Signac: Integration and clustering of chromap quantifications
Last updated: 2022-09-14
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Knit directory: chromap_vs_cellranger_scATAC_exploration_10x/
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Load packages and previously saved workspace
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.8 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 2.1.2 ✔ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(GenomicRanges)Loading required package: stats4Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'The following objects are masked from 'package:dplyr':
combine, intersect, setdiff, unionThe following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.minLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following objects are masked from 'package:dplyr':
first, renameThe following object is masked from 'package:tidyr':
expandThe following objects are masked from 'package:base':
expand.grid, I, unnameLoading required package: IRanges
Attaching package: 'IRanges'The following objects are masked from 'package:dplyr':
collapse, desc, sliceThe following object is masked from 'package:purrr':
reduceLoading required package: GenomeInfoDblibrary(Seurat)Attaching SeuratObjectlibrary(Signac)
library(EnsDb.Hsapiens.v86)Loading required package: ensembldbLoading required package: GenomicFeaturesLoading required package: AnnotationDbiLoading required package: BiobaseWelcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: 'AnnotationDbi'The following object is masked from 'package:dplyr':
selectLoading required package: AnnotationFilter
Attaching package: 'ensembldb'The following object is masked from 'package:dplyr':
filterThe following object is masked from 'package:stats':
filterload("cCRE_hg38_10x_HGMM_PBMC_chromap_fragments_MACS_q01_unionPeaks_merge100_seurat_090222_QC.RData")Merge chromap seurat objects, subset, then split into a list
chromap.combined <- merge(HGMM_10x_seurat,y = PBMC_10x_seurat)Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')# Subset for features observed in at least 20 cells
filtFeatures <- FindTopFeatures(chromap.combined,min.cutoff=20)
chromap.combined <- subset(chromap.combined, features = VariableFeatures(filtFeatures))
# Split by sample into a list
chromap.list <- SplitObject(chromap.combined, split.by="Sample")Iterate over sample list, subset based on QC metrics, and generate lsi embeddings
for (i in 1:length(chromap.list)) {
chromap.list[[i]] <- subset(x = chromap.list[[i]],
features = VariableFeatures(filtFeatures),
subset = nCount_peaks > 1000 &
nCount_peaks < 100000 &
FRiP > 0.15 &
blacklist_fraction < 0.05 &
nucleosome_signal < 4 &
TSS.enrichment > 2
)
chromap.list[[i]] <- FindTopFeatures(chromap.list[[i]], min.cutoff = 'q0')
chromap.list[[i]] <- RunTFIDF(chromap.list[[i]], assay="peaks")
chromap.list[[i]] <- RunSVD(chromap.list[[i]])
chromap.list[[i]] <- RunUMAP(chromap.list[[i]], reduction = "lsi", dims = 2:30)
}Performing TF-IDF normalizationWarning in RunTFIDF.default(object = GetAssayData(object = object, slot =
"counts"), : Some features contain 0 total countsRunning SVDScaling cell embeddingsWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session12:28:17 UMAP embedding parameters a = 0.9922 b = 1.11212:28:17 Read 5636 rows and found 29 numeric columns12:28:17 Using Annoy for neighbor search, n_neighbors = 3012:28:17 Building Annoy index with metric = cosine, n_trees = 500% 10 20 30 40 50 60 70 80 90 100%[----|----|----|----|----|----|----|----|----|----|**************************************************|
12:28:18 Writing NN index file to temp file /tmp/Rtmp4AqXVl/filee43d7377b738
12:28:18 Searching Annoy index using 1 thread, search_k = 3000
12:28:20 Annoy recall = 100%
12:28:28 Commencing smooth kNN distance calibration using 1 thread
12:28:41 Initializing from normalized Laplacian + noise
12:28:41 Commencing optimization for 500 epochs, with 221718 positive edges
12:28:57 Optimization finished
Performing TF-IDF normalizationWarning in RunTFIDF.default(object = GetAssayData(object = object, slot =
"counts"), : Some features contain 0 total countsRunning SVD
Scaling cell embeddings
12:35:09 UMAP embedding parameters a = 0.9922 b = 1.112
12:35:09 Read 11236 rows and found 29 numeric columns
12:35:09 Using Annoy for neighbor search, n_neighbors = 30
12:35:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:35:13 Writing NN index file to temp file /tmp/Rtmp4AqXVl/filee43d30218077
12:35:13 Searching Annoy index using 1 thread, search_k = 3000
12:35:19 Annoy recall = 100%
12:35:28 Commencing smooth kNN distance calibration using 1 thread
12:35:42 Initializing from normalized Laplacian + noise
12:35:42 Commencing optimization for 200 epochs, with 459300 positive edges
12:35:57 Optimization finished# Subset and process the combined dataset
chromap.combined <- subset(
x = chromap.combined,
features = VariableFeatures(filtFeatures),
subset = nCount_peaks > 1000 &
nCount_peaks < 100000 &
FRiP > 0.15 &
blacklist_fraction < 0.05 &
nucleosome_signal < 4 &
TSS.enrichment > 2
)Get filtered dimensions
table(chromap.combined$Sample)
HGMM PBMC
5636 11236 lapply(chromap.list, dim)$HGMM
[1] 713685 5636
$PBMC
[1] 713685 11236Repeat here on the combined object
chromap.combined <- FindTopFeatures(chromap.combined, min.cutoff = 'q0')
chromap.combined <- RunTFIDF(chromap.combined, assay="peaks")Performing TF-IDF normalizationchromap.combined <- RunSVD(chromap.combined)Running SVDScaling cell embeddingschromap.combined <- RunUMAP(chromap.combined, reduction = "lsi", dims = 2:30)12:45:09 UMAP embedding parameters a = 0.9922 b = 1.11212:45:09 Read 16872 rows and found 29 numeric columns12:45:09 Using Annoy for neighbor search, n_neighbors = 3012:45:09 Building Annoy index with metric = cosine, n_trees = 500% 10 20 30 40 50 60 70 80 90 100%[----|----|----|----|----|----|----|----|----|----|**************************************************|
12:45:14 Writing NN index file to temp file /tmp/Rtmp4AqXVl/filee43d381c167
12:45:14 Searching Annoy index using 1 thread, search_k = 3000
12:45:24 Annoy recall = 100%
12:45:34 Commencing smooth kNN distance calibration using 1 thread
12:45:45 Initializing from normalized Laplacian + noise
12:45:47 Commencing optimization for 200 epochs, with 667204 positive edges
12:46:08 Optimization finishedIntegrate all data
# Find integration anchors
integration.anchors <- FindIntegrationAnchors(object.list = chromap.list,
anchor.features = rownames(chromap.combined),
reduction = "rlsi",
dims = 2:30
)Computing within dataset neighborhoodsFinding all pairwise anchorsWarning: No filtering performed if passing to data rather than countsWarning: Feature names cannot have underscores ('_'), replacing with dashes
('-')Projecting new data onto SVDProjecting new data onto SVDFinding neighborhoodsFinding anchors Found 129 anchors# Integrate LSI embeddings
# Arbitrarily reduced k.weight to 50 to get around "number of items to replace is not a multiple of replacement length" error
chromap.integrated <- IntegrateEmbeddings(anchorset = integration.anchors,
reductions = chromap.combined[["lsi"]],
new.reduction.name = "integrated_lsi",
dims.to.integrate = 1:30,
k.weight = 50
)Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')Merging dataset 1 into 2Extracting anchors for merged samplesFinding integration vectorsFinding integration vector weightsIntegrating dataCreate a new UMAP using the integrated embeddings
chromap.integrated <- RunUMAP(chromap.integrated, reduction = "integrated_lsi", dims = 2:30)12:56:10 UMAP embedding parameters a = 0.9922 b = 1.11212:56:10 Read 16872 rows and found 29 numeric columns12:56:10 Using Annoy for neighbor search, n_neighbors = 3012:56:10 Building Annoy index with metric = cosine, n_trees = 500% 10 20 30 40 50 60 70 80 90 100%[----|----|----|----|----|----|----|----|----|----|**************************************************|
12:56:13 Writing NN index file to temp file /tmp/Rtmp4AqXVl/filee43d1a73aff2
12:56:13 Searching Annoy index using 1 thread, search_k = 3000
12:56:19 Annoy recall = 100%
12:56:25 Commencing smooth kNN distance calibration using 1 thread
12:56:33 Initializing from normalized Laplacian + noise
12:56:34 Commencing optimization for 200 epochs, with 671784 positive edges
12:56:46 Optimization finishedDimPlot(chromap.integrated, group.by = "Sample")
Identify clusters
chromap.integrated <- FindNeighbors(object = chromap.integrated, reduction = 'integrated_lsi', dims = 2:30)Computing nearest neighbor graphComputing SNNchromap.integrated <- FindClusters(object = chromap.integrated, verbose = FALSE, algorithm = 3, resolution = 0.5)
DimPlot(object = chromap.integrated, label = TRUE) + NoLegend()
Compute gene activity matrix and insert this as a pseudo RNA assay
gene.activities <- GeneActivity(chromap.integrated)Extracting gene coordinatesExtracting reads overlapping genomic regions
Extracting reads overlapping genomic regions# Add the gene activity matrix to the Seurat object as a new assay and normalize it
chromap.integrated[['RNA']] <- CreateAssayObject(counts = gene.activities)
chromap.integrated <- NormalizeData(
object = chromap.integrated,
assay = 'RNA',
normalization.method = 'LogNormalize',
scale.factor = median(chromap.integrated$nCount_RNA)
)
DefaultAssay(chromap.integrated) <- 'RNA'Plot the same marker genes from Seurat vignette
FeaturePlot(
object = chromap.integrated,
features = c('MS4A1', 'CD3D', 'LEF1', 'NKG7', 'TREM1', 'LYZ'),
pt.size = 0.1,
max.cutoff = 'q95',
ncol = 3
)
Save workspace
save.image("cCRE_hg38_10x_HGMM_PBMC_chromap_fragments_MACS_q01_unionPeaks_merge100_seurat_090222_QC_integrated.RData")
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.5 (Ootpa)
Matrix products: default
BLAS/LAPACK: /nas/longleaf/rhel8/apps/r/4.1.0/lib/libopenblas_haswellp-r0.3.5.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.18.3
[3] AnnotationFilter_1.18.0 GenomicFeatures_1.46.5
[5] AnnotationDbi_1.56.2 Biobase_2.54.0
[7] Signac_1.7.0.9003 SeuratObject_4.0.4
[9] Seurat_4.1.0 GenomicRanges_1.46.1
[11] GenomeInfoDb_1.30.1 IRanges_2.28.0
[13] S4Vectors_0.32.4 BiocGenerics_0.40.0
[15] forcats_0.5.1 stringr_1.4.0
[17] dplyr_1.0.9 purrr_0.3.4
[19] readr_2.1.2 tidyr_1.2.0
[21] tibble_3.1.8 ggplot2_3.3.6
[23] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.25
[3] tidyselect_1.1.2 RSQLite_2.2.10
[5] htmlwidgets_1.5.4 grid_4.1.0
[7] BiocParallel_1.28.3 Rtsne_0.15
[9] munsell_0.5.0 codetools_0.2-18
[11] ica_1.0-2 future_1.24.0
[13] miniUI_0.1.1.1 withr_2.5.0
[15] spatstat.random_2.1-0 colorspace_2.0-3
[17] filelock_1.0.2 highr_0.9
[19] knitr_1.37 rstudioapi_0.13
[21] ROCR_1.0-11 tensor_1.5
[23] listenv_0.8.0 labeling_0.4.2
[25] MatrixGenerics_1.6.0 git2r_0.30.1
[27] GenomeInfoDbData_1.2.7 polyclip_1.10-0
[29] farver_2.1.0 bit64_4.0.5
[31] rprojroot_2.0.2 parallelly_1.30.0
[33] vctrs_0.4.1 generics_0.1.2
[35] xfun_0.30 BiocFileCache_2.2.1
[37] R6_2.5.1 DelayedArray_0.20.0
[39] bitops_1.0-7 spatstat.utils_2.3-0
[41] cachem_1.0.6 assertthat_0.2.1
[43] BiocIO_1.4.0 promises_1.2.0.1
[45] scales_1.2.0 gtable_0.3.0
[47] globals_0.14.0 processx_3.5.2
[49] goftest_1.2-3 rlang_1.0.4
[51] RcppRoll_0.3.0 splines_4.1.0
[53] rtracklayer_1.54.0 lazyeval_0.2.2
[55] spatstat.geom_2.3-2 broom_1.0.0
[57] yaml_2.3.5 reshape2_1.4.4
[59] abind_1.4-5 modelr_0.1.8
[61] backports_1.4.1 httpuv_1.6.5
[63] tools_4.1.0 ellipsis_0.3.2
[65] spatstat.core_2.4-0 jquerylib_0.1.4
[67] RColorBrewer_1.1-3 ggridges_0.5.3
[69] Rcpp_1.0.8.3 plyr_1.8.7
[71] progress_1.2.2 zlibbioc_1.40.0
[73] RCurl_1.98-1.6 prettyunits_1.1.1
[75] ps_1.6.0 rpart_4.1.16
[77] deldir_1.0-6 pbapply_1.5-0
[79] cowplot_1.1.1 zoo_1.8-9
[81] SummarizedExperiment_1.24.0 haven_2.4.3
[83] ggrepel_0.9.1 cluster_2.1.2
[85] fs_1.5.2 magrittr_2.0.2
[87] RSpectra_0.16-0 data.table_1.14.2
[89] scattermore_0.8 lmtest_0.9-40
[91] reprex_2.0.1 RANN_2.6.1
[93] whisker_0.4 ProtGenerics_1.26.0
[95] fitdistrplus_1.1-6 matrixStats_0.62.0
[97] hms_1.1.1 patchwork_1.1.1
[99] mime_0.12 evaluate_0.15
[101] xtable_1.8-4 XML_3.99-0.9
[103] readxl_1.3.1 gridExtra_2.3
[105] biomaRt_2.50.3 compiler_4.1.0
[107] KernSmooth_2.23-20 crayon_1.5.1
[109] htmltools_0.5.2 mgcv_1.8-40
[111] later_1.3.0 tzdb_0.2.0
[113] lubridate_1.8.0 DBI_1.1.2
[115] dbplyr_2.1.1 rappdirs_0.3.3
[117] MASS_7.3-55 Matrix_1.4-0
[119] cli_3.3.0 parallel_4.1.0
[121] igraph_1.3.3 pkgconfig_2.0.3
[123] GenomicAlignments_1.30.0 getPass_0.2-2
[125] plotly_4.10.0 spatstat.sparse_2.1-0
[127] xml2_1.3.3 bslib_0.3.1
[129] XVector_0.34.0 rvest_1.0.2
[131] callr_3.7.0 digest_0.6.29
[133] sctransform_0.3.3 RcppAnnoy_0.0.19
[135] spatstat.data_2.1-2 Biostrings_2.62.0
[137] rmarkdown_2.12 cellranger_1.1.0
[139] leiden_0.3.9 fastmatch_1.1-3
[141] uwot_0.1.11 restfulr_0.0.13
[143] curl_4.3.2 shiny_1.7.1
[145] Rsamtools_2.10.0 rjson_0.2.21
[147] lifecycle_1.0.1 nlme_3.1-155
[149] jsonlite_1.8.0 viridisLite_0.4.0
[151] fansi_1.0.3 pillar_1.7.0
[153] lattice_0.20-45 KEGGREST_1.34.0
[155] fastmap_1.1.0 httr_1.4.2
[157] survival_3.2-13 glue_1.6.2
[159] png_0.1-7 bit_4.0.4
[161] stringi_1.7.6 sass_0.4.0
[163] blob_1.2.2 memoise_2.0.1
[165] irlba_2.3.5 future.apply_1.8.1