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 general experimental design was as follows: WT vs DKO mice at two time points, 0 and 6 days. At day 0, cells were in a preadipocyte state, while at day 6 they had differentiated into adipocytes. Each time Read More...
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Bioinformatics
How might we plot automobile weight (wt) versus miles per gallon (mpg). {{Sdet}} Possible Solution{{Esum}} ggplot(mtcars, aes(wt, mpg)) + geom_point() {{Edet}}
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Bioinformatics
In this tutorial, we are using data from Nanduri et al. 2022, Epigenetic regulation of white adipose tissue plasticity and energy metabolism by nucleosome binding HMGN proteins , published in Nature Communications . The raw count matrices are Read More...
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Bioinformatics
#misspelled object #mtcars mean(mcars$wt) Error in mean(mcars$wt): object 'mcars' not found object not found errors can be fixed by checking to make sure the object name was correctly typed and/or Read More...
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Bioinformatics
#misspelled object #mtcars mean(mcars$wt) Error in mean(mcars$wt): object 'mcars' not found object not found errors can be fixed by checking to make sure the object name was correctly typed and/or Read More...
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Bioinformatics
In this tutorial, we will continue to use data from Nanduri et al. 2022, Epigenetic regulation of white adipose tissue plasticity and energy metabolism by nucleosome binding HMGN proteins , published in Nature Communications . As a reminder, Read More...
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Bioinformatics
In this tutorial, we will continue to use data from Nanduri et al. 2022, Epigenetic regulation of white adipose tissue plasticity and energy metabolism by nucleosome binding HMGN proteins , published in Nature Communications . As a reminder, Read More...
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Bioinformatics
As we can see above, the glimpse command shows the metadata that can be used to classify the cells. Within Seurat, the metadata is used to define the "identity" of the dataset. This Read More...
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Bioinformatics
library(ggplot2) ggplot(mtcars) + geom_point(aes(wt, mpg, color = factor(cyl) shape = factor(cyl))) Error: <text>:4:18: unexpected symbol 3: geom_point(aes(wt, mpg, color = factor(cyl) 4: shape ^ Unexpected symbol in Read More...
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Bioinformatics
library(ggplot2) ggplot(mtcars) + geom_point(aes(wt, mpg, color = factor(cyl) shape = factor(cyl))) Error: <text>:4:18: unexpected symbol 3: geom_point(aes(wt, mpg, color = factor(cyl) 4: shape ^ Unexpected symbol in Read More...
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Bioinformatics
One particular critique of differential expression in single cell RNASeq analysis is p-value "inflation," where the p-values get so small that there are far too many genes exist with p-values below 0.05, even after Read More...
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Bioinformatics
Create subplots by transmission (am). {{Sdet}} Possible Solution{{Esum}} ggplot(mtcars, aes(wt, mpg)) + geom_point(aes(color = factor(cyl),shape = factor(cyl),size=qsec))+ facet_wrap(~am) {{Edet}}
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Bioinformatics
Make the size of the points change by the quarter mile time (qsec). {{Sdet}} Possible Solution{{Esum}} ggplot(mtcars, aes(wt, mpg)) + geom_point(aes(color = factor(cyl),shape = factor(cyl),size=qsec)) {{Edet}}
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Bioinformatics
What if we want to represent the number of cylinders (cyl) by color and shape? {{Sdet}} Possible Solution{{Esum}} ggplot(mtcars, aes(wt, mpg)) + geom_point(aes(color = factor(cyl),shape = factor(cyl))) {{Edet}}
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Bioinformatics
Model the trend using geom_smooth() . What is the default method used by geom_smooth() ? {{Sdet}} Possible Solution{{Esum}} ggplot(mtcars, aes(wt, mpg)) + geom_point(aes(color = factor(cyl),shape = factor(cyl),size=qsec))+ Read More...
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Bioinformatics
We can add information to our metadata by accessing and assigning to metadata columns or using ?AddMetaData() . Add condition to metadata (either wildtype of double knockout). adp$condition
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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
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
We will build a database out of all features of the 2014 Ebola genome under accession number KM233118. This data will go into a new directory named "db_2014". mkdir -p db_2014 # Get the 2014 Ebola 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
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
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
Indices in R start with 1. Incorrect usage of indexing in data structures such as vectors or data frames will not necessarily result in an error, but will often lead to unexpected results. A general subscript Read More...
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Bioinformatics
Indices in R start with 1. Incorrect usage of indexing in data structures such as vectors or data frames will not necessarily result in an error, but will often lead to unexpected results. A general subscript 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
This lesson provides an introduction to R in the context of single cell RNA-Seq analysis with Seurat. Learning Objectives Learn about options for analyzing your scRNA-Seq data. Learn about resources for learning R programming. Learn Read More...
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Bioinformatics
Learning Objectives This tutorial was 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. Throughout this tutorial we Read More...
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Bioinformatics
This page uses content directly from the Biostar Handbook by Istvan Albert. Remember to activate the bioinformatics environment and create a directory for today's work. conda activate bioinfo mkdir blast cd blast What is 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 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...