{"kind":"notebook","key":"pbmc3k.Rmd","description":"PBMC 3k analysis","reference":null,"uid":"KFtlfbCiP9Bm000A","source_code":"---\ntitle: \"PBMC 3k analysis\"\noutput: html_document\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(\n echo = TRUE\n)\n```\n\nLet's see how Rmd files are handled on Lamin's end by performing an example Seurat analysis.\n\nFirst we connect to the instance we want to use.\nThis must be done ***BEFORE*** importing `lamindb`.\nIf you don't explicitly connect, the current default instance will be used.\n\n```{r connect}\nlc <- laminr::import_module(\"lamin_cli\", silent = TRUE)\nlc$connect(\"laminlabs/training\")\n```\n\nNow we can import the `lamindb` module to run commands.\n\n```{r import}\nlibrary(laminr)\n\nln <- import_module(\"lamindb\", silent = TRUE)\n```\n\nFrom here, we're going to track every move of this session.\n\n```{r track}\n# Set up a project\nproj <- ln$Project(name = \"Basic Seurat analysis\")$save()\n\n# Start a tracked run\nln$track(project = proj)\n```\n\nGreat. Now let's get the PBMC 3k dataset in here. \n\n```{r libraries}\nlibrary(tidyverse)\nlibrary(Seurat)\n```\n\nLet's get this going.\n\n```{r load}\ncounts <- read.table(\n file = system.file('extdata', 'pbmc_raw.txt', package = 'Seurat'),\n as.is = TRUE\n)\ncells <- CreateSeuratObject(counts)\n```\n\nAnd now we're going to run it through the usual set of things, using Seurat's best practices.\n\n```{r normalise}\ncells <- NormalizeData(\n cells, normalization.method = \"LogNormalize\", scale.factor = 10000\n)\n```\n\n```{r variable-features}\ncells <- FindVariableFeatures(cells, selection.method = \"vst\", nfeatures = 2000)\n```\n\nNote that this step makes the Seurat object much more bulky.\n\n```{r scale}\nall.genes <- rownames(cells)\ncells <- ScaleData(cells, features = all.genes)\n```\n\nFrom here, we do our PCA.\n\n```{r pca}\ncells <- RunPCA(cells, features = VariableFeatures(object = cells))\n```\n\nWhich sets the stage for the clustering and non-linear dimensionality reduction steps below.\n\nWe can get a feel for the intrinsic dimensionality of the data below.\n\n```{r elbow}\nElbowPlot(cells)\n```\n\nWe then do clustering, which is Louvain clustering of a SNN graph.\n\n```{r cluster}\ncells <- FindNeighbors(cells, dims = 1:10)\ncells <- FindClusters(cells, resolution = 0.5)\n```\n\nAnd from here, we run a UMAP for visualization.\n \n```{r umap}\ncells <- RunUMAP(cells, dims = 1:10)\n```\n\nNow let's look at it.\n\n```{r plot-umap}\n# note that you can set `label = TRUE` or use the LabelClusters function to\n# help label individual clusters\nDimPlot(cells, reduction = \"umap\")\n```\n\nMore or less what was expected.\n\nAnd let's color by some features.\n\n```{r plot-features}\nFeaturePlot(cells, features = c(\"MS4A1\", \"GNLY\", \"CD3E\", \"CD14\", \"FCER1A\", \"FCGR3A\", \"LYZ\", \"PPBP\", \"CD8A\"))\n```\n\nCool. It's really ugly when you visualize it in the Rmd interface, but when you knit this thing, it's ok.\n\nOk, and from here, we're going to set this as a Lamin artifact and save it out accordingly.\n\nWe tried to save it as a h5ad object, but that didn't work. So we're just going to go ahead and output as a rds, which is standard when you're handing Seurat.\nThere are ways to convert it to h5ad that are beyond the scope of this test here.\n\nAnd now we save.\n\n```{r save-rds}\nsaveRDS(cells, \"pbmc3k_processed.rds\")\n```\n\nAnd now we turn the saved file into a Lamin artifact.\n\n```{r save-artifact}\n# Use this rather than lamin_save\nln$Artifact(\"pbmc3k_processed.rds\", key = \"pbmc3k/pbmc3k_processed.rds\")$save()\n```\n\nAnd now we end the session.\n\n```{r finish}\n# optional: close out the tracked run so it shows up neatly in the hub\nln$finish()\n```\n","hash":"iql9g166mbSmqZ300IMADw","id":50,"updated_at":"2025-10-01T10:06:28.998309+00:00","is_locked":false,"reference_type":null,"created_at":"2025-10-01T10:06:28.998309+00:00","_aux":null,"version_tag":null,"is_latest":true,"branch":{"id":1,"uid":"mmmmmmmmmmmm","_aux":null,"name":"main","created_at":"2025-06-01T22:18:55.628024+00:00","description":"The main & default branch of the instance.","_status_code":0},"created_by":{"id":9,"uid":"gQNxnqcL","name":"Luke Zappia (DI)","handle":"luke_at_di","created_at":"2025-08-21T15:29:14.271846+00:00","updated_at":"2026-04-29T10:32:29.974999+00:00"},"space":{"id":1,"uid":"aaaaaaaaaaaa"},"runs":[{"id":60,"uid":"WD6vpHARh3HU6dLO","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2025-10-01T10:06:30.585374+00:00","entrypoint":null,"extra_data":null,"started_at":"2025-10-01T10:06:30.488963+00:00","updated_at":"2026-01-11T06:55:00.052388+00:00","description":null,"finished_at":"2025-10-01T10:06:53.181368+00:00","_status_code":0,"reference_type":null,"_is_consecutive":true,"input_artifacts":[],"output_artifacts":[{"id":97,"key":"pbmc3k/pbmc3k_processed.rds","uid":"VugfUMiwR8OtlnIU000L","_aux":null,"hash":"o-GTLDXWJtBPpPfjffeuhA","kind":null,"size":207742,"otype":null,"suffix":".rds","n_files":null,"_real_key":null,"is_latest":true,"is_locked":false,"_hash_type":"md5","created_at":"2025-10-01T10:06:42.133141+00:00","extra_data":null,"updated_at":"2025-10-01T10:06:42.133141+00:00","description":null,"version_tag":null,"n_observations":null,"_key_is_virtual":true,"_overwrite_versions":false}],"json_values":[],"created_by":{"id":9,"uid":"gQNxnqcL","name":"Luke Zappia (DI)","handle":"luke_at_di","created_at":"2025-08-21T15:29:14.271846+00:00","updated_at":"2026-04-29T10:32:29.974999+00:00"},"report":{"id":98,"key":null,"uid":"1XHPE7ksoK9lZhWx0000","_aux":null,"hash":"SpPFknnaXSpd86ZTcRK75g","kind":"__lamindb_run__","size":1030127,"otype":null,"suffix":".html","n_files":null,"_real_key":null,"is_latest":true,"is_locked":false,"_hash_type":"md5","created_at":"2025-10-01T10:07:03.447486+00:00","extra_data":null,"updated_at":"2025-10-01T10:07:03.447486+00:00","description":"Report of run WD6vpHARh3HU6dLO","version_tag":null,"n_observations":null,"_key_is_virtual":true,"_overwrite_versions":false}}],"ulabels":[],"references":[],"projects":[{"id":2,"uid":"Ket9GTtrDHjb","url":null,"_aux":null,"abbr":null,"name":"Basic Seurat analysis","is_type":false,"end_date":null,"is_locked":false,"created_at":"2025-06-30T14:33:16.262744+00:00","start_date":null,"updated_at":"2025-06-30T14:33:16.262744+00:00","description":null,"_status_code":0}],"ablocks":[],"created_on":{"id":1,"uid":"mmmmmmmmmmmm","name":"main"},"run":null}

Input	Output
	000L pbmc3k/pbmc3k_processed.rds VugfUMiw (208 kB)

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