The dgCMatrix class is a class of sparse numeric matrices in the compressed, sparse, column-oriented format. To easily tell which original object any particular cell came from, you can set the add.cell.ids parameter with an c(x, y) vector, which will prepend the given identifier to the beginning of each cell name. graph.type The type of graphs used for the hierarchical clustering, could . Prevailing single-cell platforms such as Seurat [7], Scran [6, 8], Monocle [3, 9], and Scanpy [10] provide numerous analytics tools. Seurat v3 is the . library ("Seurat") # from https . Milo is a tool for analysis of complex single cell datasets generated from replicated multi-condition experiments, which detects changes in composition between conditions. of 3 variables: $ barcodes: chr "AAACAGCCAAACTGCC-5" "AAACAGCCAACAGGAT-1 . Matrix. I can share R objects if that helps. It does not support combining SCE with assay in delayedArray format. I have a Seurat object of 8 patients. I have also tried using the Seurat::as.sparse () function, but it returns the same type of S4 object that I cannot convert to data.frame. Based on the code you provide in Seurat_to_anndata.ipynb I am trying to import a "old Seurat object" into scanpy. The Seurat object. columns in object metadata, PC scores To review, open the file in an editor . In this example, we save our toy sparse matrix into file named "sparse_matrix.mtx". 原文见Seurat - Guided Clustering Tutorial, Compiled: April 17, 2020 #1 Seurat安装 install.packages("Seurat") #2 数据下载. We have had the most success using the graph clustering approach implemented by Seurat.In ArchR, clustering is performed using the addClusters() function which permits additional clustering parameters to be passed to the Seurat::FindClusters() function via ..In our hands, clustering using Seurat::FindClusters() is deterministic . B. oh, I see. The object structure is rather complex, since it contains multi-level lists and slots. We can make a Seurat object from the sparce matrix as follows: srat <- CreateSeuratObject(counts = filt.matrix) srat ## An object of class Seurat ## 36601 features across 10194 samples within 1 assay ## Active assay: RNA (36601 features, 0 variable features) Let's make a "SoupChannel", the object needed to run SoupX. Thanks, Mark FeaturePlot(object = pbmc_small, features.plot = 'PC1') Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. This website uses cookies to improve your experience while you navigate through the website. Normalized values are stored in the "RNA" slot of the gbm object. ## An object of class Seurat ## 36601 features across 10194 samples within 1 assay ## Active assay: RNA (36601 features, 0 variable features) Let's erase adj.matrix from memory to save RAM, and look at the Seurat object a bit closer. By default, Seurat employs a global-scaling normalization method "LogNormalize" that normalizes the feature expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the result. big <-readRDS ("my_seurat_object") ### Construct the cds object #Extract data, phenotype data, and feature data from the SeuratObject . #校正细胞的表达值，将count数量根据表达总量矫正之后，乘以10000，并进行对数转换. by.r: Specifications of the columns used for merging rowData. Introduction. The Seurat object. Currently, combineSCE function only support combining SCE objects with assay in dgCMatrix format. Note: This is not a complete Seurat object. dgCMatrix-class: Compressed, sparse, column-oriented numeric matrices Description. This is my metadata. 6 x 10 sparse Matrix of class "dgCMatrix" . #> 6 x 6 sparse Matrix of class "dgCMatrix" #> HSPC_001 HSPC_002 HSPC_003 HSPC_004 HSPC_006 HSPC_008 #> Clec1b . B. The readSeurat() function can be used to create a Seurat object. There are 2,700 single cells that were sequenced on the Illumina NextSeq 500. merge() merges the raw count matrices of two Seurat objects and creates a new Seurat object with the resulting combined raw count matrix. 1. writeMM(obj = sp_matrix, file="sparse_matrix.mtx") We can load the saved sparse matrix data into sparse matrix using readMM function. This data comes from an hdf5 file that I had to open using the Seurat package. If set as NULL, the rownames of rowData tables will be used to merging . # from the Seurat pancreas example # we now have pan_celseq2, fully annotated in metadata column "celltype", and pan_smartseq2, ready to be annotated data_url = "https://scrnaseq-workshop.s3-us-west-2.amazonaws.com" pan_celseq2 <- readRDS(url(file.path(data_url, "pan_celseq2.rds"))) pan_celseq2@meta.data %>% head() #> orig.ident nCount_RNA . For example Seurat DoHeatmap gives such a picture by which we can see these set of genes are being expressed on which group of cells. So the Matrix package doesn't define a direct conversion from dgRMatrix to dgCMatrix, but does define a conversion from the general RsparseMatrix (which dgRMatrix inherits from) to CsparseMatrix (which dgCMatrix inherits from). . Is the ExportSeuratObject function not detecting these extra columns? . dgCMatrix. The raw data can be found here. # Initialize the Seurat object with the raw (non-normalized data). Comments. 12. I want to add metadata to that so that I have origin of each cell. - I have created some meta data columns using the AddMetaData function in seurat. A dgCMatrix or matrix object is also acceptable, and no samples will be integrated. test_seurat <-CreateSeuratObject(counts = test, project = " test ", min.cells = 0, min.features = 0) Raw table2matrix.R This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. Hi, I had the same issue. The "version" corresponds to the version of Seurat that the h5Seurat file is based on. While differential abundance (DA) is commonly quantified in discrete cell clusters, Milo uses partally overlapping neighbourhoods of cells on a KNN graph. dgCMatrix. A vector of names of Assay, DimReduc, and Graph objects contained in a Seurat object can be had by using names. calculate enrichment scores, return a Seurat object including these score matrix. 单细胞数据未来会朝着多样本发展，因此数据整合是一项必备技能。cellranger中自带了aggr的整合功能，而这篇文章（Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA-sequencing）的作者也正是这么做得到的组合后的表达矩阵，然后用 Read10X 读入 Ask questions Read10X - invalid dimnames given for "dgTMatrix" object I'm trying to load the output of scrna alignment into a datamatrix with Read10X . label: An optional label for the file. Batch Correction Lab. Comes up when I subset the seurat3 object and try to subcluster. assay: Assay to pull expression values from; defaults to RNA.. slot: Slot to pull expression values from; defaults to data.It is recommended to use sparse data (such as log-transformed or raw counts) instead of dense data (such as the scaled slot) to avoid performance bottlenecks in the Cerebro interface.. file ### 校正表达值. I would appreciate your help on scRNA-seq analysis, as I am doing a comparison between 2 scRNA-seq datasets ; I am using SEURAT package and after I use RunCCA() and AlignSubspace(), as described here : mary matrix of gene expression of Seurat, SingleCellExperiment, and anndata objects (Scanpy). Let's assume you have a Seurat object but generated tables of differentially expressed genes and enriched pathways using other tools/methods than those built into cerebroApp. The object structure is rather complex, since it contains multi-level lists and slots. The group 'data' stores the primary matrix of gene expression of Seurat, SingleCellExperiment, and anndata objects (Scanpy). Where in the object is the count data stored? obj. dim (seurat_object) saveRDS (seurat_object, file = "subset.rds") #readRDS ("subset.rds") 3. Batch Correction Lab. Hi, below is a bug report for very large data. dgCMatrix. seurat_obj: A Seurat object. Tushar-87 changed the title data frame to dcgMatrix and then seurat object data frame to dgCMatrix and then seurat object on Feb 11, 2020. Code to produce the heatmap of Seurat correlation matrix vs cotan heatmap on . We can use writeMM function to save the sparse matrix object into a file. . load using Matrix and Seurat R packages with "Read10X" function to yield unfiltered dgCMatrix containing 27, 998 genes across 40, 712 cell profiles; initialize Seurat object with filters min.cells =3, min.genes = 1000 class. str commant allows us to see all fields of the class: datamatrix <- Read10X(data.dir = data_dir , gene.column = 1, unique.features = TRUE) The matrix C is the adjacency matrix of the built CKNN graph. We have a simple function to convert a Seurat object to a cellexalvrR object prior to export. A workaround is to convert the slot to a regular matrix before the conversion (see below). C[i,j] = 1 means that there is an edge between sample i . 5.1 Clustering using Seurat's FindClusters() function. I have an extremely big sparse matrix (4,941,643,682 elements, 5.6Gb R object in memory) that I need to convert to a 3-column data.frame (row, column, value) and then dump it into a text file for further processing. class. In this tutorial we will go over the analysis of a panel of samples using Pagoda2 and Conos. in the same object. Still, it's an open question (for me) of how to use generic matrix methods (such as MASS::ginv, for example), with sparseMatrix objects without resolving to as.matrix. Working with a sparse matrix in R. Sparse matrices are necessary for dealing with large single-cell RNA-seq datasets. And "as.data.frame" and "as.data.frame.matrix" attempt to create a 245k-column x 20k-row data.frame. As an example we load single-cell RNA-sequencing data of B cells extracted from the dataset published by Jerby-Arnon et al. I have tried the "summary" method described here with no luck (I have . . Normalized values are stored in the "RNA" slot of the gbm object. We will look at how different batch correction methods affect our data analysis. Please see the other vignettes for more realistic examples using larger scRNA-seq data sets and gene set collections based on MSigDB. In this lab, we will look at different single cell RNA-seq datasets collected from pancreatic islets. I use the following code to run convert my seurat object to the monocle_cds: seurat. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to idetnify subpopulations and run differential expression within idividual samples. 12. I thought you were after a similar thing, but sadly the accepted answer does not solve it. Seurat object from SCP files Seurat's Read10X function expects the three files that comprise an Feature-Barcode Matrix to have specific names and format, all located in one directory. Peripheral Blood Mononuclear Cells (PBMC) 是10X Genomics dataset page提供的一个数据，包含2700个单细胞，出自Illumina NextSeq 500平台。 PBMCs是来自健康供体具有相对少量RNA（around 1pg RNA/cell）的原代细胞。 An n by n binary dgCMatrix object C, where n is the number of input samples. Hello, The wrapper was designed to read in a velocyto-produced loom file into a Seurat object and run the velocity estimation pipeline (gene.relative.velocity.estimates) without needing to keep track of which matrix is which.We aren't able to provide much more help than technical expertise; conceptual questions should be directed to the velocyto team. You can try to find the name of the graph object stored in the seurat object and specifiy it in the FindClusters function: 4. 校正表达值. The gbm object we have created has the class Seurat. It can be used to identify groups of cells with high or low expression levels of a marker gene. > Seurat::pbmc_small An object of class Seurat 230 features across 80 samples within 1 assay Tushar-87 closed this on Feb 12, 2020. Consider setting 'min.sz' > 1. By default, Seurat employs a global-scaling normalization method "LogNormalize" that normalizes the feature expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the result. Dear all, happy and healthy new year ! In this implementation the non-zero elements in the columns are sorted into increasing row order. # Get assay data from the default assay in a Seurat object GetAssayData (object = pbmc_small, slot = "data") [1: 5, 1: 5] #> 5 x 5 sparse Matrix of class "dgCMatrix" #> ATGCCAGAACGACT CATGGCCTGTGCAT GAACCTGATGAACC TGACTGGATTCTCA #> MS4A1 . An object of class Seurat 13714 features across 2700 samples within 1 assay Active assay: RNA (13714 features, 0 variable features) [3]: # Lets examine a few genes in the first thirty cells pbmc.data [ c ( "CD3D" , "TCL1A" , "MS4A1" ), 1 : 30 ] . Serun singlecell data analysis notebook [10]: # FeatureScatter is typically used to visualize feature-feature relationships, but ˓→can be used # for anything calculated by the object, i.e. This example uses the pbmc small data set included in the Seurat package and a single contrived gene set. object: Seurat object. まだプレリリース版のSeruat v3.0ですが、 10Xのサイトで以下のように言及されたことにより、こちらを使用する人が増えている気がします。 Seurat 3.0 is specifically designed to handle the type of multi-data experiments enabled by Feature Barcoding technology, and can also read the latest output file produced by Cell Ranger 3.0 引用：https . subset (pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 4000 & percent.mt < 7.5) #> An object of class Seurat #> 15604 features across 2083 samples within 1 assay . 2 Seurat object. To demonstrate this we will use the the Seurat pbmc_small example data. Perhaps it'd be a good idea to add that kind of workaround to the Seurat::as.SingleCellExperiment.seurat function (an alternative would be to clean the internet from legacy Seurat objects, which is perhaps less realistic?) ## 目的：for loop iterate 两个样本的文件 ## 思路：执行三个命令 (1) 使用Read10x()读取计数数据；（2）使用CreateSeuratObject()从读取的数据中创建Seurat对象；（3）根据样本名将Seurat对象赋值给一个新的变量（好处是不会覆盖上一次迭代中创建的Seurat对象） mtx_dir = "/Users . ## Seurat object keeps the data in sparse matrix form sparse.size <-object.size (x = pbmc.data) sparse.size ## [1] 29861992 bytes # Let's examine the sparse counts matrix # The columns are indexed by 10x cell barcodes (each 16 nt long), # and the rows are the gene names. The gbm object we have created has the class Seurat. What is in there? Di erent platforms have their specialty. seurat_object_norm <- NormalizeData (seurat_object) # # 查看矫正前后的数据. slot: The slot within the Seurat object to retrieve data from. Have a look at the gbm object by running str(gbm). Pagoda2 is a package aimed at analysis of standalone datasets. Doing `%%R suppressPackageStartupMessages(library(Seurat)) #3, min.features = 200) refset` works well but when I then try to do `%%R #convert the Seurat object to a SingleCellExperiment object refset_sce <- as.SingleCellExperiment . This website uses cookies to improve your experience while you navigate through the website. In this implementation the non-zero elements in the columns are sorted into increasing row order. Interacting with the Seurat object Handling multiple assays. SCUBI partitions the coordinate space into small non-overlapping squares and visualizes the aggregated information across cells falling in each square. A list of count matrices that will be integrated using the IntegrationAnchors features they should have the same rownames. We first load one spatial transcriptomics dataset into Seurat, and then explore the Seurat object a bit for single-cell data storage and manipulation. Exercise: A. dgCMatrix. ## An object of class Seurat ## 13714 features across 2700 samples within 1 assay ## Active assay: RNA (13714 features, 0 variable features) pbmc.data[c("CD3D","TCL1A","MS4A1"), 1:30] ## 3 x 30 sparse Matrix of class "dgCMatrix" Then I remembered that in version 2 of Seurat there was a function that converted the seurat object to a h5ad file. So I took the source function of version 2 as a base and went on adapting and modifying it until I had a function that converts the seurat object to an h5ad file, which is exactly what we need to give as input to SCCAF. One 10X Genomics Visium dataset will be analyzed with Seurat in this tutorial, and you may explore other dataset sources from various sequencing technologies, and other computational toolkits listed in this (non-exhaustive . …is a class of sparse numeric matrices in the compressed, sparse, column-oriented format. Object shape/dimensions can be found using the dim, ncol, and nrow functions; cell and feature names can be found using the colnames and rownames functions, respectively, or the dimnames function. DoHeatmap(object ,genes.use=a vector of genes) Or we can plot a set of genes on seurat feature lot to they are being expressed more on which cluster. 1.039889 1.172368 #> Kdm3a . assay: The assay within the Seurat object to retrieve data from. To export those tables, you just need to put it in the right place, following a "method" and "name" scheme. (Cell, 2018). Sign up for free to join this conversation on . All you need to do is split your matrix into RNA and ADT, create your Seurat object with RNA data and then add the ADT data with: seurat_obj_with_rna_only[["ADT"]] <- CreateAssayObject(counts = your_adt_matrix) For efficiency, Seurat uses sparse matrices so don't forget to convert your data matrices to sparse. To decrease the size, the object only contains gene expression values and cluster annotations. proj_name: Name of the project that will be the prefix of the file name. It's public data anyway. This mode visualizes the expression of a single gene. The "project" attribute corresponds to the project value of a Seurat object; the "active.assay" attribute is the name of the default assay and must be present in the "assays" group. Some gene sets have size one. Merging Two Seurat Objects. We will look at how different batch correction methods affect our data analysis. scDIOR implements the single-cell data IO between R (Seurat, SingleCellExperiment and Monocle . test replications elapsed relative user.self sys.self 1 asdgCMatrix_(m * 1) 1000 0.028 1.00 0.028 0.000 2 as(m, "dgCMatrix") 1000 0.287 10.25 0.284 0.004 For larger matrices the difference in performance gain is smaller: Row order attempt to create a 245k-column x 20k-row data.frame version & ;... Will use the the Seurat object if you choose to keep it //personal.broadinstitute.org/jlchang/ '' > Seurat object if you to! Up when I subset the seurat3 object and try to subcluster https: ''. Similar thing, but sadly the accepted answer does not solve it made of head metadata! Currently, combineSCE function only support combining SCE with assay in delayedArray format sparse_matrix.mtx quot. Up for free to join this conversation on at the gbm object we have has! Seriph78.Github.Io < /a > Converting an extremely large R S4 dgCMatrix to data frame are... Metadata to that so that I have tried the & quot ; AAACAGCCAACAGGAT-1 data anyway BioTuring! - GitHub Pages < /a > dgCMatrix cells while I want to color clusters by sample by.r Specifications! Default ) throuhgout the samples up for free to join this conversation.!, since it contains multi-level lists and slots clusters by sample the assay the... Clustering, could class is a tool for analysis of standalone datasets ) & # x27 dgCMatrix. Only to perform scaling on the Illumina NextSeq 500 to subcluster & quot ; fused Seurat object file an. Information across cells falling in each square will look at the gbm object we have created has class! Default ) the built CKNN graph ; AAACAGCCAAACTGCC-5 & quot ; standard quot! Values and cluster annotations object only contains gene expression table to matrix.mtx · GitHub /a... Different batch correction methods affect our data analysis tried the & quot ; AAACAGCCAACAGGAT-1 dgCMatrix quot. In BioTuring size, the object is also acceptable, and they allow some computations to be more.... Sets and gene set collections based on MSigDB our toy sparse matrix format... < >! If set as NULL, the default in ScaleData is only to perform scaling the. Github Plus < /a > dgCMatrix high or low expression levels of a gene! Affect our data analysis scRNA-seq data sets and gene set collections based on our toy sparse matrix...... Want to color clusters by sample vs cotan heatmap - seriph78.github.io < /a > Seurat. At the moment UMAP just shows a bunch of cells while I want to add metadata to that that! Slot: the slot within the Seurat object with no luck ( I have had. Into file named & quot ; class for sparse numeric matrices in the previous function call for more realistic using. Error in CreateSeuratObject... < /a > the Seurat object a bit for single-cell IO... Gbm ) integration of single cell rank-based gene set collections based on a 245k-column x data.frame... Require less memory than dense matrices, and no samples will be the of! The assay within the Seurat pbmc_small example data ) jerby_b_cells # & gt ; an: //gist.github.com/xie186/332eff13dcac50f101f91494402b4bd1 '' > object! Slot: the slot within the Seurat pbmc_small example data ; an ) & # x27 min.sz... An edge between sample I < /a > the Seurat object to retrieve data from a single gene the elements... Of assay, DimReduc, and no samples will be analyzing the a dataset of Peripheral Blood Mononuclear (! File named & quot ; summary & quot ; standard & quot ; method described here with luck! Dgcmatrix < /a > dgCMatrix assay: the slot within the Seurat can. Is only to perform scaling on the Illumina NextSeq 500 non-overlapping squares and the... If set as NULL, the rownames of rowData tables will be the... Information across cells falling in each square collected from pancreatic islets in Seurat in!, since it contains multi-level lists and slots explore the Seurat object can be used to.. Addmetadata function in Seurat seem to show in BioTuring you navigate through the website ) jerby_b_cells &. The non-zero elements in the matrix C is the count data stored seurat-wrappers - GitHub Introduction the project that will be used to merging an... Free to join this conversation on high or low expression levels of a marker gene str... Blood Mononuclear cells ( PBMC ) freely available from 10X Genomics # from.. < /a > Creating a Seurat object from SCP files < /a > 12 open the name! Github < /a > Converting an extremely large R S4 dgCMatrix... < /a > Hi, I had same. There is an edge between sample I Seurat & quot ; as.data.frame.matrix & quot ; combineSCE function only combining. Converting merged Seurat object containing the graphs built with scRNA-seq data by running str ( gbm ) were after similar! Cells falling in each dgcmatrix to seurat object available from 10X Genomics an extremely large R dgCMatrix... Previously identified variable features ( 2,000 by default ) at how different batch correction affect. Of 3 variables: $ barcodes: chr & quot ; and quot! For this tutorial, we save our toy sparse matrix format... /a. Which detects changes in composition between conditions spatial transcriptomics dataset into Seurat, and they allow some computations to more! Into Seurat, SingleCellExperiment and Monocle discuss the internals of the gbm by... Matrix C is the & quot ; RNA & quot ; corresponds to version. ; as.data.frame.matrix & quot ; RNA & quot ; summary & quot ; standard & quot ; RNA quot... A dgCMatrix object made of data IO between R ( Seurat, and explore... Set collections based on the matrix package dgcmatrix to seurat object PBMC ) freely available from 10X Genomics only to scaling... Retrieve data from proj_name: name of the file name in a object! I have created has the class Seurat I thought you were after a similar,. Mononuclear cells ( PBMC ) freely available from 10X Genomics 6 obs while you navigate through the website features in. Marker gene had by using names is the ExportSeuratObject function not detecting these extra columns > 12 into small squares. Generated from replicated multi-condition experiments, which detects changes in composition between conditions object have. Output of the dgCMatrix class with examples slot of the columns used for merging rowData contains! Have a look at different single cell RNA-seq datasets collected from pancreatic islets structure! ; and & quot ; & gt ; 1 in this lab, we look! & lt ; - NormalizeData ( seurat_object ) # # 查看矫正前后的数据 with scRNA-seq data were a. That there is an edge between sample I CreateSeuratObject... < /a >.! ; str ( gbm ) storage and manipulation between sample I cotan heatmap on ExportSeuratObject... Plus < /a > dgCMatrix built with scRNA-seq data sets and gene...! Extra columns 2 Seurat object back to dgCMatrix < /a > 2 Seurat object to retrieve data.! Head ( metadata ) ) & # x27 ; ll discuss the internals of the gbm.... The gbm object · GitHub < /a > Introduction ScaleData is only to scaling! To identify groups of cells while I want to color clusters by sample dgCMatrix object made of it. Single cells that were sequenced dgcmatrix to seurat object the Illumina NextSeq 500 values throuhgout the.... Only contains gene expression table to matrix.mtx · GitHub dgcmatrix to seurat object /a > Seurat... And they allow some computations to be more efficient for analysis of complex single cell rank-based set... They allow some computations to be more efficient shows a bunch of cells with or. Lab, we & # x27 ; objects as input is still in an experimental stage '' Seurat! To review, open the file in an editor Pages < /a > oh, I see: the within! Null, the default in ScaleData is only to perform scaling on the previously identified variable (. Larger scRNA-seq data sets and gene set... < /a > oh, I had the same issue seriph78.github.io! The object structure is rather complex, since it contains multi-level lists and slots cotan heatmap on pbmc_small data. Assay: the assay within the Seurat object to retrieve data from matrix.... Explore the Seurat object containing the graphs built with scRNA-seq data sets and gene set based... //Www.R-Bloggers.Com/2020/03/What-Is-A-Dgcmatrix-Object-Made-Of-Sparse-Matrix-Format-In-R/ '' > scDIOR workflow scubi partitions the coordinate space into small non-overlapping squares and visualizes dgcmatrix to seurat object expression of marker... Collections based on Converting an extremely large R S4 dgCMatrix to data frame identified features! Standard & quot ; RNA & quot ; slot of the gbm object we have has. File in an editor but these don & # x27 ; min.sz & # ;... In Seurat space into small non-overlapping squares and visualizes the aggregated information across cells falling in each.... Improve your experience while you navigate through the website the previously identified features... Batch correction methods affect our data analysis up when I subset the seurat3 object and to! Analysis of standalone datasets to review, open the file name to produce heatmap. Squares and visualizes the expression of a marker gene from 10X Genomics cells... Github Pages < /a > oh, I had the same issue small non-overlapping squares visualizes. > oh, I see has the class Seurat a look at the moment UMAP just shows bunch! Heatmap - seriph78.github.io < /a > 2 Seurat object can be used to create a 245k-column x data.frame! Detecting these extra columns SingleCellExperiment and Monocle AddMetaData function in Seurat scDIOR workflow and they allow some to. //Stackoverflow.Com/Questions/63114747/Converting-An-Extremely-Large-R-S4-Dgcmatrix-To-Data-Frame '' > Spaniel - GitHub Pages < /a > dgCMatrix xxx genes with constant expression and... 2,000 by default ) object back to dgCMatrix < /a > Converting Seurat!