scanpy anndata Objects from a Manuscript¶
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:
Each anndata object contains several elements crucial for comprehensive data analysis:
.X: Filtered, normalized, and log-transformed count matrix..raw: Original, filtered raw count matrix..obsm['MAGIC_imputed_data']: Imputed count matrix using MAGIC algorithm..obsm['tsne']: t-SNE maps (as presented in the manuscript), generated using scaled diffusion components..obs['clusters']: Cell clustering information..obs['palantir_pseudotime']: Cell pseudo-time ordering, as determined by Palantir..obs['palantir_diff_potential']: Palantir-determined differentiation potential of cells..obsm['palantir_branch_probs']: Probabilities of cells branching into different lineages, according to Palantir..uns['palantir_branch_probs_cell_types']: Labels for Palantir branch probabilities..uns['ct_colors']: Color codes for cell types, as used in the manuscript..uns['cluster_colors']: Color codes for cell clusters, as used in the manuscript.In [1]:
import scanpy as sc
# Read in the data, with backup URLs provided
adata_Rep1 = sc.read(
"../data/human_cd34_bm_rep1.h5ad",
backup_url="https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep1.h5ad",
)
adata_Rep2 = sc.read(
"../data/human_cd34_bm_rep2.h5ad",
backup_url="https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep2.h5ad",
)
adata_Rep3 = sc.read(
"../data/human_cd34_bm_rep3.h5ad",
backup_url="https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep3.h5ad",
)
Out[2]:
AnnData object with n_obs × n_vars = 5780 × 14651
obs: 'clusters', 'palantir_pseudotime', 'palantir_diff_potential'
uns: 'cluster_colors', 'ct_colors', 'palantir_branch_probs_cell_types'
obsm: 'tsne', 'MAGIC_imputed_data', 'palantir_branch_probs'
Out[3]:
AnnData object with n_obs × n_vars = 6501 × 14913
obs: 'clusters', 'palantir_pseudotime', 'palantir_diff_potential'
uns: 'cluster_colors', 'ct_colors', 'palantir_branch_probs_cell_types'
obsm: 'tsne', 'MAGIC_imputed_data', 'palantir_branch_probs'
Out[4]:
AnnData object with n_obs × n_vars = 12046 × 14044
obs: 'clusters', 'palantir_pseudotime', 'palantir_diff_potential'
uns: 'cluster_colors', 'ct_colors', 'palantir_branch_probs_cell_types'
obsm: 'tsne', 'MAGIC_imputed_data', 'palantir_branch_probs' Converting anndata Objects to Seurat Objects Using R¶
For researchers working with R and Seurat, the process to convert anndata objects to Seurat objects involves the following steps:
Set Up R Environment and Libraries:
Seurat and anndata.Download and Read the Data:
curl::curl_download to download the anndata from the provided URLs.read_h5ad method from the anndata library.Create Seurat Objects:
CreateSeuratObject function to convert the data into Seurat objects, incorporating counts and metadata from the anndata object.In [ ]:
# this cell only exists to allow running R code inside this python notebook using a conda kernel
import sys
import os
# Get the path to the python executable
python_executable_path = sys.executable
# Extract the path to the environment from the path to the python executable
env_path = os.path.dirname(os.path.dirname(python_executable_path))
print(
f"Conda env path: {env_path}\n"
"Please make sure you have R installed in the conda environment."
)
os.environ['R_HOME'] = os.path.join(env_path, 'lib', 'R')
%load_ext rpy2.ipython
In [6]:
%%R
library(Seurat)
library(anndata)
create_seurat <- function(url) {
file_path <- sub("https://s3.amazonaws.com/dp-lab-data-public/palantir/", "../data/", url)
if (!file.exists(file_path)) {
curl::curl_download(url, file_path)
}
data <- read_h5ad(file_path)
seurat_obj <- CreateSeuratObject(
counts = t(data$X),
meta.data = data$obs,
project = "CD34+ Bone Marrow Cells"
)
tsne_data <- data$obsm[["tsne"]]
rownames(tsne_data) <- rownames(data$obs)
colnames(tsne_data) <- c("tSNE_1", "tSNE_2")
seurat_obj[["tsne"]] <- CreateDimReducObject(
embeddings = tsne_data,
key = "tSNE_"
)
imputed_data <- t(data$obsm[["MAGIC_imputed_data"]])
colnames(imputed_data) <- rownames(data$obs)
rownames(imputed_data) <- rownames(data$var)
seurat_obj[["MAGIC_imputed"]] <- CreateAssayObject(counts = imputed_data)
fate_probs <- as.data.frame(data$obsm[["palantir_branch_probs"]])
colnames(fate_probs) <- data$uns[["palantir_branch_probs_cell_types"]]
rownames(fate_probs) <- rownames(data$obs)
seurat_obj <- AddMetaData(seurat_obj, metadata = fate_probs)
return(seurat_obj)
}
human_cd34_bm_Rep1 <- create_seurat("https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep1.h5ad")
human_cd34_bm_Rep2 <- create_seurat("https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep2.h5ad")
human_cd34_bm_Rep3 <- create_seurat("https://s3.amazonaws.com/dp-lab-data-public/palantir/human_cd34_bm_rep3.h5ad")
R[write to console]: Loading required package: SeuratObject
R[write to console]: Loading required package: sp
R[write to console]:
Attaching package: ‘SeuratObject’
R[write to console]: The following object is masked from ‘package:base’:
intersect
WARNING: The R package "reticulate" only fixed recently
an issue that caused a segfault when used with rpy2:
https://github.com/rstudio/reticulate/pull/1188
Make sure that you use a version of that package that includes
the fix.
R[write to console]:
Attaching package: ‘anndata’
R[write to console]: The following object is masked from ‘package:SeuratObject’:
Layers
R[write to console]: Warning:
R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')
R[write to console]: Warning:
R[write to console]: Data is of class matrix. Coercing to dgCMatrix.
R[write to console]: Warning:
R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')
R[write to console]: Warning:
R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')
R[write to console]: Warning:
R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')
R[write to console]: Warning:
R[write to console]: Data is of class matrix. Coercing to dgCMatrix.
R[write to console]: Warning:
R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')
R[write to console]: Warning:
R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')
R[write to console]: Warning:
R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')
R[write to console]: Warning:
R[write to console]: Data is of class matrix. Coercing to dgCMatrix.
R[write to console]: Warning:
R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')
R[write to console]: Warning:
R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')
An object of class Seurat 29302 features across 5780 samples within 2 assays Active assay: RNA (14651 features, 0 variable features) 1 layer present: counts 1 other assay present: MAGIC_imputed 1 dimensional reduction calculated: tsne
An object of class Seurat 29826 features across 6501 samples within 2 assays Active assay: RNA (14913 features, 0 variable features) 1 layer present: counts 1 other assay present: MAGIC_imputed 1 dimensional reduction calculated: tsne
An object of class Seurat 28088 features across 12046 samples within 2 assays Active assay: RNA (14044 features, 0 variable features) 1 layer present: counts 1 other assay present: MAGIC_imputed 1 dimensional reduction calculated: tsne
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