Bioinformatics

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.

Introduction An artificial intelligence system called a convolutional neural network (CNN) has gained a lot of popularity recently. For jobs like image recognition, where we want to teach a computer to recognize things in a picture, they are especially well suited. CNNs operate by dissecting an image into increasingly minute components, or “features.” The network then examines each feature and searches for patterns shared by various objects. For instance, a CNN might come to understand that some pixel patterns are frequently linked to faces, while others are linked to vehicles or trees.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter Single-cell spatial transcriptome data is a new and advanced technology that combines the study of individual cells’ genes and their location in a tissue to understand the complex cellular and molecular differences within it. This allows scientists to investigate how genes are expressed and how cells interact with each other with much greater detail than before.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter The T-cell receptor (TCR) is a special molecule found on the surface of a type of immune cell called a T-cell. Think of T-cells like soldiers in your body’s defense system that help identify and attack foreign invaders like viruses and bacteria. The TCR is like a sensor or antenna that allows T-cells to recognize specific targets, kind of like how a key fits into a lock.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter Introduction Are you a machine learning practitioner or data analyst looking to broaden your skill set? Look nowhere else! This blog post will offer an introduction to deep learning, which is currently the hottest topic in machine learning. Using the well-known MNIST dataset) and the Keras package, we will investigate the potential of deep learning.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter The problem Let me be clear, when you have gazillions of data points in a scatter plot, you want to deal with the overplotting to avoid drawing misleading conclusions. Let’s start with a single-cell example. Load the libraries: library(dplyr) library(Seurat) library(patchwork) library(ggplot2) library(ComplexHeatmap) library(SeuratData) set.seed(1234) prepare the data data("pbmc3k") pbmc3k #> An object of class Seurat #> 13714 features across 2700 samples within 1 assay #> Active assay: RNA (13714 features, 0 variable features) ## routine processing pbmc3k<- pbmc3k %>% NormalizeData(normalization.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter Start here Docker is a great tool to ensure reproducibility of your computing work. I was using the bioconductor image on google cloud, but the image does not have the gsutil command. You can install once in the container, but once you exit the container, the gsutil command will be gone. You will need to modify the docker image if you want to keep using it.

In this blog post, I am going to show you how to make a correlation heatmap with p-values and significant values labeled in the heatmap body. Let’s use the PBMC single cell data as an example. You may want to read my previous blog post How to do gene correlation for single-cell RNAseq data. Load libraries library(dplyr) library(Seurat) library(patchwork) library(ggplot2) library(ComplexHeatmap) library(SeuratData) library(hdWGCNA) library(WGCNA) set.seed(1234) prepare the data data("pbmc3k") pbmc3k #> An object of class Seurat #> 13714 features across 2700 samples within 1 assay #> Active assay: RNA (13714 features, 0 variable features) ## routine processing pbmc3k<- pbmc3k %>% NormalizeData(normalization.

In my last blog post, I showed that pearson gene correlation for single-cell data has flaws because of the sparsity of the count matrix. One way to get around it is to use the so called meta-cell. One can use KNN to find the K nearest neighbors and collapse them into a meta-cell. You can implement it from scratch, but one should not re-invent the wheel. For example, you can use metacells.

Load libraries library(dplyr) library(Seurat) library(patchwork) library(ggplot2) library(ComplexHeatmap) library(SeuratData) set.seed(1234) prepare the data data("pbmc3k") pbmc3k #> An object of class Seurat #> 13714 features across 2700 samples within 1 assay #> Active assay: RNA (13714 features, 0 variable features) ## routine processing pbmc3k<- pbmc3k %>% NormalizeData(normalization.method = "LogNormalize", scale.factor = 10000) %>% FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>% ScaleData() %>% RunPCA(verbose = FALSE) %>% FindNeighbors(dims = 1:10, verbose = FALSE) %>% FindClusters(resolution = 0.