Search Results for: sample acquisition

Back Services: Biophysics Facility offers DSC as an open-access instrument. First-time users must complete training before gaining access to the instrument reservation calendar. Location: Building 50, room 3123 Description: The differential scanning calorimeter measures the constant pressure 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 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...

Back Services: This instrument is not user accessible.  We provide both data collection and data analysis services. Location: Building 50, room 3331 Description: An analytical ultracentrifuge is equipped with absorption and interference optical systems that monitor Read More...

Back Services: Biophysics Facility offers MP as an open-access instrument.  First-time users must complete a short training session before gaining access to the instrument training calendar.  Training includes mass distribution analysis of a Read More...

Back Services: Biophysics Facility offers Tycho as an open-access instrument.  This instrument is very easy to use, and no formal training is required.  Core staff will help with their first experiment of new Read More...

The Cancer and Inflammation Program – Microbiome and Genetics Core (CIP-MGC) grew out of the former CIP Genetics Core to meet the increasing need for sequencing and analysis of commensal microbiota within CIP and NCI. The Read More...

Back Services: Biophysics Facility offers MDS as an open-access instrument. First-time users must complete a short training session before gaining access to the instrument reservation calendar. Training includes the KD determination of a standard molecular Read More...

Back Services: Biophysics Facility offers ZetaView as an open-access instrument. First-time users must complete a short training session before using it for the first time. Training includes instrument calibration and size analysis of a standard Read More...

Many established and emerging technologies are available to CCR scientists.  This technology-rich environment makes the CCR a unique place to conduct scientific research.  Through the OSTR, the CCR continues to find Read More...

The Clinical Support Laboratory offers processing, tracking, and testing of a broad range of clinical samples. Support can begin at the early stages of clinical trial development to aid in developing a comprehensive strategy for Read More...

Back Services: Biophysics Facility offers MST as an open-access instrument.  First-time users must complete a short training session before gaining access to the instrument reservation calendar.  Training includes the KD determination of a Read More...

Back Services: Biophysics Facility offers fluorometers as open-access instruments.  First-time users must complete a short training session before gaining access to the instrument reservation calendar. Location: Building 50, room 3226 Description: Some substances reemit light after Read More...

Back Services: Biophysics Facility offers DLS as an open-access instrument. First-time users must complete a short training session before gaining access to the instrument reservation calendar. Training includes DLS analysis of small- and large-molecular size Read More...

The Blood Processing Core monitors viral load in patients with HIV and performs sequential studies using samples obtained from patients with cancer, AIDS, chronic granulomatous disease, or other diseases associated with immunologic dysfunction. The core Read More...

NIH Intramural CryoEM Consortium (NICE) serves intramural investigators in all NIH IC’s. NICE provides access to state-of-the-art Titan Krios cryo-electron microscopes for atomic-resolution structure determination of protein, macromolecular complexes, membrane receptors, cellular organelles, and Read More...

The CHTN is a unique NCI-supported resource that provides human tissues and fluids from routine procedures to investigators who utilize human biospecimens in their research. Unlike tissue banks, the CHTN works prospectively with each investigator Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More New CREx User Survey The CREx Team is carrying out a CREx User Survey. If you haven’t Read More...

Back Services: Biophysics Facility offers ITC calorimeters as open-access instruments.  First-time users must complete a short training session before gaining access to the instrument reservation calendar.  Training includes performing a test experiment and Read More...

NanoString Technology The nCounter® Analysis System is an automated, multi-application, digital detection and counting system which directly profiles up to 800 molecules simultaneously from a single sample using a novel barcoding technology. Profile hundreds of mRNAs, Read More...

The NIH Metabolomics Consortium (NIH-MC) is a shared research resource that performs metabolomics analysis (and related small molecule research) for investigators at all Institutes and Centers across the NIH Intramural Research Program. The NIH-MC is Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More NIH Research Festival The NIH Research Festival highlights the groundbreaking science and the vibrant NIH community driving our Read More...

The centrally funded Statistics team within the Advanced Biomedical Computational Science group at the Frederick National Lab provides statistical consultation and data analysis support for NCI laboratories. We have broad-range expertise in biomedically relevant areas Read More...

Back Services: We offer a limited sample processing service using standard SEC-MALS and FFF protocols.  This service is intended for the occasional users of this system.  Researchers who expect to use this instrument Read More...

Back Services: We offer a limited sample processing service using standard SEC-MALS and FFF protocols.  This service is intended for the occasional users of this system.  Researchers who expect to use this instrument Read More...

The Division of Veterinary Resources’ (DVR) mission is to contribute to the advancement of NIH biomedical research programs by providing excellence in animal care, customer service, and specialized research services to support intramural research efforts. Read More...

Home About the Biophysics Core Biophysics Core Technologies We provide consultations and advice on the applications of biophysical methods. If you would like to discuss the requirements of a particular technique or consider selecting the 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...

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...

The Spatial Imaging Technology Resource (formerly the Nanoscale Protein Analysis Section of the Collaborative Protein Technology Resource or CPTR) provides expertise and service in state-of-the-art protein analysis technologies to advance CCR research in basic discovery Read More...

In order to meet increasing demands from both NIH intramural and extramural communities for access to a small angle X-ray scattering (SAXS) resource, the Center for Cancer Research (CCR) under the leadership of Drs. Jeffrey Read More...

The function of the SAIP is to collaborate with NCI investigators in the development of mouse models, new molecular imaging probes for early detection and therapy, monitor tumors in vivo, and perform drug efficacy studies Read More...

The Functional Genomics Laboratory (formerly, the RNAi Screening Facility) of the National Center for Advancing Translational Sciences (NCATS) assist investigators with all stages of project planning and execution, beginning with assay development through genome-wide siRNA Read More...

[tabby title="Home"] About NICE-NIH Intramural CryoEM Consortium  NIH Intramural CryoEM Consortium (NICE) serves intramural investigators in all NIH IC’s. NICE provides access to state-of-the-art Titan Krios cryo-electron microscopes for atomic-resolution structure determination of Read More...

The Division of Veterinary Resources Animal Surgery has supported large animal research and clinical care for more than 40 years. DVR surgeons provide outstanding and highly specialized large animal (rabbits, ungulates, etc.) research support in a Read More...

Home Biophysics Core Technologies Biophysics Core Services The Biophysics Core The Biophysics Core Facility (BCF) was established in 2008 in the NHLBI as an advanced service laboratory providing expertise, training, consultations, and instrument access for intramural Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More New Resources on CREx Interested in imaging large scale biological samples?  Volume electron microscopy allows for Read More...

DTP maintains a repository of synthetic compounds and pure natural products that are available to investigators for non-clinical research purposes. The Repository collection is a uniquely diverse set of more than 200,000 compounds that have been Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Technology Event Biophysical Methods for Protein Interactions Monday, May 15 – Friday, May 19, 2023 This workshop will review the strategies of Read More...

[embed]https://youtu.be/pfZp5Vgsbw0[/embed] Menarini Silicon Biosystems has combined the ability to manipulate individual cells using DEP technology with high-quality image-based cell selection to create the DEPArray™ System. This Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More NIH Cores on CREx NIBIB BEPS Micro Analytical Immunochemistry Unit The Micro Analytical Immunochemistry Unit employs a variety Read More...

Home About the Biophysics Core Biophysics Core Services [tabby title="Instrumentation"] NHLBI Biophysics Core The Biophysics Core Facility: Overview Core Facilities provide scientific resources, cutting-edge technologies and novel approaches to support DIR scientists. Availability of Read More...

The Specimen Resource Locator (SRL) is a biospecimen resource database designed to help researchers locate resources that may have the samples needed for their investigational use. This publicly searchable database includes information about biospecimen banks Read More...

The Staff Scientists/Clinicians (SSSC) Technical Enrichment Program (STEP) was established to provide SSSC’s an opportunity to compete for funding to gain comprehensive training in state-of-the-art techniques available through CCR Cores and Facilities. The Read More...

NCI LASP Animal Research Technology Support (ARTS) provides customized technical support for basic and translational animal-based research to the scientific community. We offer a wide array of services ranging from expert colony management to the Read More...

Proteomic Data Common (PDC) represents the NCI’s largest public repository of proteogenomic comprehensive tumor datasets, essentially a Proteogenomic Cancer Atlas. It was developed to advance our understanding of how proteins help shape the risk, Read More...

The CCR Genomics Core is located in Building 41 on the NIH Bethesda campus. The primary goal of the Core is to provide investigators from CCR/NCI and other NIH Institutes access to genomic technologies and Read More...

The Department of Laboratory Medicine provides state-of-the-art laboratory testing in support of Clinical Center patient care and will serve as a center of excellence in research and training in laboratory medicine, particularly in areas which 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 Laboratory Animal Sciences Program (LASP) of the Frederick National Laboratory operates a Gnotobiotics Facility (GF) to support research focused on the role of microbiota in cancer inflammation, pathogenesis, and treatment response. The GF can Read More...

The NCI High-Throughput Imaging Facility (HiTIF) works in a collaborative fashion with NCI/NIH Investigators by providing them with the necessary expertise, instrumentation, and software to develop and execute advanced High-Throughput Imaging (HTI) assays. These Read More...

The Biophysics Core’s mission is to provide support in the study of macromolecular interactions, dynamics, and stability by offering consultations, training, professional collaborations, and instrument access. General Services Multi-technique molecular interaction studies, Kinetic 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...

Protein and Metabolite Characterization Core (PMCC), formerly known as the Protein Characterization Lab (PCL),  offers various technologies to CCR investigators to characterize proteins and metabolites. The core develops and applies state-of-the-art analytical technologies, primarily mass 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...

How many rows per sample are in the scaled_counts data frame? scaled_counts |> group_by(dex, sample) |> summarize(n=n()) #there are multiple functions that can be used here `summarise()` has grouped Read More...

How many rows per sample are in the scaled_counts data frame? ::: {.cell} scaled_counts |> group_by ( dex , sample ) |> summarize ( n = n ()) #there are multiple functions that can be used here ::: {.cell-output .cell-output-stderr} ` Read More...

No summary available.

03/06/2024 - The NCI Cancer Diagnosis Program in the Division of Cancer Treatment and Diagnosis is hosting a workshop, “Digital Pathology Imaging (DPI) in Cancer Clinical Trials and Research.” This workshop will Read More...

02/21/2024 - The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires easy access to Read More...

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...

colnames() will return a vector of column names from our data frame. We can use this vector and [] subsetting to easily modify column names. For example, let's rename the column "Sample" to & Read More...

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...

01/23/2026 - In the second class of Statistical Analysis of Research Data (SARD) hosted by NCI Center for Cancer Training, participants will learn about: Inferential statistics Two sample tests of means Small versus large sample consideration Read More...

01/23/2026 - This is the second class of the Statistical Analysis of Research Data (SARD) series offered by NCI Center for Cancer Training. In this class participants will learn about: Inferential statistics  Two sample Read More...

03/26/2025 - Developing artificial intelligence (AI) schemes to assist the clinician towards enabling precision medicine approaches requires development of objective markers that are predictive of disease response to treatment or prognostic of longer-term patient survival. The Read More...

The goal of quality control is to keep only high quality cells (i.e., remove low quality cells (dead or dying cells), cell-free RNA, or doublets). Low quality cells will impact downstream analyses. Take care Read More...

05/14/2024 - This presentation will explain the difference between the mean and standard deviation of a set of values and the standard error of the mean. The parameters involved in comparing two normally distributed populations relative Read More...

Create a data frame summarizing the mean counts_scaled by sample from the scaled_counts data frame. {{Sdet}} Possible Solution{{Esum}} scaled_counts |> group_by(sample) |> summarize(Mean_counts_scaled=mean(counts_scaled)) {{ Read More...

Create a data frame summarizing the mean counts_scaled by sample from the scaled_counts data frame. ::: {.cell} scaled_counts |> group_by ( sample ) |> summarize ( Mean_counts_scaled = mean ( counts_scaled )) :::

The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . We are going to use the filtlowabund_scaledcounts_airways.txt file Read More...

Lesson 2 Exercise Questions: Part 1 (BaseR subsetting and Factors) The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . We are going Read More...

Using what you have learned about select() and filter() , use the pipe ( |> ) to create a subset data frame from scaled_counts that only includes the columns 'sample', 'cell', 'dex', 'transcript', and 'counts_scaled' and Read More...

Using what you have learned about select() and filter() , use the pipe ( |> ) to create a subset data frame from scaled_counts that only includes the columns 'sample', 'cell', 'dex', 'transcript', and 'counts_scaled' and Read More...

From the interesting_trnsc data frame select the following columns and save to an object: sample, dex, transcript, counts_scaled. {{Sdet}} Possible Solution{{Esum}} interesting_trnsc_s

No summary available.

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...

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...

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...

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...

04/09/2026 - In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering several trainings that cover general concepts behind statistics and epidemiology. These trainings will help participants Read More...

07/23/2025 - This presentation will explore the next emerging stage where biobanking is characterised by being part of system - Biobanking 4.0. Biobanking has always been about data generation with tissue specimens providing the biological and genomic Read More...

03/17/2025 - In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a several trainings that cover general concepts behind statistics and epidemiology. These trainings will help Read More...

02/27/2025 - NGS produces millions of sequences per sample and the challenge is to identify where in the genome each sequence came from. Fortunately, there are aligners that help scientists with this task. After this class, Read More...

12/03/2024 - In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This four-part lecture series will Read More...

11/08/2024 - Learn microbiome analysis basics in R with phyloseq. This workshop will cover different types of analysis frequently used in microbiome studies, including sample diversity, community composition, and differential taxa. The techniques we learn will 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...

09/18/2024 - Learn how to use the FlowJo™ workspace, including how to load files, evaluate sample quality, draw gates, and generate tabular and graphical layouts.   Designed for those new to the software Read More...

08/08/2024 - What are common statistical analyses for binary data? What is the distribution of your binary dependent variable? What is the difference from normally distributed data? How do you model the binary outcome with multiple Read More...

06/20/2024 - What are common statistical analyses for continuous data? Can you check whether your continuous outcome is normally distributed? What are the methods when the data are not normal? How do you model the outcome Read More...

This tutorial assumes that all pre-processing steps (read demultiplexing, FASTQ QC, reference based alignment, error correction) have been completed. At this stage of the analysis, a gene-by-cell count matrix has been generated for each sample.

Once we have read in the matrices, the next step is to create a Seurat object. The Seurat object will be used to store the raw count matrices, sample information, and processed data (normalized counts, 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...

Integration is the process of aligning the same cell types across samples, treatments, data sets, batches, etc. Clustering should represent biological differences and not technical artifacts. Integration is not always necessary. You should run through 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...

Differential expression analysis is the process of identifying genes that have a significant difference in expression between two or more groups. For many sequencing experiments, regardless of methodology, differential analysis lays the foundation of the Read More...

In addition to the Heatmap and the FeaturePlot shown previously, two other options easily accessible through Seurat are the Dot Plot and Violin Plot. The dot plot can visualize relative expression level and expression fraction. Read More...

The metadata in the Seurat object is located in adp@metadata and contains the information associated with each cell. We can access the metadata using: head(adp@meta.data) #using head to return only the Read More...

Because of the different layers, you will need to break down the object to apply different thresholds by group or sample. The easiest way to do this is to work with the metadata and use Read More...

In this tutorial, we are using data from Nanduri et al. 2022, Epigenetic regulation of white adipose tissue plasticity and energy metabolism by nucleosome binding HMGN proteins , published in Nature Communications . The raw count matrices are Read More...

03/26/2024 - Dear Colleagues,   Developed by expert statisticians and programmers, SUDAAN is a software package designed for researchers who work with study data. SUDAAN procedures properly account for correlated observations, clustering, weighting, stratification, and Read More...

03/20/2024 - Our series of talks continues with two 20-minute presentations.  There will be light refreshments (coffee and donuts) provided, so please consider attending in person! We encourage attendees to stay and chat with Read More...

To access the NIH Partek Flow server, go to https://partekflow.cit.nih.gov/flow and enter the user's NIH username and then password. Note User may have selected a password different than that Read More...

Log into Partek Flow at https://partekflow.cit.nih.gov/flow . This example will use the nci_ccr_sf_example_scrna project, so click on it. Click on "Add data". Select Single cell, Read More...

Accessing Partek Flow at NIH and tips for data transfer Learning objectives Instructions for accessing Partek Flow NCI researchers can find instructions for accessing Partek Flow at https://bioinformatics.ccr.cancer.gov/btep/partek-flow-bulk-and-single-cell-rna-seq-data-analysis/ . But Read More...

Partek Flow is a start-to-finish solution for analyzing high dimensional multi-omics sequencing data. It is a point-and-click software and is suitable for those who wish to avoid the steep learning curve associated with analyzing sequencing Read More...

The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . You can obtain the data outside of class here . The diffexp_ Read More...

The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . You can obtain the data outside of class here . The diffexp_ Read More...

Lesson 4 Exercise Questions: Tidyverse The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . You can obtain the data outside of Read More...

The following represents the basic ggplot2 template: ggplot(data = ) + (mapping = aes()) We need three basic components to create a plot: the data we want to plot , geom function(s) , and mapping aesthetics . Notice the + symbol Read More...

Lesson 5 Exercise Questions: Tidyverse The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . You can obtain the data outside of 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...

#Setting a theme my_theme

The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . You can obtain the data outside of class here . The diffexp_ Read More...

Lesson 4 Exercise Questions: Tidyverse The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . You can obtain the data outside of Read More...

Before diving into subsetting with dplyr , let's take a step back and learn to subset with base R. Subsetting a data frame is similar to subsetting a vector; we can use bracket notation [] . However, 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...

We will continue working with the airway data for this lesson. Let's import the data. scaled_counts

The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . You can obtain the data outside of class here . The diffexp_ Read More...

We will continue working with the airway data for this lesson. Let's import the data. ::: {.cell} scaled_counts

Lesson 2 Exercise Questions: Part 2 (Tidyverse) The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . You can obtain the data outside Read More...

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...

We can also use mutate to coerce variables. To mutate across multiple columns, we need to use the function across() with the select helper where() . #view sscaled glimpse(sscaled) Rows: 127,408 Columns: 6 $ sample 508, 508, 508, 508, 508, 508, 508, 508, 508, 508, 508, 5… $ cell "N61311& Read More...

We can also use mutate to coerce variables. To mutate across multiple columns, we need to use the function across() with the select helper where() . ::: {.cell} #view sscaled glimpse ( sscaled ) ::: {.cell-output .cell-output-stdout} Rows: 127,408 Columns: 6 $ sample 508, 508, 508, 508, 508, 508, 508, 508, 508, 508, 508, 5… $ Read More...

ggplot2 is an R graphics package from the tidyverse collection. It allows the user to create informative plots quickly by using a 'grammar of graphics' implementation, which is described as "a coherent system for Read More...

There are rules regarding the naming of objects. 1. Avoid spaces or special characters EXCEPT '_' and '.' 2. No numbers or underscores at the beginning of an object name. For example: 1a:1:2: unexpected symbol ## 1: 1a ## ^ Note It is Read More...

We can convert to wide format using pivot_wider() , which takes three main arguments: 1. the data we are reshaping 2. the column that includes the new column names - names_from 3. the column that includes the Read More...

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...

We can convert to wide format using pivot_wider() , which takes three main arguments: 1. the data we are reshaping 2. the column that includes the new column names - names_from 3. the column that includes the Read More...

Now let's filter the rows based on a condition. Let's look at only the treated samples in scaled_counts using the function filter() . filter() requires the df as the first argument followed by Read More...

"Keep raw data separate from analyzed data" -- datacarpentry.org For large genomic data sets, you may want to include a project folder with two main subdirectories (i.e., raw_data and data_ Read More...

There are data sets available in R to practice with or showcase different packages. For today's lesson and the remainder of this course, we will use data from the Bioconductor package airway to showcase Read More...

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...

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...

07/17/2025 - NIDDK Biostats Seminar Series: From Research Study Design to Collecting, Managing, and Analyzing Data. Learning Objectives 1. The learner should know the difference between observational studies, clinical trials (drug and non-drug studies), and secondary data ( Read More...

Long read sequencing was recently named 2022’s method of the year by Nature Methods . Long read sequencing technologies, those that generate sequence reads with lengths of 10s of kilobases or longer have several advantages over Read More...

Listed below are the video recordings of past BTEP events (classes, seminars, workshops). Videos are hosted on various servers and may play slightly differently. Some videos may be downloaded for local viewing. Recorded Videos of Read More...

12/05/2024 - This one and a half hour online training will provide a demonstration of how to identify cell types based on statistics, visualization, and canonical markers. One Peripheral blood mononuclear cells (PBMCs) sample will be Read More...

11/06/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

10/29/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

10/22/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

10/15/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

Following Cell Ranger and/or other pre-processing tools, you will have a gene-by-cell counts table for each sample. The three most popular frameworks for analyzing these count matrices include: R ( Seurat ). Seurat, brought to you Read More...

When using SingleR, the 3 primary parameters are the experimental dataset, the reference dataset, and the labels being used. Continuing with the main labels of the MouseRNASeq dataset on the full dataset looks like this: annot = Read More...

The Seurat Object is a data container for single cell RNA-Seq and related data. It is an S4 object, which is a type of data structure that stores complex information (e.g., scRNA-Seq count matrix, Read More...

Clustering is used to group cells by similar transcriptomic profiles. Seurat uses a graph based clustering method. You can read more about it here . The first step is to compute the nearest neighbors of each 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...

One particular critique of differential expression in single cell RNASeq analysis is p-value "inflation," where the p-values get so small that there are far too many genes exist with p-values below 0.05, even after 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...

1. Introduction and Learning Objectives This tutorial has been 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 that Read More...

03/22/2024 - This 3-hour seminar is tailored for biologists, data analysts, and researchers who are eager to dive into the essentials of computational flow cytometry analysis using R. Flow cytometry is a crucial technique in cell Read More...

03/05/2024 - This in-person workshop will show participants how to identify cell types based on statistics, visualization, and canonical markers. One Peripheral blood mononuclear cells (PBMCs) sample will be used to illustrate a basic Single Cell Read More...

Course Overview Partek Flow is a start-to-finish solution for analyzing high dimensional multi-omics sequencing data. It is a point-and-click software and is suitable for those who wish to avoid the steep learning curve associated with Read More...

Accessing Partek Flow at NIH and tips for data transfer Learning objectives After consulting this guide, participants will Know how to access Partek Flow at NIH. Be able to transfer data from NCI CCR Sequencing Read More...

Tidy data implies that we have one observation per row and one variable per column. This generally means data is in a long format. However, whether data is tidy or not will depend on what Read More...

The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . We are going to use the filtlowabund_scaledcounts_airways.txt file Read More...

Lesson 3 Exercise Questions: BaseR dataframe manipulation and factors The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . We are going Read More...

Tidy data implies that we have one observation per row and one variable per column. This generally means data is in a long format. However, whether data is tidy or not will depend on what Read More...

“Tidy datasets are all alike, but every messy dataset is messy in its own way.” –– Hadley Wickham. Messy data sets tend to share five common problems: Column headers are values, not variable names. Multiple variables Read More...

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...

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...

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...

How do I determine which EM technique should be used for my research? Schedule a free consultation with the Head of EMC, Dr. Ru-ching Hsia, to discuss the aim of your research project. Dr.& Read More...

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