Closed carl-coyle closed 3 years ago
Tricky to track down the issue from this- could you produce the error again, run traceback()
right after, and post the output here? Hopefully that'll help a bit, thanks!
Hi there,
Thanks so much for your response. Please see below the traceback.
[cid:E1D5EBFC-5404-4531-B2F8-365E5A13B3AF@kcl.ac.uk] On 24 Nov 2020, at 12:47, Helena L. Crowell notifications@github.com<mailto:notifications@github.com> wrote:
Tricky to track down the issue from this- could you produce the error again, run traceback() right after, and post the output here? Hopefully that'll help a bit, thanks!
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I think you forgot to send the traceback()
output?
Oops sorry. I thought I sent it as an image.
See test output below:
traceback() 6: (function(x, i, exact) if (is.matrix(i)) as.matrix(x)[[i]] else .subset2(x, i, exact = exact))(x, ..., exact = exact) 5:
[[.data.frame
(y, y_cols$target) 4: y[[y_cols$target]] 3: as.vector(y) 2: intersect(rownames(x), y[[y_cols$target]]) 1: plotDiffHeatmap(x = sce, y = rowData(da_res2$res))
On 24 Nov 2020, at 13:39, Helena L. Crowell notifications@github.com<mailto:notifications@github.com> wrote:
I think you forgot to send the traceback() output?
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Okay, getting close- could you also please post the output of
sce
head(rowData(da_res2$res))
sce class: SingleCellExperiment dim: 41 4476877 metadata(4): experiment_info cluster_codes SOM_codes delta_area assays(2): counts exprs rownames(41): CD16 CD19 ... HLA-DR CD56 rowData names(4): channel_name marker_name marker_class used_for_clustering colnames: NULL colData names(4): sample_id condition patient_id cluster_id reducedDimNames(1): UMAP altExpNames(0):
head(rowData(da_res2$res)) DataFrame with 6 rows and 3 columns cluster_id p_val p_adj
B_Cell_CD16+_CD185+IGD+CD38+ B_Cell_CD16+_CD185+IGD+CD38+ 0.1914092 0.4029668 B_Cell_CD185+IGD+CD38+ B_Cell_CD185+IGD+CD38+ 0.1184251 0.3028183 B_Cell_CD24+CD185+_CD25Dim B_Cell_CD24+CD185+_CD25Dim 0.0196324 0.0654414 B_Cell_CD24+CD185neg_CD25Dim B_Cell_CD24+CD185neg_CD25Dim 0.2507808 0.4179680 B_Cell_CD24+IGD+CD38neg_CD185+CD25Dim B_Cell_CD24+IGD+CD38neg_CD185+CD25Dim 0.5057959 0.6976495 B_Cell_CD24+IGD+CD38neg_CD185negCD25+ B_Cell_CD24+IGD+CD38neg_CD185negCD25+ 0.8771161 0.8912320 On 25 Nov 2020, at 10:44, Helena L. Crowell > wrote:
Okay, getting close- could you also please post the output of
sce head(rowData(da_res2$res))
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Could you try evaluating the following and tell me what it returns (TRUE
or FALSE
)? It seems the function enters an if-statement that it shouldn't, resulting in the data being processed as DS results (rather than DA results).
x <- sce
y <- rowData(da_res2$res)
kids <- levels(y$cluster_id)
same <- vapply(cluster_codes(x), function(u) identical(levels(u), kids), logical(1))
k <- names(cluster_codes(x))[same][1]
x$cluster_id <- cluster_ids(x, k)
y <- data.frame(y, check.names = FALSE)
y <- dplyr::mutate_if(y, is.factor, as.character)
any(rownames(x) %in% unlist(y))
The code didn’t work with the following y <- rowData(da_res2)$res so I changed it to y <- rowData(da_res2$res). I don’t know if that affects the results.
Below is the output of the code. It seems to return TRUE
x <- sce y <- rowData(da_res2$res)
kids <- levels(y$cluster_id) same <- vapply(cluster_codes(x), function(u) identical(levels(u), kids), logical(1)) k <- names(cluster_codes(x))[same][1] x$cluster_id <- cluster_ids(x, k)
y <- data.frame(y, check.names = FALSE) y <- dplyr::mutate_if(y, is.factor, as.character) any(rownames(x) %in% unlist(y)) [1] TRUE On 25 Nov 2020, at 13:22, Helena L. Crowell notifications@github.com<mailto:notifications@github.com> wrote:
Could you try evaluating the following and telling me what it returns (TRUE or FALSE)? It seams the function enters an if-statement that it shouldn't, resulting the data to be processed as DS results (rather than DA results).
x <- sce y <- rowData(da_res2)$res
kids <- levels(y$cluster_id) same <- vapply(cluster_codes(x), function(u) identical(levels(u), kids), logical(1)) k <- names(cluster_codes(x))[same][1] x$cluster_id <- cluster_ids(x, k)
y <- data.frame(y, check.names = FALSE) y <- dplyr::mutate_if(y, is.factor, as.character) any(rownames(x) %in% unlist(y))
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Yes, your fix is correct- my apologies. So the bug occurs for the following reason:
plotDiffHeatmap
tries to determine whether y
are DS or DA resultsy
's rownames contain markers (rownames(x)
)I will think about how to fix this and make the interpretation of results more accurate and robust to such occurrences. Meanwhile, I'd suggest re-labeling the cluster(s) such that they are distinguishable from the marker names. The function should then run smoothly; please let me know if so / if not!
Thats makes perfect and yes that is correct one of the clusters was labelled only CD8. I have changed this now and it worked.
Thank you so much for your time on this. It is very much appreciated.
Carl.
On 25 Nov 2020, at 13:32, Helena L. Crowell notifications@github.com<mailto:notifications@github.com> wrote:
Yes, your fix is correct- my apologies. So the bug occurs for the following reason:
I will think about how to fix this and make the interpretation of results more accurate and robust to such occurrences. Meanwhile, I'd suggest re-labeling the cluster(s) such that they are distinguishable from the marker names. The function should then run smoothly.
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Hi there, I've ran Differential abundance analysis on my data and have managed to pull out things like top table etc however when I try and run the plotDiffHeatmap it turns up the following error.
I am quite new to R so not too sure what this error in referring to. Apologies if this also doesn't give as much information needed to address a solution id be happy to provide more information which is deemed necessary.
Session Info. R version 4.0.3 (2020-10-10) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS High Sierra 10.13.6
**Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale: [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages: [1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages: [1] lme4_1.1-25 Matrix_1.2-18 diffcyt_1.10.0 cowplot_1.1.0 CATALYST_1.14.0
[6] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0 Biobase_2.50.0 GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
[11] IRanges_2.24.0 S4Vectors_0.28.0 BiocGenerics_0.36.0 MatrixGenerics_1.2.0 matrixStats_0.57.0
[16] readxl_1.3.1 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[21] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0
[26] CytoML_2.2.1 flowWorkspace_4.2.0 flowCore_2.2.0
loaded via a namespace (and not attached): [1] backports_1.2.0 circlize_0.4.11 drc_3.0-1 plyr_1.8.6 igraph_1.2.6
[6] ConsensusClusterPlus_1.54.0 splines_4.0.3 BiocParallel_1.24.1 scater_1.18.3 TH.data_1.0-10
[11] digest_0.6.27 viridis_0.5.1 fansi_0.4.1 magrittr_2.0.1 cluster_2.1.0
[16] limma_3.46.0 aws.signature_0.6.0 openxlsx_4.2.3 ComplexHeatmap_2.6.2 modelr_0.1.8
[21] RcppParallel_5.0.2 sandwich_3.0-0 cytolib_2.2.0 jpeg_0.1-8.1 colorspace_2.0-0
[26] rvest_0.3.6 ggrepel_0.8.2 haven_2.3.1 xfun_0.19 crayon_1.3.4
[31] RCurl_1.98-1.2 jsonlite_1.7.1 hexbin_1.28.1 graph_1.68.0 survival_3.2-7
[36] zoo_1.8-8 glue_1.4.2 gtable_0.3.0 nnls_1.4 zlibbioc_1.36.0
[41] XVector_0.30.0 GetoptLong_1.0.4 DelayedArray_0.16.0 ggcyto_1.18.0 BiocSingular_1.6.0
[46] car_3.0-10 Rgraphviz_2.34.0 shape_1.4.5 abind_1.4-5 scales_1.1.1
[51] mvtnorm_1.1-1 edgeR_3.32.0 DBI_1.1.0 Rcpp_1.0.5 plotrix_3.7-8
[56] viridisLite_0.3.0 clue_0.3-57 rsvd_1.0.3 foreign_0.8-80 FlowSOM_1.22.0
[61] tsne_0.1-3 httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.1 farver_2.0.3
[66] pkgconfig_2.0.3 XML_3.99-0.5 scuttle_1.0.0 uwot_0.1.9 dbplyr_2.0.0
[71] locfit_1.5-9.4 labeling_0.4.2 tidyselect_1.1.0 rlang_0.4.8 reshape2_1.4.4
[76] munsell_0.5.0 cellranger_1.1.0 tools_4.0.3 cli_2.1.0 generics_0.1.0
[81] broom_0.7.2 ggridges_0.5.2 aws.s3_0.3.21 yaml_2.2.1 knitr_1.30
[86] fs_1.5.0 zip_2.1.1 nlme_3.1-150 RBGL_1.66.0 sparseMatrixStats_1.2.0
[91] xml2_1.3.2 compiler_4.0.3 rstudioapi_0.13 beeswarm_0.2.3 curl_4.3
[96] png_0.1-7 reprex_0.3.0 statmod_1.4.35 stringi_1.5.3 RSpectra_0.16-0
[101] lattice_0.20-41 nloptr_1.2.2.2 vctrs_0.3.5 pillar_1.4.6 lifecycle_0.2.0
[106] GlobalOptions_0.1.2 RcppAnnoy_0.0.17 BiocNeighbors_1.8.1 irlba_2.3.3 data.table_1.13.2
[111] bitops_1.0-6 R6_2.5.0 latticeExtra_0.6-29 gridExtra_2.3 RProtoBufLib_2.2.0
[116] rio_0.5.16 vipor_0.4.5 codetools_0.2-18 boot_1.3-25 MASS_7.3-53
[121] gtools_3.8.2 assertthat_0.2.1 rjson_0.2.20 withr_2.3.0 multcomp_1.4-15
[126] GenomeInfoDbData_1.2.4 hms_0.5.3 ncdfFlow_2.36.0 grid_4.0.3 beachmat_2.6.1
[131] minqa_1.2.4 DelayedMatrixStats_1.12.0 carData_3.0-4 Cairo_1.5-12.2 Rtsne_0.15
[136] lubridate_1.7.9.2 base64enc_0.1-3 ggbeeswarm_0.6.0**