Tag: H5AD
h5ad cellxgene to R
I am trying to bring this small dataset (1496 cells, 43MB) from cellxgene. 1 R SeuratDisk h5ad to h5seurat and then to seurat. library(SeuratDisk) SeuratDisk::Convert(“local.h5ad”, dest = “local.h5seurat”, overwrite=TRUE) g <- SeuratDisk::LoadH5Seurat(“local.h5seurat”, meta.data=FALSE, misc=FALSE) Validating h5Seurat file Initializing RNA with data Adding counts for RNA Adding feature-level metadata for RNA…
from seurat to scanpy data conversion and re-scaling
Hello everyone I am working on spatial transcriptome data . For analysis I used seurat R package. Specifically, I’ve been working with 4 samples that have a batch effect. To integrate them, I) I used the anchor-based integration method along with SCT normalization. II) From clustering analysis, I found some…
Analysis a scRNAseq object that already has cell type annotation
Analysis a scRNAseq object that already has cell type annotation 0 Hi all I have recently downloaded a publicly available scRNAseq dataset that I want to analyse. The goal will be to do some differential expression analysis between two specific cell type clusters. The raw file was in .h5ad format,…
RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding
RevGel-seq sample preparation workflow Experiments were performed with the RevGel-seq protocol, capable of analyzing 10,000 input cells per sample with the specially designed gelation device (Fig. S3). The individual steps shown in Fig. 1A, from cell-barcoded bead coupling to library preparation for sequencing, are more fully described below: Cell labeling Cells…
How to include spatial coordinate data into anndata within scanpy
Hello I am working on 10X visium spatial transcriptome data which was processed in seurat pacakge with image data. For my downstream analysis, I am trying to import seurat normalized data into scanpy. For this I converted seurat object to h5ad using these steps. SaveH5Seurat(test_object, overwrite = TRUE, filename =…
Analysis of Smart-Seq3 data with kallisto-bustools
Tutorial:Analysis of Smart-Seq3 data with kallisto-bustools 0 Based on @dsull’s answer. The smart-seq3 analysis is handled by the kallisto-bustools pipeline. Create a conda environment if needed. Create the conda environment file and save it as env_kb.yml. # conda environment for kallisto bustools pipeline name: kb channels: – bioconda – conda-forge…
scRNAseq analysis – h5ad file conversion to Seurat format
scRNAseq analysis – h5ad file conversion to Seurat format 0 Hi all. I have a single .h5ad file that contains scRNAseq data from several samples. I would like to convert it so that I can open it in Seurat (I am comfortable with R, but not with Python). I have…
Single cell database scrna dB for bioinformatics database development (1)
Single cell database construction High quality integrated single cell database If readers just want to get a ready-made single-cell database with rich content and add it to their own PC or linux The server , You can skip the following detailed theoretical tutorial Database download link : Click to download…
Obs_name
Obs_name 0 Can someone please help explain what the Obs_name represents? I exported the anndata matrix (h5ad) and attempted to transform it to the Seurat object using something similar to the following: exprs <- t(adata$X) colnames(exprs) <- adata$obs_names$to_list() rownames(exprs) <- adata$var_names$to_list() # Create the Seurat object seurat <- CreateSeuratObject(exprs) #…
Best compression for single cell RNA-seq object
Best compression for single cell RNA-seq object 0 I generated a scRNA-seq object (counts, PCA, UMAP embeddings, DEGs etc.) in Scanpy or Seurat. What is the best data structure to store this in to reduce the size of the object? I’m considering H5AD (scanpy/anndata), RDS or H5Seurat (Seurat), or Loom…