Frederick, MD
Core Facility
Repositories
The Mouse Modeling Core assists NIH investigators by generating and preserving genetically-engineered mouse strains. Services include scientific consultation, gene-targeting in mouse embryonic stem cells, micro-injection of nucleic acids, proteins, or ES cells into mouse embryos, Read More...
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Bioinformatics
The next step is to import data to the project. Click on the "Add data" button and select "Bulk". RNA sequencing is the default option and since FASTQ files will be Read More...
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Bioinformatics
Let's load in a count matrix from airway to work with and reshape. aircount
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Bioinformatics
Let's check out the structure of the data. {{Sdet}} Possible Solution{{Esum}} str(mtcars) ## 'data.frame': 32 obs. of 11 variables: ## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... ## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... ## $ disp: num 160 160 108 258 360 ... ## $ hp : num 110 110 93 110 175 105 245 62 95 123 ... ## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... ## $ wt : num 2.62 2.88 2.32 3.21 3.44 ... ## $ qsec: num 16.5 17 18.6 19.4 17 ... ## $ vs : Read More...
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Bioinformatics
Explore this gene expression dataset a bit. How many samples (columns) and genes (row names) does this dataset have? {{Sdet}} Solution{{Esum}} This dataset contains 6 samples ( HBR_1.bam HBR_2.bam HBR_3.bam UHR_1.bam UHR_2. Read More...
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Bioinformatics
06/29/2022 - THIS EVENT HAS BEEN RESCHEDULED FROM 6/22 TO 6/29 AT 11:00 AM USING THE SAME MEETING LINK. We will go over RNA-seq from experimental design, data import options, data normalization options, to to statistical tests and biological Read More...
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Bioinformatics
Let's use read.delim to load the data. aircount<-read.delim("./data/head50_airway_nonnorm_count.txt", row.names = 1) head(aircount) Accession.SRR1039508 Accession.SRR1039509 ENSG00000000003.TSPAN6 679 448 ENSG00000000005.TNMD 0 0 ENSG00000000419. Read More...
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Bioinformatics
sessionInfo() R version 4.2.3 (2023-03-15) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Big Sur ... 10.16 Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/ Read More...
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Bioinformatics
From the paper where this data was obtained, the following (incomplete) list of gene markers was obtained: Mmp3: preadipocytes Mki67: proliferating cells Fabp4: differentiating beige adipocytes and differentiated beige adipocytes Scd1: differentiated beige adipocytes Ucp1: Read More...
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Bioinformatics
There are many steps that can be taken following subsetting (i.e., filtering by rows and columns); one of which is reordering rows. In the tidyverse, reordering rows is largely done by arrange() . Arrange will Read More...
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Bioinformatics
Here, R will be used to generate principal components plot for the HBR and UHR study. Principal components plots are a popular way to visualize how samples in RNA sequencing cluster based on gene expression. %% Read More...
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Bioinformatics
We can use the function prcomp() to run PCA on the first four columns of the iris data. The function takes numeric data. colnames(iris)[1:4] ## [1] "Sepal.Length" "Sepal.Width" "Petal. Read More...
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Bioinformatics
Prior to sending our data into the heatmap generating algorithm, it is a good idea to sacle. There are several reasons for doing this Variables in the data might not have the same units, thus Read More...
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Bioinformatics
Prior to sending our data into the heatmap generating algorithm, it is a good idea to sacle. There are several reasons for doing this Variables in the data might not have the same units, thus Read More...
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Bioinformatics
The following questions will help you gain more confidence in exploring data through heatmap. We will work with a subset of the Human Brain Reference (HBR) and Universal Human Reference (UHR) RNA sequencing dataset and Read More...
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Bioinformatics
Scaling is important during cluster analysis because it reduces the influence that variables with high magnitude values will have on distance. (https://medium.com/analytics-vidhya/why-is-scaling-required-in-knn-and-k-means-8129e4d88ed7). A common method for scaling is Read More...
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Bioinformatics
In tab delimited files, data columns are separated by tabs. To import tab-delimited files there are several options. There are base R functions such as read.delim() and read.table() as well as the readr Read More...
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Bioinformatics
Excel files are the primary means by which many people save spreadsheet data. .xls or .xlsx files store workbooks composed of one or more spreadsheets. Importing excel files requires the R package readxl. While this Read More...
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Bioinformatics
1. Introduction and Learning Objectives This tutorial has been designed to demonstrate common secondary analysis steps in a scRNA-Seq workflow. We will start with a merged Seurat Object with multiple data layers representing multiple samples that Read More...
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Bioinformatics
As you can see from the image, there are several accessor functions to access the data from the object: assays() - access matrix-like experimental data (e.g., count data). Rows are genomic features (e.g., Read More...
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Bioinformatics
Data import and reshape Objectives 1. Learn to import multiple data types 2. Data reshape with tidyr : pivot_longer() , pivot_wider() , separate() , and unite() Installing and loading packages So far we have only worked with objects that Read More...
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Bioinformatics
Objectives To explore Bioconductor, a repository for R packages related to biological data analysis. To better understand S4 objects as they relate to the Bioconductor core infrastructure. To learn more about a popular Bioconductor S4 Read More...
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Introduction to dplyr and the %>% Objectives Today we will begin to wrangle data using the tidyverse package, dplyr . To this end, you will learn: how to filter data frames using dplyr how to employ Read More...
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Bioinformatics
Managing Bioinformatics Projects with Jupyter Lab Learning Objectives After this class, participants will have obtained the foundation needed to start using Jupyter Lab as an all-in-one place to maintain code, output, and other description of Read More...
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Bioinformatics
Load the data For these exercises, you will explore the titanic data from kaggle.com , which was downloaded from here . You will need to download the data and load into R. As this is a Read More...
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Bioinformatics
Visualizing clusters with heatmaps Objectives Introduce the heatmap and dendrogram as tools for visualizing clusters in data. Learn to construct cluster heatmap using the package pheatmap . Learn how to save a non-ggplot2 plot. Introduce ggplotify Read More...
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Bioinformatics
Scatter plots and plot customization Objectives Learn to customize your ggplot with labels, axes, text annotations, and themes. Learn how to make and modify scatter plots to make fairly different overall plot representations. Load a Read More...
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Bioinformatics
Visualizing clusters with heatmaps Objectives Introduce the heatmap and dendrogram as tools for visualizing clusters in data. Learn to construct cluster heatmap using the package pheatmap . Learn how to save a non-ggplot2 plot. Introduce ggplotify Read More...
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Bioinformatics
Visualizing clusters with heatmaps Objectives Introduce the heatmap and dendrogram as tools for visualizing clusters in data. Learn how to work with the package pheatmap . Learn how to save a non-ggplot2 plot. Introduce ggplotify to Read More...
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Bioinformatics
For tabular data in the form of csv files, which could contain multiple columns, the columns do not print to the terminal nicely aligned. The column command can fix this. The options and arguments in Read More...
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Bioinformatics
All solutions use the pipe. Solutions have multiple possibilities. Q1. Import the file "./data/filtlowabund_scaledcounts_airways.txt" and save to an object named sc. Create a data frame from sc that only Read More...