Intro to Python For Life Sciences
In this workshop, we’ll take a tour through the many uses of Python for life scientists, focusing on single-cell RNA sequencing analysis.
2 Session Class:
OPTIONAL Python Boot Camp, Saturday, September 17, 12-2pm
Saturday, September 17, 3-6PM
Sunday, September 18, 3-6PM
Data scientists and developers love Python for its power and simplicity. Life scientists love it because it’s named after a reptile, but also for its extreme utility in analyzing biological “big data” such as massive single-cell RNA sequencing matrices or days of animal behavior video data. The open-source, user-friendly aspects of Python underpin its popularity across scientific disciplines as well as in the “real world” (e.g. data science, finance). Come learn what the buzz, or perhaps the hiss, is all about in this introductory workshop - no previous coding experience required.
Participants should have prior Python experience or complete the Intro to Python course on DataCamp before the workshop.
In this two-part workshop, participants will learn:
- How to execute simple single-cell RNA sequencing analysis in the Scanpy package
- Gain familiarity with more advanced analysis methods, including time series , gene regulatory network, and harmonization/integration across samples.
- Apply single-cell RNAseq analysis principles to single-cell data in other modalities: spatial gene expression, ‘multi-omics’, cell surface profiling.
- Overview of Python in other life science areas: animal behavior analysis, 3D structure prediction
Then, during the first session, we will discuss the exciting new world of single-cell RNA sequencing (scRNAseq), which has exploded in popularity over the past decade. We will take a deep dive into the Scanpy scRNAseq analysis ‘ecosystem’ (https://scanpy.readthedocs.io/en/stable/). We will explore sample human brain cell scRNAseq data, perform quality control steps, and finally cluster the cells based on gene expression in 2D space.
During the second session, we will discuss more advanced scRNAseq analyses including data harmonization/normalization, time series modeling, and the discovery of gene regulatory networks. We will move on to a “sampler platter” of single-cell analysis of other modalities, including single-cell spatial transcriptomics, ‘multi-omics’ (dual scRNAseq + scATACseq), and cell surface marker profiling. We will finish with a discussion of Python’s versatile use in other life science settings, including high-throughput video behavioral analysis via machine learning and the use of Python in 3D structural prediction software.
Please bring a laptop with you to both sessions.
COVID-19 Safety Notice:
Participants age 5+ must show proof of full vaccination (we recommend using the Excelsior Pass). Masks are required to be worn for the duration of the event when indoors. If you are feeling unwell, suspect that you have been exposed to COVID-19 or test positive, please do not attend. You can find a list of symptoms and a self-checker tool on the CDC website. Upon entry, all new guests are required to and show a vaccination card and sign in/out for contract tracing.
If you have had COVID-19 within the past 15 days, a negative PCR or Rapid test is required before coming to Genspace. Please show Genspace staff your proof of negative test results. Given the rapidly changing circumstances surrounding the pandemic, please note that terms of this event are subject to change, and we will update you ASAP.
COVID-19 Class Cancellation Policy
If you test positive or suspect you have COVID, please let us know ASAP (info@genspace.org). If you cancel after our 7-day policy, we cannot refund your ticket, but we can exchange and offer credits toward future classes. If you have signed up for Biohacker Boot Camp, we will automatically transfer your registration to the next month’s dates unless you tell us otherwise. For all other classes, we will provide a personalized discount code to apply the cost of your class to a future workshop of your choice.
In this workshop, we’ll take a tour through the many uses of Python for life scientists, focusing on single-cell RNA sequencing analysis.
2 Session Class:
OPTIONAL Python Boot Camp, Saturday, September 17, 12-2pm
Saturday, September 17, 3-6PM
Sunday, September 18, 3-6PM
Data scientists and developers love Python for its power and simplicity. Life scientists love it because it’s named after a reptile, but also for its extreme utility in analyzing biological “big data” such as massive single-cell RNA sequencing matrices or days of animal behavior video data. The open-source, user-friendly aspects of Python underpin its popularity across scientific disciplines as well as in the “real world” (e.g. data science, finance). Come learn what the buzz, or perhaps the hiss, is all about in this introductory workshop - no previous coding experience required.
Participants should have prior Python experience or complete the Intro to Python course on DataCamp before the workshop.
In this two-part workshop, participants will learn:
- How to execute simple single-cell RNA sequencing analysis in the Scanpy package
- Gain familiarity with more advanced analysis methods, including time series , gene regulatory network, and harmonization/integration across samples.
- Apply single-cell RNAseq analysis principles to single-cell data in other modalities: spatial gene expression, ‘multi-omics’, cell surface profiling.
- Overview of Python in other life science areas: animal behavior analysis, 3D structure prediction
Then, during the first session, we will discuss the exciting new world of single-cell RNA sequencing (scRNAseq), which has exploded in popularity over the past decade. We will take a deep dive into the Scanpy scRNAseq analysis ‘ecosystem’ (https://scanpy.readthedocs.io/en/stable/). We will explore sample human brain cell scRNAseq data, perform quality control steps, and finally cluster the cells based on gene expression in 2D space.
During the second session, we will discuss more advanced scRNAseq analyses including data harmonization/normalization, time series modeling, and the discovery of gene regulatory networks. We will move on to a “sampler platter” of single-cell analysis of other modalities, including single-cell spatial transcriptomics, ‘multi-omics’ (dual scRNAseq + scATACseq), and cell surface marker profiling. We will finish with a discussion of Python’s versatile use in other life science settings, including high-throughput video behavioral analysis via machine learning and the use of Python in 3D structural prediction software.
Please bring a laptop with you to both sessions.
COVID-19 Safety Notice:
Participants age 5+ must show proof of full vaccination (we recommend using the Excelsior Pass). Masks are required to be worn for the duration of the event when indoors. If you are feeling unwell, suspect that you have been exposed to COVID-19 or test positive, please do not attend. You can find a list of symptoms and a self-checker tool on the CDC website. Upon entry, all new guests are required to and show a vaccination card and sign in/out for contract tracing.
If you have had COVID-19 within the past 15 days, a negative PCR or Rapid test is required before coming to Genspace. Please show Genspace staff your proof of negative test results. Given the rapidly changing circumstances surrounding the pandemic, please note that terms of this event are subject to change, and we will update you ASAP.
COVID-19 Class Cancellation Policy
If you test positive or suspect you have COVID, please let us know ASAP (info@genspace.org). If you cancel after our 7-day policy, we cannot refund your ticket, but we can exchange and offer credits toward future classes. If you have signed up for Biohacker Boot Camp, we will automatically transfer your registration to the next month’s dates unless you tell us otherwise. For all other classes, we will provide a personalized discount code to apply the cost of your class to a future workshop of your choice.
Paul Frazel (he/him) is a Ph.D. candidate in the Neuroscience and Physiology program at NYU Medical School. He is originally from Chicago, and has a B.A. in Biology and Philosophy from Dartmouth College. At NYU, Paul is involved in multiple single-cell RNA sequencing projects examining differential gene expression between brain cell types, in both health and disease. Particularly, Paul is interested in understanding the mysteries of noncoding “junk” DNA, and how this genomic dark matter impacts the risk of Alzheimer’s disease.
Twitter: @SciPaulie