Tag: UMAP

how to ensure that embedding for the new data is initialized based on the embedding of the existing data in UMAP? 0 Hello all, I have a merged dataset of mouse-human and by applying umap on the data I get some shared cluster of mouse-human. Now I would like to…

Babesia genomes lack enzymes of the complete haem synthesis system as compared to Plasmodium genomes Babesia genomes comprise four chromosomes, and range in size from 6 to 15 Mb [e.g. Babesia microti (6.44 Mb), Babesia bovis (8.18 Mb), Babesia bigemina (12.84 Mb), Babesia ovata (14.45 Mb), Babesia ovis (8.38 Mb) and Babesia divergens (9.65 Mb) (Additional file…

Dermal cell characterization by 10X scRNA-seq. a, Workflow used to obtain dermal cells of adult and aged mouse back skin. Single-cell suspensions were enriched separately for EpCAM–CD45– and CD45+ cells by FACS. Transcriptomes of sorted single cells were then analyzed by 10X scRNA-seq. For CD45+ cells, n = 7 mice for the adult…

Development of the BRCA-mt signature. UMAP representation of BRCA-mt and BRCA-wt samples from all evaluated cohorts without (a) and after (b) batch adjustment (N = 653). Volcano plots showing differentially expressed miRNAs between BRCA-mutated and wild-type samples without (c) and after (d) batch adjustment (N = 521); red markings represent miRNAs with P < 0.01 and FC > 1.5…

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…

normalize for sample size in single cell rna seq cluster frequencies data 0 Hello, I have some single-cell RNA seq of different mice tissues and two different conditions (treated, untreated). After generating the UMAP plots and the resulted cells clusters I would like to calclute the frequency of treated vs…

Epigenomics, a rapidly advancing field within genomics, delves into the intricate mechanisms that regulate gene expression through chemical modifications to DNA and associated proteins. These epigenetic changes have profound implications for various biological processes and can influence the development of diseases. As the understanding of epigenomics expands, so does the…

Single-cell and single-nucleus RNA sequencing of sarcoma subtypes In this work, we studied sarcomas from varying tissue origins, including osteosarcoma (OS), Ewing sarcoma (ES), and desmoplastic small round cell tumor (DSRCT) (Fig. 1). We used different dissociation protocols: Miltenyi Tumor Dissociation Kit, cold-active protease derived from Bacillus licheniformis, and Nuclei EZ…

DOI: 10.18129/B9.bioc.MatrixQCvis     This package is for version 3.13 of Bioconductor; for the stable, up-to-date release version, see MatrixQCvis. Shiny-based interactive data-quality exploration for omics data Bioconductor version: 3.13 Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here…

Annotation of short-read scRNA-seq data with isoform-level information Using fluorescence-activated cell sorting (FACS), we isolated the Lineage-negative, cKit (Cd117) positive (LK) cell fraction of mouse bone marrow cells, a population containing stem and progenitor cells14 from young (8 weeks old, n = 3) and aged (72+ weeks old, n = 3) mice. We generated…

Removing specific cluster based on one gene down regulation 0 Hello, I’m using seurat pipeline to analyze the single cell RNA-seq data. I m stuck with one problem I found two clusters with out gene A expression but this specific gene is expressed in all other clusters. It is important…

Hi, I have two samples of Spatial transcriptome (Visium), one for wild type and one for knock out that I have analyzed using SpaceRanger count (I am new in this type of analysis).I need to compare the annotated UMAP plot of wild type and knock out samples. I am following…

HCPL – Hybrid subcellular protein localiser Figure 1 presents an overview of the HPA dataset, the HPA challenge, and our HCPL solution. The HCPL system (Fig. 1b) receives multi-channel images, segments individual cells using the HPA Cell Segmentator (Methods), and analyses each cell in turn to estimate its visual integrity and the…

Hi all, I have a scRNA dataset which has 16 individuals and I have the count matrix for all of these. I made 16 seurat objects and after that I have merged all the objects using the “merge” function of Seurat. I have done the normalization and scaling of the…

Liger find a sub cluster under one cluster 0 Good morning, I am trying to use liger to find my sub clusters for one cell clusters. However, I got trouble. I noticed that my sub clusters looks like: They are not really clustering. I am trying to use different parameters,…

Study cohort and performance during NMP An overview of the overall study population is presented in Table 1 (individual data are given in Supplementary Table 1). Detailed information on study livers and analysis is provided as workflow scheme in Fig. 1. The decision to apply NMP was based on one or a combination…

Welcome to the latest installment of our ongoing blog series where we explore computer vision related datasets, this one is from a new Kaggle competition! In this post we’ll use the open source FiftyOne computer vision toolset to explore Google Research’s Image Matching Challenge 2023 – Reconstruct 3D Scenes from…

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…

Hi, The problem that you got is related with the fact that you need to provide a list of comparisons to be made to the function stat_compare_means(), and you are providing a character/factor variable. So, if you adjust your code to: vp_case1 <- function(gene_signature, file_name, test_sign, y_max){ plot_case1 <- function(signature){…

remove batch effect 0 Good afternoon, I integrated about 20 studies on different organs and used merge to combine them, followed by using harmony to remove the batch effect. However, the UMAP plot I generated did not successfully remove the batch effect, as several clusters were just from one study…

Trophoblast cell states in the early maternal–fetal interface. a, Schematic representation of the maternal–fetal interface during the first trimester of human pregnancy. b, Histological overview (haematoxylin and eosin (H&E) staining) of the implantation site of donor P13 (approximately 8–9 PCW) (n = 1). Black outlines indicate trophoblast microenvironments in space. c, Uniform manifold…

Goddard, E. T., Bozic, I., Riddell, S. R. & Ghajar, C. M. Dormant tumour cells, their niches and the influence of immunity. Nat. Cell Biol. 20, 1240–1249 (2018). Article  CAS  PubMed  Google Scholar  Malladi, S. et al. Metastatic latency and immune evasion through autocrine inhibition of WNT. Cell 165, 45–60…

Published: 28 March 2023| Version 1 | DOI: 10.17632/fwczkb6xw3.1 Contributors: Description This study was designed to capture the changes in CD8 T cell phenotypes in murine glioma (CT-2A) model post immunotherapy with AAV-LIGHT (TNFSF14). Tumour infiltrating CD45 cells were isolated by flow sorting and subject to targeted single-cell transcriptome sequencing…

Clustering with monocle 3 0 Hello everyone, I was trying to analyze a scRNA dataset (chromium 10x) using monocle 3. I realized that changing the resolution parameter when applying modularity maximization did not change the capacity to discriminate subpopulations. The number of cells is 2k and UMAP visualization separates the…

Hello I am working on 10X visium spatial transcriptome data which was processed in seurat pacakge with image data. For my downstream analysis, I am trying to import seurat normalized data into scanpy. For this I converted seurat object to h5ad using these steps. SaveH5Seurat(test_object, overwrite = TRUE, filename =…

I would like to include the ribosomal genes (for normalisation, plotting etc) in the Seurat object but not use them in PCA, UMAP etc, so I remove them from HVGs. This is how I do it with ScaleData approach. s <- NormalizeData(s, normalization.method=”LogNormalize”) s <- FindVariableFeatures(s,selection.method=”vst”, nfeatures=2500) # remove ribo…

(A) Locus view for the CAD locus with nearby gene CNNM2. We provide the position of the sentinel CAD variant (rs11191416) and the putative functional variant identified in the pooled CRISPR screen (rs78260931). The LD proxies and sgRNAs tested are also shown. ATAC-seq and RNA-seq data in resting teloHAEC are…

Spatial-CITE-seq workflow design and application to diverse mouse tissue types and human tonsil for co-mapping of proteins and whole transcriptome. a, Scheme of spatial-CITE-seq. A cocktail of ADTs is applied to a PFA-fixed tissue section to label a panel of ~200–300 protein markers in situ. Next, a set of DNA…

SIDA framework To achieve supervised integration, we propose to use a domain adaptation deep learning network architecture, which is able to incorporate cell type labels to inform data integration. As shown in Fig. 1, this network architecture takes training pairs generated by cells from different batches as input and passes the…

Design and evaluation of spatial epigenome–transcriptome cosequencing with E13 mouse embryo. a, Schematic workflow. b, Comparison of number of unique fragments and fraction of reads in peaks (FRiP) in spatial ATAC–RNA-seq and spatial CUT&Tag–RNA-seq. c, Gene and UMI count distribution in spatial ATAC–RNA-seq and spatial CUT&Tag–RNA-seq. Number of pixels in…

scRNAseq package and cell type annotations 1 @0dc78e90 Last seen 4 hours ago Australia I would like to annotate or label UMAP clusters with cell types in a multiple myeloma single-cell RNA-seq dataset using the scRNAseq R package in conjunction with the SingleR package, if possible. I found a dataset…

Single-cell nucleus RNA-seq profiling of Hip and PFC A schematic of nuclei isolation and the snRNA-seq workflow from the Hip and PFC is shown in Fig. 1a. Using the droplet-based single-nucleus method, we captured 72,226, and 67,698 nuclei from the Hip and PFC, respectively, in the 9 mice (3 per group)….

Why my single cell cluster is splitted? 0 Hi all, I am analyzing my single nuclei dataset. I did clustering and cluster annotation. And I observed sth that I couldn’t explain why it is happening. Below is the umap projection of my dataset. As you see the MN_1 cluster is…

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…

The number of cells in each cell-type biases the null distributions and statistics of the Z-scores method In this study, we refer to Z-score as the scaled Pearson R value of a cis-link between an ATACseq peak and a nearby gene against its matched trans-link null distribution (the Z-scores method,…

Hi I have spent a few days to learn how totalvi analyze CITE-seq data and I am bit confused by the contrasting steps between Scanpy and totalvi: I have used Scanpy for 10x scRNA-seq for over 2 years now and I love it.The typical steps are as follows: Read in…

Data Imputation for performing UMAP 1 Hi guys! Currently I am working on a dataset with gene ID, it’s expression values and patient IDs. I want to use the UMAP method to process the data and compare results with a previous study. That study used a K-means clustering method. At…

Filtering genes study-specific before Harmony integration? 0 Hello all, I have download two scRNAseq experiments from two different studies. After creating the seurat file using (features, matrix, counts), I removed the doublets, normalize these two samples using harmony and, in general, the result is pretty decent. However, there are some…

Advices on Box-Cox transformation (powerTransform function) before UMAP clustering process statistics 0 Hi guys, Currently I am analysing some gene expression data. The dataset was analyzed in several studies before. I have identify one particular study and they used a standard K-mean clustering to identify different phenotypes. My main goal…

A comprehensive catalogue of ARGs from the human microbiome We created a catalogue of ARGs across both the human microbiome and reference genomes by locating open reading frames (ORFs) on metagenomic assemblies from 8972 human microbiome samples spanning gut (7589), oral cavity (746), skin (380), airway (118), nasal cavity (55),…

We seek a talented, energetic, and collaborative bioinformatician to design bioinformatics pipelines and analyze multi-platform data as part of the development of our flagship platform A 3 D 3 a: Adaptive, AI-augmented, Drug Discovery and Development. With expertise in genomics and the design and deployment of bioinformatics tools, the Data…

The Computational Biology Core at the University of Connecticut’s Institute for Systems Genomics is offering a virtual Single Cell RNA-seq workshop March 20-23, 2023. The workshop will be run on a high performance computing cluster, and cover the basics of Linux and writing/submitting scripts. The goal is to familiarize attendees…

Summary of a Single-cell Guided Pipeline to Aid Repurposing of Drugs Using scRNA-seq data, ASGARD repurposes drugs for disease by fully accounting for the cellular heterogeneity of patients (Fig. 1, Formula 1 in “Methods” section). In ASGARD, every cell cluster in the diseased sample is paired to that in the normal…

Superimpose UMAPs 0 Hi! I am starting out with scRNA analysis and I need to superimpose 2 UMAPs: x = sc.pl.umap(labeled_adata, color=”n_counts”) y = sc.pl.umap(unlabeled_adata) Could anyone help me out with the code please? Superimpose Overlay UMAP • 35 views Login before adding your answer. Read more here: Source link

Bioinformatics pipeline Aiming to genetically separate, characterize, and individually identify persons who contributed to multi-person blood mixtures from single-cell transcriptome sequencing (scRNA-seq) data, we have developed a bioinformatics pipeline called de-goulash (Fig. 1a)24. We applied de-goulash on scRNA-seq datasets that we de-novo generated from multi-person blood mixtures and on in silico…

The Brainy Insights The rising partnerships between information technology (IT) and pharma companies can boost the development of the bioinformatics services market over the forecast years. North America emerged as the largest market for the global bioinformatics services market, with a 35.6% share of the market revenue in 2022. Newark,…

Neuron-specific removal of the APOE gene in tauopathy mice Our laboratory previously generated mouse lines expressing a floxed human APOE3 or APOE4 gene45 and a Cre recombinase gene under the control of a neuron-specific synapsin-1 promoter (Syn1-Cre)46. These floxed APOE-KI (fE) mice express homozygous human APOE3 or APOE4 in place…

Hello, I have single cell and bulk RNA seq data for both of which I have performed some basic analysis. For the bulk RNA seq data I have performed DESe2 and I have gotten a list of DE genes. I would like to make a UMAP where the cells are…

AJIVE integration captures biologically joint-acting and cohort-specific variation AJIVE is an extension of JIVE that employs the use of thresholded Singular Value Decomposition (SVD) to create low-dimension approximations of input data matrices (Fig. 1). Then, through utilizing Principal Angle Analysis, identifies low-dimension subspace bases that either share variation structure (Joint) or…

I would like to make a UMAP where the cells are colored by the average expression of the bulk signature genes but I am not confident that I did it correctly. I would like to use scanpy for it. I did the below: bulk_de_genes_up_list = bulk_de_genes[‘Gene’].tolist() # Subset the data…

singleR annotation shows overlapping clusters 0 Hello everyone, I used singleR to annotate my clustered Seurat dataset. Before annotating using singleR I did manual annotation using marker genes. Below is the result of my manual annotation Then, I used singleR and below is the annotation results. The result shows that…

cell type annotation in single cell pbmc data 0 Hello everyone I am analyzing single cell data (16 samples) for the first time. I followed multiple tutorials for the QC and normalization analysis. I have few queries which I would like to discuss. 1) After normalization of all samples ,…

How to make a UMAP for single cell data and color cells by average expression of a list of cells in scanpy? 0 Hello, I have single cell and bulk RNA seq data for both of which I have performed some basic analysis. For the bulk RNA seq data I…

Cell clustering and cluster composition of snRNA-seq on human livers. (A-B) Quality control metrics of snRNA-seq across different samples by violin plot and UMAP plot. (C) UMAP plot visualization of clusters based on group conditions. (D) Violin plot of expression level for signature genes of each cluster. (E) Relative proportion…

NMDS: how many axes to plot? 1 Hi all, I have a question on NMDS. I runned an NMDS on my data and it extracts 4 axes. I now have to plot my results, but I don’t know if I should plot all axes combination or just the first 2….

Fibroblast identification through single-cell RNA-sequencing analysis of whole-tissue homogenates derived from human NSCLC tumor samples. a Schematic illustrating sample processing and analysis methodology used to generate the target lung drop-seq (TLDS) dataset, comprised of human control (n = 6) and NSCLC (n = 12) samples. The Figure was partly generated using Servier Medical Art,…

seurat v3 object ASSAYS: RNA: mRNA expression data DIMENSIONALITY REDUCTION projected: Data was projected on the main AML dataset from Cohorts A and B. scanorama: Data was integrated with Scanorama, using the patient as Batch key umap: umap computed from Scanorama components METADATA patient: Patient ct: Projected cell type (Triana…

doi: 10.3389/fcell.2022.1071243. eCollection 2022. Affiliations Expand Affiliations 1 Department of Anatomy, Biochemistry and Physiology, Honolulu, HI, United States. 2 Center for Cardiovascular Research, Honolulu, HI, United States. 3 Department of Medicine, Honolulu, HI, United States. 4 Department of Quantitative Health Sciences, Honolulu, HI, United States. 5 Genomics and Bioinformatics Shared…

Run the usual steps including clustering and visualization via something like UMAP and then color the UMAP by these four markers. That assumes that the surface marker separation you see in flow holds true on a transcriptional level which is not necessarily the case. For example, hematopoietic surface-defined populations such…

Wang, J., Garancher, A., Ramaswamy, V. & Wechsler-Reya, R. J. Medulloblastoma: from molecular subgroups to molecular targeted therapies. Annu. Rev. Neurosci. 41, 207–232 (2018). Article  CAS  PubMed  Google Scholar  Cavalli, F. M. G. et al. Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell 31, 737–754.e736 (2017). Article  CAS  PubMed  PubMed Central …

Software Figure preparation: CorelDRAW x8 (Corel); Bioinformatic analyses: R v 4.0.3 (R Foundation for Statistical Computing). Computational resources Analyses were run on a desktop computer with an Intel Core i9-10900L CPU (3.70 GHz, 10 cores, 20 threads) with 120 GB RAM running Windows 10 Pro (v21H2). Data preprocessing scRNA-seq data sets…

Niikawa N, Matsuura N, Fukushima Y, Ohsawa T, Kajii T. Kabuki make-up syndrome: a syndrome of mentalretardation, unusual facies, large and protruding ears, and postnatal growth deficiency. J Pediatrics. 1981;99:565–9. CAS  Article  Google Scholar  Kuroki Y, Suzuki Y, Chyo H, Hata A, Matsui I. A new malformation syndrome of long…

Low expression of key transcripts in scRNAseq dataset after deeper sequencing 1 I am having an issue with my single cell data set. This alert was shown after I requested additional reads per cell to be incorporated with the initial data we received. We have previously performed single cell on…

lncRNA expression according to breast cancer clinicopathological subtypes To identify lncRNAs expressed by specific breast cancer subtypes or associated with clinicopathological features, we analyzed RNA-sequencing data from two large independent breast cancer cohorts: SCAN-B (n = 3455)17 and TCGA-BRCA (n = 1095). We focused on lncRNAs annotated in the Ensembl18 v93 non-coding reference transcriptome…

Mapping molecular changes across malignant transformation We generated single-cell data for 81 samples collected from eight FAP and seven non-FAP donors (Fig. 1a and Supplementary Tables 1 and 2). For each tissue, we performed matched scATAC-seq and snRNA-seq (10x Genomics). We obtained high-quality single-cell chromatin accessibility profiles for 447,829 cells…

Hi all, I am new to R and Seurat, and I am following Seurat tutorials to find anchors between RNA-seq and ATAC-seq data according to: Combining the two tutorials is difficult for a cell line data set I am using for SNARE-seq Human here. I managed to run the following…

scRNAseq analysis – h5ad file conversion to Seurat format 0 Hi all. I have a single .h5ad file that contains scRNAseq data from several samples. I would like to convert it so that I can open it in Seurat (I am comfortable with R, but not with Python). I have…

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…

Why do UMAP on all scRNA-seq samples rather than a UMAP for each treatment? 1 When analyzing scRNA-seq data, why do people pool all their data across treatments and run UMAP on the combined dataset rather than running a separate UMAP on each treatment group? For example, say you’re looking…

Introduction Keloid is excessive fibrosis of the skin that extends beyond the area of injury and does not regress.1 Keloid can occur in the joints and mouth after several years of severe injury, including burns, chemical injury, wound, and surgical incision.2 Keloids on the joints affect the quality of life,…

Integration of APC scRNA-seq data reveals heterogeneity of adipocyte progenitor cells In a previous study9, we defined Lin−Sca1+CD142+ APCs as adipogenesis regulatory (Areg) cells and demonstrated that these cells are both refractory toward adipogenesis and control adipocyte formation of APCs through paracrine signaling. In contrast, Merrick et. al.4 observed that Lin−CD142+ cells…

Integrating Bulk RNA-seq data with Single cell RNA seq data 0 Hello all, recently, I had been trying to integrate bulk RNAseq data into single-cell data where I treat each sample in my bulk RNAseq data as a single cell and integrate it into the single-cell data based on the…

Adding numbers and characters to legend key in ggplot2 of UMAP clusters 0 Hi everyone, I have a UMAP cluster, however there are so many clusters that the descriptions look clunky if i put them on the umap…but then there are too many colors if it just colors. So, i…

1. Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 20, 404–416 (2019). CAS  PubMed  Google Scholar  2. Marine, J. C., Dawson, S. J. & Dawson, M. A. Non-genetic mechanisms of therapeutic resistance in cancer. Nat. Rev. Cancer 20, 743–756 (2020). CAS …

tSNE and UMAP of scATAC-seq data looks like spaghetti 0 I would like to use R to generate cluster my 20k cells from a single cell ATAC-seq experiment. I ran PCA then selected the first 50 components, which were put into tSNE’s normalize_input() then Rtsne(). This is the result I…

Correct usage of FindConservedMarkers() in Seurat 0 Dear all, I have a Seurat object of a certain cell type with a UMAP of 7 clusters. I also have information about the sample’s origin (primary tumor/metastatic) in my metadata. Looking at the UMAP I can clearly see that clusters 1 and…

DOI: 10.18129/B9.bioc.GEOexplorer     This is the development version of GEOexplorer; to use it, please install the devel version of Bioconductor. GEOexplorer: an R/Bioconductor package for gene expression analysis and visualisation Bioconductor version: Development (3.14) GEOexplorer is a Shiny app that enables exploratory data analysis and differential gene expression of…

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…

The Biostar Herald publishes user submitted links of bioinformatics relevance. It aims to provide a summary of interesting and relevant information you may have missed. You too can submit links here. This edition of the Herald was brought to you by contribution from zx8754, Istvan Albert, and was edited by…

A question about scRNA-seq analysis 0 Dear all, please may I ask for a suggestion : I have a scRNA-seq dataset, which has 2 groups (Control and Model). I would like to process all the samples into cluster by tSNE or UMAP , then classification or differentiation the cell types….

Best compression for single cell RNA-seq object 0 I generated a scRNA-seq object (counts, PCA, UMAP embeddings, DEGs etc.) in Scanpy or Seurat. What is the best data structure to store this in to reduce the size of the object? I’m considering H5AD (scanpy/anndata), RDS or H5Seurat (Seurat), or Loom…

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 >…

Sorry for lateness, I wanted to do something similar. This is what I did for reference: Using a Seurat generated gene list for input into ClusterProfiler to see the GO or KEGG terms per cluster. I’ll keep the meat and potatoes of the Seurat vignette in this tutorial: library(dplyr) library(Seurat)…

I am trying to set up VisCello shiny app for hosting some of our single cell data, analyzed in Seurat: github.com/qinzhu/VisCello I am using the following code though running into a problem, the app launches however I dont see the umaps and when I click over to differential expression the…

Hello, I have output from a single cell sequencing run that has both the VDJ and gene expression data. For the same cells, we also used a hybrid capture approach to sequence the TCR sequences. I have compared the TCR sequences across the two approaches and I have found a…

UMAP vs “rigorous” t-SNE 1 I’ve heard a lot of people discussing UMAP recently as though it has essentially superseded t-SNE for visualizing scRNA-seq data. UMAP is certainly impressive, but it seems to me that there are a lot of things one can do to pretty dramatically improve the output…

Experience with Cytobank 0 Dear all We are medical doctors and immunologists working with 16 color phenotype FCS files created on patient blood PBMC samples. We would like to independently work with a clustering algorithm that is intuitive and easy to manage. We have been exploring Cytobank briefly but we…

I’ve been trying to delve into the data from whole genome sequencing, specifically by looking at the already existing data in the 1000 genome project and gnomad, and I have a lot of questions. Does gnomAD contain the 1000gp samples? I’ve found many vcf including these: ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/hd_genotype_chip/ ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/integrated_sv_map/ ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase1/analysis_results/integrated_call_sets/ gnomad.broadinstitute.org/downloads…

Corelate TCR data to clusters/GEX/CITEseq data 1 Hello everyone, I just added my TCR VDJ data as metadata to my Seurat object (as described in the tutorial here). So, I basically ended up with two different collumns of metadata where my barcodes are assigned to the clonotypes and the cdr3…