Search Results for: black bear

Using the annotation_colors argument, we can reassign the colors of the sample to treatment mapping legend. pheatmap(mat,scale="row", annotation_col = dfh, annotation_colors=list(dex=c(trt="orange", Read More...

Using the annotation_colors argument, we can reassign the colors of the sample to treatment mapping legend. pheatmap(mat,scale="row", annotation_col = dfh, annotation_colors=list(dex=c(trt="orange", Read More...

class ( a1 $ dose ) ## [1] "numeric" As it turns out, dose is really an experimental factor, so if we specify factor(dose) it will be interpreted as categorical or discrete. Before fixing the x axis Read More...

A title for our heatmap can be included using the main argument pheatmap(mat,scale="row", annotation_col = dfh, annotation_colors =list(dex=c(trt="orange",untrt="black")), color= Read More...

The Seurat v5 object doesn’t require all assays have the same cells. In this case, Cells() can be used to return the cell names of the default assay while colnames() can be used to Read More...

readRDS("./data/diffexp_results_edger_airways.rds") |> #read data select(transcript, logFC, FDR) |> #select columns of interest filter(transcript == "TSPAN6" | transcript=="DPM1" ) |> #filter ggplot(aes(x= Read More...

::: {.cell} readRDS ( "./data/diffexp_results_edger_airways.rds" ) |> #read data select ( transcript , logFC , FDR ) |> #select columns of interest filter ( transcript == "TSPAN6" | transcript == "DPM1" ) |> #filter ggplot ( aes ( Read More...

What geoms would you use to draw each of the following named plots? a. Scatterplot b. Line chart c. Histogram d. Bar chart e. Pie chart (Question taken from https://ggplot2-book.org/individual-geoms.html .) {{ Read More...

Lesson 5 Exercise Questions: ggplot2 What geoms would you use to draw each of the following named plots? a. Scatterplot b. Line chart c. Histogram d. Bar chart e. Pie chart (Question taken from https://ggplot2 Read More...

ggplot2 uses a default coordinate system (the Cartesian coordinate system). This isn't super important until we want to do something like make a map (See coord_quickmap() ) or create a pie chart (See coord_ Read More...

Many graphs, like scatterplots, plot the raw values of your dataset. Other graphs, like bar charts, calculate new values to plot: bar charts, histograms, and frequency polygons bin your data and then plot bin counts, Read More...

How do we ultimately get our figures to a publishable state? The bread and butter of pretty plots really falls to the additional non-data layers of our ggplot2 code. These layers will include code to Read More...

Load in the comma separated file "./data/countB.csv" and save to an object named gcounts . {{Sdet}} Solution } gcounts `...1` colnames ( gcounts )[ 1 ] ## 1 Tspan6 703 567 867 71 970 242 ## 2 TNMD 490 482 18 342 935 469 ## 3 DPM1 921 797 622 661 8 500 ## 4 SCYL3 335 216 222 774 979 793 ## 5 FGR 574 574 515 584 941 344 ## 6 CFH 577 792 672 104 192 936 ## 7 FUCA2 798 766 995 27 756 546 ## 8 GCLC 822 874 923 705 667 522 ## 9 NFYA 622 793 918 868 334 64 {{Edet}} Plot the Read More...

There is no single best method for RNA-Seq data analysis - it depends on your definition of best, and even then it varies over time and with the particular goals and specifics of a given Read More...

DAVID can also generate gene clusters where those gene that cluster together share common annotations. We can view the cluster information as a heatmap where green represents an association between the gene and an annotation. Read More...

It maybe helpful to split the heatmap into different portions to illustrate clusters more efficiently. Here, we can the heatmap into two columns using the argument cutree_col and setting this to 2. Doing so will Read More...

A title for our heatmap can be included using the main argument pheatmap(mat,scale="row", annotation_col = dfh, annotation_colors =list(dex=c(trt="orange",untrt="black")), color= Read More...

A title for our heatmap can be included using the main argument pheatmap(mat,scale="row", annotation_col = dfh, annotation_colors =list(dex=c(trt="orange",untrt="black")), color= Read More...

It maybe helpful to split the heatmap into different portions to illustrate clusters more efficiently. Here, we can split the heatmap into two columns using the argument cutree_col and setting this to 2. Doing so Read More...

It maybe helpful to split the heatmap into different portions to illustrate clusters more efficiently. Here, we can split the heatmap into two columns using the argument cutree_col and setting this to 2. Doing so Read More...

Understanding data distribution can help us decide appropriate downstream steps in analysis such as which statistical test to use. A histogram is a good way to visualize distribution. It divides the data into bins or Read More...

Understanding data distribution can help us decide appropriate downstream steps in analysis such as which statistical test to use. A histogram is a good way to visualize distribution. It divides the data into bins or Read More...

To change the y axis scale, we will need a specific function. These functions generally start with scale_y... . In our case we want to reverse our axis so that increasingly negative is going in Read More...

We can modify many aspects of the figure legend using the function guide() . Let's see how that works and go ahead and customize some theme arguments. Notice that the legend position is specified in Read More...

What geoms would you use to draw each of the following named plots? a. Scatterplot b. Line chart c. Histogram d. Bar chart e. Pie chart (Question taken from https://ggplot2-book.org/individual-geoms.html .) {{ Read More...

Lesson 5 Exercise Questions: ggplot2 What geoms would you use to draw each of the following named plots? a. Scatterplot b. Line chart c. Histogram d. Bar chart e. Pie chart (Question taken from https://ggplot2 Read More...

ggplot2 uses a default coordinate system (the Cartesian coordinate system). This isn't super important until we want to do something like make a map (See coord_quickmap() ) or create a pie chart (See coord_ Read More...

Many graphs, like scatterplots, plot the raw values of your dataset. Other graphs, like bar charts, calculate new values to plot: bar charts, histograms, and frequency polygons bin your data and then plot bin counts, Read More...

To change the y axis scale, we will need a specific function. These functions generally start with scale_y.... In our case we want to reverse our axis so that increasingly negative is going in Read More...

How do we ultimately get our figures to a publishable state? The bread and butter of pretty plots really falls to the additional non-data layers of our ggplot2 code. These layers will include code to Read More...

We can modify many aspects of the figure legend using the function guide(). Let's see how that works and go ahead and customize some theme arguments. Notice that the legend position is specified in Read More...

::: {.cell output-location='slide'} ggplot(exdata) + geom_point(aes(x=Number.of.Transcripts, y = Total.Counts, fill=Treatment), shape=21,size=3) + #can change labels of fill levels along with colors scale_fill_manual(values=c("purple& Read More...

Colors are assigned to the fill and color aesthetics in aes(). We can change the default colors by providing an additional layer to our figure. To change the color, we use the scale_color functions: Read More...

Colors are assigned to the fill and color aesthetics in aes(). We can change the default colors by providing an additional layer to our figure. To change the color, we use the scale_color functions: Read More...

NISC’s role within NHGRI, and more broadly across NIH, aims to advance genome sequencing and its many applications, with a goal not simply to produce sequence data, but to produce the infrastructure required to Read More...

The LCBG Microscopy Core offers imaging technologies and training. The Core has established instrumentation for for 2D and 3D imaging of both fixed and living specimens.

ggplot2 will automatically assign colors to the categories in our data. Colors are assigned to the fill and color aesthetics in aes() . We can change the default colors by providing an additional layer to our Read More...

These steps can be used to create a publish worthy figure. For example, let's create a volcano plot of our differential expression results. A volcano plot is a type of scatterplot that shows statistical Read More...

Lesson 6 Exercise Questions: ggplot2 Putting what we have learned to the test: The following questions synthesize several of the skills you have learned thus far. It may not be immediately apparent how you would go Read More...

ggplot2 will automatically assign colors to the categories in our data. Colors are assigned to the fill and color aesthetics in aes() . We can change the default colors by providing an additional layer to our Read More...

Often we will apply multiple functions to wrangle a data frame into the state that we need it. For example, maybe you want to select and filter. What are our options? We could run one Read More...

Help Session Lesson 4 Plotting with ggplot2 For the following plots, let's use the diamonds data ( ?diamonds ). The diamonds dataset comes in ggplot2 and contains information about ~54,000 diamonds, including the price, carat, color, clarity, and Read More...

Each of you will need to create an account on DNAnexus, which hosts the GOLD online learning platform. This login and password is different from the login/password you will use for the GOLD system. Read More...

Data Analysis Overview RNASEQ - Data Analysis WorkFlow Mostly Computational intensive task requiring signigicant computer hardware. Quality Control Sample quality and consistency Is Trimming appropriate - quality/adaptors Alignment/Mapping Reference Target (Sequence and annotation) Read More...

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Recall that we can use the ggsave command to save a ggplot. However, heatmaps generated using the pheatmap package are not ggplots, there for we need to turn to either the image export feature (Figure 7) Read More...

Play with the theme to make this a bit nicer. Change font style to "Times". Change all font sizes to 12 pt font. Bold the legend title and the axes titles. Increase the size Read More...

Recall that we can use the ggsave command to save a ggplot. However, heatmaps generated using the pheatmap package are not ggplots, therefore we need to turn to either the image export feature (Figure 7) in Read More...

Recall that we can use the ggsave command to save a ggplot. However, heatmaps generated using the pheatmap package are not ggplots, therefore we need to turn to either the image export feature (Figure 7) in Read More...

Let's draw an image on here, just to show that we can add one. There are two options. We can add an image as if it was another figure or we can simply draw Read More...

Below, we assign the heatmap to the R object hm_ph and we can import this back to R in the future. hm_ph

Above, we learned about the number of tooth length measurements taken at each dose and supplement combination using the default stat="count" transformation of geom_bar . But what if we want to specify Read More...

Above, we learned the number of tooth length measurements taken at each dose and supplement combination using the default stat_count transformation of geom_bar, but what if we want to specify and plot exactly Read More...

ggplot2 will automatically assign colors to the categories in our data. Colors are assigned to the fill and color aesthetics in aes() . We can change the default colors by providing an additional layer to our Read More...

When you have a lot of colors and you want to keep these colors consistent, you can use the following convenient functions to set a name attribute for a vector of colors. Let's do Read More...

Stat Transformations: Bar plots, box plots, and histograms Objectives Review the grammar of graphics template Learn about the statistical transformations inherent to geoms Review data types Create bar plots, box & whisker plots, and histograms. Read More...

/* Whole document: */ body{ font-family: Times; font-size: 16pt; } Stat Transformations: Bar plots, box plots, and histograms Objectives Review the grammar of graphics template Learn about the statistical transformations inherent to geoms Review data types Create bar Read More...

Where can I find examples of different types of plots? The R Graph Gallery is a fantastic resource for R data visualization. The gallery can be used to explore different plot types and the code Read More...

R Course FAQs Where can I find examples of different types of plots? The R Graph Gallery is a fantastic resource for R data visualization. The gallery can be used to explore different plot types Read More...

The following questions synthesize several of the skills you have learned thus far. It may not be immediately apparent how you would go about answering these questions. Remember, the R community is expansive, and there Read More...

We now know enough to put our new skills to use to make a volcano plot from RNASeq data. A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude Read More...

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...

These steps can be used to create a publish worthy figure. For example, let's create a volcano plot of our differential expression results. A volcano plot is a type of scatterplot that shows statistical Read More...

ggplot2 will automatically assign colors to the categories in our data. Colors are assigned to the fill and color aesthetics in aes() . We can change the default colors by providing an additional layer to our Read More...

Often we will apply multiple functions to wrangle a data frame into the state that we need it. For example, maybe you want to select and filter. What are our options? We could run one Read More...

Lesson 6 Exercise Questions: ggplot2 Putting what we have learned to the test: The following questions synthesize several of the skills you have learned thus far. It may not be immediately apparent how you would go Read More...

Let's explore how piping streamlines this. Piping (using %>%) allows you to employ multiple functions consecutively, while improving readability. The output of one function is passed directly to another without storing the intermediate Read More...

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...

Objectives Review the grammar of graphics template. Learn about the statistical transformations inherent to geoms. Learn more about fine tuning figures with labels, legends, scales, and themes. Learn how to save plots with ggsave() . Review Read More...

In this lesson, attendees will learn how to transform, summarize, and reshape data using functions from the tidyverse. Learning Objectives Continue to wrangle data using tidyverse functionality. To this end, you should understand: how to Read More...

Data visualization with ggplot2 Objectives To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures. Read More...

In this lesson, attendees will learn how to transform, summarize, and reshape data using functions from the tidyverse. Learning Objectives Continue to wrangle data using tidyverse functionality. To this end, you should understand: how to Read More...

Introduction to ggplot2 Objectives Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective figures. Data Visualization in the tidyverse Read More...

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...

Syllabus for “Bioinformatics for Beginners" Instructors: Co-Instructors: To participate in these courses, you need a computer, reliable internet connection and a web browser. All classes and help sessions will be held online. This class Read More...

Database for Annotation, Visualization and Integrated Discovery (DAVID) - an overview Lesson 17 review In the previous class, we got an overview of functional and pathway analysis, which help to put RNA sequencing results into biological Read More...

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...

Multi-figure panel Objectives Combine multiple plots into a single figure Learn how to use patchwork and cowplot The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel Read More...

Objectives Combine multiple plots into a single figure Learn how to use aspects of cowplot and patchwork The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel Read More...

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...

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...

Introduction to ggplot2 Objectives Learn how to import spreadsheet data. Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective Read More...

Below, we assign the heatmap to the R object hm_ph. hm_ph {"x":{"data":[{"x":[4.875,2.875,null,2.875,2.875,null,2.875,1.75,null,1.75,1.75,null,1.75,1,null,1,1,null,1.75,2.5,null,2.5,2.5,null,2.5,2,null,2,2,null,2.5,3,null,3,3,null,2.875,4,null,4,4, Read More...

Below, we assign the heatmap to the R object hm_ph and we can import this back to R in the future. hm_ph {"x":{"data":[{"x":[4.875,2.875,null,2.875,2.875,null,2.875,1.75, Read More...

Objectives Review the grammar of graphics template. Learn about the statistical transformations inherent to geoms. Learn more about fine tuning figures with labels, legends, scales, and themes. Learn how to save plots with ggsave() . Review Read More...

Data visualization with ggplot2 Objectives To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures. Read More...

Introduction to Data Wrangling with the Tidyverse Objectives Wrangle data using tidyverse functionality (i.e., dplyr ). To this end, you should understand: 1. how to use common dplyr functions (e.g., select() , group_by() , arrange() , mutate() , Read More...

01/25/2022 - Speaker: Anant Madabhushi, Ph.D. Donnell Institute Professor of Biomedical Engineering Director, Center for Computational Imaging and Personalized Diagnostics (CCIPD) Case Western Reserve University (CWRU),  Cleveland Anant Madabhushi, PhD, is a Research Scientist at Read More...

10/22/2015 - /* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Read More...

01/29/2015 - /* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Read More...