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Create a volcano plot for the differential expression analysis results for the hcc1395 data (hint: import hcc1395_deg_chr22_with_significance.csv) {{Sdet}}{{Ssum}}Solution{{Esum}} import pandas import matplotlib.pyplot as plt import seaborn Read More...

Import hcc1395_top_deg_normalized_counts.csv and create an expression heatmap. Use the Viridis color palette. {{Sdet}}{{Ssum}}Solution{{Esum}} hcc1395_top_deg_normalized_counts=pandas.read_csv("./hcc1395_top_deg_normalized_counts. Read More...

Import hcc1395_deg_chr22.csv and store it as hcc1395_deg_chr22. {{Sdet}}{{Ssum}}Solution{{Esum}} hcc1395_deg_chr22=pandas.read_csv("./hcc1395_deg_chr22.csv") {{Edet}}

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}}

Subset out the following columns from hcc1395_deg_chr22 and store it as hcc1395_deg_chr22_1. name log2FoldChange PAdj {{Sdet}}{{Ssum}}Solution{{Esum}} hcc1395_deg_chr22_1=hcc1395_deg_chr22.loc[:,["name", " Read More...

Add a column to hcc1395_deg_chr22_1 that contains the negative log10 of the PAdj value. {{Sdet}}{{Ssum}}Solution{{Esum}} import numpy hcc1395_deg_chr22_1["-log10PAdj"]=numpy.negative(numpy.log10(hcc1395_deg_ Read More...

How many rows and columns are in hcc1395_chr22_counts? {{Sdet}}{{Ssum}}Solution{{Esum}} hcc1395_chr22_counts.shape (1335, 7) {{Edet}}

What are the column names in hcc1395_chr22_counts and how to view the first 10 rows of this data set? {{Sdet}}{{Ssum}}Solution{{Esum}} hcc1395_chr22_counts.head(10) Alternatively, use hcc1395_chr22_counts.columns to Read More...

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}}

Import hcc1395_chr22_rna_seq_counts.csv and store it as hcc1395_chr22_counts. {{Sdet}}{{Ssum}}Solution{{Esum}} import pandas hcc1395_chr22_counts=pandas.read_csv("./hcc1395_chr22_rna_seq_counts.csv") {{Edet}}

Dictionaries are key-value pairs and these are encountered as ways to specify options in some Python packages. my_dictionary={"apples":"red","oranges":"orange","bananas":" Read More...

Generate a list called twelve that contains numbers 1 through 12 and then afterwards, subset it to a list called even_numbers that contains only the even entries. Hint Google how to find the remainder of a Read More...

This course will be composed of for classes. Each class will be followed by a 1 hour virtual help session where participants can work through some practice problems and ask questions. Participants will learn sufficient material Read More...

Download data used in this course

Label the two most differential expressed genes in the volcano plot. As a hint, first import hcc1395_deg_chr22_top_genes.csv. What message shows up upon running the code and suggest a plausible solution. {{ Read More...

Add a bar on the top of the heatmap that shows which treatment group the samples belong to. {{Sdet}}{{Ssum}}Solution{{Esum}} samples=pandas.Series({"hcc1395_normal_rep1":"orangered", "hcc1395_ Read More...

To subset a column of a pandas dataframe, the bracket notation followed by the column name can be used. For instance, to extract the Geneid column in hbr_uhr_chr22_counts do the following. hbr_ Read More...

Given a list of numbers, it is difficult to perform mathematical operations. For instance list_of_numbers=[1,2,3,4,5] Multiplying list_of_numbers by 2 will duplicate this list. However, multiplying a list of numbers by two should Read More...

Create a list called numeric grades that contains 90, 75, 80, 95, and 100. Then loop through numeric_grades and print the student's letter grade using the following criteria. >=90: A <90 but >=80: B & Read More...

Conditionals evaluate the validity of certain conditions and operators include: ==: is equal to? >: is greater than? >=: is greater than or equal to? <: is less than? <=: is less Read More...

Ipython enables users to run Python commands interactively at the terminal. It features autocomplete of commands and allows for saving of commands to a python script using %save followed by the name of the script. Read More...

Goto the course overview section in the class documents and scroll to the bottom and click on "Download data used in this course" to download some example data to local computer. Take note Read More...

The document provides useful links where participants can find help for the Python packages that were addressed during the course series. Pandas - package for working with tabular data Pandas API reference gives instructions for Read More...

This lesson will provide participants with enough knowledge to start using Python for data visualization. Specifically, participants should Be able to use the package Seaborn to Construct plots that range from very basic to elegant Read More...

import pandas import numpy import matplotlib.pyplot as plt import seaborn

After this lesson, participants will Be able to import tabular data into Python using Pandas Be able to explore and modify tabular data through various data wrangling approaches, including retrieving dimensions subsetting obtaining descriptive statistics Read More...

After this class, participants will Be able to describe Python data types and structures Become familiar with variable assignment Be able to use conditional operators and if-else statements Understand how loops and iterators can be Read More...

Loops and iterators are great for performing repeated tasks. In Python, users will see for and while loops. To learn about loops, first recreate sequencing_list. sequencing_list=["whole genome", "rna", & Read More...

There are several methods for subsetting a dictionary. See https://www.geeksforgeeks.org/get-a-subset-of-dict-in-python/. First, just enclosing one of the keys in square brackets will retrieve its associated value. my_dictionary['bananas'] yellow A for Read More...

Use the a dictionary's update attribute to add values at the end. my_dictionary.update({'pears': 'green'}) The code below will also append the pear and green value pair to my_dictionary. my_dictionary[' Read More...

Lists and tuples are one dimensional collections of data. The tuple is an immutable list, in which the elements cannot be modified. However, lists are mutable. To create a list, enclose the contents in square Read More...

Often, in bioinformatics and data science, data comes in the form of rectangular tables, which are referred to as data frames. Data frames have the following property. Study variable(s) form the columns Observation(s) Read More...

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...

Create a new Jupyter Notebook in Python 3.12 (click on the "python/3.12" tile). The new notebook has the name "Untitled.ipynb". Click on the disk icon in the notebook menu bar to Read More...

After this class, participants will be able to: Describe Python and provide rationale for using Python List tools for interacting with Python Sign onto Biowulf, start a Jupyter Lab session, and become familiar with the Read More...

Step 1 to starting Jupyter Lab on Biowulf via command is to request an interactive compute session using sinteractive with the following options. The local temporary/scratch storage space and RAM allocation can be modified base Read More...

Pandas data frames have a function shape that informs of the number of rows and number of columns in a data frame (in other words the dimensions of a tabular dataset). To get the dimensions Read More...

Figure 3 shows the first 10 rows of hbr_uhr_chr22_counts. The left most column, which contains labels starting with "0" is referred to as the row indices or row names. Users can specify a Read More...

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...

Given that the elements in a data frame can be referenced by its row and column positions, what would be the approach for extracting the element in row 60 and column 5? The solution is the command Read More...

Suppose there is a list called states. states=["florida", "alabama", "wisconsin", "tennessee"] To add another state as the last entry in the list, used the append attribute Read More...

Suppose there is a list called list1 that contains the following numbers. list1=[1,2,3,4,5] list1 [1, 2, 3, 4, 5] Next, create copy of list1 was made and assigned to variable list2. list2=list1 list2 [1, 2, 3, 4, 5] Then insert 0 as the first item Read More...

Python external packages are found at the Python Package Index. To install a package from PyPi, just use pip install package_name, where package_name can be any package of choice. For instance, to install Read More...

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...

on the front page is the NIH Bioinformatics Calendar (search by topic, or organizer, info on current, future and past events) Licenses are available to online learning platforms (Dataquest and Coursera), where you can learn Read More...

The next exercise is to practice creating a scatter plot on a biologically relevant dataset. Namely, the differential expression results from the hbr and uhr RNA sequencing study will be used to create a scatter Read More...

Before getting started, make sure to start a Jupyter Lab session with the default resources via HPC OnDemand. Hint Be sure to start the Jupyter Lab session in `/data/$USER/pies_data'. Where $USER is Read More...

Open a web browser on local computer (Google Chrome is recommended) and go to https://hpcondemand.nih.gov/, which is the URL for Biowulf's HPC OnDemand. Once at HPC OnDemand, sign in with participant' Read More...

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...

Navigate back to the HPC OnDemand website by clicking "HPC OnDemand" at the top left corner. Then click on the "Jupyter" tab to launch a Jupyter Lab session. The subsequent page Read More...

Pandas is a popular Python package used to work with tabular data. To work with Pandas, first activate it using the import command. import pandas Sometimes the name of a package is long, so users Read More...

This exercise will use the read_csv function of Pandas to import a comma separated value (csv) file called hbr_uhr_chr22_rna_seq_counts.csv, which contains RNA sequencing gene expression counts from the Read More...

import pandas import seaborn import matplotlib.pyplot as plt counts1=pandas.read_csv("./hbr_uhr_top_deg_normalized_counts.csv", index_col=[0]) seaborn.clustermap(counts1,z_score=0,cmap="viridis", figsize=(5,5)) plt. Read More...

Pandas enables mathematical operations on data frames. For instance, one might want to sum the total counts across all samples for each gene. The sum function can be used to do this. Setting axis=1 will Read More...

To plot using Seaborn, start the command with seaborn followed by the plot type (where plot type can be any plot, for instance if the user wants a scatter plot then the command would be Read More...

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...

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...

An abundance of external packages make scientific computing and data presentation easy. For instance, the packages matplotlib and seaborn are good tools for generating data visualizations. With a few lines of code, scientists can generate Read More...

Seaborn is a popular Python plotting package, which is the tool that will be introduced in this lesson. Seaborn is an extension of and builds on Matplotlib and is oriented towards statistical data visualization. However, Read More...

sequencing_list[1]="single cell RNA" sequencing_list ['whole genome', 'single cell RNA', 'whole exome'] sequencing_tuple[1]="single cell RNA" TypeError Traceback (most recent call last) Cell In[48], line 1 ----> 1 Read More...

This class will use Jupyter Lab installed on Biowulf for interactions with Python. To get started, open a Terminal (if working on a Mac) or a Command Prompt (if working on Windows) and sign into Read More...

Assuming Python is installed, just type python at the command prompt to start using Python. Hit control-d to exit back to the command prompt. The downside to this is that users cannot save the commands Read More...

Biowulf (HPC OnDemand is recommended). Use Python locally on government furnished personal computer via NIH Anaconda Professional License. This will require users to install Anaconda to local computer. See BTEP's Topic Spotlight on NIH' Read More...

Python can be run at the command prompt Ipython Run python script at the command prompt Integrated Development Environments such as: Spyder Pycharm Visual Studio Code from Microsoft has extensions that support Python scripting R Read More...

Arguments and options for Python commands are enclosed in parentheses. In general, the anatomy is command(argument, option). For example, the command below is print and it will display the argument, "Hello BTEP" Read More...

Integrated Development Environments or IDEs are ideal for scripting in Python as well as other languages. See https://ritza.co/comparisons/pycharm-vs-spyder-vs-jupyter-vs-visual-studio-vs-anaconda-vs-intellij.html for a breakdown of of common ones such as Spyder, Pycharm, VS Read More...

General purpose scripting language Facilitates reuse and reproducibility Can be used to analyze large datasets Extensive external packages that can be used for Data wrangling Data visualization Single cell RNA sequencing analysis Working with biological Read More...

General purpose scripting language Analyze and visualize large datasets Reusability and reproducibility Versioning and keeping track of changes is possible when analyzing data using scripts Easy to learn External packages that enhances functionality Python Package Read More...

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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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...

06/12/2025 - Thi cla will wrap up the Python Introductory Education erie by howing participant how to create data vi ualization . Experience with Python i not needed for attendance. Participant are required to have acce to Read More...

06/10/2025 - Thi cla of the Python Introductory Education erie will introduce participant to working with and wrangling tabular data u ing the package, Panda . Experience with Python i not needed for attendance. Participant are required Read More...

06/05/2025 - In the econd cla of the Python Introductory Education erie , participant will tart to dive into Python. Participant will walk away with knowledge of common Python data type and tructure a well a how Read More...

06/03/2025 - Thi i the fir t cla of the Python Introductory Education erie . Here, participant will learn how to acce and interact with Python, obtain an under tanding of command yntax for thi programming language, Read More...

01/14/2025 - In this talk, we will cover the basics of survival analysis as a statistical methodology developed for censored data. Special emphasis will be on visualizing survival data, comparing survival-time distributions for subject groups, and Read More...

Discover expert help with analysis, processing applications, and licensed software packages.

Discover expert help with analysis, processing applications, and licensed software packages.

The Neurorehabilitation and Biomechanics Research Section (also referred to as the NAB LAB) is a multidisciplinary group of highly qualified scientists, clinical and technical staff, and trainees with diverse backgrounds including medical, physical therapy, neuroscience 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...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More New NIH Resource Resources Advance your research with the NIH Mouse Imaging Facility (MIF) The NIH Mouse Imaging Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More New NIH Resource Resources Derive Greater Insights and Accelerate your Research Using Bioinformatic Tools! CREx is an NIH Read More...

Recently, the NIH HPC Team has provided on demand access to HPC resources via web browser through integration of Open OnDemand , an open source project supporting "nearly 400 HPC centers around the world" . This Read More...

09/23/2024 - Come to the Fair!  In the morning session, several different groups will speak about their training and education programs. The afternoon session will be devoted to learning about research using AI across Read More...

MAB provides visual communication solutions across all media to the entire NIH community. Our Medical Illustration section provides a complete range of biomedical visualization services, including manuscript/textbook figures, infographics, 3D modeling, animation, technical diagrams, Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More New NIH Resource Spotlight The NIH Lab Managers Working Group have developed a new NIH-wide database of cold Read More...

The Clinical Flow Cytometry Laboratory provides extensive support for NCI clinical protocols by providing diagnostic testing for leukemia and lymphoma in patients either on NCI clinical protocols or undergoing testing to determine eligibility for NCI Read More...

The NIH Clinical Center Pharmacy Department provides pharmaceutical care to inpatients and outpatients on NIH intramural research protocols at the NIH Clinical Center. The Clinical Center facility encompasses 200 inpatient beds, 93 day-hospital stations, and 15 clinics. Clinical Read More...

The rapid advancement of single-cell technology has provided new powerful tools to answer many biological questions, such as identifying new or rare cell populations and characterizing the complexities of tumor heterogeneity. Realizing the great potential Read More...

September 8, 2022 crex.nih.gov CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Science & Technology Seminars and Training Events Upcoming Seminars and Educational Opportunities The following Read More...

October 6, 2022 crex.nih.gov CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Science & Technology Seminars and Training Events Upcoming Seminars and Educational Opportunities The following 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...

The Behavioral Health Core (BHC) is comprised of the two separate but interrelated components: Psychosocial Support and Research Program, and, Health Psychology and Neurobehavioral Research Program., These teams provide specialized expertise, such as patient-reported and Read More...

Cores & Facilities From centralized laboratories to collaborative resources and technologies available to CCR Investigators. NCI Cores Centralized laboratories providing broad access to cutting-edge technologies and specialized expertise. Browse NCI Cores Collaborative Resources are technologies and Read More...

The Mouse Metabolic Phenotyping Center (MMPC)Live Program provides standardized, high quality, unique, and hard to find phenotyping services for mouse models of diabetes, obesity, and related metabolic disorders. Emerging as the next iteration of Read More...

The Bloomington Drosophila Stock Center (BDSC) collects, maintains, and distributes genetically defined strains of Drosophila melanogaster for research and education. The BDSC supports a large, worldwide community of scientists using Drosophila as a model organism Read More...

CREx News & Updates June 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...

CREx News & Updates April 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...

CCR Office of Science and Technology Resources (OSTR) The Center for Cancer Research (CCR) Office of Science and Technology Resources (OSTR) explores and evaluates the latest technologic advancements and then makes them available to all Read More...

CREx News & Updates March 2022 Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Site Spotlight NIA Nonhuman Primate Core The NIA Nonhuman Primate Core supports multi-disciplinary translational aging projects for Read More...

CREx News & Updates August 2022 Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Site Spotlight NCATS Functional Genomics Laboratory (FGL) FGL is designed to help NIH Investigators use the latest 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...

CREx News & Updates May 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...

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...

Most BTEP courses include detailed course materials including lesson content, additional resources, and lesson associated data. These course materials are listed here so that learners can easily return to and review concepts taught in class Read More...

Register now for an upcoming seminar on January 22, 2025 , “Revealing Tumor-Promoting Signals in Breast Cancer Subtypes: A NIDAP Analysis of Single-Cell Data” . This one-hour talk will feature guest speaker and NIDAP developer, Josh Meyer, who will Read More...

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