Search Results for: plasma treatment

TCIA is a service which de-identifies and publishes medical images of cancer for download. New data proposals are reviewed by an Advisory Group on a quarterly basis. The data are organized as “collections”; typically, patients’ Read More...

The Office of Collaborative Biostatistics, Office of the Clinical Director, is the statistical and data management component of CCR. The Section provides statistical leadership and data management consultation for CCR’s major clinical activities and Read More...

The National Cancer Institute (NCI) is developing a national repository of Patient-Derived Models (PDMs) comprised of patient-derived xenografts (PDXs), patient-derived organoids (PDOrg), and in vitro patient-derived tumor cell cultures (PDCs) and cancer-associated fibroblasts (CAFs). These 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...

The BRB Preclinical Biologics Repository is a central repository that supplies reagents to the broad research community. This NCI-sponsored repository is managed by NCI's Biological Resources Branch (BRB) in the Development Therapeutics Program (DTP) of Read More...

The Molecular Pathology Unit is intended to spearhead opportunities for bridging basic and clinical research efforts by more precisely optimizing the development, characterization, and utilization of models of human disease. The initiative approach includes both 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...

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...

The Biological Products Core provides the AIDS research community with high-quality purified preparations of various strains of Human Immunodeficiency Virus (HIV) and Simian Immunodeficiency Virus (SIV), economically prepared by leveraging the economy of scale. Materials 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...

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...

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...

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 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 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 Clinical Image Processing Service (CIPS) offers timely and accurate advanced image processing of diagnostic radiology images for clinical care, research, and training. CIPS’ functions include clinical services and scientific researches. Established Technologies CIPS can Read More...

CREx News & Updates July 2022 Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Click below to learn how easy it is to navigate the CREx platform. These short videos will 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 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...

Mouse models of human cancer have had a profound impact on our current understanding of the mechanisms of tumorigenesis and the pathways regulated by cancer-related genes. These models hold the promise of serving as critical Read More...

12/11/2025 - This talk will focus on analyses of the Patient Outcomes Repository for Treatment (PORT) which is a large registry of chronic pain treatment outcomes from patients seen in the pain clinics at the University Read More...

06/16/2025 - In 2017, Dr. Rol joined the World Health Organization' International Agency for Re earch on Cancer (IARC-WHO), motivated to improve equal acce to high-quality healthcare for everyone. Currently, he lead an IARC team dedicated to Read More...

The code below generates a data frame, dfh, that contains information on the treatment group in which a sample was assigned. To create a data frame in R, we use the data.frame command. In Read More...

The code below generates a data frame, dfh, that contains information on the treatment group in which a sample was assigned. To create a data frame in R, we use the data.frame command. In Read More...

The code below generates a data frame, dfh, that contains information on the treatment group in which a sample was assigned. To create a data frame in R, we use the data.frame command. In Read More...

03/24/2026 - Overview This 3-day, virtual workshop will explore how foundation models —a powerful class of advanced  AI models  —can transform cancer research and clinical care. We will Read More...

Scatterplots are useful for visualizing treatment–response comparisons, associations between variables, or paired data (e.g., a disease biomarker in several patients before and after treatment). Holmes and Huber, 2021 Because scatter plots involve mapping each Read More...

05/03/2021 - Abstract: We will discuss technical advantages of a personalized and tumor-informed multiplex PCR next generation sequencing assay, called Signatera™, that enables a sensitive, specific, and dynamic detection of molecular disease burden in cell-free DNA ( Read More...

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

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

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...

Create a data frame called annotation_df that contains the sample and treatment group information that we will add to the legend for this heatmap. {{Sdet}} Solution{{Esum}} annotation_df % column_to_rownames ( "sample& 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...

09/09/2025 - Join&nb p;the National Heart, Lung, and Blood In titute (NHLBI)&nb p;for a hybrid work hop to explore how medicine will be tran formed by the current artificial intelligence ( Read More...

04/23/2025 - Dr. Ruppin's research is focused on multi-disciplinary, algorithmic, computationally driven analysis of large-scale biomedical omics and clinical data, with an emphasis on AI and machine / deep learning approaches. Overall, it centers on three 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...

05/20/2024 - Hybrid (in-person location in Rockville, MD) Virtual attending via  WebEx Meeting, link will be available two weeks prior to the meeting date . Attend the  2024 Co-Clinical Imaging Research Resource Program (CIRP) Read More...

05/02/2024 - The explosion of biomedical big data and information in the past decade or so has created new opportunities for discoveries to improve the treatment and prevention of human diseases. As such, the field of 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...

There is an approach to data analysis known as "split-apply-combine", in which the data is split into smaller components, some type of analysis is applied to each component, and the results are combined. Read More...

Now, if we want the top five transcripts with the greatest median scaled counts by treatment, we need to organize our data frame and then return the top rows. We can use arrange() to arrange Read More...

There is an approach to data analysis known as "split-apply-combine", in which the data is split into smaller components, some type of analysis is applied to each component, and the results are combined. Read More...

Now, if we want the top five transcripts with the greatest median scaled counts by treatment, we need to organize our data frame and then return the top rows. We can use arrange() to arrange 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 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...

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...

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...

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

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...

Create the design.csv file using the nano editor. Recall that the design files contain nothing more than a column with sample names and a column informing of sample treatment condition. Some of the R 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...

05/23/2023 - In this webinar, University of Wisconsin-Madison’s Dr. Tiwari shares how she and her laboratory have created machine learning (ML) techniques to better understand the basic biology of tumors through non-invasive imaging, Read More...

04/16/2026 - The ability to measure gene expression levels for individual cells (vs. pools of cells) and with spatial resolution is crucial to address many important biological and medical questions, such as the study of stem Read More...

01/14/2026 - During this event, Dr. Emilie Roncali, an associate professor at UC Davis, is tackling an intersection of two critical data science concepts in cancer research: theranostics and digital twins. Dr. Roncali will give you Read More...

09/29/2025 - Plea e u e thi link to acce overview, regi tration, and other information: http ://event .cancer.gov/nci/od -data-jamboree Childhood cancer i a rare di ea e with ~15,000 ca e diagno ed Read More...

07/08/2025 - Join Dr . Eytan Ruppin (pre enter) and Timothy haw (moderator) a they pre ent on four approache for predicting how patient re pond to checkpoint immunotherapy. Approach #1: Predicting patient re pon e to the Read More...

05/29/2025 - Prostate cancer exhibits significant intratumoral heterogeneity, driving a spectrum of phenotypes ranging from indolent disease to aggressive metastasis, challenging risk stratification and treatment. A key feature of this heterogeneity is clonal diversity, encompassing both Read More...

01/22/2025 - Alzheimer’s Disease (AD) presents significant challenges in prevention and treatment despite decades of research advancements. Innovative AI/ML approaches enable analysis of real-world data sources, such as electronic health records (EHRs) Read More...

01/14/2025 - Join leaders from  NCI’s Human Tumor Atlas Network  (HTAN) to hear about the program’s collaborative efforts to develop comprehensive atlases incorporating cellular, molecular, and histological dimensions across various cancer types and stages.  Read More...

01/09/2025 - Alzheimer’s Disease (AD) presents significant challenges in prevention and treatment despite decades of research advancements. Innovative AI/ML approaches enable analysis of real-world data sources, such as electronic health records (EHRs) Read More...

10/29/2024 - In this presentation, you will get an overview of the Cancer Digital Slide Archive (CDSA) platform. CDSA is an open source, web-based platform for storage, visualization, and management of digitized whole slide images.& Read More...

10/15/2024 - On October 15th-16th, 2024, the NCI Office of Data Sharing (ODS) is hosting the Annual Data Sharing Symposium titled  Driving Cancer Advances through Impactful Research  inside the Clinical Read More...

10/10/2024 - Digital Pathology has advanced significantly in the past decade, evolving beyond assisting pathologists to now informing molecular and genetic properties of tumors. Recent breakthroughs in machine learning and AI, particularly in big data and Read More...

08/29/2024 - Dr. Mardis is an internationally recognized expert in cancer genomics, with ongoing interests in the integrated characterization of cancer genomes, defining DNA-based somatic and germline interactions and RNA-based pathways, and immune microenvironments that lead Read More...

07/17/2024 - Dear Colleagues,   Please join us on Wednesday, July 17, 2024, when Dr. Olivier Gevaert from Stanford University will discuss leveraging data at different scales for personalized diagnosis, prognosis, and therapy in the fields of Read More...

06/06/2024 - The CCR Genomics Core Facility is pleased to host a virtual technology workshop with EpiCypher on CUT&RUN library prep/sequencing Presentation overview: The location of histone post-translational modifications and chromatin-associated proteins Read More...

05/10/2024 - Please join us for the May Data Sharing and Reuse Seminar featuring Dr. Ali Loveys and Fiona Meng from FI Consulting. They will be sharing their presentation on Laying the Foundation for AI-Ready Data. Read More...

05/06/2024 - Have you been looking for ways to use artificial intelligence (AI) in clinical practice but not sure where to start? Attend this webinar for tips from Dr. Anant Madabhushi on applying AI in precision Read More...

04/23/2024 - Dr. Stephanie Harmon obtained her Ph.D. in medical physics at the University of Wisconsin–Madison and did her postgraduate training at Leidos Biomedical Research where she worked within the Molecular Imaging Read More...

04/19/2024 - Dear Colleagues,    This webinar will introduce TumorDecon, a computational tool that's at an early stage of contributing to the intersection of bioinformatics and oncology.   TumorDecon aims to Read More...

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...

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...

03/04/2024 - The NCI Cancer Imaging Program presents a new monthly webinar series highlighting advancements in our imaging community. Please join us for our next lecture in the series Dr. McNally earned her Ph.D. in 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...

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 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...

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...

#Setting a theme my_theme

Use pivot_longer to reshape countB. Your reshaped data should look the same as the data below. {{Sdet}} Solution } library ( tidyverse ) countB % rownames_to_column ( "Feature" ) countB_l ## 1 Tspan6 1 703 71 ## 2 Tspan6 2 567 970 ## 3 Tspan6 3 867 242 ## 4 TNMD 1 490 342 ## 5 TNMD 2 482 935 ## 6 Read More...

Excel files are the primary means by which many people save spreadsheet data. .xls or .xlsx files store workbooks composed of one or more spreadsheets. Importing excel files requires the R package readxl . While this Read More...

In lesson 3, we learned how to read and save excel spreadsheet data to a R object using the tidyverse package readxl . Today we will use some example data from an excel spreadsheet to learn the 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...

There is an approach to data analysis known as "split-apply-combine", in which the data is split into smaller components, some type of analysis is applied to each component, and the results are combined. Read More...

09/27/2023 - Partek Flow is your 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 Read More...

Prior to differential expression analysis, we need to generate a design.csv file that contains the samples and their corresponding treatment conditions. Note that csv stands for comma separated value so the columns in these Read More...

Next, we need to generate the counts (ie. number of reads that map to a transcript). But first, change back into the ~/biostar_class/snidget folder and then take a moment to think about how Read More...

06/29/2023 - Please join us for a special presentation about the Fred Hutchinson’s data journal to the cloud, including an innovative cloud platform (Cirro:  https://cirro.bio/ ) to streamline data collection, Read More...

05/19/2023 - Patient-derived cancer models (PDCM) have become an essential tool in both cancer research and preclinical studies. Each model type offers unique advantages and is better suited for specific research areas: cell lines are low 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...

What is bioinformatics? Bioinformatics integrates biology, statistics, and computer science to develop and apply theory, methods, and tools for the collection, storage, and analysis of biological and related data. Bioinformatics plays a critical role in Read More...

07/11/2024 - Please join us for this special event featuring three speakers on the topic of Single-Cell Spatial Transcriptomics. George Zaki, Ph.D., Director, Applied Scientific Computing, BACS, The Frederick National Lab for Cancer Research, NCI/ 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...

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...

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...

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...

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

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

01/19/2024 - Trey Ideker, Ph.D., is a professor of medicine, bioengineering, and computer science, and former chief of genetics at the University of California San Diego (UCSD). Additionally, he is director or co-director of the 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...

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...

Let's grab some data. library ( tidyverse ) acount_smeta % dplyr :: rename ( "Feature" = "...1" ) acount #differential expression results dexp % filter ( ! Feature %in% dexp $ feature ) ## # A tibble: 48,176 × 9 ## Feature SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 ## ## 1 Read More...

Help Session Lesson 6 Let's grab some data. library ( tidyverse ) acount_smeta % dplyr :: rename ( "Feature" = "...1" ) acount #differential expression results dexp % filter ( ! Feature %in% dexp $ feature ) ## # A tibble: 48,176 × 9 ## Feature SRR1039508 SRR1039509 SRR1039512 Read More...

All solutions should use the pipe. Import the file "./data/filtlowabund_scaledcounts_airways.txt" and save to an object named sc . Create a subset data frame from sc that only includes the columns Read More...

Help Session Lesson 5 All solutions should use the pipe. Import the file "./data/filtlowabund_scaledcounts_airways.txt" and save to an object named sc . Create a subset data frame from sc that only 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...

Help Session Lesson 3 Loading data Import data from the sheet "iris_data_long" from the excel workbook (file_path = "./data/iris_data.xlsx"). Make sure the column names are unique and Read More...

dplyr : joining, tranforming, and summarizing data frames Objectives Today we will continue to wrangle data using the tidyverse package, dplyr . We will learn: how to join data frames using dplyr how to transform and create Read More...

Data import and reshape Objectives 1. Learn to import multiple data types 2. Data reshape with tidyr : pivot_longer() , pivot_wider() , separate() , and unite() Installing and loading packages So far we have only worked with objects that Read More...

10/13/2023 - Zhiyong Lu, Ph.D. will present AI in Medicine: Improving Access to Literature Data for Knowledge Discovery at the monthly Data Sharing and Reuse Seminar.  The explosion of biomedical big data and Read More...

Below, you will find questions and answers brought up in the course polls for the BTEP Bioinformatics for Beginners course series that took place from September 13th, 2022 to December 13th, 2022. Question 1 : Normalization - when to Read More...

BTEP Bioinformatics for Beginners (September 13th, 2022 - December 13th, 2022) Questions and Answers Below, you will find questions and answers brought up in the course polls for the BTEP Bioinformatics for Beginners course series that took Read More...

Lesson 15 Practice Objectives Previously, we performed QC on the Golden Snidget RNA sequencing data, aligned the sequencing reads to its genome, and obtained expression counts. We can now finally perform differential expression analysis, to find Read More...

Lesson 16 Practice Objectives In this lesson, we learned about the classification based approach for RNA sequencing analysis. In this approach, we are aligning our raw sequencing reads to a reference transcriptome rather than a genome. 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...

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...

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...