Tag: monocle3

Monocle3 transition to seurat

Monocle3 transition to seurat 1 I know to transition from Seurat to monocle3 one has to use library(Seurat Wrappers) and then use the function: as.cell_data_set(seuratobject). But, after reclustering in monocle3, i would like to go back to Seurat to perform differential analysis. How do i do this? How do i…

Continue Reading Monocle3 transition to seurat

Monocle3 garnett

Monocle3 garnett 0 I am trying to annotate my cells using garnett, but i keep getting this error at this stage. Error: is(object = cds, class2 = “CellDataSet”) is not TRUE. Here is my code: library(garnett) library(org.Mm.eg.db) classifier <- readRDS(“./mmLung_20191017.RDS”) trac <- classify_cells(trac, classifier, db = org.Mm.eg.db, cluster_extend = TRUE,…

Continue Reading Monocle3 garnett

Scvelo vs Monocle3

Scvelo vs Monocle3 0 Hi Biostars, I tried to do trajectory analysis but got an error message here Help with error velocyto so I tried Monocle3 and I feel it was a lot faster and simpler than using scVelo/velocyto. However, with Monocle3, how can we know where the root note…

Continue Reading Scvelo vs Monocle3

differences between trajectories in conditions with Monocle3 or other tools

differences between trajectories in conditions with Monocle3 or other tools 0 hi, I ran the analysis of my dataset with monocle3 twice: the first on WT cells, the second on mutant cells. I wonder if there was a method to compare the differences between the two trajectories (wild type and…

Continue Reading differences between trajectories in conditions with Monocle3 or other tools

Violin plot (Monocle 3) – Troubleshooting

Violin plot (Monocle 3) – Troubleshooting 0 I am trying to generate some violin plots in monocle on a cell dataset object. Basically, I’m trying to visualize expression of certain marker genes in different clusters of a cell dataset object. I tried to follow this online documentation using the plot_genes_violin…

Continue Reading Violin plot (Monocle 3) – Troubleshooting

Abnormal developmental trajectory and vulnerability to cardiac arrhythmias in tetralogy of Fallot with DiGeorge syndrome

Generation and characterisation of patient-specific hiPSCs and hiPSC-CMs hiPSC lines were established from two TOF-DG patients, two TOF-ND patients, and two healthy controls with pluripotency markers and germ layer markers verified (Supplementary Figs. 1 and 2). Whole genome sequencing confirmed, respectively, the presence and the absence of 22q11.2 deletion in the…

Continue Reading Abnormal developmental trajectory and vulnerability to cardiac arrhythmias in tetralogy of Fallot with DiGeorge syndrome

TimeTalk uses single-cell RNA-seq datasets to decipher cell-cell communication during early embryo development

Curation of early-embryo development single-cell RNA-seq data sets for studying cell-cell communication To identify and study eLRs, we collected public early embryo development scRNA-seq datasets from the mouse MII-oocyte stage to the late blastocyst stage to ensure that scRNA-seq datasets represented every stage of early embryo development. In addition, to…

Continue Reading TimeTalk uses single-cell RNA-seq datasets to decipher cell-cell communication during early embryo development

R studio freezes when monocle3 is installed – RStudio IDE

Hi, R studio server freezes and the .Rproj.user and .Rhistory files must be deleted to connect to an already running session. This behaviour is only resolved by uninstalling monocle3 and comes back when I re-install monocle3. I tried the development and beta branch of Monocle3. I had the same issue…

Continue Reading R studio freezes when monocle3 is installed – RStudio IDE

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…

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

Monocle3 error when passing gene_module_df directly to plot_cells()

Monocle3 error when passing gene_module_df directly to plot_cells() 0 Hello I’m a new computational biologist and I’m following the Monocle3’s guide on finding modules of co-regulated genes cole-trapnell-lab.github.io/monocle3/docs/differential/#gene-modules But when I run the plot_cells() function, it keeps showing an error after I pass gene_module_df directly to plot_cells(): Error in !is.null(dim(genes))…

Continue Reading Monocle3 error when passing gene_module_df directly to plot_cells()

Tox4 regulates transcriptional elongation and reinitiation during murine T cell development

Pan-hematopoietic Tox4 deletion reduces number of multipotential progenitors and impairs T cell development To understand the role of TOX4 in development, we generated Tox4 conditional knockout mice by the CRISPR-Cas9 methodology, and two loxP sites in the same orientation were inserted upstream and downstream of exons 4–6, respectively (Supplementary Fig. 1a). Considering…

Continue Reading Tox4 regulates transcriptional elongation and reinitiation during murine T cell development

Understanding learn_graph and ncenter in Monocle3

Understanding learn_graph and ncenter in Monocle3 0 Hi all I have some questions about how Monocle3’s learn_graph function and ncenter parameters work. Background: I’m using Monocle3 to calculate trajectories for a dataset that consists of two cohorts – control and treated. Cleaning, dimensionality reduction and clustering were all done in…

Continue Reading Understanding learn_graph and ncenter in Monocle3

Motixafortide and G-CSF to mobilize hematopoietic stem cells for autologous transplantation in multiple myeloma: a randomized phase 3 trial

Patient demographics were comparable across study cohorts From 22 January 2018 to 30 October 2020, a total of 122 patients from 18 sites in five countries were enrolled and randomized 2:1 to receive either motixafortide + G-CSF (80 patients) or placebo + G-CSF (42 patients) for HSPC mobilization (Fig. 1 and Extended Data Fig. 1a). Demographics between the two treatment…

Continue Reading Motixafortide and G-CSF to mobilize hematopoietic stem cells for autologous transplantation in multiple myeloma: a randomized phase 3 trial

Gene duplicate

Gene duplicate 1 Hi there, I am pretty new to single cell RNA seq and I am trying to learn by doing analysis for a data that has been published already. I am using monocle3 and I realized that some Ensembl IDs that are the same and I was wondering…

Continue Reading Gene duplicate

Trajectory analysis using Monocle3 with Seurat sub-clustering

Trajectory analysis using Monocle3 with Seurat sub-clustering 0 Hi all, I am analyzing single cell RNA-seq data using Seurat and trying to do trajectory analysis using Monocle3. My analysis pipeline is below. # QC, NormalizeData, FindVariableFeatures for each sample independetly SelectIntegrationFeatures > FindIntegrationAnchors > IntegrateData > > ScaleData > RunPCA…

Continue Reading Trajectory analysis using Monocle3 with Seurat sub-clustering

single-cell pseudotime analysis with monocle3

single-cell pseudotime analysis with monocle3 0 I have a question regarding pseudotime analysis. It seems, this analysis is done over only a certain cell type. However, I am wondering if we can apply it on all the cells we got from UMAP out of single-cell analysis. Can we? Also, If…

Continue Reading single-cell pseudotime analysis with monocle3

exchange the monocle data with Seurat

exchange the monocle data with Seurat 0 Hi everyone! I am pretty new to single cell RNA seq analysis. I am using monocle 3 for my analysis. the data file that I have downloaded has already normalized data and it was coming with ensembl ID and I was able to…

Continue Reading exchange the monocle data with Seurat

r – monocle3 installation issues

I have R 4.2.1 and the latest R studio build 554 on a macOS Monterey. I am trying to install monocle3 using the following : BiocManager::install(‘monocle3’) I get a really long error message BiocManager::install(“monocle3”) ‘getOption(“repos”)’ replaces Bioconductor standard repositories, see ‘?repositories’ for details replacement repositories: CRAN: cran.rstudio.com/ Bioconductor version 3.15…

Continue Reading r – monocle3 installation issues

Monocle3 or cytotrace with scanpy script : bioinformatics

Hey guys, maybe this question is obvious, but I have been trying to find an answer to this for a while now and just wanted to ask. I am analysing single cell data with scanpy and need to do differentiation trajectory prediction. My project leader (not a bioinformatician) recommended monocle3…

Continue Reading Monocle3 or cytotrace with scanpy script : bioinformatics

Alternatives and detailed information of monocle3

Licence: other No description or website provided. Projects that are alternatives of or similar to monocle3 dropEst Pipeline for initial analysis of droplet-based single-cell RNA-seq data Stars: ✭ 71 (-58.24%) Mutual labels:  single-cell-rna-seq StackedDAE Stacked Denoising AutoEncoder based on TensorFlow Stars: ✭ 23 (-86.47%) Mutual labels:  single-cell-rna-seq kmer-homology-paper Manuscript for functional prediction…

Continue Reading Alternatives and detailed information of monocle3

dataframe – uwot is throwing an error running the Monocle3 R package’s “find_gene_module()” function, likely as an issue with how my data is formatted

I am trying to run the Monocle3 function find_gene_modules() on a cell_data_set (cds) but am getting a variety of errors in this. I have not had any other issues before this. I am working with an imported Seurat object. My first error came back stating that the number of rows…

Continue Reading dataframe – uwot is throwing an error running the Monocle3 R package’s “find_gene_module()” function, likely as an issue with how my data is formatted

Monocle3 differential expression failed when active.assay is not “RNA”

after run estimate_size_factors, data with active.assay = ‘integrated’ works too, but no deg in the result. > [email protected] = ‘integrated’ > cds_raw <- as.cell_data_set(seurat_object) Warning: Monocle 3 trajectories require cluster partitions, which Seurat does not calculate. Please run ‘cluster_cells’ on your cell_data_set object > cds <- cluster_cells(cds_raw) > pr_graph_test_res <-…

Continue Reading Monocle3 differential expression failed when active.assay is not “RNA”

Single-cell delineation of lineage and genetic identity in the mouse brain

STICR lentiviral library preparation and validation We synthesized a high-complexity lentivirus barcode library that encodes approximately 60–70 million distinct oligonucleotide RNA sequences (STICR barcodes). STICR barcodes comprised three distinct oligonucleotide fragments cloned sequentially into a multicloning site within the 3′ UTR of an enhanced green fluorescent protein (eGFP) transgene under…

Continue Reading Single-cell delineation of lineage and genetic identity in the mouse brain

Which trajectory method is better !?

Which trajectory method is better !? 2 Hello I was engaged with a basic problem. I have dataset consist ~2000 cells and composed 8-9 clusters using Seurat package, then I transfer Seurat object to the Monocle. I tried monocle2 and monocle3. The problem is, how to make the trajectory ?…

Continue Reading Which trajectory method is better !?