This data is used for the Seurat version of the batch correction and integration tutorial on the Galaxy Training Network. The input data was provided by Seurat in the 'Integrative Analysis in Seurat v5' tutorial. The input dataset provided here has been filtered to include only cells for which nFeature_RNA > 1000. The other datasets were produced on Galaxy. The original dataset was published as: Ding, J., Adiconis, X., Simmons, S.K. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38, 737–746 (2020). https://doi.org/10.1038/s41587-020-0465-8.

Test Data for Galaxy Tutorial "Clustering 3k PBMCs with Seurat"

The data is derived from the 3k PBMC data used in scanpy & Seurat tutorials. In comes in the AnnData h5ad format.

Processed 3k PBMCs from a Healthy Donor from 10x Genomics, available at https://scanpy.readthedocs.io/en/stable/generated/scanpy.datasets.pbmc3k_processed.html Original 10X data available at http://cf.10xgenomics.com/samples/cell-exp/1.1.0/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz from this website: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k

The changes made to the original scanpy.datasets.pbmc3k_processed() data are described in this github issue: https://github.com/scverse/scverse-tutorials/issues/51

See jupyter notebook for details.

`bcr_phylo_tutorial.zip` is used in the Reconstruction and analysis of B-cell lineage trees from single cell data using Immcantation tutorial.

`immcantation-BCR-Seurat-tutorial.zip` is used in the Integration of BCR and GEX data tutorial.

Necessary datasets to run the Immcantation 10x Tutorial. Below is the description of the files in the data set.

BCR_data_sample1.tsv: data corresponding to the first sample (sample 1) of the two samples analyzed in the 10x tutorial. This is the sample used to show the Change-O steps.

filtered_contig_annotations.csv: filtered contig annotations file for sample 1, output of cellranger vdj.

filtered_contig.fasta: sequence fasta file for sample 1, output of cellranger vdj.

BCR_data.tsv: AIRR rearrangement file containing the data for both samples 1 and 2 used in the 10x tutorial.

BCR.data_08112023.rds: R dataframe object containing the single-cell BCR sequencing data for both samples 1 and 2 used in the 10x tutorial.

GEX.data_08112023.rds: Seurat object containing the single-cell gene expression data used in the 10x tutorial.

The demo datasets for scMetabolismThe demo data is the dataset of Peripheral Blood Mononuclear Cells (PBMC) from 10X Genomics open access dataset (~2,700 single cells, also used by Seurat tutorial).

IFN-beta treated and control PBMCs from 8 donorstwo groups of PBMCs from Kang et al. 2017 (https://www.nature.com/articles/nbt.4042), analyzed using the Seurat sample alignment strategy as explained in the tutorial at https://satijalab.org/seurat/v2.4/immune_alignment.html and then converted to h5ad format* ~14000 cells* IFN-treated and unstimulated PBMCs from 8 donors* donor identities determined using demuxlet (see GSE96583)

This is a single cell transcriptomics dataset containing roughly 3,000 PBMCs. The original data was downloaded from the Seurat 3k PBMC tutorial: https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html. We reprocessed the dataset using the Besca package (https://github.com/bedapub/besca).

Remark: See paper: https://arxiv.org/abs/2208.05229 results on cell cycle analysis discussed there. "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data: Paper: CENP-A overexpression promotes distinct fates in human cells, depending on p53 status Daniel Jeffery, Alberto Gatto, Katrina Podsypanina, Charlène Renaud-Pageot, Rebeca Ponce Landete, Lorraine Bonneville, Marie Dumont, Daniele Fachinetti & Geneviève Almouzni https://www.nature.com/articles/s42003-021-01941-5

Data: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9861/

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data: Paper: Roider, T., Seufert, J., Uvarovskii, A. et al. Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels. Nat Cell Biol 22, 896–906 (2020). doi: https://doi.org/10.1038/s41556-020-0532-x Data: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/VRJUNV

Subdirectory names like "FL1", ""DLBCL"... - corresponds to:

SampleName Tissue Sex Entity Diagnosis Subclassification Batch CellrangerVersion FL3 Lymph node M B cell Lymphoma Follicular Lymphoma NA 1 2.1.1 FL1 Lymph node M B cell Lymphoma Follicular Lymphoma NA 1 2.1.1 FL2 Lymph node M B cell Lymphoma Follicular Lymphoma NA 1 2.1.1 DLBCL1 Lymph node F B cell Lymphoma Diffuse large B cell lymphoma Germinal Center subtype 1 2.1.1 tFL1 Lymph node F B cell Lymphoma Transformed Follicular Lymphoma Germinal Center subtype 1 2.1.1 DLBCL2 Lymph node M B cell Lymphoma Diffuse large B cell lymphoma Germinal Center subtype 1 2.1.1 rLN1 Lymph node M NA Reactive Lymphadenitis NA 1 2.1.1 DLBCL3 Lymph node F B cell Lymphoma Diffuse large B cell lymphoma non-Germinal Center subtype 2 3.0.2 FL4 Lymph node M B cell Lymphoma Follicular Lymphoma NA 2 3.0.2 rLN3 Lymph node F NA Reactive Lymphadenitis NA 2 3.0.2 rLN2 Lymph node M NA Reactive Lymphadenitis NA 2 3.0.2 tFL2 Lymph node F B cell Lymphoma Transformed Follicular Lymphoma Germinal Center subtype 2 3.0.2

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

These are processed AnnData objects (converted from Seurat objects) for GeneTrajectory tutorials (https://github.com/KlugerLab/GeneTrajectory-python/):Human myeloid dataset analysisMyeloid cells were extracted from a publicly available 10x scRNA-seq dataset (https:// support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc 10k v3). QC was performed using the same workflow in (https://github.com/satijalab/ Integration2019/blob/master/preprocessing scripts/pbmc 10k v3.R). After standard normalization, highly-variable gene selection and scaling using the Seurat R package, we applied PCA and retained the top 30 principal components. Four sub-clusters of myeloid cells were identified based on Louvian clustering with a resolution of 0.3. Wilcoxon rank-sum test was employed to find cluster-specific gene markers for cell type annotation.For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel, each bandwidth is determined by the distance to its k-nearest neighbor, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 5 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 0.5% − 75% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (11,21,8) to extract three gene trajectories.Mouse embryo skin data analysisWe separated out dermal cell populations from the newly collected mouse embryo skin samples. Cells from the wildtype and the Wls mutant were pooled for analyses. After standard normalization, highly-variable gene selection and scaling using Seurat, we applied PCA and retained the top 30 principal components. Three dermal celltypes were stratified based on the expression of canonical dermal markers, including Sox2, Dkk1, and Dkk2. For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel bandwidth, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 10 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 1% − 50% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (9,16,5) to sequentially extract three gene trajectories. To compare the differences between the wiltype and the Wls mutant, we stratified Wnt-active UD cells into seven stages according to their expression profiles of the genes binned along the DC gene trajectory.

Remark: For trajectory inference discussion for that dataset, see paper: https://www.mdpi.com/1099-4300/22/11/1274 "Minimum Spanning vs. Principal Trees for Structured Approximations of Multi-Dimensional Datasets Alexander Chervov, Jonathan Bac and Andrei Zinovyev

For cell cycle analysis see: https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data: Gene expressions count matrix. Single cell RNA sequencing data. 447 cells , 24748 genes Mouse Liver Hepatoblast in vivo.

Paper: Hepatology. 2017 Nov;66(5):1387-1401. doi: 10.1002/hep.29353. Epub 2017 Sep 29. A single-cell transcriptomic analysis reveals precise pathways and regulatory mechanisms underlying hepatoblast differentiation Li Yang 1 2 , Wei-Hua Wang 1 2 , Wei-Lin Qiu 1 3 , Zhen Guo 1 , Erfei Bi 4 , Cheng-Ran Xu 1

Data: GSE90047 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90047 Downloaded from: https://cytotrace.stanford.edu/#shiny-tab-dataset_download

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

This archive contains 10x matrix data (processed with cellranger) used for the Introduction to single cell RNAseq analysis workshop taught by the Danish National Sandbox for Health Data Science. The course repo can be found on Github.

Data.zip contains 6 runs on Spermatogonia development. 3 from healthy individuals and 3 from azoospermic individuals. Data has been already preprocessed using cellranger and can be loaded using Seurat (R) or scanpy (python).

Integration Skript:

library(Seurat)
library(tidyverse)
library(Matrix)

#cite <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Merge AAA mit Cite AAA/Cite_seq_v0.41.rds")
#CD45 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/CD45.rds")
AAA <- readRDS("C:/Users/alex/sciebo/AAA_Zhao_v4.rds")
cite <- readRDS("C:/Users/alex/sciebo/CITE_Seq_v0.5.rds")
all4 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/Schrader_All4_Rohanalyse/all4_220228.rds")

#fuse lists
c <- list(cite, all4, AAA)
names(c) <- c("cite", "all4", "AAA")

pancreas.list <- c[c("cite", "all4", "AAA")]
for (i in 1:length(pancreas.list)) {
pancreas.list[[i]] <- SCTransform(pancreas.list[[i]], verbose = FALSE)
}

pancreas.features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000)
#options(future.globals.maxSize= 6091289600)
#pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = pancreas.features,
#verbose = FALSE) #future.globals.maxsize was to low. changed it to options(future.globals.maxSize= 1091289600)
#identify anchors

#alternative from tutorial (https://satijalab.org/seurat/articles/integration_introduction.html)
#memory.limit(9999999999)
features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000)
pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = features)
pancreas.anchors <- FindIntegrationAnchors(object.list = pancreas.list, normalization.method = "SCT", anchor.features = pancreas.features, verbose = FALSE)
pancreas.integrated <- IntegrateData(anchorset = pancreas.anchors, normalization.method = "SCT",
verbose = FALSE)

setwd("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper")

saveRDS(pancreas.integrated, file = "integrated_AAA_Cite_AMI.rds")

saveRDS(cd45, file = "integrated_AAA_Cite_CD45.rds")

seurat <- pancreas.integrated

#seurat <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/integrated_d5_cite.rds")

DefaultAssay(object = seurat) <- "integrated"
seurat <- FindVariableFeatures(seurat, selection.method = "vst", nfeatures = 3000)
seurat <- ScaleData(seurat, verbose = FALSE)
seurat <- RunPCA(seurat, npcs = 30, verbose = FALSE)
seurat <- FindNeighbors(seurat, dims = 1:30)
seurat <- FindClusters(seurat, resolution = 0.5)
seurat <- RunUMAP(seurat, reduction = "pca", dims = 1:30)
DimPlot(seurat, reduction = "umap", split.by = "treatment") + NoLegend()

DimPlot(seurat, label = T, repel = T) + NoLegend()

DefaultAssay(object = seurat) <- "ADT"
adt_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.3)
write.csv(adt_marker_integrated, file = "adt_marker_all4_integrated.csv")

DefaultAssay(object = seurat) <- "RNA"
RNA_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.5)
write.csv(RNA_marker_integrated, file = "RNA_marker_all4_integrated.csv")

DimPlot(seurat, label = T, repel = T, split.by = "tissue") + NoLegend()

FeaturePlot(seurat, features = "Cd40", order = T, label = T)
FeaturePlot(seurat, features = "Ms.CD40", order = T, label = T)

#####
#leanup:
> seurat@meta.data[["sen_score1"]] <- NULL
> seurat@meta.data[["sen_score2"]] <- NULL
> seurat@meta.data[["sen_score3"]] <- NULL
> seurat@meta.data[["sen_score4"]] <- NULL
> seurat@meta.data[["sen_score5"]] <- NULL
> seurat@meta.data[["sen_score6"]] <- NULL
> seurat@meta.data[["sen_score7"]] <- NULL
> seurat@meta.data[["pANN_0.25_0.1_1211"]] <- NULL
> seurat@meta.data[["DF.classifications_0.25_0.1_1211"]] <- NULL
> seurat@meta.data[["DF.classifications_0.25_0.1_466"]] <- NULL
> seurat@assays[["prediction.score.celltype"]] <- NULL
> seurat@meta.data[["predicted.celltype"]] <- NULL
> seurat@meta.data[["DF.classifications_0.25_0.1_184"]] <- NULL
> seurat@meta.data[["DF.classifications_0.25_0.1_953"]] <- NULL
> seurat@meta.data[["integrated_snn_res.3"]] <- NULL
> seurat@meta.data[["RNA_snn_res.3"]] <- NULL
> seurat@meta.data[["SingleR"]] <- NULL
> seurat@meta.data[["SingleR_fine"]] <- NULL
> seurat@meta.data[["ImmGen"]] <- NULL
> seurat@meta.data[["ImmGen_fine"]] <- NULL
> seurat@meta.data[["percent.mt"]] <- NULL
> seurat@meta.data[["nCount_integrated"]] <- NULL
> seurat@meta.data[["nFeature_integrated"]] <- NULL
> seurat@meta.data[["S.Score"]] <- NULL
> seurat@meta.data[["G2M.Score"]] <- NULL
> seurat@meta.data[["Phase"]] <- NULL
> seurat@meta.data[["sen_score8"]] <- NULL
> seurat@meta.data[["sen_score9"]] <- NULL
> seurat@meta.data[["sen_score10"]] <- NULL
> seurat@meta.data[["sen_score11"]] <- NULL
> seurat@meta.data[["sen_score12"]] <- NULL
> seurat@meta.data[["sen_score13"]] <- NULL
> seurat@meta.data[["sen_score14"]] <- NULL
> seurat@meta.data[["sen_score15"]] <- NULL
> seurat@meta.data[["sen_score16"]] <- NULL
> seurat@meta.data[["sen_score17"]] <- NULL
> seurat@meta.data[["sen_score18"]] <- NULL
> seurat@meta.data[["sen_score19"]] <- NULL
seurat@meta.data[["pANN_0.25_0.1_184"]] <- NULL
seurat@meta.data[["pANN_0.25_0.1_953"]] <- NULL
seurat@meta.data[["pANN_0.25_0.1_466"]] <- NULL