Tag: WNN

Converting scMultiome data to loom using SEURAT

I’m using scMultiom data with the CCAF tool to predict cell cycle phases. CCAF : github.com/plaisier-lab/ccAF CCAF requires a loom file as input. I converted the output h5 file from cellranger-arc and atac_fragment.tsv.gz to a loom file using Seurat’s code. library(Seurat) library(Signac) library(EnsDb.Hsapiens.v86) library(dplyr) library(ggplot2) library(SeuratDisk) inputdata.10x <- Read10X_h5(“D:/Halima’s Data/Thesis_2/1_GD428_21136_Hu_REH_Parental/outs/filtered_feature_bc_matrix.h5”)…

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Help with weighted nearest neighbor analysis

Help with weighted nearest neighbor analysis 0 Hi all, I try to use this vignette on my single cell multiome data and not sure how to get the gene to run the function below. The data from endothelial cell and fibroblast. Would you please have a suggestion? I appreciate your…

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Seurat IntegrateData function returning an error

Hello, I am trying to integrate the data by correcting for batch effects per patient and I’m running into this error while executing the IntegrateData function, how do I fix this? Is it because the sepsis2HTO_HAB3 (7th dataset) has too few samples (66) to be properly integrated? analyseFinalList = function(objlist,…

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A universal tool for predicting differentially active features in single-cell and spatial genomics data

singleCellHaystack methodology For a detailed description of the original singleCellHaystack implementation (version 0.3.2) we refer to Vandenbon and Diez19. In brief, singleCellHaystack uses the distribution of cells inside an input space to predict DAFs. First, it infers a reference distribution \(Q\) of all cells in the space by estimating the…

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WNN in Seurat

Dear all, I am trying to follow the WNN vignette here satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html After the steps below, I would like to annotate my clusters, hence I need to know the markers which best represent each cluster. pbmc <- FindMultiModalNeighbors(pbmc, reduction.list = list(“pca”, “lsi”), dims.list = list(1:50, 2:50)) pbmc <- RunUMAP(pbmc, nn.name

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WNN (Seurat v4) vs. totalVI (scvi-tools) for CITE-seq data

WNN (Seurat v4) vs. totalVI (scvi-tools) for CITE-seq data 0 I want to build a UMAP from CITE-seq data with a joint embedding of the scRNA-seq and protein ab data. What’s the ‘best’ method in terms of representing the most accurate embedding? In the totalVI paper, they say totalVI >…

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