Scanpy-proceeded data cannot be run on Scissor

[hyjforesight]

Hello Scissor, My scRNA-seq workflow is based on Scanpy. After Scanpy proceeded with my data, I saved it into h5ad file. Then, I use R Markdown to convert my hd5ad file into a Seurat object. Then run Scissor on it. However, I got Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL'. Could you please help me with this issue? We'll cite your paper and make the acknowledgment when we publish our data. I've shared the h5ad file and its converted rds file with your gmail. Thanks! Best, YJ

attach R Markdown code

# python code in R Markdown
import numpy as np
import pandas as pd
import scanpy as sc
ACT_sub2 = sc.read('C:/Users/Park_Lab/Documents/ACT_sub2.h5ad')
sc.pl.umap(ACT_sub2, color='leiden', legend_loc='right margin', title='', frameon=False, use_raw=False)

# all below is R code in R Markdown
library(reticulate)
Sys.setenv(LANG = "en_US")
library(Seurat)
library(ggplot2)
library(sctransform)
library(dplyr)
library(patchwork)
library(DT)
memory.limit(512000)
options(future.globals.maxSize = 256000*1024^2)

# Get the expression matrix
exprs <- t(py$ACT_sub2$X)
colnames(exprs) <- py$ACT_sub2$obs_names$to_list()
rownames(exprs) <- py$ACT_sub2$var_names$to_list()
# Create the Seurat object
seurat <- CreateSeuratObject(exprs)
# Set the expression assay
seurat <- SetAssayData(seurat, "data", exprs)
# Add observation metadata
seurat <- AddMetaData(seurat, py$ACT_sub2$obs)
# Add fetaure metadata
seurat[["RNA"]][["n_cells"]] <- py$ACT_sub2$var["n_cells"]
# Add embedding
embedding <- py$ACT_sub2$obsm["X_umap"]
rownames(embedding) <- py$ACT_sub2$obs_names$to_list()
colnames(embedding) <- c("umap_1", "umap_2")
seurat[["umap"]] <- CreateDimReducObject(embedding, key = "umap_")
seurat
ACT_sub2 <- SetIdent(seurat, value = "leiden")
DimPlot(ACT_sub2, reduction = "umap")

# run Scissor
phenotype <- TCGA_phenotype_input
tag <- c('0', '1')    # name the phenotype 0 (uninterested) first, then phenotype 1 (interested)
GeneMatrix_MGI_input  <- as.matrix(GeneMatrix_MGI)    # conver gene martix (data.frame) to 'matrix'
infos <- Scissor(bulk_dataset=GeneMatrix_MGI_input, sc_dataset=ACT_sub2, phenotype=phenotype, tag = tag, alpha = 0.3, family = "binomial", Save_file = "ACT_sub2_Scissor.RData")    # alpha should be tested to make Scissor selected cells<20%

[sunduanchen]

Hi YJ,

You should construct a cell-cell network slot in Seurat. i.e., @graphs$RNA_snn cannot be null.

You can use other ways to achieve this and I will update Scissor code to allow users to choose their desired slot name.

Hope this helps.

Best, Duanchen

On Sun, 6 Feb 2022 at 13:06, Yuanjian Huang @.***> wrote:

Hello Scissor, My scRNA-seq workflow is based on Scanpy. After Scanpy proceeded with my data, I saved it into h5ad file. Then, I use R Markdown to convert my hd5ad file into a Seurat object. Then run Scissor on it. However, I got Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL'. Could you please help me with this issue? Thanks! Best, YJ

attach R Markdown code

python code in R Markdownimport numpy as npimport pandas as pdimport scanpy as scACT_sub2 = sc.read('C:/Users/Park_Lab/Documents/ACT_sub2.h5ad')

sc.pl.umap(ACT_sub2, color='leiden', legend_loc='right margin', title='', frameon=False, use_raw=False)

R code in R Markdown

library(reticulate) Sys.setenv(LANG = "en_US") library(Seurat) library(ggplot2) library(sctransform) library(dplyr) library(patchwork) library(DT) memory.limit(512000) options(future.globals.maxSize = 256000*1024^2)

Get the expression matrixexprs <- t(py$ACT_sub2$X)

colnames(exprs) <- py$ACT_sub2$obs_names$to_list() rownames(exprs) <- py$ACT_sub2$var_names$to_list()# Create the Seurat objectseurat <- CreateSeuratObject(exprs)# Set the expression assayseurat <- SetAssayData(seurat, "data", exprs)# Add observation metadataseurat <- AddMetaData(seurat, py$ACT_sub2$obs)# Add fetaure metadataseurat[["RNA"]][["n_cells"]] <- py$ACT_sub2$var["n_cells"]# Add embeddingembedding <- py$ACT_sub2$obsm["X_umap"] rownames(embedding) <- py$ACT_sub2$obs_names$to_list() colnames(embedding) <- c("umap_1", "umap2")seurat[["umap"]] <- CreateDimReducObject(embedding, key = "umap")seuratACT_sub2 <- SetIdent(seurat, value = "leiden") DimPlot(ACT_sub2, reduction = "umap")

5. run Scissorphenotype <- TCGA_phenotype_inputtag <- c('0', '1') # name the phenotype 0 (uninterested) first, then phenotype 1 (interested)GeneMatrix_MGI_input <- as.matrix(GeneMatrix_MGI) # conver gene martix (data.frame) to 'matrix'infos <- Scissor(bulk_dataset=GeneMatrix_MGI_input, sc_dataset=ACT_sub2, phenotype=phenotype, tag = tag, alpha = 0.3, family = "binomial", Save_file = "ACT_sub2_Scissor.RData") # alpha should be tested to make Scissor selected cells<20%

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[hyjforesight]

Hello @sunduanchen I gave it up and now I'm using Seurat to cluster my data and do Scissor. Thanks! Best, YJ

[hyjforesight]

Hello @sunduanchen, I've copied integrated_snn slot to RNA_snn slot.

Merge2.combined@graphs$RNA_snn=Merge2.combined@graphs$integrated_snn

Do I also need to copy integrated_nn slot to RNA_nn slot.

Merge2.combined@graphs$RNA_nn=Merge2.combined@graphs$integrated_nn

Thanks! Best, Yuanjian