1 Introduction
I spent some time playing with cellranger and its various options for treating gex/atac and their combined datatypes. I think it is unlikely that more than ~ 100 cells are likely to be detected in any combination of options. I therefore thought it would be interesting to attempt treating the samples as separate expression/atac datasets and see if I can learn about the state of the experiment from them.
2 Annotation data
It might prove the case that downloading annotations from biomart does not work, I have been getting a large number of timeouts and failures recently.
rn_biomart <- load_biomart_annotations(species = "rnorvegicus", year = 2022, month = "oct",
symbol_columns = "external_gene_name", overwrite = TRUE)## Using mart: ENSEMBL_MART_ENSEMBL from host: oct2022.archive.ensembl.org.## Successfully connected to the rnorvegicus_gene_ensembl database.## Finished downloading ensembl gene annotations.## Finished downloading ensembl structure annotations.## Including symbols, there are 30560 vs the 54991 gene annotations.## Not dropping haplotype chromosome annotations, set drop_haplotypes = TRUE if this is bad.## Saving annotations to rnorvegicus_biomart_annotations.rda.## Finished save().## Returning a df with 26 columns and 1426516 rows.3 Metadata
My annotation collection tool is unaware of atac data. I think therefore I will try runing it and just see what happens.
sample_sheet <- "sample_sheets/all_samples.xlsx"
rn_meta <- gather_preprocessing_metadata(sample_sheet,
species = "rnorvegicus_7.2_107")## Did not find the condition column in the sample sheet.## Filling it in as undefined.## Did not find the batch column in the sample sheet.## Filling it in as undefined.## Checking the state of the condition column.## Checking the state of the batch column.## Checking the condition factor.## Writing new metadata to: sample_sheets/all_samples_modified.xlsx## Deleting the file sample_sheets/all_samples_modified.xlsx before writing the tables.It fails utterly.
4 Create gex summarized Experiment
rn_se <- create_se(sample_sheet, gene_info = rn_annot, file_column = "gex_count_table") %>%
set_conditions(fact = "treatment") %>%
set_batches(fact = "replicate")## Reading the sample metadata.## Did not find the condition column in the sample sheet.## Filling it in as undefined.## Did not find the batch column in the sample sheet.## Filling it in as undefined.## Checking the state of the condition column.## Checking the state of the batch column.## Checking the condition factor.## The sample definitions comprises: 8 rows(samples) and 8 columns(metadata fields).## Warning in create_se(sample_sheet, gene_info = rn_annot, file_column = "gex_count_table"): Some samples
## were removed when cross referencing the samples against the count data.## Matched 23139 annotations and counts.## The final summarized experiment has 23139 rows and 8 columns.## The numbers of samples by condition are:##
## oxycodone saline
## 2 2## The number of samples by batch are:##
## r1 r2
## 2 25 Check it out
## png
## 2## Library sizes of 4 samples,
## ranging from 31,404,515 to 59,790,154.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the hpgltools package.
## Please report the issue to the authors.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.## png
## 2## A non-zero genes plot of 4 samples.
## These samples have an average 43.89 CPM coverage and 16993 genes observed, ranging from 16121 to
## 17693.
5.1 Normalize
## Removing 11208 low-count genes (11931 remaining).## transform_counts: Found 277 values equal to 0, adding 1 to the matrix.rn_disheat <- plot_disheat(rn_norm)
pp(file = "images/rn_disheat.png")
rn_disheat[["plot"]]
dev.off()## png
## 2## A heatmap of pairwise sample distances ranging from:
## 86.762864144945 to 187.411008453202.
rn_corheat <- plot_corheat(rn_norm)
pp(file = "images/rn_corheat.png")
rn_corheat[["plot"]]
dev.off()## png
## 2## A heatmap of pairwise sample correlations ranging from:
## 0.728550215536099 to 0.92793236561988.
## png
## 2## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by oxycodone, saline
## Shapes are defined by r1, r2.
## Removing 11208 low-count genes (11931 remaining).## transform_counts: Found 318 values less than 0.## transform_counts: Found 318 values equal to 0, adding 1 to the matrix.## png
## 2## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by oxycodone, saline
## Shapes are defined by r1, r2.
6 Compare the two conditions
pairs <- all_pairwise(rn_se, model_svs = "svaseq", force = TRUE,
model_fstring = "~ 0 + condition", filter = TRUE)## oxycodone saline
## 2 2## Removing 11208 low-count genes (11931 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 1552 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into
## integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into
## integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into
## integers.## conditions
## oxycodone saline
## 2 2## conditions
## oxycodone saline
## 2 2## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into
## integers.## conditions
## oxycodone saline
## 2 2## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## sln_vs_xyc
## basic_vs_deseq 0.6590
## basic_vs_dream 0.7010
## basic_vs_ebseq 0.8425
## basic_vs_edger 0.6577
## basic_vs_noiseq 0.8905
## deseq_vs_dream 0.9106
## deseq_vs_ebseq 0.6616
## deseq_vs_edger 0.9996
## deseq_vs_noiseq 0.6794
## dream_vs_ebseq 0.6339
## dream_vs_edger 0.9091
## dream_vs_noiseq 0.7200
## ebseq_vs_edger 0.6615
## ebseq_vs_noiseq 0.8728
## edger_vs_noiseq 0.6788## Deleting the file excel/pairs.xlsx before writing the tables.## Looking for subscript invalid names, start of extract_keepers.## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 saline_vs_oxycodone 117 7 41 3 21 1## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the UpSetR package.
## Please report the issue to the authors.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the UpSetR package.
## Please report the issue to the authors.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.## Plot describing unique/shared genes in a differential expression table.
## Deleting the file excel/sig.xlsx before writing the tables.## A set of genes deemed significant according to limma.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## limma_up limma_down
## saline_vs_oxycodone 21 1## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf
## Error in `plot.window()`:
## ! need finite 'xlim' valuesLimma volcano plot
In theory, the column ‘external_gene_name’ should provide reasonable genenames, but I am having some troubles extracting it from ensembl.
pp(file = "images/gex_volcano.png")
table[["plots"]][["saline_vs_oxycodone"]][["limma_vol_plots"]]
dev.off()## png
## 2
## [1] 21 72## [1] 1 72gp_up <- simple_gprofiler(ups, species = "rnorvegicus")
tt <- plot_enrichresult(gp_up[["REAC_enrich"]])## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?7 Examine the ATAC results
Given my lack of experience with ATAC data, I am currently using
as a guide.
## Loading required package: BiocGenerics## Loading required package: generics##
## Attaching package: 'generics'## The following objects are masked from 'package:base':
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## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff, setequal, union##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:hpgltools':
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## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
## rownames, sapply, saveRDS, table, tapply, unique, unsplit, which.max, which.min## Loading required package: S4Vectors## Loading required package: stats4##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:utils':
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## import## Loading required package: rtracklayer## Loading required package: GenomicFeatures## Loading required package: AnnotationDbi## Loading required package: Biobase## Welcome to Bioconductor
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## Vignettes contain introductory material; view with 'browseVignettes()'. To cite
## Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following object is masked from 'package:hpgltools':
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## noteslibrary(ChIPpeakAnno)
library(Rsamtools)
bam_outputs <- c(
"preprocessing/1_saline_atac/outputs/40hisat_rnorvegicus_norway_grcr8/rnorvegicus_norway_grcr8_genome-paired.bam",
"preprocessing/2_oxycodone_atac/outputs/40hisat_rnorvegicus_norway_grcr8/rnorvegicus_norway_grcr8_genome-paired.bam",
"preprocessing/3_saline_atac/outputs/40hisat_rnorvegicus_norway_grcr8/rnorvegicus_norway_grcr8_genome-paired.bam",
"preprocessing/4_oxycodone_atac/outputs/40hisat_rnorvegicus_norway_grcr8/rnorvegicus_norway_grcr8_genome-paired.bam")
bam_qc <- bamQC(bam_outputs[1], outPath = NULL)
possibleTag <- combn(LETTERS, 2)
possibleTag <- c(paste0(possibleTag[1, ], possibleTag[2, ]),
paste0(possibleTag[2, ], possibleTag[1, ]))
## This seems a bit stupid? I already know what the primary sam tags are, should I not just
## have a list with their names and meanings?
bamTop100 <- scanBam(BamFile(bam_outputs[1], yieldSize = 100),
param = ScanBamParam(tag = possibleTag))[[1]]$tag
tags <- names(bamTop100)[lengths(bamTop100)>0]
tags## [1] "AS" "MD" "XG" "NH" "NM" "XM" "XN" "XO" "YS" "ZS" "YT"gal = readBamFile(bam_outputs[1], tag = tags, asMates = TRUE, bigFile = TRUE)
out_dir <- "preprocessing/1_saline_atac/outputs/99atac_qc"
dir.create(out_dir, recursive = TRUE)## Warning in dir.create(out_dir, recursive = TRUE): 'preprocessing/1_saline_atac/outputs/99atac_qc' already
## existsshifted_bam <- file.path(out_dir, "shifted.bam")
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| 99% | |================================================================================================| 100%R version 4.5.1 (2025-06-13)
Platform: x86_64-pc-linux-gnu
locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C
attached base packages: stats4, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: Rsamtools(v.2.26.0), ChIPpeakAnno(v.3.44.0), TxDb.Rnorvegicus.UCSC.rn7.refGene(v.3.15.0), GenomicFeatures(v.1.62.0), AnnotationDbi(v.1.72.0), Biobase(v.2.70.0), BSgenome.Rnorvegicus.UCSC.rn7(v.1.4.3), BSgenome(v.1.78.0), rtracklayer(v.1.70.1), BiocIO(v.1.20.0), Biostrings(v.2.78.0), XVector(v.0.50.0), GenomicRanges(v.1.62.1), Seqinfo(v.1.0.0), IRanges(v.2.44.0), ATACseqQC(v.1.34.0), S4Vectors(v.0.48.1), BiocGenerics(v.0.56.0), generics(v.0.1.4), edgeR(v.4.8.2), ruv(v.0.9.7.1), hpgltools(v.2026.03), testthat(v.3.3.2) and reticulate(v.1.45.0)
loaded via a namespace (and not attached): R.methodsS3(v.1.8.2), dichromat(v.2.0-0.1), GSEABase(v.1.72.0), progress(v.1.2.3), PROPER(v.1.42.0), HDF5Array(v.1.38.0), restez(v.2.1.5), vctrs(v.0.7.2), ggtangle(v.0.1.1), digest(v.0.6.39), png(v.0.1-9), corpcor(v.1.6.10), ggrepel(v.0.9.8), MASS(v.7.3-65), fontLiberation(v.0.1.0), reshape2(v.1.4.5), httpuv(v.1.6.17), foreach(v.1.5.2), qvalue(v.2.42.0), withr(v.3.0.2), xfun(v.0.57), ggfun(v.0.2.0), ellipsis(v.0.3.3), survival(v.3.8-6), memoise(v.2.0.1), clusterProfiler(v.4.18.4), gson(v.0.1.0), systemfonts(v.1.3.2), tidytree(v.0.4.7), gtools(v.3.9.5), R.oo(v.1.27.1), DEoptimR(v.1.1-4), prettyunits(v.1.2.0), KEGGREST(v.1.50.0), promises(v.1.5.0), otel(v.0.2.0), httr(v.1.4.8), restfulr(v.0.0.16), rhdf5filters(v.1.22.0), rhdf5(v.2.55.13), rstudioapi(v.0.18.0), UCSC.utils(v.1.6.1), DOSE(v.4.4.0), curl(v.7.0.0), h5mread(v.1.2.1), randomForest(v.4.7-1.2), polyclip(v.1.10-7), SparseArray(v.1.10.10), RBGL(v.1.86.0), RcppEigen(v.0.3.4.0.2), ade4(v.1.7-24), xtable(v.1.8-8), stringr(v.1.6.0), desc(v.1.4.3), evaluate(v.1.0.5), S4Arrays(v.1.10.1), BiocFileCache(v.3.0.0), preprocessCore(v.1.72.0), hms(v.1.1.4), filelock(v.1.0.3), polynom(v.1.4-1), VennDiagram(v.1.8.2), magrittr(v.2.0.5), later(v.1.4.8), ggtree(v.4.0.5), lattice(v.0.22-9), genefilter(v.1.92.0), robustbase(v.0.99-7), XML(v.3.99-0.23), cowplot(v.1.2.0), matrixStats(v.1.5.0), ggupset(v.0.4.1), pillar(v.1.11.1), nlme(v.3.1-169), pwalign(v.1.6.0), iterators(v.1.0.14), caTools(v.1.18.3), compiler(v.4.5.1), stringi(v.1.8.7), minqa(v.1.2.8), SummarizedExperiment(v.1.40.0), devtools(v.2.5.0), GenomicAlignments(v.1.46.0), plyr(v.1.8.9), crayon(v.1.5.3), abind(v.1.4-8), gridGraphics(v.0.5-1), locfit(v.1.5-9.12), bit(v.4.6.0), UpSetR(v.1.4.0), dplyr(v.1.2.1), fastmatch(v.1.1-8), fastcluster(v.1.3.0), codetools(v.0.2-20), crosstalk(v.1.2.2), bslib(v.0.10.0), plotly(v.4.12.0), multtest(v.2.66.0), remaCor(v.0.0.20), mime(v.0.13), splines(v.4.5.1), Rcpp(v.1.1.1), tidydr(v.0.0.6), dbplyr(v.2.5.2), knitr(v.1.51), blob(v.1.3.0), seqLogo(v.1.76.0), BiocVersion(v.3.22.0), AnnotationFilter(v.1.34.0), lme4(v.2.0-1), fs(v.2.0.1), Rdpack(v.2.6.6), EBSeq(v.2.8.0), pkgbuild(v.1.4.8), openxlsx(v.4.2.8.1), ggplotify(v.0.1.3), tibble(v.3.3.1), Matrix(v.1.7-5), statmod(v.1.5.1), fANCOVA(v.0.6-1), tweenr(v.2.0.3), pkgconfig(v.2.0.3), tools(v.4.5.1), cachem(v.1.1.0), RhpcBLASctl(v.0.23-42), rbibutils(v.2.4.1), cigarillo(v.1.0.0), RSQLite(v.2.4.6), viridisLite(v.0.4.3), DBI(v.1.3.0), numDeriv(v.2016.8-1.1), fastmap(v.1.2.0), rmarkdown(v.2.31), scales(v.1.4.0), grid(v.4.5.1), usethis(v.3.2.1), gprofiler2(v.0.2.4), AnnotationHub(v.4.0.0), broom(v.1.0.12), sass(v.0.4.10), patchwork(v.1.3.2), BiocManager(v.1.30.27), graph(v.1.88.1), varhandle(v.2.0.6), farver(v.2.1.2), reformulas(v.0.4.4), aod(v.1.3.3), scatterpie(v.0.2.6), mgcv(v.1.9-4), yaml(v.2.3.12), MatrixGenerics(v.1.22.0), cli(v.3.6.5), purrr(v.1.2.1), lifecycle(v.1.0.5), mvtnorm(v.1.3-6), lambda.r(v.1.2.4), sessioninfo(v.1.2.3), backports(v.1.5.1), Vennerable(v.3.1.0.9000), BiocParallel(v.1.44.0), annotate(v.1.88.0), gtable(v.0.3.6), rjson(v.0.2.23), parallel(v.4.5.1), ape(v.5.8-1), limma(v.3.66.0), jsonlite(v.2.0.0), TFBSTools(v.1.48.0), bitops(v.1.0-9), ggplot2(v.4.0.2), NOISeq(v.2.54.0), bit64(v.4.6.0-1), brio(v.1.1.5), yulab.utils(v.0.2.4), zip(v.2.3.3), futile.options(v.1.0.1), jquerylib(v.0.1.4), GOSemSim(v.2.36.0), R.utils(v.2.13.0), pbkrtest(v.0.5.5), lazyeval(v.0.2.3), pander(v.0.6.6), shiny(v.1.13.0), htmltools(v.0.5.9), enrichplot(v.1.30.5), GO.db(v.3.22.0), formatR(v.1.14), rappdirs(v.0.3.4), blockmodeling(v.1.1.8), ensembldb(v.2.34.0), glue(v.1.8.0), TFMPvalue(v.1.0.0), GenomicScores(v.2.22.0), httr2(v.1.2.2), gdtools(v.0.5.0), RCurl(v.1.98-1.18), InteractionSet(v.1.38.0), rprojroot(v.2.1.1), treeio(v.1.34.0), motifStack(v.1.54.0), futile.logger(v.1.4.9), gridExtra(v.2.3), EnvStats(v.3.1.0), boot(v.1.3-32), preseqR(v.4.0.0), universalmotif(v.1.28.0), igraph(v.2.2.3), variancePartition(v.1.40.2), R6(v.2.6.1), sva(v.3.58.0), tidyr(v.1.3.2), DESeq2(v.1.50.2), ggiraph(v.0.9.6), gplots(v.3.3.0), labeling(v.0.4.3), cluster(v.2.1.8.2), Rhdf5lib(v.1.32.0), regioneR(v.1.42.0), pkgload(v.1.5.1), aplot(v.0.2.9), GenomeInfoDb(v.1.46.2), DirichletMultinomial(v.1.52.0), nloptr(v.2.2.1), ProtGenerics(v.1.42.0), DelayedArray(v.0.36.1), tidyselect(v.1.2.1), ggforce(v.0.5.0), xml2(v.1.5.2), fontBitstreamVera(v.0.1.1), KernSmooth(v.2.23-26), S7(v.0.2.1), fontquiver(v.0.2.1), data.table(v.1.18.2.1), htmlwidgets(v.1.6.4), fgsea(v.1.36.2), RColorBrewer(v.1.1-3), biomaRt(v.2.66.2), rlang(v.1.2.0), lmerTest(v.3.2-1) and ggnewscale(v.0.5.2)
## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset 03a4e43defdc53e7038116087cb006b05404d424## This is hpgltools commit: Tue Apr 7 15:44:04 2026 -0400: 03a4e43defdc53e7038116087cb006b05404d424this_save <- paste0(gsub(pattern = "\\.Rmd", replace = "", x = rmd_file), "-v", ver, ".rda.xz")
message("Saving to ", this_save)## Saving to index-v20260408.rda.xzLS0tCnRpdGxlOiAiVHJlYXRpbmcgc2NSTkEgYW5kIEFUQUMgYXMgYnVsay4iCmF1dGhvcjogImF0YiBhYmVsZXdAZ21haWwuY29tIgpkYXRlOiAiYHIgU3lzLkRhdGUoKWAiCm91dHB1dDoKICBodG1sX2RvY3VtZW50OgogICAgY29kZV9kb3dubG9hZDogdHJ1ZQogICAgY29kZV9mb2xkaW5nOiBzaG93CiAgICBkZl9wcmludDogcGFnZWQKICAgIGZpZ19jYXB0aW9uOiB0cnVlCiAgICBmaWdfaGVpZ2h0OiA3CiAgICBmaWdfd2lkdGg6IDcKICAgIGhpZ2hsaWdodDogemVuYnVybgogICAga2VlcF9tZDogZmFsc2UKICAgIG1vZGU6IHNlbGZjb250YWluZWQKICAgIG51bWJlcl9zZWN0aW9uczogdHJ1ZQogICAgc2VsZl9jb250YWluZWQ6IHRydWUKICAgIHRoZW1lOiByZWFkYWJsZQogICAgdG9jOiB0cnVlCiAgICB0b2NfZmxvYXQ6CiAgICAgIGNvbGxhcHNlZDogZmFsc2UKICAgICAgc21vb3RoX3Njcm9sbDogZmFsc2UKICBybWRmb3JtYXRzOjpyZWFkdGhlZG93bjoKICAgIGNvZGVfZG93bmxvYWQ6IHRydWUKICAgIGNvZGVfZm9sZGluZzogc2hvdwogICAgZGZfcHJpbnQ6IHBhZ2VkCiAgICBmaWdfY2FwdGlvbjogdHJ1ZQogICAgZmlnX2hlaWdodDogNwogICAgZmlnX3dpZHRoOiA3CiAgICBoaWdobGlnaHQ6IHplbmJ1cm4KICAgIHdpZHRoOiAzMDAKICAgIGtlZXBfbWQ6IGZhbHNlCiAgICBtb2RlOiBzZWxmY29udGFpbmVkCiAgICB0b2NfZmxvYXQ6IHRydWUKICBCaW9jU3R5bGU6Omh0bWxfZG9jdW1lbnQ6CiAgICBjb2RlX2Rvd25sb2FkOiB0cnVlCiAgICBjb2RlX2ZvbGRpbmc6IHNob3cKICAgIGZpZ19jYXB0aW9uOiB0cnVlCiAgICBmaWdfaGVpZ2h0OiA3CiAgICBmaWdfd2lkdGg6IDcKICAgIGhpZ2hsaWdodDogemVuYnVybgogICAga2VlcF9tZDogZmFsc2UKICAgIG1vZGU6IHNlbGZjb250YWluZWQKICAgIHRvY19mbG9hdDogdHJ1ZQotLS0KCjxzdHlsZSB0eXBlPSJ0ZXh0L2NzcyI+CmJvZHksIHRkIHsKICBmb250LXNpemU6IDE2cHg7Cn0KY29kZS5yewogIGZvbnQtc2l6ZTogMTZweDsKfQpwcmUgewogZm9udC1zaXplOiAxNnB4Cn0KPC9zdHlsZT4KCmBgYHtyIG9wdGlvbnMsIGluY2x1ZGU9RkFMU0V9CmxpYnJhcnkoImhwZ2x0b29scyIpCmxpYnJhcnkoInJldGljdWxhdGUiKQp0dCA8LSBkZXZ0b29sczo6bG9hZF9hbGwoIn4vaHBnbHRvb2xzIikKa25pdHI6Om9wdHNfa25pdCRzZXQoCiAgd2lkdGggPSAxMjAsIHByb2dyZXNzID0gVFJVRSwgdmVyYm9zZSA9IFRSVUUsIGVjaG8gPSBUUlVFKQprbml0cjo6b3B0c19jaHVuayRzZXQoZXJyb3IgPSBUUlVFLCBkcGkgPSA5NikKbHVhX2ZpbHRlcnMgPC0gcm1hcmtkb3duOjpwYW5kb2NfbHVhX2ZpbHRlcl9hcmdzKCJwYW5kb2Mtem90eHQubHVhIikKb2xkX29wdGlvbnMgPC0gb3B0aW9ucygKICBkaWdpdHMgPSA0LCBzdHJpbmdzQXNGYWN0b3JzID0gRkFMU0UsIGtuaXRyLmR1cGxpY2F0ZS5sYWJlbCA9ICJhbGxvdyIpCmdncGxvdDI6OnRoZW1lX3NldChnZ3Bsb3QyOjp0aGVtZV9idyhiYXNlX3NpemUgPSAxMCkpCnJ1bmRhdGUgPC0gZm9ybWF0KFN5cy5EYXRlKCksIGZvcm1hdCA9ICIlWSVtJWQiKQpwcmV2aW91c19maWxlIDwtICIiCnZlciA8LSBmb3JtYXQoU3lzLkRhdGUoKSwgIiVZJW0lZCIpCgojI3RtcCA8LSBzbShsb2FkbWUoZmlsZW5hbWU9cGFzdGUwKGdzdWIocGF0dGVybj0iXFwuUm1kIiwgcmVwbGFjZT0iIiwgeD1wcmV2aW91c19maWxlKSwgIi12IiwgdmVyLCAiLnJkYS54eiIpKSkKcm1kX2ZpbGUgPC0gImluZGV4LlJtZCIKYGBgCgojIEludHJvZHVjdGlvbgoKSSBzcGVudCBzb21lIHRpbWUgcGxheWluZyB3aXRoIGNlbGxyYW5nZXIgYW5kIGl0cyB2YXJpb3VzIG9wdGlvbnMgZm9yCnRyZWF0aW5nIGdleC9hdGFjIGFuZCB0aGVpciBjb21iaW5lZCBkYXRhdHlwZXMuICBJIHRoaW5rIGl0IGlzCnVubGlrZWx5IHRoYXQgbW9yZSB0aGFuIH4gMTAwIGNlbGxzIGFyZSBsaWtlbHkgdG8gYmUgZGV0ZWN0ZWQgaW4gYW55CmNvbWJpbmF0aW9uIG9mIG9wdGlvbnMuICBJIHRoZXJlZm9yZSB0aG91Z2h0IGl0IHdvdWxkIGJlIGludGVyZXN0aW5nCnRvIGF0dGVtcHQgdHJlYXRpbmcgdGhlIHNhbXBsZXMgYXMgc2VwYXJhdGUgZXhwcmVzc2lvbi9hdGFjIGRhdGFzZXRzCmFuZCBzZWUgaWYgSSBjYW4gbGVhcm4gYWJvdXQgdGhlIHN0YXRlIG9mIHRoZSBleHBlcmltZW50IGZyb20gdGhlbS4KCiMgQW5ub3RhdGlvbiBkYXRhCgpJdCBtaWdodCBwcm92ZSB0aGUgY2FzZSB0aGF0IGRvd25sb2FkaW5nIGFubm90YXRpb25zIGZyb20gYmlvbWFydCBkb2VzCm5vdCB3b3JrLCBJIGhhdmUgYmVlbiBnZXR0aW5nIGEgbGFyZ2UgbnVtYmVyIG9mIHRpbWVvdXRzIGFuZCBmYWlsdXJlcwpyZWNlbnRseS4KCmBgYHtyfQpybl9iaW9tYXJ0IDwtIGxvYWRfYmlvbWFydF9hbm5vdGF0aW9ucyhzcGVjaWVzID0gInJub3J2ZWdpY3VzIiwgeWVhciA9IDIwMjIsIG1vbnRoID0gIm9jdCIsCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIHN5bWJvbF9jb2x1bW5zID0gImV4dGVybmFsX2dlbmVfbmFtZSIsIG92ZXJ3cml0ZSA9IFRSVUUpCnJuX2dmZiA8LSBsb2FkX2dmZl9hbm5vdGF0aW9ucygifi9saWJyYXJpZXMvZ2Vub21lL2dmZi9ybm9ydmVnaWN1c183LjJfMTA3LmdmZiIpCgpybl9hbm5vdCA8LSBybl9iaW9tYXJ0W1siZ2VuZV9hbm5vdGF0aW9ucyJdXQoKI3JuX2V4YW1wbGUgPC0gZ2V0X2Jpb21hcnRfZXhhbXBsZV9nZW5lKHNwZWNpZXMgPSAicm5vcnZlZ2ljdXMiLAojICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgaG9zdCA9ICJ1c2Vhc3QuZW5zZW1ibC5vcmciLCBhcmNoaXZlID0gRkFMU0UpCmBgYAoKIyBNZXRhZGF0YQoKTXkgYW5ub3RhdGlvbiBjb2xsZWN0aW9uIHRvb2wgaXMgdW5hd2FyZSBvZiBhdGFjIGRhdGEuICBJIHRoaW5rCnRoZXJlZm9yZSBJIHdpbGwgdHJ5IHJ1bmluZyBpdCBhbmQganVzdCBzZWUgd2hhdCBoYXBwZW5zLgoKYGBge3J9CnNhbXBsZV9zaGVldCA8LSAic2FtcGxlX3NoZWV0cy9hbGxfc2FtcGxlcy54bHN4Igpybl9tZXRhIDwtIGdhdGhlcl9wcmVwcm9jZXNzaW5nX21ldGFkYXRhKHNhbXBsZV9zaGVldCwKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBzcGVjaWVzID0gInJub3J2ZWdpY3VzXzcuMl8xMDciKQpgYGAKCkl0IGZhaWxzIHV0dGVybHkuCgojIENyZWF0ZSBnZXggc3VtbWFyaXplZCBFeHBlcmltZW50CgpgYGB7cn0Kcm5fc2UgPC0gY3JlYXRlX3NlKHNhbXBsZV9zaGVldCwgZ2VuZV9pbmZvID0gcm5fYW5ub3QsIGZpbGVfY29sdW1uID0gImdleF9jb3VudF90YWJsZSIpICU+JQogIHNldF9jb25kaXRpb25zKGZhY3QgPSAidHJlYXRtZW50IikgJT4lCiAgc2V0X2JhdGNoZXMoZmFjdCA9ICJyZXBsaWNhdGUiKQpgYGAKCiMgQ2hlY2sgaXQgb3V0CgpgYGB7cn0Kcm5fbGlic2l6ZSA8LSBwbG90X2xpYnNpemUocm5fc2UpCnBwKGZpbGUgPSAiaW1hZ2VzL3JuX2xpYnNpemUucG5nIikKcm5fbGlic2l6ZVtbInBsb3QiXV0KZGV2Lm9mZigpCnJuX2xpYnNpemUKcm5fbnogPC0gcGxvdF9ub256ZXJvKHJuX3NlLCB5X2ludGVyY2VwdCA9IDAuNzUpCnBwKGZpbGUgPSAiaW1hZ2VzL3JuX256LnBuZyIpCnJuX256W1sicGxvdCJdXQpkZXYub2ZmKCkKcm5fbnoKYGBgCgojIyBOb3JtYWxpemUKCmBgYHtyfQpybl9ub3JtIDwtIG5vcm1hbGl6ZShybl9zZSwgdHJhbnNmb3JtID0gImxvZzIiLCBjb252ZXJ0ID0gIm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