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The base data type (e.g., numeric, character, logical, etc.) and the class (dataframe, matrix, etc.) will be important for what you can do with an object. Learn more about an object with the following: Read More...
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Vectors are probably the most used commonly used object type in R. A vector is a collection of values that are all of the same type (numbers, characters, etc.). The columns that make up a Read More...
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Explore the data. What is the structure of the data? Try str() . What are the column names? Try colnames() . How can you get help if you do not know how to use these functions? {{Sdet}} Read More...
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Vectors are probably the most used commonly used object type in R. A vector is a collection of values that are all of the same type (numbers, characters, etc.). The columns that make up a Read More...
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import matplotlib.pyplot as plt import numpy x=numpy.array([0,1,2,3,4,5,6,7,8]) y=numpy.array([0.5,2,5,6,7,10,13,14,16]) plt.scatter(x,y) slope, intercept=numpy.polyfit(x,y,1) plt.plot(x,slope*x+intercept) plt.text(1,14,'y='+str(round(slope,3))+' Read More...
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Data frames hold tabular data comprised of rows and columns; they can be created using data.frame() . To understand more about the structure of an object and data frame, consider the following functions: str() displays Read More...
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Data frames hold tabular data comprised of rows and columns; they can be created using data.frame() . To understand more about the structure of an object and data frame, consider the following functions: str() displays Read More...
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To view the column headings of a data frame use the column function. For instance, hbr_uhr_chr22_counts.columns HBR_1.bam HBR_2.bam HBR_3.bam UHR_1.bam UHR_2.bam UHR_3.bam The str.replace Read More...
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To view the column headings of a data frame use the column function. For instance, hbr_uhr_chr22_counts.columns HBR_1.bam HBR_2.bam HBR_3.bam UHR_1.bam UHR_2.bam UHR_3.bam The str.replace Read More...
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In Python, variables are assigned to values using "=". Users can assign variables to integers, float, or string. perfect=100 perfect 100 mole=6.02e23 mole 6.02e+23 btep_class="Python Introductory Education Series" btep_class ' Read More...
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The command type can be used to identify data types and structures in Python. type(100) This will return int for integer as 100 is an integer. int type(3.1415926) This will return float as 3.1415926 has decimals. float Read More...
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#create a vector of gene names transcript_names <- c("TSPAN6", "TNMD", "SCYL3", "GCLC") transcript_names [1] "TSPAN6" "TNMD" "SCYL3" & Read More...
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Panda's loc function allows for subsetting by row or column names. For instance, to retrieve the Geneid column, do the following. The ":" denotes get every row. hbr_uhr_chr22_counts.loc[:,['Geneid']] Read More...
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Panda's loc function allows for subsetting by row or column names. For instance, to retrieve the gene id column, do the following. The ":" denotes get every row. hbr_uhr_chr22_counts.loc[:,[' Read More...
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In Python, variables are assigned to values using "=". test1_score=100 test1_score 100 mole=6.02e23 mole 6.02e+23 btep_class="Python Introductory Education Series" btep_class 'Python Introductory Education Series' The command type( Read More...
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How many genes start with the letter "C" in hcc1395_chr22_counts? {{Sdet}}{{Ssum}}Solution{{Esum}} hcc1395_chr22_counts.loc[hcc1395_chr22_counts.loc[:,'Geneid'].str.startswith("C")] {{Edet}}
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How many genes start with the letter "C" in hcc1395_chr22_counts? {{Sdet}}{{Ssum}}Solution{{Esum}} hcc1395_chr22_counts.loc[hcc1395_chr22_counts.loc[:,'Geneid'].str.startswith("C")] {{Edet}}
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The Protein Expression Laboratory produces proteins to help CCR investigators achieve their research goals with the lowest possible cost in the shortest time. PEL is operated by Leidos Biomedical Research Inc. on behalf of NCI Read More...
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The object that we imported, scaled_counts , is a data frame. Let's learn a bit more about our data frame. First, we can learn more about the structure of our data using str() . We Read More...
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To explore tidyverse functionality, let's read in some data and take a look. #let's use our differential expression results dexp "ENSG00000000003", "ENSG00000000419", "ENSG00000000457", "E… $ albut untrt, Read More...
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The data type of an R object affects how that object can be used or will behave. Examples of base R data types include numeric, integer, complex, character, and logical. R objects can also have Read More...
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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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Let's learn a bit more about our data frame. First, we can learn more about the structure of our data using str() . str(scaled_counts) ## 'data.frame': 127408 obs. of 18 variables: ## $ feature : chr "ENSG00000000003& Read More...
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At this point, you have seen the term "factor" pop up a few times. Factors can be thought of as vectors which are specialized for categorical data. Given R’s specialization for statistics, Read More...
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Remove ".bam" from the column headers of hcc1395_deg_chr22. {{Sdet}}{{Ssum}}Solution{{Esum}} hcc1395_deg_chr22.columns=hcc1395_deg_chr22.columns.str.replace(".bam", "") {{Edet}}
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Remove ".bam" from the column headers of hcc1395_deg_chr22. {{Sdet}}{{Ssum}}Solution{{Esum}} hcc1395_deg_chr22.columns=hcc1395_deg_chr22.columns.str.replace(".bam", "") {{Edet}}
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#Create a list My_exp <- list(c("N052611", "N061011", "N080611", "N61311" ), c("SRR1039508", "SRR1039509", "SRR1039512", "SRR1039513", & Read More...
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The command type can be used to identify data types and structures in Python. type(100) int type(3.1415926) float type("bioinformatics") str
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An important step to learning any new programming language and data analysis is to understand its data types and structures. Common data types and structures that will be encountered include the following. Text (str) Numeric Read More...
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An important step to learning any new programming language and data analysis is to understand its data types and data structures. Common data types and structures that will be encountered include the following. Text (str) Read More...
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All of the objects we imported in the previous lesson, were data frames. In this lesson, we will learn how to view and find out more information regarding the data stored in a data frame. Read More...
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Read the documentation! Know what the functions you use actually do. Read package vignettes. Keep your code neat and clean. You can do this by following a style guide, for example, the tidyverse style guide. Read More...
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Read the documentation! Know what the functions you use actually do. Read package vignettes. Practice! The more you engage with R programming the more proficient you will become. With time, handling error messages will become Read More...
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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...
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Notice that "sample" was treated as numeric, rather than as a character vector. If we intend to work with this column, we will need to convert it or coerce it to a character Read More...
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The data type of an R object affects how that object can be used or will behave. Examples of base R data types include numeric, integer, complex, character, and logical. R objects can also have Read More...
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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...
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Objectives To understand some of the most basic features of the R language including: Creating R objects and understanding object types Using mathematical operations Using comparison operators Creating, subsetting, and modifying vectors By the end Read More...
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For this lesson, we will use sample metadata and differential expression results from the airway RNA-Seq project. Let's begin by importing the data. #sample information smeta ## 1 GSM1275862 N61311 untrt untrt SRR10395… 126 SRX384345 SRS50… SAMN0242… ## 2 Read More...
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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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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...
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While bracket notation is useful, it is not always the most readable or easy to employ, especially for beginners. This is where dplyr comes in. The dplyr package in the tidyverse world simplifies data wrangling Read More...
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Notice that "sample" was treated as numeric, rather than as a character vector. If we intend to work with this column, we will need to convert it or coerce it to a character Read More...
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R objects have certain attributes, and these attributes will be important for how they can interact with certain methods / functions. Understanding the mode (storage type) or the class of an object will be important for Read More...
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Bioinformatics
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...
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Bioinformatics
For this lesson, we will use sample metadata and differential expression results derived from the airway RNA-Seq project. See here for instructions on accessing the data. Let's begin by importing the data. #sample information Read More...
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Data types are familiar in many programming languages, but also in natural language where we refer to them as the parts of speech, e.g. nouns, verbs, adverbs, etc. Once you know if a word Read More...
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This exercise will use the differential gene expression analysis table from the hbr and uhr study. hbr_uhr_deg_chr22=pandas.read_csv("./hbr_uhr_deg_chr22.csv") The info() attribute will retrieve Read More...
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For this exercise, stay in the /data/username/pies_2023 folder, which should be the present working directory (use pwd to check). If not in the /data/username/pies_2023 folder, change into it. Copy the hbr_ Read More...
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Let's pretend that the sample IDs were numeric rather than of type character. smeta$SampleID <- c(1:nrow(smeta)) smeta SampleName cell dex albut Run avgLength Experiment Sample 1 GSM1275862 N61311 untrt untrt Read More...
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There are many packages and functions available in R programming for performing PCA. Some of the most popular functions are stats::prcomp(), stats::princomp(), FactoMineR::PCA(), and ade4::dudi.pca(). These functions largely differ in Read More...
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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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HALO HALO is a powerful 2D image analysis tool with AI capabilities to perform annotations and analysis on bright-field and immunofluorescent images for digital pathology and numerous other applications. It offers a set of algorithms Read More...
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Wide-field microscopy This is the technical term for a conventional fluorescence microscope. Typically the entire field of view of the specimen is illuminated with excitation light and then all of the emitted fluorescence is collected Read More...
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Home Please watch the following instructional video to learn how to order and reserve microscopes usinf BookItLab
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FRET (Fluorescence Resonance Energy Transfer) FRET is a light microscopy method for detecting protein‐protein interactions within intact cells. FCS (Fluorescence Correlation Spectroscopy) Fluorescence correlation spectroscopy (FCS) is a correlation analysis of fluctuation of the Read More...
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Multicolor imaging Fluorescence imaging is used for protein localization and colocalization in 3D. Multi-color imaging is necessary to observe colocalization of several proteins in the same cell. Many fluorescent proteins are now available for multi-color Read More...
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Our Mission The NCI Optical Microscopy Cores are instrumental in advancing cancer research through optical measurements and analysis. Serving the Center for Cancer Research community, which is home to approximately 250 Principal and Senior Investigators across 50 Read More...
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Zeiss LSM980 with Airyscan 2 Confocal Microscope Inverted microscope Two PMTs and four GaAsP detectors for confocal imaging 4 color Airyscan 2 “super-resolution” detector Airyscan Multiplex modes for increased acquisition speed with high sensitivity 5x, 10x, 20x, 40x, 63 Read More...
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Ross Lake, B.A.Core Headlaker@mail.nih.govBuilding 37, Room 1066240-760-6824 Ross Lake joined the Microscopy Core of the Laboratory of Genitourinary Cancer Pathogenesis (LGCP) in 2003. He became the Core Head of the Core Read More...
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Training Contact the LCBG Microscopy Core to discuss your experiment: Meet with Ross Lake, Core Head, to discuss your specific project. If the project is feasible, schedule an appointment to receive training and use the Read More...
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2024 Senatorov IS, Bowman J, Jansson KJ, Alilin AN, Capaldo BJ, Lake R, Riba M, Abbey YC, Mcknight C, Zhang X, Raj S, Beshiri ML, Shinn P, Ngyuyen H, Thomas CJ, Corey E, Kelly K. Castrate Read More...
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Techniques The Laboratory of Cancer Biology and Genetics Microscopy Core houses multiple systems that can be used to analyze cell structure, protein expression, and cell dynamics using immunofluorescence. These include inverted epifluorescence microscopes, a confocal Read More...
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Contact the LCBG Microscopy Core to discuss your experiment Please login to the Bookitlab with your NIH credentials and register as user. Once you have registered on the website, coordinate a Read More...
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WIDE-FIELD MICROSCOPY Nikon Ti 2000 wide-field microscope Capabilities: This microscope is suited for live cell imaging (4D) and fixed cells (3D) imaging. This microscope produces high quality wide-field images that may be improved by Read More...
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Our Team Tatiana S. Karpova Ph.D.Core Headkarpovat@nih.govBuilding 41, Room C615240-760-6637 David A. Ball Ph.D.Core Biologistballa@nih.govBuilding 41, Room B114D240-760-6577 Mohamadreza Fazel, Ph.D.Core Biologistmohamadreza. Read More...
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Software Image Acquisition Commercial imaging systems of LRBGE Optical Microscopy Core are controlled by acquisition software specifically designed for the appropriate microscope, such as ZEN (Zeiss confocal microscopes), Nikon Elements (Nikon), Imspector (Abberior). Custom-built HILO Read More...
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General Usage Policies Principal investigators should read this document and sign it. PI’s/postdocs should attach a short-written summary of the project to the signed doc. If the project changes, Read More...
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Leica SP8 LSCM with white light laser The SP8 LIGHTNING confocal microscope allows you to make proper and detailed observations of fast biological processes. Your experimental work will have the benefit of super-resolution, high-speed imaging, Read More...
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2024 L. Balagopalan, T. Moreno, H. Qin, B. C. Angeles, T. Kondo, J. Yi, K. M. McIntire, N. Alvinez, S. Pallikkuth, M. E. Lee, H. Yamane, A. D. Tran, P. Youkharibache, R. E. Cachau, N. Taylor, Read More...
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Software LAS Leica Application Suite X (LAS X) is the one software platform for all Leica microscopes: It integrates confocal, wide field, stereo, super-resolution, and light-sheet instruments from Leica Microsystems. MetaMorph The MetaMorph® Microscopy Automation Read More...
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Zeiss LSM 880 NLO Laser Scanning Microscope Laser scanning microscope: 355nm UV laser 405nm, 594nm, 561nm and 633nm diode lasers Argon laser (458nm, 488nm and 514nm lines) Coherent Chameleon Vision II tunable (680nm – 1080nm) IR multiphoton Read More...
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Jan Wisniewski, Ph.D.Head of EIB Microscopy and Digital Imaging Facilitywisniewj@nih.govBuilding 10, Room 4A05240-858-3605 Dr Wisniewski received his Ph.D. from the Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Read More...
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2024 Kim G, Chen Z, Li J, Luo J, Castro-Martinez F, Wisniewski J, Cui K, Wang Y, Sun J, Ren X, Crawford SE, Becerra SP, Zhu J, Liu T, Wang S, Zhao K, Wu Read More...
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Macro’s for Image Processing Dr. Wisniewski wrote a number of custom ImageJ Macros aimed at streamlining image processing tasks such as: Software Imaris The world’s leading Interactive Microscopy Image Analysis software company, actively Read More...
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Sign-Up for Microscopes and Computers Sign-up for EIB Microscopy Facility microscopes and image processing workstations is mandatory – please e-mail facility manager (“Contact” tab) and include your preferred date/time etc. Your request will be confirmed Read More...
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Yokogawa CV8000 High-throughput spinning disk confocal microscope The Yokogawa CV8000 is HiTIF second high-throughput imaging system. The CV8000 feature set is similar to the CV7000.When compared to the latter, the CV8000 has a newer Read More...
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Gianluca Pegoraro, PhD gianluca.pegoraro@nih.gov Facility Head 240-760-6696 Bldg. 41, Room B909 Dr. Pegoraro received his Ph.D. in Molecular Genetics from the International School of Superior Studies in Trieste, Italy in 2004. After Read More...
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Gianluca Pegoraro, PhD gianluca.pegoraro@nih.gov Facility Head 240-760-6696 Bldg. 41, Room B909 Dr. Pegoraro received his Ph.D. in Molecular Genetics from the International School of Superior Studies in Trieste, Italy in 2004. After Read More...
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HiTIF Protocols Cell Fixation PFA Cell Fixation with the Blue Washer Cell Seeding Seeding Cells in the imaging plates using the Multidrop Combi Functional Genomic Screens RNAi/CRISPR-KO Screen Assay Development SOP siRNA/sgRNA Spotting Read More...
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Image Analysis Signals Imaging Artist Next generation image analysis and management platform for high-content screening and cell imaging data. Quickly process, analyze, share, and store the vast volumes of data. Cell Profiler CellProfiler is free, Read More...
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2025 Sebastian R, Sun EG, Fedkenheuer M, Fu H, Jung S, Thakur BL, Redon CE, Pegoraro G, Tran AD, Gross JM, Mosavarpour S, Kusi NA, Ray A, Dhall A, Pongor LS, Casellas R, Aladjem MI. Mechanism Read More...
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A typical HTI screening project at HiTIF can be subdivided in 5 phases: Pre-Development The investigator contacts the Facility Head to inquire about the current availability of instrumentation time and manpower for the project to be Read More...
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ZEISS Elyra 7 with Lattice SIM² Type: Wide-field structured illumination microscope Capabilities: Super resolution 2D and 3D imaging of live or fixed cells Apotome SIM: 170 nm lateral, 450 nm axial Apotome SIM2: 140 nm lateral, 300 nm axial Lattice Read More...
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Stephen Lockett, Ph.D. Director, OMAL locketts@nih.gov 301-846-5515 Valentin Magidson, Ph.D. Scientist magidsonv@mail.nih.gov 301-846-6092 Will Heinz, Ph.D. Scientist heinzwf@nih.gov 301-846-1239 David Read More...
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Confocal Microscope Image of Fixed Mouse Embryonic Fibroblast (MEF) Cells Actin proteins (stained red) and Tubulin proteins (stained green) are involved in a number of cellular process such as cell motility, cell division and maintenance Read More...
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2024 Coutinho, L. L., Femino, E. L., Gonzalez, A. L., Moffat, R. L., Heinz, W. F., Cheng, R. Y. S., Lockett, S. J., Rangel, M. C., Ridnour, L. A. & Wink, D. A. NOS2 and Read More...
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Image Analysis Capabilities: Custom Development Software Supported Software Platforms Arivis Vision 4D Arivis is software for image processing, analysis, and display for working with multi-channel 2D, 3D and 4D images of almost unlimited size independent Read More...
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Training We recommend that users become familiar with the principals of fluorescence labeling and optical microscopy before arranging for training. We recommend the following sites for learning about microscopy: Introductions to Fluorescence Microscopy Fluorescence Labeling Read More...
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Research Mission The goal of OMAL’s research is to understand molecular mechanisms driving carcinogenesis and the reversal of this process through treatment, by utilization and advancement of optical microscopy techniques. These techniques include multiplex Read More...
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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...
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Our Team Michael Kruhlak, Ph.D.Core Headkruhlakm@nih.govBuilding 37, Room B114E240-858-3342 Dr. Kruhlak received his Ph.D. from the Department of Biochemistry and Molecular Biology at The University of Calgary Medical Read More...
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Image Processing Capabilities: Please contact Michael Kruhlak if you have any questions regarding image analysis.
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BookIt The NCI Microscopy Core is using a new system to reserve a microscope. Use this link to access the BookIt software. Click on the NIH Login to sign in using your NIH PIV ID Read More...
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2024 Mizunuma M, Redon CE, Saha LK, Tran AD, Dhall A, Sebastian R, Taniyama D, Kruhlak MJ, Reinhold WC, Takebe N, Pommier Y. Acetalax (Oxyphenisatin Acetate, NSC 59687) and Bisacodyl Cause Oncosis in Triple-Negative Breast Read More...
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Software Several software packages are available to users Arivis Vision 4D Arivis Vision4D is a modular software for working with multi-channel 2D, 3D and 4D images of almost unlimited size independent of Read More...
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Contact the CCR Microscopy Core to discuss your experiment Thank you for your interest in using the CCR Microscopy Core. Please login to the Bookitlab with your NIH credentials and register Read More...
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Chris Combs, Ph.D. Facility Head Address: Bldg. 10 Rm 6n-309 10 Center Dr Bethesda, MD 20892 combsc@nhlbi.nih.gov Phone: 301.480.4078 Tatiana Karpova, Ph.D. Facility Head Address: Bldg. 41 Rm C615 41 Center Dr Bethesda, MD 20892 Read More...
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General Microscopy Resources Confocal Listserv Email discussion list focused on confocal microscopy, but also including topics on fluorescence microscopy and digital imaging. MicroscopyU Online learning platform by Nikon. An online source for Microscopy education. Leica Read More...
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Optical Microscopy Core of the Experimental Immunology Branch (CCR/NCI) announces the introductory classes to Image J/FIJI: December 16th and 18th, 2020, 10 am to 4 pm with 1 hr break. Dr. Jan Wisniewski “Streamline Your Image Analysis: Read More...
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2024 Date: Tuesday, October 15, 2024 Time and Location: 11 am EST, ZOOM (INVITATION BY LMIG LIST SERVER) Speaker: Dr. Diego Presman (U Buenos Aires) Title: “Insights on Glucocorticoid Receptor Activity Through Live Cell Imaging” Summary: Eucaryotic transcription factors ( Read More...
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PLEASE, JOIN US AT LMIG ZOOM SEMINAR! DATE: Tuesday, January 14th, 2025 TIME AND LOCATION: 11 am EST, ZOOM (INVITATION BY LMIG LIST SERVER)Dr. Yun-Xing Wang (NIH/NCI) “Determining 3D structures of individual molecules in Read More...
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PLEASE, JOIN US AT LMIG ZOOM SEMINAR! DATE: Tuesday, January 14th, 2025 TIME AND LOCATION: 11 am EST, ZOOM (INVITATION BY LMIG LIST SERVER) Dr. Yun-Xing Wang (NIH/NCI) “Determining 3D structures of individual 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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Learning Objectives Learn about data structures including factors, lists, data frames, and matrices. Load, explore, and access data in a tabular format (data frames) Learn to write out (export) data from the R environment Data Read More...
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This lesson will introduce data wrangling with R. Attendees will learn to filter data using base R and tidyverse (dplyr) functionality. Learning Objectives Understand the concept of tidy data. Become familiar with the tidyverse packages. Read More...
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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...
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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...
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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...
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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...
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Objectives To explore Bioconductor, a repository for R packages related to biological data analysis. To better understand S4 objects as they relate to the Bioconductor core infrastructure. To learn more about a popular Bioconductor S4 Read More...
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Lesson 1: Introduction to Biowulf, Unix, and R Learning Objectives Learn about why you may want to use R on Biowulf. Refresh Unix and R skills. This lesson will not be hands on. Why use R Read More...
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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...
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R basics Objectives : To understand some of the most basic features of the R language including: Creating R objects and understanding object types Using mathematical operations Using comparison operators Creating, subsetting, and modifying vectors By Read More...
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Data frames Objectives To be able to load, explore, and access data in a tabular format. To this end, students should understand the following: 1. how to import and export data 2. how to create, summarize, and Read More...
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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...
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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...
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Let's use read.delim to load the data. aircount<-read.delim("./data/head50_airway_nonnorm_count.txt", row.names = 1) head(aircount) Accession.SRR1039508 Accession.SRR1039509 ENSG00000000003.TSPAN6 679 448 ENSG00000000005.TNMD 0 0 ENSG00000000419. Read More...
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Bioinformatics
General steps to debug R code: Read the message carefully. Check for typos and missing punctuation. Check your global environment for created objects (or use ls()). Check object attributes (Use str(), dim(),typeof(), class(), etc.) Read More...
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Bioinformatics
General steps to debug R code: Read the message carefully. Check for typos and missing punctuation. Check your global environment for created objects (or use ls()). Check object attributes (Use str(), dim(),typeof(), class(), etc.) Read More...
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CRTP
How do I determine which EM technique should be used for my research? Schedule a free consultation with the Head of EML, Dr. Ru-ching Hsia, to discuss the aim of your research project. Dr.& Read More...
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CRTP
empanada v1.2 now released empanada-napari now comes with: MitoNet v1, NucleoNet v0.1 and DropNet v0.1(and other new useful modules) Empanada (EM PANoptic Any Dimension Annotation) is a tool Read More...