diff --git a/README.md b/README.md index 7f2a5289..740c0fda 100755 --- a/README.md +++ b/README.md @@ -17,6 +17,14 @@ Palantir has been implemented in Python3 and can be installed using: A tutorial on Palantir usage and results visualization for single cell RNA-seq data can be found in this notebook: http://nbviewer.jupyter.org/github/dpeerlab/Palantir/blob/master/notebooks/Palantir_sample_notebook.ipynb +## Processed data and metadata + +`scanpy anndata` objects are available for download for the three replicates generated in the manuscript: +- [Replicate 1 (Rep1)](https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep1.h5ad) +- [Replicate 2 (Rep2)](https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep2.h5ad) +- [Replicate 3 (Rep3)](https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep3.h5ad) + +This notebook details how to use the data in `Python` and `R`: http://nbviewer.jupyter.org/github/dpeerlab/Palantir/blob/master/notebooks/manuscript_data.ipynb ## Comparison to trajectory detection algorithms Notebooks detailing the generation of results comparing Palantir to trajectory detection algorithms are available [here](https://github.com/dpeerlab/Palantir/blob/master/notebooks/comparisons) diff --git a/notebooks/manuscript_data.ipynb b/notebooks/manuscript_data.ipynb new file mode 100644 index 00000000..2277e576 --- /dev/null +++ b/notebooks/manuscript_data.ipynb @@ -0,0 +1,501 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "68a5c2f5-9391-4170-b5ea-9df9ad5eafb4", + "metadata": {}, + "source": [ + "# Access and Analyze `scanpy anndata` Objects from a Manuscript\n", + "\n", + "This guide provides steps to access and analyze the `scanpy anndata` objects associated with a recent manuscript. These objects are essential for computational biologists and data scientists working in genomics and related fields. There are three replicates available for download:\n", + "\n", + "- [Replicate 1 (Rep1)](https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep1.h5ad)\n", + "- [Replicate 2 (Rep2)](https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep2.h5ad)\n", + "- [Replicate 3 (Rep3)](https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep3.h5ad)\n", + "\n", + "Each `anndata` object contains several elements crucial for comprehensive data analysis:\n", + "\n", + "1. `.X`: Filtered, normalized, and log-transformed count matrix.\n", + "2. `.raw`: Original, filtered raw count matrix.\n", + "3. `.obsm['MAGIC_imputed_data']`: Imputed count matrix using MAGIC algorithm.\n", + "4. `.obsm['tsne']`: t-SNE maps (as presented in the manuscript), generated using scaled diffusion components.\n", + "5. `.obs['clusters']`: Cell clustering information.\n", + "6. `.obs['palantir_pseudotime']`: Cell pseudo-time ordering, as determined by Palantir.\n", + "7. `.obs['palantir_diff_potential']`: Palantir-determined differentiation potential of cells.\n", + "8. `.obsm['palantir_branch_probs']`: Probabilities of cells branching into different lineages, according to Palantir.\n", + "9. `.uns['palantir_branch_probs_cell_types']`: Labels for Palantir branch probabilities.\n", + "10. `.uns['ct_colors']`: Color codes for cell types, as used in the manuscript.\n", + "11. `.uns['cluster_colors']`: Color codes for cell clusters, as used in the manuscript.\n", + "12. `.varm['mast_diff_res_pval']`: MAST algorithm p-values for differential expression analysis across clusters.\n", + "13. `.varm['mast_diff_res_statistic']`: Statistical values from MAST for differential expression.\n", + "14. `.uns['mast_diff_res_columns']`: Column names for MAST differential expression results.\n", + "\n", + "## Python Code for Data Access:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "63f356a7-3856-4596-a7b3-9fc05cc3029a", + "metadata": { + "execution": { + "iopub.execute_input": "2023-11-28T21:20:46.755293Z", + "iopub.status.busy": "2023-11-28T21:20:46.755059Z", + "iopub.status.idle": "2023-11-28T21:20:59.646740Z", + "shell.execute_reply": "2023-11-28T21:20:59.645355Z", + "shell.execute_reply.started": "2023-11-28T21:20:46.755266Z" + } + }, + "outputs": [], + "source": [ + "import scanpy as sc\n", + "\n", + "# Read in the data, with backup URLs provided\n", + "adata_Rep1 = sc.read(\n", + " \"../data/human_cd34_bm_rep1.h5ad\",\n", + " backup_url=\"https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep1.h5ad\",\n", + ")\n", + "adata_Rep2 = sc.read(\n", + " \"../data/human_cd34_bm_rep2.h5ad\",\n", + " backup_url=\"https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep2.h5ad\",\n", + ")\n", + "adata_Rep3 = sc.read(\n", + " \"../data/human_cd34_bm_rep3.h5ad\",\n", + " backup_url=\"https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep3.h5ad\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bee4a735-7c47-415a-b1e3-ee776998dbd5", + "metadata": { + "execution": { + "iopub.execute_input": "2023-11-28T21:20:59.650053Z", + "iopub.status.busy": "2023-11-28T21:20:59.649313Z", + "iopub.status.idle": "2023-11-28T21:20:59.659463Z", + "shell.execute_reply": "2023-11-28T21:20:59.658910Z", + "shell.execute_reply.started": "2023-11-28T21:20:59.650021Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 5780 × 14651\n", + " obs: 'clusters', 'palantir_pseudotime', 'palantir_diff_potential'\n", + " uns: 'cluster_colors', 'ct_colors', 'palantir_branch_probs_cell_types'\n", + " obsm: 'tsne', 'MAGIC_imputed_data', 'palantir_branch_probs'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_Rep1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "515e6760-8f95-42d6-87ba-1a2375797ccf", + "metadata": { + "execution": { + "iopub.execute_input": "2023-11-28T21:20:59.660313Z", + "iopub.status.busy": "2023-11-28T21:20:59.660133Z", + "iopub.status.idle": "2023-11-28T21:20:59.676952Z", + "shell.execute_reply": "2023-11-28T21:20:59.676283Z", + "shell.execute_reply.started": "2023-11-28T21:20:59.660295Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 6501 × 14913\n", + " obs: 'clusters', 'palantir_pseudotime', 'palantir_diff_potential'\n", + " uns: 'cluster_colors', 'ct_colors', 'palantir_branch_probs_cell_types'\n", + " obsm: 'tsne', 'MAGIC_imputed_data', 'palantir_branch_probs'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_Rep2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "61d7a8e0-0916-4099-8982-5599d7166104", + "metadata": { + "execution": { + "iopub.execute_input": "2023-11-28T21:20:59.678250Z", + "iopub.status.busy": "2023-11-28T21:20:59.677863Z", + "iopub.status.idle": "2023-11-28T21:20:59.691822Z", + "shell.execute_reply": "2023-11-28T21:20:59.691131Z", + "shell.execute_reply.started": "2023-11-28T21:20:59.678220Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 12046 × 14044\n", + " obs: 'clusters', 'palantir_pseudotime', 'palantir_diff_potential'\n", + " uns: 'cluster_colors', 'ct_colors', 'palantir_branch_probs_cell_types'\n", + " obsm: 'tsne', 'MAGIC_imputed_data', 'palantir_branch_probs'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_Rep3" + ] + }, + { + "cell_type": "markdown", + "id": "b057a720-f0f4-40b0-8bcf-02efc9b2124d", + "metadata": { + "execution": { + "iopub.execute_input": "2023-11-28T19:21:40.634650Z", + "iopub.status.busy": "2023-11-28T19:21:40.634039Z", + "iopub.status.idle": "2023-11-28T19:21:40.647637Z", + "shell.execute_reply": "2023-11-28T19:21:40.646498Z", + "shell.execute_reply.started": "2023-11-28T19:21:40.634595Z" + } + }, + "source": [ + "## Converting `anndata` Objects to `Seurat` Objects Using R\n", + "\n", + "For researchers working with R and Seurat, the process to convert `anndata` objects to Seurat objects involves the following steps:\n", + "\n", + "1. **Set Up R Environment and Libraries**:\n", + " - Load the necessary libraries: `Seurat` and `anndata`.\n", + "\n", + "2. **Download and Read the Data**:\n", + " - Use `curl::curl_download` to download the `anndata` from the provided URLs.\n", + " - Read the data using the `read_h5ad` method from the `anndata` library.\n", + "\n", + "3. **Create Seurat Objects**:\n", + " - Use the `CreateSeuratObject` function to convert the data into Seurat objects, incorporating counts and metadata from the `anndata` object.\n", + " - Transfer additional data like tSNE embeddings, imputed gene expressions, and cell fate probabilities into the appropriate slots in the Seurat object.\n", + "\n", + "### R Code Snippet:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "562d56fb-80dc-4f44-8266-3ca559e79106", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "# this cell only exists to allow running R code inside this python notebook using a conda kernel\n", + "import sys\n", + "import os\n", + "\n", + "# Get the path to the python executable\n", + "python_executable_path = sys.executable\n", + "\n", + "# Extract the path to the environment from the path to the python executable\n", + "env_path = os.path.dirname(os.path.dirname(python_executable_path))\n", + "\n", + "print(\n", + " f\"Conda env path: {env_path}\\n\"\n", + " \"Please make sure you have R installed in the conda environment.\"\n", + ")\n", + "\n", + "os.environ['R_HOME'] = os.path.join(env_path, 'lib', 'R')\n", + "\n", + "%load_ext rpy2.ipython" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ed46f119-e8be-45ba-b447-b46e8b947cf8", + "metadata": { + "execution": { + "iopub.execute_input": "2023-11-28T21:21:01.081154Z", + "iopub.status.busy": "2023-11-28T21:21:01.080675Z", + "iopub.status.idle": "2023-11-28T21:23:08.313753Z", + "shell.execute_reply": "2023-11-28T21:23:08.313058Z", + "shell.execute_reply.started": "2023-11-28T21:21:01.081128Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: Loading required package: SeuratObject\n", + "\n", + "R[write to console]: Loading required package: sp\n", + "\n", + "R[write to console]: \n", + "Attaching package: ‘SeuratObject’\n", + "\n", + "\n", + "R[write to console]: The following object is masked from ‘package:base’:\n", + "\n", + " intersect\n", + "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " WARNING: The R package \"reticulate\" only fixed recently\n", + " an issue that caused a segfault when used with rpy2:\n", + " https://github.com/rstudio/reticulate/pull/1188\n", + " Make sure that you use a version of that package that includes\n", + " the fix.\n", + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: \n", + "Attaching package: ‘anndata’\n", + "\n", + "\n", + "R[write to console]: The following object is masked from ‘package:SeuratObject’:\n", + "\n", + " Layers\n", + "\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Data is of class matrix. Coercing to dgCMatrix.\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Data is of class matrix. Coercing to dgCMatrix.\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Data is of class matrix. Coercing to dgCMatrix.\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", + "\n", + "R[write to console]: Warning:\n", + "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", + "\n" + ] + } + ], + "source": [ + "%%R\n", + "library(Seurat)\n", + "library(anndata)\n", + "\n", + "create_seurat <- function(url) {\n", + " file_path <- sub(\"https://s3.amazonaws.com/dp-lab-data-public/palantir/\", \"../data/\", url)\n", + " if (!file.exists(file_path)) {\n", + " curl::curl_download(url, file_path)\n", + " }\n", + " data <- read_h5ad(file_path)\n", + " \n", + " seurat_obj <- CreateSeuratObject(\n", + " counts = t(data$X), \n", + " meta.data = data$obs,\n", + " project = \"CD34+ Bone Marrow Cells\"\n", + " )\n", + " tsne_data <- data$obsm[[\"tsne\"]]\n", + " rownames(tsne_data) <- rownames(data$obs)\n", + " colnames(tsne_data) <- c(\"tSNE_1\", \"tSNE_2\")\n", + " seurat_obj[[\"tsne\"]] <- CreateDimReducObject(\n", + " embeddings = tsne_data,\n", + " key = \"tSNE_\"\n", + " )\n", + " imputed_data <- t(data$obsm[[\"MAGIC_imputed_data\"]])\n", + " colnames(imputed_data) <- rownames(data$obs)\n", + " rownames(imputed_data) <- rownames(data$var)\n", + " seurat_obj[[\"MAGIC_imputed\"]] <- CreateAssayObject(counts = imputed_data)\n", + " fate_probs <- as.data.frame(data$obsm[[\"palantir_branch_probs\"]])\n", + " colnames(fate_probs) <- data$uns[[\"palantir_branch_probs_cell_types\"]]\n", + " rownames(fate_probs) <- rownames(data$obs)\n", + " seurat_obj <- AddMetaData(seurat_obj, metadata = fate_probs)\n", + "\n", + " return(seurat_obj)\n", + "}\n", + "\n", + "human_cd34_bm_Rep1 <- create_seurat(\"https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep1.h5ad\")\n", + "human_cd34_bm_Rep2 <- create_seurat(\"https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep2.h5ad\")\n", + "human_cd34_bm_Rep3 <- create_seurat(\"https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep3.h5ad\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a7c8b823-4d18-4252-acc1-4a9f51f929b9", + "metadata": { + "execution": { + "iopub.execute_input": "2023-11-28T21:23:08.315660Z", + "iopub.status.busy": "2023-11-28T21:23:08.315364Z", + "iopub.status.idle": "2023-11-28T21:23:08.361153Z", + "shell.execute_reply": "2023-11-28T21:23:08.360630Z", + "shell.execute_reply.started": "2023-11-28T21:23:08.315642Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "An object of class Seurat \n", + "29302 features across 5780 samples within 2 assays \n", + "Active assay: RNA (14651 features, 0 variable features)\n", + " 1 layer present: counts\n", + " 1 other assay present: MAGIC_imputed\n", + " 1 dimensional reduction calculated: tsne\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "human_cd34_bm_Rep1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "094067ac-b251-4e37-8d67-eedc2641b8fa", + "metadata": { + "execution": { + "iopub.execute_input": "2023-11-28T21:23:08.362383Z", + "iopub.status.busy": "2023-11-28T21:23:08.361964Z", + "iopub.status.idle": "2023-11-28T21:23:08.400063Z", + "shell.execute_reply": "2023-11-28T21:23:08.399518Z", + "shell.execute_reply.started": "2023-11-28T21:23:08.362356Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "An object of class Seurat \n", + "29826 features across 6501 samples within 2 assays \n", + "Active assay: RNA (14913 features, 0 variable features)\n", + " 1 layer present: counts\n", + " 1 other assay present: MAGIC_imputed\n", + " 1 dimensional reduction calculated: tsne\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "human_cd34_bm_Rep2" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6fb000c4-41ee-4147-aba8-08c0e6f7deb5", + "metadata": { + "execution": { + "iopub.execute_input": "2023-11-28T21:23:08.401196Z", + "iopub.status.busy": "2023-11-28T21:23:08.400878Z", + "iopub.status.idle": "2023-11-28T21:23:08.441148Z", + "shell.execute_reply": "2023-11-28T21:23:08.440627Z", + "shell.execute_reply.started": "2023-11-28T21:23:08.401171Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "An object of class Seurat \n", + "28088 features across 12046 samples within 2 assays \n", + "Active assay: RNA (14044 features, 0 variable features)\n", + " 1 layer present: counts\n", + " 1 other assay present: MAGIC_imputed\n", + " 1 dimensional reduction calculated: tsne\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "human_cd34_bm_Rep3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e208ff84-85d0-40f7-b08d-9153537b088a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "da1", + "language": "python", + "name": "da1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}