Closed MayaCyTOFnewbie closed 2 years ago
Hi again,
I found issue #35 and modified the isotope list accordingly:
isotope_list2 <- c(CATALYST::isotope_list, list(BCKG=190)) sce = compCytof(x = sce, isotope_list = isotope_list2)
then ran the plot
plotSpillmat(sce)
which works! with a warning I don't understand:
Warning messages:
1: In sprintf("%2.2f", colSums(sm) * 100 - 100, "%") :
one argument not used by format '%2.2f'
2: It is deprecated to specify `guide = FALSE` to remove a guide. Please use `guide = "none"` instead.
additionally, the plot generated is missing channels 169:176
I tried to fix using suggestion from issue #204 but I don't have a "panel" object.
I'm not sure if it is relevant, but I think that some the staining for the single stained beads didn't work:
table(sce$bc_id)
111 112 113 114 115 116 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
2159 1254 587 1240 1095 946 1079 1081 993 1752 1880 **1** 1600 1972 1197 1459 1227 1769 22832 1023 63 860 1316 1141 1271 3498 **1** 1150
163 164 165 166 167 168 169 170 171 172 173 174 175 176
1282 2663 2947 2081 4154 3694 3520 3019 1935 1911 1212 1623 5007 6474
BR
Maya
sprintf("%2.2f", colSums(sm) * 100 - 100, "%") :
one argument not used by format '%2.2f'
The extra "%" is not take into account. "%2.2f" should be replaced by "%2.2f%s". Alt. the extra "%" should be removed and included in the format string as %%.
Hi Sam,
Thank you for the quick reply! Can you please translate your reply to a medium/beginner level R programmer? how do I remove the extra "%" and include in the format string as %%?
Hi Maya, My comment is intended to the package manager or an advanced programmer that would like to find and correct this error. The initial error message is a warning, and should not change your result. Just ignore it unless you feel a numerical value is wrong. Best.
Hm, I don't really understand how table
can give **1**
for 146/147; never seen this before. Could you do table(sce$bc_id == "146/7")
? Not sure this will really help, but I don't fully understand the issue from the output above.
Hi,
the issue is that I am missing isotopes 169:176 in my spillover matrix (attached)
I also noticed that some of my single stained beads didn't work. I added this information because I thought it might be the reason for the warning that I don't understand
table(sce$bc_id ==146)
FALSE TRUE 97967 1
thanks again!
Maya
I also now ran into this issue. What I noticed is that after removing entries in the spillover matrix via sm[, colSums(sm) != 0]
here, the subsetting operation bc_chs <- chs[rowData(x)$is_bc]
here is invalid since chs
and rowData(x)$is_bc
are of different length. This should be corrected and will also address #204 I guess.
Relates to #204 (in progress)
Hi!
I am trying to run compensation matrix, currently without samples, just to test if I can make the code work. The single-antibody stained bead sample ran with EQ beads. I get the error: Error in .check_sm(sm, isotope_list) : The supplied spillover matrix seems to be invalid. All isotopes should appear in
isotope_list
.Thanks!
Maya
here is what I did:
read fcs file
ff =read.FCS(file.path(dir,"20211220_CompBeads_01.FCS"), transformation = FALSE, truncate_max_range = FALSE)
check colnames
colnames(ff)
data in SCE format
sce = prepData(ff)
view number of events per sample
table(sce$sample_id)
view non-mass channels
names(int_colData(sce))
specify mass channels stained for & debarcode
bc_ms <- c(111:116,141:176) sce <- assignPrelim(sce, bc_ms, verbose = FALSE)
Estimation of distance separation cutoffs
sce <- applyCutoffs(estCutoffs(sce))
compute & extract spillover matrix
sce <- computeSpillmat(sce) sm <- metadata(sce)$spillover_matrix
do some sanity checks
chs <- channels(sce) ss_chs <- chs[rowData(sce)$is_bc] all(diag(sm[ss_chs, ss_chs]) == 1) all(sm >= 0 & sm <= 1)
Spillover matrix heatmap
plotSpillmat(sce)
Error in .check_sm(sm, isotope_list) : The supplied spillover matrix seems to be invalid. All isotopes should appear in
isotope_list
.[[2]] [1] "Pd102Di" "Rh103Di" "Pd104Di" "Pd105Di" "Pd106Di" "Pd108Di" "Pd110Di" "Cd111Di" "Cd112Di" "In113Di" "Cd114Di" "In115Di" "Cd116Di"
[14] "La139Di" "Ce140Di" "Pr141Di" "Nd142Di" "Nd143Di" "Nd144Di" "Nd145Di" "Nd146Di" "Sm147Di" "Nd148Di" "Sm149Di" "Nd150Di" "Eu151Di"
[27] "Sm152Di" "Eu153Di" "Sm154Di" "Gd155Di" "Gd156Di" "Gd157Di" "Gd158Di" "Tb159Di" "Gd160Di" "Dy161Di" "Dy162Di" "Dy163Di" "Dy164Di"
[40] "Ho165Di" "Er166Di" "Er167Di" "Er168Di" "Tm169Di" "Er170Di" "Yb171Di" "Yb172Di" "Yb173Di" "Yb174Di" "Lu175Di" "Yb176Di" "BCKG190Di" [53] "Ir191Di" "Ir193Di"
Matrix products: default
locale: [1] C
attached base packages: [1] grid tcltk stats4 stats graphics grDevices utils datasets methods base
other attached packages: [1] circlize_0.4.13 ComplexHeatmap_2.9.3 data.table_1.14.2 premessa_0.2.6 pals_1.7
[6] CytoML_2.4.0 flowWorkspace_4.4.0 ggpubr_0.4.0 RColorBrewer_1.1-2 forcats_0.5.1
[11] dplyr_1.0.7 purrr_0.3.4 readr_2.0.2 tidyr_1.1.4 tibble_3.1.5
[16] tidyverse_1.3.1 uwot_0.1.10 Matrix_1.3-4 cytofclean_1.0.3 scales_1.1.1
[21] cowplot_1.1.1 tcltk2_1.2-11 pheatmap_1.0.12 cytutils_0.1.0 stringr_1.4.0
[26] flowCut_1.3.1 flowAI_1.23.0 CytoNorm_0.0.6 remotes_2.4.1 ggplot2_3.3.5
[31] FlowSOM_2.1.24 igraph_1.2.7 flowCore_2.4.0 flowDensity_1.27.2 CATALYST_1.17.3
[36] SingleCellExperiment_1.15.2 SummarizedExperiment_1.23.4 Biobase_2.52.0 GenomicRanges_1.45.0 GenomeInfoDb_1.29.5
[41] IRanges_2.27.2 S4Vectors_0.30.0 BiocGenerics_0.39.2 MatrixGenerics_1.5.4 matrixStats_0.61.0
[46] devtools_2.4.2 usethis_2.1.3 FlowRepositoryR_1.23.0
loaded via a namespace (and not attached): [1] scattermore_0.7 knitr_1.36 irlba_2.3.3 multcomp_1.4-17 DelayedArray_0.19.1
[6] RCurl_1.98-1.5 doParallel_1.0.16 generics_0.1.1 ScaledMatrix_1.1.0 callr_3.7.0
[11] TH.data_1.1-0 proxy_0.4-26 ggpointdensity_0.1.0 tzdb_0.1.2 lubridate_1.8.0
[16] xml2_1.3.2 assertthat_0.2.1 viridis_0.6.2 xfun_0.27 hms_1.1.1
[21] evaluate_0.14 fansi_0.5.0 dbplyr_2.1.1 caTools_1.18.2 readxl_1.3.1
[26] Rgraphviz_2.36.0 DBI_1.1.1 ellipsis_0.3.2 RSpectra_0.16-0 ggcyto_1.21.0
[31] ggnewscale_0.4.5 backports_1.3.0 cytolib_2.5.3 RSEIS_4.0-3 RcppParallel_5.1.4
[36] sparseMatrixStats_1.5.3 vctrs_0.3.8 Cairo_1.5-12.2 GEOmap_2.4-4 abind_1.4-5
[41] cachem_1.0.6 withr_2.4.2 ggforce_0.3.3 aws.signature_0.6.0 RPMG_2.2-3
[46] prettyunits_1.1.1 splancs_2.01-42 cluster_2.1.2 dotCall64_1.0-1 crayon_1.4.1
[51] drc_3.0-1 labeling_0.4.2 pkgconfig_2.0.3 tweenr_1.0.2 vipor_0.4.5
[56] pkgload_1.2.3 changepoint_2.2.2 rlang_0.4.11 lifecycle_1.0.1 sandwich_3.0-1
[61] modelr_0.1.8 rsvd_1.0.5 dichromat_2.0-0 cellranger_1.1.0 rprojroot_2.0.2
[66] polyclip_1.10-0 graph_1.70.0 carData_3.0-4 zoo_1.8-9 reprex_2.0.1
[71] base64enc_0.1-3 beeswarm_0.4.0 ggridges_0.5.3 GlobalOptions_0.1.2 processx_3.5.2
[76] png_0.1-7 viridisLite_0.4.0 rjson_0.2.20 bitops_1.0-7 ConsensusClusterPlus_1.57.0 [81] KernSmooth_2.23-20 spam_2.7-0 DelayedMatrixStats_1.15.4 shape_1.4.6 jpeg_0.1-9
[86] rstatix_0.7.0 ggsignif_0.6.3 aws.s3_0.3.21 beachmat_2.9.1 memoise_2.0.0
[91] magrittr_2.0.1 plyr_1.8.6 hexbin_1.28.2 gplots_3.1.1 zlibbioc_1.38.0
[96] compiler_4.1.1 RFOC_3.4-6 plotrix_3.8-2 clue_0.3-60 cli_3.0.1
[101] XVector_0.33.0 ncdfFlow_2.38.0 ps_1.6.0 MASS_7.3-54 tidyselect_1.1.1
[106] stringi_1.7.5 RProtoBufLib_2.5.1 yaml_2.2.1 BiocSingular_1.9.1 latticeExtra_0.6-29
[111] ggrepel_0.9.1 tools_4.1.1 parallel_4.1.1 rio_0.5.27 rstudioapi_0.13
[116] foreach_1.5.1 foreign_0.8-81 gridExtra_2.3 MBA_0.0-9 farver_2.1.0
[121] Rtsne_0.15 rgeos_0.5-8 digest_0.6.28 BiocManager_1.30.16 Rcpp_1.0.7
[126] car_3.0-11 broom_0.7.9 scuttle_1.3.1 RcppAnnoy_0.0.19 IDPmisc_1.1.20
[131] httr_1.4.2 colorspace_2.0-2 rvest_1.0.2 XML_3.99-0.8 fs_1.5.0
[136] splines_4.1.1 fields_12.5 RBGL_1.68.0 scater_1.21.3 sp_1.4-5
[141] mapproj_1.2.7 sessioninfo_1.1.1 jsonlite_1.7.2 testthat_3.1.0 R6_2.5.1
[146] pillar_1.6.4 htmltools_0.5.2 nnls_1.4 glue_1.4.2 fastmap_1.1.0
[151] BiocParallel_1.27.4 BiocNeighbors_1.11.0 class_7.3-19 codetools_0.2-18 maps_3.4.0
[156] pkgbuild_1.2.0 mvtnorm_1.1-3 utf8_1.2.2 lattice_0.20-45 flowViz_1.57.2
[161] Rwave_2.6-0 curl_4.3.2 ggbeeswarm_0.6.0 colorRamps_2.3 gtools_3.9.2
[166] zip_2.2.0 openxlsx_4.2.4 survival_3.2-13 rmarkdown_2.11 desc_1.4.0
[171] munsell_0.5.0 e1071_1.7-9 GetoptLong_1.0.5 GenomeInfoDbData_1.2.6 iterators_1.0.13
[176] haven_2.4.3 reshape2_1.4.4 gtable_0.3.0