Bioinformatics

Multi-omics data analysis is a cutting-edge approach in biology that involves studying and integrating information from multiple biological “omics” sources. These omics sources include genomics (genes and their variations), transcriptomics (gene expression and RNA data), proteomics (proteins and their interactions), metabolomics (small molecules and metabolites), epigenomics (epigenetic modifications), and more. By analyzing data from various omics levels together, we can gain a more comprehensive and detailed understanding of biological systems and their complexities.

Spatial transcriptome cellular niche analysis using 10x xenium data go to https://www.10xgenomics.com/resources/datasets There is a lung cancer and a breast cancer dataset. Let’s work on the lung cancer one. https://www.10xgenomics.com/resources/datasets/xenium-human-lung-preview-data-1-standard 37G zipped file! wget https://s3-us-west-2.amazonaws.com/10x.files/samples/xenium/1.3.0/Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE/Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE_outs.zip unzip Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE_outs.zip sudo tar xvzf cell_feature_matrix.tar.gz cell_feature_matrix/ cell_feature_matrix/barcodes.tsv.gz cell_feature_matrix/features.tsv.gz cell_feature_matrix/matrix.mtx.gz read in the data with Seurat We really only care about the cell by gene count matrix which is inside the cell_feature_matrix folder, and the cell location x,y coordinates: cells.

Introductioon In scRNA-seq data analysis, one of the most crucial and demanding tasks is determining the optimal resolution and cluster number. Achieving an appropriate balance between over-clustering and under-clustering is often intricate, as it directly impacts the identification of distinct cell populations and biological insights. The clustering algorithms have many parameters to tune and it can generate more clusters if e.g., you increase the resolution parameter. However, whether the newly generated clusters are meaningful or not is a question.

Disclaimer: This post is sponsored by Watershed Omics Bench platform. I have personally tested the platform. The opinions and views expressed in this post are solely those of the author and do not represent the views of my employer Variant calling is the process of identifying and categorizing genetic variants in sequencing data. It is a critical step in the analysis of whole-genome sequencing (WGS) and whole-exome sequencing (WES) data, as it allows researchers to identify potential disease-causing mutations.

Download the data https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116256 cd data/GSE116256 wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE116nnn/GSE116256/suppl/GSE116256_RAW.tar tar xvf GSE116256_RAW.tar rm GSE116256_RAW.tar Depending on how the authors upload their data. Some authors may just upload the merged count matrix file. This is the easiest situation. In this dataset, each sample has a separate set of matrix (*dem.txt.gz), features and barcodes. Total, there are 43 samples. For each sample, it has an associated metadata file (*anno.txt.gz) too. You can inspect the files in command line:

I tweeted it at https://twitter.com/tangming2005/status/1679120948140572672 I got asked to put all my posts in a central place and I think it is a good idea. And here it is! Benchmarking integration of single-cell differential expression Benchmarking atlas-level data integration in single-cell genomics A review of computational strategies for denoising and imputation of single-cell transcriptomic data Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution

What’s the drug development process? Has AI changed the course of Drug Development? To answer this question, we need first to understand the drug development process. The whole process includes the following: target identification target pharmacology and biomarker development lead identification, lead optimization Clinical research & development regulatory review of IND (investigational new drug) and later phase clinical trials post-marketing knowledge Biologics/antibodies drug development follows a similar path (you can find the map in the same link).

introduce ggside using single cell data The ggside R package provides a new way to visualize data by combining the flexibility of ggplot2 with the power of side-by-side plots. We will use a single cell dataset to demonstrate its usage. ggside allows users to create side-by-side plots of multiple variables, such as gene expression, cell type, and experimental conditions. This can be helpful for identifying patterns and trends in scRNA-seq data that would be difficult to see in individual plots.

Disclaimer: This post is sponsored by Watershed Omics Bench platform. I have personally tested the platform. The opinions and views expressed in this post are solely those of the author and do not represent the views of my employer As an experienced bioinformatician who understands the needs of biotech startups, I know the challenges that arise when analyzing genomics data. The first solution that comes to mind is cloud computing. Unsurprisingly, AWS and Google Cloud Platform (GCP) are commonly used options.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter In a previous blog post, I showed you how to make a Seurat spatial object from Vizgen spatial transcriptome data. In this post, I am going to show you how to identify clusters of neighborhood or cellular niches where specific cell types tend to co-localize. read in the data and pre-process library(Seurat) library(here) library(ggplot2) library(dplyr) # the LoadVizgen function requires the raw segmentation files which is too big.