Date: Tuesday 31st March 2020 Time: 14:00 - 15:00 UK Time Speakers: Ketil Tvermosegaard (GSK).
The slides for this event can be downloaded here.
This webinar is free for PSI Members and Non-Members.
Label-based flow cytometry allows the quantification of target features of interest by attaching fluorophores (labels) to antibodies and measuring the resulting fluorescence at the relevant wavelengths. This is widely used for cell sorting, i.e., determining cell types.
Image flow cytometry is a technology which enables single cell images in cell sorting experiments. Problematically, directly using this data for classification involves manual inspection of many thousands of images. This creates a bottleneck for analysis and scalability.
As part of an epithelial barrier project; a Medium Throughput screen was conducted to investigate whether candidate CRISPR gene knockouts modulated the proportion of cells which differentiated into ciliated cells (important for indications such as COPD and asthma).
However, the team hypothesized that traditional label-based flow cytometry did not always properly classify cell types. We were approached about developing a scalable way of using image flow cytometry for determining whether cells are ciliated. This would provide them with an alternative endpoint and a way to test their hypothesis.
In this project, we;
1) Developed Python code to extract images from the proprietary file format
2) Built a proof-of-concept convolutional neural network. Results here suggested the problem was solvable with Deep Learning
3) Initiated a Tessella Analytics Partnership project with Tessella
4) Worked with Tessella to steer their development of an appropriate architecture for the neural network, which achieved better-than-human performance
5) Applied the trained network to a validation screen and confirmed disagreements between label-based and label-free flow cytometry.
Scientific Meetings
PSI ToxSIG Webinar: Label-free Classification of Ciliated Cells using Deep Learning.
Date: Tuesday 31st March 2020 Time: 14:00 - 15:00 UK Time Speakers: Ketil Tvermosegaard (GSK).
The slides for this event can be downloaded here.
This webinar is free for PSI Members and Non-Members.
Label-based flow cytometry allows the quantification of target features of interest by attaching fluorophores (labels) to antibodies and measuring the resulting fluorescence at the relevant wavelengths. This is widely used for cell sorting, i.e., determining cell types.
Image flow cytometry is a technology which enables single cell images in cell sorting experiments. Problematically, directly using this data for classification involves manual inspection of many thousands of images. This creates a bottleneck for analysis and scalability.
As part of an epithelial barrier project; a Medium Throughput screen was conducted to investigate whether candidate CRISPR gene knockouts modulated the proportion of cells which differentiated into ciliated cells (important for indications such as COPD and asthma).
However, the team hypothesized that traditional label-based flow cytometry did not always properly classify cell types. We were approached about developing a scalable way of using image flow cytometry for determining whether cells are ciliated. This would provide them with an alternative endpoint and a way to test their hypothesis.
In this project, we;
1) Developed Python code to extract images from the proprietary file format
2) Built a proof-of-concept convolutional neural network. Results here suggested the problem was solvable with Deep Learning
3) Initiated a Tessella Analytics Partnership project with Tessella
4) Worked with Tessella to steer their development of an appropriate architecture for the neural network, which achieved better-than-human performance
5) Applied the trained network to a validation screen and confirmed disagreements between label-based and label-free flow cytometry.
Training Courses
PSI ToxSIG Webinar: Label-free Classification of Ciliated Cells using Deep Learning.
Date: Tuesday 31st March 2020 Time: 14:00 - 15:00 UK Time Speakers: Ketil Tvermosegaard (GSK).
The slides for this event can be downloaded here.
This webinar is free for PSI Members and Non-Members.
Label-based flow cytometry allows the quantification of target features of interest by attaching fluorophores (labels) to antibodies and measuring the resulting fluorescence at the relevant wavelengths. This is widely used for cell sorting, i.e., determining cell types.
Image flow cytometry is a technology which enables single cell images in cell sorting experiments. Problematically, directly using this data for classification involves manual inspection of many thousands of images. This creates a bottleneck for analysis and scalability.
As part of an epithelial barrier project; a Medium Throughput screen was conducted to investigate whether candidate CRISPR gene knockouts modulated the proportion of cells which differentiated into ciliated cells (important for indications such as COPD and asthma).
However, the team hypothesized that traditional label-based flow cytometry did not always properly classify cell types. We were approached about developing a scalable way of using image flow cytometry for determining whether cells are ciliated. This would provide them with an alternative endpoint and a way to test their hypothesis.
In this project, we;
1) Developed Python code to extract images from the proprietary file format
2) Built a proof-of-concept convolutional neural network. Results here suggested the problem was solvable with Deep Learning
3) Initiated a Tessella Analytics Partnership project with Tessella
4) Worked with Tessella to steer their development of an appropriate architecture for the neural network, which achieved better-than-human performance
5) Applied the trained network to a validation screen and confirmed disagreements between label-based and label-free flow cytometry.
Journal Club
PSI ToxSIG Webinar: Label-free Classification of Ciliated Cells using Deep Learning.
Date: Tuesday 31st March 2020 Time: 14:00 - 15:00 UK Time Speakers: Ketil Tvermosegaard (GSK).
The slides for this event can be downloaded here.
This webinar is free for PSI Members and Non-Members.
Label-based flow cytometry allows the quantification of target features of interest by attaching fluorophores (labels) to antibodies and measuring the resulting fluorescence at the relevant wavelengths. This is widely used for cell sorting, i.e., determining cell types.
Image flow cytometry is a technology which enables single cell images in cell sorting experiments. Problematically, directly using this data for classification involves manual inspection of many thousands of images. This creates a bottleneck for analysis and scalability.
As part of an epithelial barrier project; a Medium Throughput screen was conducted to investigate whether candidate CRISPR gene knockouts modulated the proportion of cells which differentiated into ciliated cells (important for indications such as COPD and asthma).
However, the team hypothesized that traditional label-based flow cytometry did not always properly classify cell types. We were approached about developing a scalable way of using image flow cytometry for determining whether cells are ciliated. This would provide them with an alternative endpoint and a way to test their hypothesis.
In this project, we;
1) Developed Python code to extract images from the proprietary file format
2) Built a proof-of-concept convolutional neural network. Results here suggested the problem was solvable with Deep Learning
3) Initiated a Tessella Analytics Partnership project with Tessella
4) Worked with Tessella to steer their development of an appropriate architecture for the neural network, which achieved better-than-human performance
5) Applied the trained network to a validation screen and confirmed disagreements between label-based and label-free flow cytometry.
Webinars
PSI ToxSIG Webinar: Label-free Classification of Ciliated Cells using Deep Learning.
Date: Tuesday 31st March 2020 Time: 14:00 - 15:00 UK Time Speakers: Ketil Tvermosegaard (GSK).
The slides for this event can be downloaded here.
This webinar is free for PSI Members and Non-Members.
Label-based flow cytometry allows the quantification of target features of interest by attaching fluorophores (labels) to antibodies and measuring the resulting fluorescence at the relevant wavelengths. This is widely used for cell sorting, i.e., determining cell types.
Image flow cytometry is a technology which enables single cell images in cell sorting experiments. Problematically, directly using this data for classification involves manual inspection of many thousands of images. This creates a bottleneck for analysis and scalability.
As part of an epithelial barrier project; a Medium Throughput screen was conducted to investigate whether candidate CRISPR gene knockouts modulated the proportion of cells which differentiated into ciliated cells (important for indications such as COPD and asthma).
However, the team hypothesized that traditional label-based flow cytometry did not always properly classify cell types. We were approached about developing a scalable way of using image flow cytometry for determining whether cells are ciliated. This would provide them with an alternative endpoint and a way to test their hypothesis.
In this project, we;
1) Developed Python code to extract images from the proprietary file format
2) Built a proof-of-concept convolutional neural network. Results here suggested the problem was solvable with Deep Learning
3) Initiated a Tessella Analytics Partnership project with Tessella
4) Worked with Tessella to steer their development of an appropriate architecture for the neural network, which achieved better-than-human performance
5) Applied the trained network to a validation screen and confirmed disagreements between label-based and label-free flow cytometry.
Careers Meetings
PSI ToxSIG Webinar: Label-free Classification of Ciliated Cells using Deep Learning.
Date: Tuesday 31st March 2020 Time: 14:00 - 15:00 UK Time Speakers: Ketil Tvermosegaard (GSK).
The slides for this event can be downloaded here.
This webinar is free for PSI Members and Non-Members.
Label-based flow cytometry allows the quantification of target features of interest by attaching fluorophores (labels) to antibodies and measuring the resulting fluorescence at the relevant wavelengths. This is widely used for cell sorting, i.e., determining cell types.
Image flow cytometry is a technology which enables single cell images in cell sorting experiments. Problematically, directly using this data for classification involves manual inspection of many thousands of images. This creates a bottleneck for analysis and scalability.
As part of an epithelial barrier project; a Medium Throughput screen was conducted to investigate whether candidate CRISPR gene knockouts modulated the proportion of cells which differentiated into ciliated cells (important for indications such as COPD and asthma).
However, the team hypothesized that traditional label-based flow cytometry did not always properly classify cell types. We were approached about developing a scalable way of using image flow cytometry for determining whether cells are ciliated. This would provide them with an alternative endpoint and a way to test their hypothesis.
In this project, we;
1) Developed Python code to extract images from the proprietary file format
2) Built a proof-of-concept convolutional neural network. Results here suggested the problem was solvable with Deep Learning
3) Initiated a Tessella Analytics Partnership project with Tessella
4) Worked with Tessella to steer their development of an appropriate architecture for the neural network, which achieved better-than-human performance
5) Applied the trained network to a validation screen and confirmed disagreements between label-based and label-free flow cytometry.
Upcoming Events
Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars
Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.
The event will open with an overview on drug development in women’s health from a clinician perspective. This talk is followed by talks about statistical challenges when planning IVF studies and analysing the menstrual cycles.
This webinar will provide an overview of surrogacy for licensing and reimbursement. In turn, the need of extensions of the SPIRIT and CONSORT statement will be defined and outlined, with case studies to support.
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Virtual Control Groups in Toxicity Studies
Lea Vaas will present how replacement of concurrent control animals by Virtual Control Groups (VCGs) in systemic toxicity studies may help in contributing to the 3R's principle of animal experimentation: Reduce, Refine, Replace.
Joint PSI/EFSPI Data Science SIG Webinar: Developing Digital Measures (Digital Biomarkers) in Drug Development – insights from Mobilise D consortium
We will share a brief overview of what Mobilise D is and why it is an important step stone in the development of digital biomarkers, and how Mobilise D outputs can be relevant for you.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
PSI Webinar: Development of Gene Therapies: Strategic, Scientific, Regulatory and Access Considerations
This webinar will cover the history of cell/gene therapy, major regulatory advances, the role of quantitative scientists in drug development of these novel therapeutics, and discuss opportunities for innovation and product advancement.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
PSI Introduction to Industry Training (ITIT) Course - 2024/2025
An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
Statisticians in the Pharmaceutical Industry Executive Office: c/o MCI UK Ltd | Unit 24/22 South | Building 4000 | Langstone Park| Langstone Road | Havant | PO9 1SA | UK