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Search Results for: 76 T2/Onc (Sleeping Beauty)

Total Results Found: 29

NCI LASP Mouse Modeling & Cryopreservation (MMC)
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...

Data Visualization with R: Question 1

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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...

Data Visualization with R: Question 2

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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...

BTEP Coding Club: R Session info

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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...

Data Wrangling with R: Reordering rows

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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...

Data Visualization with R: Perform PCA

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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...

Data Visualization with R: Scaling

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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...

Data Visualization with R: Scaling

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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...

Data Visualization with R: row-wise scaling

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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...

Data Wrangling with R: Accessors

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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...

Data Visualization with R: Lesson5

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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...