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b/cache/unnamed-chunk-21_e3e49bf8cb680c8be603eec8724ca55b.rdb similarity index 100% rename from cache/unnamed-chunk-21_8ad7b5653727d8b378d7320fd23d921e.rdb rename to cache/unnamed-chunk-21_e3e49bf8cb680c8be603eec8724ca55b.rdb diff --git a/cache/unnamed-chunk-21_8ad7b5653727d8b378d7320fd23d921e.rdx b/cache/unnamed-chunk-21_e3e49bf8cb680c8be603eec8724ca55b.rdx similarity index 100% rename from cache/unnamed-chunk-21_8ad7b5653727d8b378d7320fd23d921e.rdx rename to cache/unnamed-chunk-21_e3e49bf8cb680c8be603eec8724ca55b.rdx diff --git a/hca.md b/hca.md index 701a46f..74298ba 100644 --- a/hca.md +++ b/hca.md @@ -239,22 +239,6 @@ metadata |> 12 sci-RNA-seq 1 ``` -## Available organisms - - -``` r -metadata |> - distinct(organism, dataset_id) |> - count(organism) -``` - -``` output -# A tibble: 1 × 2 - organism n - -1 Homo sapiens 63 -``` - ### Download single-cell RNA sequencing counts The data can be provided as either "counts" or counts per million "cpm" as given diff --git a/large_data.md b/large_data.md index b696228..deaca24 100644 --- a/large_data.md +++ b/large_data.md @@ -64,7 +64,9 @@ set, as provided by the ``` r library(TENxBrainData) + sce.brain <- TENxBrainData20k() + sce.brain ``` @@ -150,7 +152,9 @@ new file at every operation, which would unnecessarily require time-consuming di ``` r tmp <- counts(sce.brain) + tmp <- log2(tmp + 1) + tmp ``` @@ -183,8 +187,11 @@ function that we used in the other workflows. ``` r library(scater) + is.mito <- grepl("^mt-", rowData(sce.brain)$Symbol) + qcstats <- perCellQCMetrics(sce.brain, subsets = list(Mt = is.mito)) + qcstats ``` @@ -253,9 +260,10 @@ by indicating the `BPPARAM` argument in `bplapply`. ``` r param <- MulticoreParam(workers = 1) + bplapply( X = c(4, 9, 16, 25), - FUN = function(x) { sqrt(x) }, + FUN = sqrt, BPPARAM = param ) ``` @@ -286,10 +294,15 @@ calculations on a Unix system: ``` r library(MouseGastrulationData) + library(scran) + sce <- WTChimeraData(samples = 5, type = "processed") + sce <- logNormCounts(sce) + dec.mc <- modelGeneVar(sce, BPPARAM = MulticoreParam(2)) + dec.mc ``` @@ -342,6 +355,7 @@ details). ``` r # 2 hours, 8 GB, 1 CPU per task, for 10 tasks. rs <- list(walltime = 7200, memory = 8000, ncpus = 1) + bpp <- BatchtoolsParam(10, cluster = "slurm", resources = rs) ``` @@ -393,7 +407,9 @@ graph-based clustering using the Louvain algorithm for community detection: ``` r library(bluster) + sce <- runPCA(sce) + colLabels(sce) <- clusterCells(sce, use.dimred = "PCA", BLUSPARAM = NNGraphParam(cluster.fun = "louvain")) ``` @@ -410,10 +426,13 @@ approximation can be largely ignored. ``` r library(scran) + library(BiocNeighbors) + clusters <- clusterCells(sce, use.dimred = "PCA", BLUSPARAM = NNGraphParam(cluster.fun = "louvain", BNPARAM = AnnoyParam())) + table(exact = colLabels(sce), approx = clusters) ``` @@ -421,19 +440,19 @@ table(exact = colLabels(sce), approx = clusters) approx exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 90 0 0 0 0 0 0 0 1 0 0 0 0 0 0 - 2 0 143 0 1 0 0 0 0 0 0 0 0 0 0 0 + 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1 3 0 0 75 0 0 0 0 0 0 0 0 0 0 0 0 - 4 0 0 0 253 0 0 0 0 0 0 0 0 144 0 0 - 5 0 0 2 0 391 1 0 0 0 1 0 3 0 0 0 - 6 0 0 0 0 0 206 51 0 0 0 1 0 0 0 0 - 7 0 0 0 0 0 3 194 0 0 1 0 0 0 0 0 - 8 0 0 0 0 2 0 0 91 0 0 0 2 0 0 0 + 4 0 0 0 342 0 0 0 0 0 0 0 0 0 0 55 + 5 0 0 0 0 74 0 0 0 0 0 0 198 0 0 0 + 6 0 0 0 0 0 210 0 0 0 0 0 0 0 0 0 + 7 0 0 0 0 0 0 245 0 0 1 0 0 0 0 0 + 8 0 0 0 0 1 0 0 95 0 0 0 0 0 0 0 9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0 - 10 0 0 0 0 0 0 0 0 0 113 8 0 0 0 0 - 11 0 0 0 0 0 0 0 0 0 0 144 0 0 0 0 - 12 0 0 0 0 2 0 0 0 0 15 0 199 0 0 0 - 13 0 0 0 0 0 0 0 0 0 0 0 0 0 146 0 - 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 + 10 0 0 0 0 0 0 0 0 0 113 0 16 0 0 0 + 11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0 + 12 0 0 2 0 321 0 0 0 0 1 0 0 0 0 0 + 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0 + 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 ``` The similarity of the two clusterings can be quantified by calculating the pairwise Rand index: @@ -441,6 +460,7 @@ The similarity of the two clusterings can be quantified by calculating the pairw ``` r rand <- pairwiseRand(colLabels(sce), clusters, mode = "index") + stopifnot(rand > 0.8) ``` @@ -455,11 +475,17 @@ the biological conclusions. ``` r set.seed(1000) + y1 <- matrix(rnorm(50000), nrow = 1000) + y2 <- matrix(rnorm(50000), nrow = 1000) + Y <- rbind(y1, y2) + exact <- findKNN(Y, k = 20) + approx <- findKNN(Y, k = 20, BNPARAM = AnnoyParam()) + mean(exact$index != approx$index) ``` @@ -487,7 +513,9 @@ library(BiocSingular) # As the name suggests, it is random, so we need to set the seed. set.seed(101000) + r.out <- runPCA(sce, ncomponents = 20, BSPARAM = RandomParam()) + str(reducedDim(r.out, "PCA")) ``` @@ -506,7 +534,9 @@ str(reducedDim(r.out, "PCA")) ``` r set.seed(101001) + i.out <- runPCA(sce, ncomponents = 20, BSPARAM = IrlbaParam()) + str(reducedDim(i.out, "PCA")) ``` @@ -546,7 +576,9 @@ This code block calculates the exact PCA coordinates. Another thing to note: PC ``` r set.seed(123) + e.out <- runPCA(sce, ncomponents = 20, BSPARAM = ExactParam()) + str(reducedDim(e.out, "PCA")) ``` @@ -661,6 +693,7 @@ We then proceed by loading all required packages and installing the PBMC dataset ``` r library(SeuratData) + InstallData("pbmc3k") ``` @@ -671,9 +704,13 @@ We then load the dataset as an `SeuratObject` and convert it to a ``` r # Use PBMC3K from SeuratData pbmc <- LoadData(ds = "pbmc3k", type = "pbmc3k.final") + pbmc <- UpdateSeuratObject(pbmc) + pbmc + pbmc.sce <- as.SingleCellExperiment(pbmc) + pbmc.sce ``` @@ -683,8 +720,11 @@ we demonstrate this here on the wild-type chimera mouse gastrulation dataset. ``` r sce <- WTChimeraData(samples = 5, type = "processed") + assay(sce) <- as.matrix(assay(sce)) + sce <- logNormCounts(sce) + sce ``` @@ -694,7 +734,9 @@ the `as.Seurat` function. ``` r sobj <- as.Seurat(sce) + Idents(sobj) <- "celltype.mapped" + sobj ``` @@ -734,6 +776,7 @@ package. ``` r example_h5ad <- system.file("extdata", "krumsiek11.h5ad", package = "zellkonverter") + readH5AD(example_h5ad) ``` @@ -758,6 +801,7 @@ chimera mouse gastrulation dataset. ``` r out.file <- tempfile(fileext = ".h5ad") + writeH5AD(sce, file = out.file) ``` @@ -985,15 +1029,18 @@ Use the function `system.time` to obtain the runtime of each job. ``` r sce.brain = logNormCounts(sce.brain) -system.time({i.out <- runPCA(sce.brain, ncomponents = 20, +system.time({i.out <- runPCA(sce.brain, + ncomponents = 20, BSPARAM = ExactParam(), BPPARAM = SerialParam())}) -system.time({i.out <- runPCA(sce.brain, ncomponents = 20, +system.time({i.out <- runPCA(sce.brain, + ncomponents = 20, BSPARAM = ExactParam(), BPPARAM = MulticoreParam(workers = 2))}) -system.time({i.out <- runPCA(sce.brain, ncomponents = 20, +system.time({i.out <- runPCA(sce.brain, + ncomponents = 20, BSPARAM = ExactParam(), BPPARAM = MulticoreParam(workers = 3))}) ``` diff --git a/md5sum.txt b/md5sum.txt index b39df98..7e50b5a 100644 --- a/md5sum.txt +++ b/md5sum.txt @@ -8,8 +8,8 @@ "episodes/eda_qc.Rmd" "1e88f395d30778f4526532deea43eb03" "site/built/eda_qc.md" "2024-09-06" "episodes/cell_type_annotation.Rmd" "66af56b730aaa88e937bc1743afb471a" "site/built/cell_type_annotation.md" "2024-09-08" "episodes/multi-sample.Rmd" "2d38d9903358ea8a8067abd82a1f1f54" "site/built/multi-sample.md" "2024-09-08" -"episodes/large_data.Rmd" "bbe443f474a0823122658effa2beb57e" "site/built/large_data.md" "2024-09-06" -"episodes/hca.Rmd" "6db220495ae4ae56d33e4ca5b5f9b8ae" "site/built/hca.md" "2024-09-06" +"episodes/large_data.Rmd" "b9710492c6792ea435778c4e42f27e02" "site/built/large_data.md" "2024-09-09" +"episodes/hca.Rmd" "e01d3fd1e07f158bed08b72d657ae1d1" "site/built/hca.md" "2024-09-09" "instructors/instructor-notes.md" "cae72b6712578d74a49fea7513099f8c" "site/built/instructor-notes.md" "2024-09-06" "learners/reference.md" "40fc1d0be2412d2d9d434a5bc84e4de8" "site/built/reference.md" "2024-09-06" "learners/setup.md" "25772142a26fe3c0cebbe650f5683269" "site/built/setup.md" "2024-09-06"