Frederick, MD
Core Facility
The Optical Microscopy Analysis Core (OMAC), formerly known as the Optical Microscopy Analysis Lab (OMAL), focuses its research and development activities to quantitatively understand the molecular basis of three-dimensional (3D) cell organization, motility, invasion, and Read More...
Bethesda, MD
Core Facility
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.
Bethesda, MD
Core Facility
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...
Bethesda, MD
Trans NIH Facility
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...
Bethesda, MD
Collaborative
As a multi-user facility, the different instruments provide a wide range of imaging modes for EIB scientists, from standard immunohistochemistry, through brightfield and wide-field epifluorescence imaging, to highly complex live cell confocal microscopy and super-resolution Read More...
Web Page
Bioinformatics
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...
Web Page
Confocal
Nikon SoRa Spinning Disk Capabilities: Inverted microscope Photo-metrics BSI sCMOS camera Yokogawa SoRa CSU-W1 spinning disk unit Super-resolution, confocal and wide-field imaging modes 4x, 10x, 20x and 60x objective lenses Mad City Labs 500 mm piezo Read More...
Web Page
Bioinformatics
There are several functions that you will see repeatedly as you use R more and more. One of those is c() , which is used to combine its arguments to form a vector. Vectors are probably Read More...
Web Page
Bioinformatics
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...
Web Page
Bioinformatics
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...
Web Page
Bioinformatics
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...
Web Page
Bioinformatics
R Crash Course: A few things to know before diving into wrangling Learning the Basics Objectives 1. Learn about R objects 3. Learn how to recognize and use R functions 4. Learn about data types and accessors Console Read More...