)
RcppArmadillo * 0.12.6.6.1 2023-12-04 [1] CRAN (R 4.2.0)
RCurl 1.98-1.14 2024-01-09 [1] CRAN (R 4.2.3)
remotes 2.4.2.1 2023-07-18 [1] CRAN (R 4.2.0)
rhdf5 * 2.42.1 2023-04-07 [1] Bioconductor
rhdf5filters 1.10.1 2023-03-24 [1] Bioconductor
Rhdf5lib 1.20.0 2022-11-01 [1] Bioconductor
rlang 1.1.3 2024-01-10 [1] CRAN (R 4.2.3)
rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.2.0)
S4Vectors * 0.36.2 2023-02-26 [1] Bioconductor
scales 1.3.0 2023-11-28 [1] CRAN (R 4.2.3)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
shiny 1.8.0 2023-11-17 [1] CRAN (R 4.2.3)
sparseMatrixStats * 1.10.0 2022-11-01 [1] Bioconductor
stringi 1.8.3 2023-12-11 [1] CRAN (R 4.2.3)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.2.3)
SummarizedExperiment * 1.28.0 2022-11-01 [1] Bioconductor
tibble 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
usethis * 2.2.3 2024-02-19 [1] CRAN (R 4.2.3)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.2.3)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.2.3)
withr 3.0.0 2024-01-16 [1] CRAN (R 4.2.3)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
XVector 0.38.0 2022-11-01 [1] Bioconductor
zlibbioc 1.44.0 2022-11-01 [1] Bioconductor

[1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library

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[hira-anis]

Figured this out. Here is the what happened.. I used this response #507 (comment)
However, i didn't realize that the updated version directly provides a Summarized Experiment for import10xFeature. You don't need to use seRNA2 <- summarizedExperiment ... and can directly run,

seRNA <- import10xFeatureMatrix(input = files, names = names) #Works!
ArchR_proj <- addGeneExpressionMatrix(input=ArchR_proj, seRNA=seRNA, force = TRUE)

running seRNA2 <- summarizedExperiment(seRNA) is basically squaring and creates issues in
assay(seRNA) <- Matrix::Matrix(assay(seRNA), sparse=TRUE)