Entering edit mode
Hi!
thank you very much for all the work on the flow packages; they are extremely helpful.
However, I did encounter a problem with creating and employing a new compensation object
to a flowset or gatingset constructed after read.flowSet() with no prior compensation.
1) subset data to use for compensation
> tmp_data <- gating_set[which(pData(gating_set)$well_ID=="G10" & pData(fs)$plate==1)]
> tmp_data
A GatingSet with 1 samples
> getData(tmp_data)
A flowSet with 1 experiments.
column names:
HDR-T FSC-A FSC-H SSC-A SSC-H V1-A V2-A B1-A B2-A B3-A B4-A R1-A R2-A
2) make empty matrix and change some value
> tmp_chn <- getData(tmp_data[[1]])@parameters@data$name # get channels for compensation matrix
> tmp_chn <- tmp_chn[!grepl("[FS]SC|HDR-T",tmp_chn)]
> tmp_mat <- matrix(nrow=length(tmp_chn), ncol=length(tmp_chn), data=0)
> colnames(tmp_mat) <- rownames(tmp_mat) <- tmp_chn
> tmp_mat[2,4] <- 0.1 # make some arbitrary change
> tmp_mat
V1-A V2-A B1-A B2-A B3-A B4-A R1-A R2-A
V1-A 0 0 0 0.0 0 0 0 0
V2-A 0 0 0 0.1 0 0 0 0
B1-A 0 0 0 0.0 0 0 0 0
B2-A 0 0 0 0.0 0 0 0 0
B3-A 0 0 0 0.0 0 0 0 0
B4-A 0 0 0 0.0 0 0 0 0
R1-A 0 0 0 0.0 0 0 0 0
R2-A 0 0 0 0.0 0 0 0 0
3) make compensation object and compensate flowset
> tmp_mat <- compensation(tmp_mat)
> tmp_mat
Compensation object 'defaultCompensation':
V1-A V2-A B1-A B2-A B3-A B4-A R1-A R2-A
V1-A 0 0 0 0.0 0 0 0 0
V2-A 0 0 0 0.1 0 0 0 0
B1-A 0 0 0 0.0 0 0 0 0
B2-A 0 0 0 0.0 0 0 0 0
B3-A 0 0 0 0.0 0 0 0 0
B4-A 0 0 0 0.0 0 0 0 0
R1-A 0 0 0 0.0 0 0 0 0
R2-A 0 0 0 0.0 0 0 0 0
> tmp_comp <-compensate(tmp_data, tmp_mat)
Fehler in solve.default(spillover) :
Lapackroutine dgesv: System ist genau singulär: U[1,1] = 0
> tmp_comp <-compensate(getData(tmp_data), tmp_mat)
Fehler in solve.default(spillover) :
Lapackroutine dgesv: System ist genau singulär: U[1,1] = 0
> traceback()
16: solve.default(spillover)
15: solve(spillover)
14: .local(x, spillover, ...)
13: compensate(x, spillover@spillover)
12: compensate(x, spillover@spillover)
11: .local(x, spillover, ...)
10: FUN(if (use.exprs) exprs(y) else y, ...)
9: FUN(if (use.exprs) exprs(y) else y, ...)
8: FUN(X[[i]], ...)
7: lapply(sampleNames(x), function(n) {
y <- x[[n]]
FUN(if (use.exprs)
exprs(y)
else y, ...)
})
6: structure(lapply(sampleNames(x), function(n) {
y <- x[[n]]
FUN(if (use.exprs)
exprs(y)
else y, ...)
}), names = sampleNames(x))
5: fsApply(x, compensate, spillover)
4: fsApply(x, compensate, spillover)
3: .local(x, spillover, ...)
2: compensate(getData(tmp_data), tmp_mat)
1: compensate(getData(tmp_data), tmp_mat)
The error message translates (roughly) to:
Error in solve default solve.default(spillover) :
Lapackroutine dgesv: System is exactly singular: U[1,1] = 0
Any suggestions are highly appreciated! Thank you & keep up the good work!
> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 19.04
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
locale:
[1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C
[3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggthemes_4.2.0 ggcyto_1.12.0
[3] ggplot2_3.2.0 openCyto_1.22.2
[5] flowWorkspace_3.32.0 ncdfFlow_2.30.1
[7] BH_1.69.0-1 RcppArmadillo_0.9.600.4.0
[9] flowCore_1.50.0
loaded via a namespace (and not attached):
[1] Biobase_2.44.0 splines_3.6.1 R.utils_2.9.0
[4] ellipse_0.4.1 gtools_3.8.1 RcppParallel_4.4.3
[7] assertthat_0.2.1 stats4_3.6.1 latticeExtra_0.6-28
[10] RBGL_1.60.0 robustbase_0.93-5 pillar_1.4.2
[13] lattice_0.20-38 glue_1.3.1 digest_0.6.20
[16] RColorBrewer_1.1-2 colorspace_1.4-1 Matrix_1.2-17
[19] R.oo_1.22.0 plyr_1.8.4 pcaPP_1.9-73
[22] pkgconfig_2.0.2 fda_2.4.8 zlibbioc_1.30.0
[25] purrr_0.3.2 corpcor_1.6.9 mvtnorm_1.0-11
[28] scales_1.0.0 flowStats_3.42.0 tibble_2.1.3
[31] flowViz_1.48.0 withr_2.1.2 BiocGenerics_0.30.0
[34] hexbin_1.27.3 lazyeval_0.2.2 mnormt_1.5-5
[37] magrittr_1.5 crayon_1.3.4 IDPmisc_1.1.19
[40] mclust_5.4.5 ks_1.11.5 R.methodsS3_1.7.1
[43] MASS_7.3-51.4 graph_1.62.0 tools_3.6.1
[46] data.table_1.12.2 flowClust_3.22.0 matrixStats_0.54.0
[49] stringr_1.4.0 munsell_0.5.0 cluster_2.1.0
[52] compiler_3.6.1 rlang_0.4.0 grid_3.6.1
[55] tcltk_3.6.1 labeling_0.3 gtable_0.3.0
[58] rrcov_1.4-7 R6_2.4.0 gridExtra_2.3
[61] dplyr_0.8.3 clue_0.3-57 KernSmooth_2.23-15
[64] Rgraphviz_2.28.0 stringi_1.4.3 Rcpp_1.0.1
[67] DEoptimR_1.0-8 tidyselect_0.2.5
