Search Results for: dual or triple color Fluorospot

We can add the sample treatment annotation by setting the annotation_col argument to dfh in pheatmap. We use annotation_col rather than annotation_col because the samples IDs are listed along the horizontal axis Read More...

Because we build plots using layers in ggplot2. We can add multiple geoms to a plot to represent the data in unique ways. #We can combine geoms; here we combine a scatter plot with a # 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...

The geom functions require a mapping argument. The mapping argument includes the aes() function, which "describes how variables in the data are mapped to visual properties (aesthetics) of geoms" (ggplot2 R Documentation). If 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...

The geom functions require a mapping argument. The mapping argument includes the aes() function, which "describes how variables in the data are mapped to visual properties (aesthetics) of geoms" (ggplot2 R Documentation). If Read More...

pheatmap(mat,scale="row", color=colorRampPalette(c("navy", "white", "red"))(50)) #pheatmap(mat,scale="row", color=brewer.pal(5, "Spectral"))

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}}

Color the points from question 3 by Pclass. Remember that Pclass is a proxy for socioeconomic status. While the values are treated as numeric upon loading, they are really categorical and should be treated as such. Read More...

Let's take a look at a bar plot constructed using the default stat="count" transformation. Below, we plot the number of tooth length measurements taken at each dose. Setting color="black& Read More...

The geom functions require a mapping argument. The mapping argument includes the aes() function, which "describes how variables in the data are mapped to visual properties (aesthetics) of geoms" (ggplot2 R Documentation). If 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...

Because we build plots using layers in ggplot2. We can add multiple geoms to a plot to represent the data in unique ways. #We can combine geoms; here we combine a scatter plot with a # 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...

Below we generate the basic heatmap using the pheatmap package. It is simple, just use the pheatmap command and include the data that we want to construct a heatmap of as the argument. In the Read More...

To look at how these metrics correlate, we can use FeatureScatter() , which can be used to visualize feature-feature relationships and also be applied to other data stored in our Seurat object (e.g., metadata columns, Read More...

Look at the distribution of nCount_RNA with a Violin plot: # set colors cnames% ggplot(aes(color=orig.ident, x=nCount_RNA, fill= orig.ident)) + geom_density(alpha = 0.2) + theme_classic() + scale_x_log10() + geom_vline( Read More...

Now, let's take a look at the number of detected features. VlnPlot(adp, features = "nFeature_RNA", group.by="orig.ident") + scale_fill_manual(values=cnames) Warning: Default search for " Read More...

(Inspired by Visualizing Data in the Tidyverse, a Coursera lesson) Consider whether the plot type you have chosen is the best way to convey your message Make your plot visually appealing Careful color selection - 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...

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...

{{Sdet}} Solution{{Esum}} library ( ggplot2 ) ggplot ( iris ) + geom_point ( aes ( Petal.Length , Petal.Width , color = Species )) {{Edet}}

{{Sdet}} Solution{{Esum}} ggplot ( iris ) + geom_point ( aes ( Petal.Length , Petal.Width , color = Species )) + coord_fixed ( ratio = 1 , ylim = c ( 0 , 2.75 ), xlim = c ( 0 , 7 )) + scale_y_continuous ( breaks = c ( 0 , 0.5 , 1 , 1.5 , 2 , 2.5 )) + scale_x_continuous ( breaks = c ( 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 )) {{Edet}}

As we have discussed, R objects are used to store things created in R to memory. This includes plots created with ggplot2 . dot_plot

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 cut of each diamond. --- R4 Read More...

As we have discussed, R objects are used to store things created in R to memory. This includes plots. scatter_plot

This page contains content taken directly from the Biostar Handbook by Istvan Albert. Activate the bioinformatics environment. conda activate bioinfo First let's make a place to store today's work. In your biostar_class Read More...

What is one way we could use to visualize gene expression? {{Sdet}} Answer{{Esum}} We can use a heatmap which plots gene expression values on a color scale and allows us to discern gene expression Read More...

This page contains content directly from the Biostar Handbook by Istvan Albert. Always remember to activate your bioinformatics environment. conda activate bioinfo What is a sequence pattern? A sequence pattern is a sequence of bases Read More...

This exercise will use Seaborn's clustermap function to construct a gene expression heatmap of top differentially expressed genes in the HBR and UHR study. Heatmaps are another common visualization in RNA sequencing and allow 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...

Below we generate a basic heatmap using the pheatmap package. We use the pheatmap command and include the data that we want to construct a heatmap of as the argument. In the heatmap below, we Read More...

Below we generate a basic heatmap using the pheatmap package. We use the pheatmap command and include the data that we want to construct a heatmap of as the argument. In the heatmap below, we Read More...

geom_bar() uses stat_count() by default: it counts the number of cases at each x position. --- ggplot2 documentation stat_count() requires mapping for either an x OR a y variable but not both. Read More...

There are quite a few parameters to adjust figure labels. To re-position labels, see label_x , label_y , hjust , and vjust . These each take either a single value to move all labels or a vector Read More...

Until this point we have been plotting raw data with geom_point() , but now we will be introducing geoms that transform and plot new values from your data. Many graphs, like scatterplots, plot the raw Read More...

#Plot ggplot(pcaData) + aes(PC1, PC2, color = Species, shape = Species) + geom_point(size = 2) + coord_fixed() + xlab("PC1: 73%")+ #x axis label text ylab("PC2: 23%") # y axis label text Automating % Variance in axis 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...

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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...

Until this point we have been plotting raw data with geom_point() , but now we will be introducing geoms that transform and plot new values from your data. Many graphs, like scatterplots, plot the raw Read More...

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

A heatmap is a graphical representation of data where the individual values contained in a matrix are represented as colors. --- R Graph Gallery Heatmaps are appropriate when we have lots of data because color Read More...

A heatmap is a graphical representation of data where the individual values contained in a matrix are represented as colors. --- R Graph Gallery Heatmaps are appropriate when we have lots of data because color Read More...

A heatmap is a graphical representation of data where the individual values contained in a matrix are represented as colors. --- R Graph Gallery Heatmaps are appropriate when we have lots of data because color 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 a categorical or discrete. Before fixing the x Read More...

The NCI Clinical Research Correlatives Core provides non-CLIA-certified spectral flow cytometric assays to support clinical trials conducted in the CCR. The core specializes in immunophenotyping and immune monitoring assays. Established Technologies Spectral flow cytometry (Cytek), Read More...

The facilities at AIM are available for use by the entire NIH intramural research community. While we welcome users with any size imaging project, AIM specializes in large, yearlong (or longer), collaborative research efforts with Read More...

The PPS encompasses all scientific analyses related to pharmacology, once the specimen has been collected and stored. There is a multi-step process to evaluate how the drug is being handled by the body after administration. Read More...

/*color variables main= #1E1E1E secondery= #333333 highlight= #073254 */ * { box-sizing: border-box; } body, html { font-family: "Open Sans", sans-serif; } .clearfix:before, .clearfix:after { content: " "; display: table; } .clearfix:after { clear: both; } h1, h2, h3, h4, h5, h6 { font-weight: 300; } body Read More...

:root { --primary-action: rgb(0, 0, 0) !important; --primary-action: rgb(0, 0, 0) !important; } .container { width: 1440px; max-width: 100%; } #h2 { font-size: 2.2rem; margin-left: 20px; } /* Lazy Load Styles */ .card-image { display: block; min-height: 20rem; /* layout hack */ background: #fff center center no-repeat; background-size: cover; filter: blur(3 Read More...

The CCR Building 41 Flow Cytometry Core is a full-service facility within the Center for Cancer Research that supports over 150 users representing 26 laboratories. The Core Facility provides instrument and software training, technical expertise, assay development, and Read More...

The NIA Nonhuman Primate (NHP) Core Facility maintains a colony of rhesus macaques at the National Institutes of Health Animal Center. Established in 2012, the Core's mission is to offer research services supporting multi-disciplinary translational aging Read More...

07/28/2025 - The "Data Vi ualization in R" erie focu e on u ing ggplot and the broader tidyver e eco y tem to create in ightful and cu tomizable vi ualization . It cover Read More...

07/01/2025 - Learn how to vi ualize equencing and analy i re ult effectively. Thi e ion de cribe the application of the web-ba ed interactive OmicCirco in R hiny to con truct circular plot with Read More...

10/09/2024 - This one-hour online training will provide detailed information and demonstrations on how to manage data in Excel.    By the end of this training, attendees will be able to:   Filter data by text, numbers, and Read More...

09/27/2024 - This session describes the application of the web-based interactive OmicCircos in R Shiny to construct circular plots with desired biological features. Example data from human and mouse genomes will be used to demonstrate over Read More...

09/06/2024 - This course provides an introduction to data visualization using R. Participants will learn data visualization with base R and using the R package ggplot2 to explore various types of data visualizations, including scatter plots, Read More...

VlnPlot(adp, features = "percent.mt", group.by="orig.ident") + scale_fill_manual(values=cnames) + geom_hline(yintercept=10,color="red") Warning: Default search for "data" layer in " 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...

02/16/2024 - This one-hour training will provide detailed information and demonstrations on how to manage data in Excel. By the end of this course, the participants will recognize how to filter data by text, numbers, and 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...

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...

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...

A way to add variables to a plot beyond mapping them to an aesthetic is to use facets or subplots. There are two primary functions to add facets, facet_wrap() and facet_grid() . If faceting Read More...

There are other reasons you may be interested in using pivot_wider or pivot_longer . In my experience, most uses revolve around plotting criteria. For example, you may want to plot two different but related Read More...

There are other reasons you may be interested in using pivot_wider or pivot_longer . In my experience, most uses revolve around plotting criteria. For example, you may want to plot two different but related Read More...

You do not need to load a package to visually explore data. Rather, you can use base R graphics for plotting (from the graphics package). This plotting is fairly different from ggplot2 , which is based 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...

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...

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...

A way to add variables to a plot beyond mapping them to an aesthetic is to use facets or subplots. There are two primary functions to add facets, facet_wrap() and facet_grid() . If faceting 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...

11/17/2023 - This session describes the application of the web-based interactive OmicCircos in R Shiny to construct circular plots with desired biological features. Example data from human and mouse genomes will be used to demonstrate over Read More...

Plots condense complex and busy tabular data into a form that is easier to interpret. An expression heatmap is a common visualization used in RNA sequencing analysis. A heatmap shows numerical data on a color Read More...

In this portion of the class, it is very important that you have IGV already opened on your computer. See Figure 1 and Figure 2 on how to load the relevant alignment outputs for HBR_1 and UHR_1 Read More...

For visualizing the HBR and UHR alignment results, we will use the built in Human hg38 genome. To do this, we will just goto the genome selection box and select hg38 (Figure 2). In Figure 3, we Read More...

Which reference genome are we using in this IGV session to view the alignment results for samples hcc1395_normal_rep1 and hcc1395_tumor_rep2? On what chromosome are the sequencing data mapping to? {{Sdet}} Solution{{ Read More...

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...

Seaborn is a popular visualization package for Python. Users can use its scatterplot function to generate scatter plots (in this case a volcano plot, which is a special type of scatter plot). The scatterplot function Read More...

Protein Characterization Laboratory (PCL) offers various technologies to CCR investigators to characterize proteins and metabolites. The laboratory develops and applies state-of-the-art analytical technologies, primarily mass spectrometry, liquid chromatography, and Surface Plasmon Resonance (SPR), to advance Read More...

The research conducted within the Synthetic Biologics Core (SBC) Facility has a dual role: Generate chemical biology tools and drug candidates for molecular targets identified by NCI research groups, Develop novel effective methods and tools Read More...

What is Visium FFPE v2 with CytAssist? Visium FFPE v2 is sequencing-based spatial profiling technology developed by 10x Genomics. This assay can take mouse or human tissue sections on normal glass slides as input and Read More...

LCMB Microscopy Core offers live cell imaging technologies as well as super-resolution, fluorescence lifetime and confocal imaging systems for immunofluorescence. Our confocal instruments are a Leica SP8 laser scanning confocal microscope and a Nikon spinning Read More...

The OSTR offers cutting-edge technology platforms to the CCR scientific community through centralized facilities. The videos accessed through this page are designed to introduce the various scientific methodologies OSTR makes available through the cores on Read More...

Clinical Support Laboratory – Flow Cytometry Section is a laboratory specializing in providing immunophenotyping support of NCI intramural clinical trials, though assessments may also be performed using cells from Non-human primates and other species. The CSL Read More...

The Genomics and Pharmacology Facility is part of the NCI's Center for Cancer Research (CCR), within the Developmental Therapeutics Branch. Its mission is to manage and assess molecular interaction data obtained through multiple platforms, increase Read More...

Molecular Cytogenetics Core Facility facilitates the assessment of structural and numerical genomic changes in pre-cancer and cancer research models. This core provides comprehensive support for the cytogenetic analysis of cells from human and research animal Read More...

What is Xenium? Xenium is a high-resolution, imaging-based in situ spatial profiling technology from 10x Genomics that allows for simultaneous expression analysis of RNA targets (currently in range of 100’s) within the same tissue section. Read More...

OMAL focuses its research and development activities to quantitatively understand the molecular basis of three-dimensional (3D) cell organization, motility, invasion, and differentiation using fixed samples and live, 3D tissue culture models (i.e., translational models). 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.

The Flow Cytometry Core (LGI) offers established technologies to support studies using flow cytometry and cell sorting. Established Technologies Applications that run on FACS Caliburs include: Immunophenotyping (up to 4-color), Intracellular markers, including cytokines and Read More...

The NIH Center for Human Immunology, Inflammation, and Autoimmunity (CHI) is a trans-NIH resource whose mission is to provide a collaborative hub of advanced translational immunology for NIH clinical and pre-clinical studies. This uniquely structured Read More...

Our operational objectives are to provide state-of-the-art OMICS technologies in support of the Genetics Branch (GB) investigators and collaborators. Research Services Wet Lab Single cell isolation from fresh, frozen, and FFPE tissue, DNA/RNA extractions Read More...

The Pan-Microbial Serology Facility (PMSF) is part of the Center for Cancer Research (CCR) at the National Cancer Institute (NCI). The PMSF focuses on determining individualized pan-microbial immune profiles associated with human diseases including immunological Read More...

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...

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...

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...

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...

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...

Lesson 14 Practice Objectives Here, we will practice using the Integrative Genome Viewer (IGV) to visualize the hcc1395 RNA sequencing alignment results. About the data and launching IGV We were introduced to the hcc1395 RNA sequencing Read More...

Lesson 14: Visualizing alignment results Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 13 Review Previously, we used the application HISAT2 to align the raw sequencing data from the Human Brain Read More...

Lesson 14: Visualizing alignment results Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 13 Review Previously, we used the application HISAT2 to align the raw sequencing data from the Human Brain Read More...

Lesson 17: RNA sequencing review 2 Learning objectives This lesson will serve as comprehensive review of Module 2. We will spend roughly the first hour reviewing the Module 2 material the second hour answering specific questions from the poll Read More...

Lesson 15: Finding differentially expressed genes Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 14 review In the previous lesson, we learned to visualize RNA sequencing alignment results in the Integrative Read More...

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...