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SS/SC Brown Bag Seminar Series

The seminar should give staff scientists a platform to present and discuss their science.  As SS/SC we are highly specialized and have greatly varying scientific interests, and we liked for the seminar to represent this; thus, the choice of topic and format will be speaker driven.

The seminar could be:
regular 45 min talk about your research,  chalk talk, summary of cool new stuff you heard about at a conference you recently went to, talk you need feedback for, presentation of a manuscript you liked to get feedback on, talk about a project that just can’t get off the ground and needs troubleshooting, presentation of a new technology/possible equipment purchases, presentations by several SSSC together or several shorter presentations.

We are also well aware that there is no time and location that fits all.  We booked 37/2041 for the first Thursday of each month from 12(noon) to 1PM, however, if you wish to present a seminar at another time / location just find a room that suits you better and let us know.

If you would like to schedule your talk or have any questions, please contact:
Christina Stuelten (christu@mail.nih.gov), Yoshimi Greer (greery@mail.nih.gov) or Emily Tai (taic@mail.nih.gov)

2021 Fall/Winter Series – "SS/SC Alumni - Where are they now?"

We are inviting our alumni who moved on to other positions in NCI, other ICs in NIH, FDA, academia or industry to share their experience with us.

Date and Time

Title

Speaker

Affiliation

WebEx Link/Sildes

9/13 (Mon) 11:30 -12:30

Staying at NIH BUT leaving the bench

Zack Howard

Center for Scientific Review, NIH

11/1 (Mon) noon-1:00

Outside of the lab BUT close to the science

Ana Robles

Office of Cancer Clinical Proteomics Research, DCTD, NCI

1/26 (Wed) noon-1:00

Research in a regulatory agency: the advantages of the researcher/assessor model at OPQ/CDER

Carole Sourbier

Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, FDA


3/23 (Wed) noon-1:00

Supporting Cancer Research through Planning and Analysis

Diane Palmieri

Center for Research Strategy, NCI

4/22 (Fri) noon-1:00

Transition from Staff Scientist to Pharma

Even Walseng

AstraZeneca R&D Biologics Engineering



2021 Spring Series – 

Understanding Tumor Heterogeneity and Plasticity through the Lens of Machine Learning and Mathematics

Maxwell Lee will review different approaches to understand tumor heterogeneity and plasticity.  The topics includes using mixture models to deconvolute bulk RNA-seq data, clustering and classification of single cell RNA-seq to study tumor subtypes, and mathematic tools to predict cancer cell evolution. His talk abstract, outline, the topic of each week are listed below.  Slides will be posted Monday morning before the talk and the video recording will be available afterwards.

Monday 10:00AM-11:00AM

WebEx Link:  https://cbiit.webex.com/cbiit/j.php?MTID=mc31fec457279698a34e0aa990172743e 

Meeting number:  157 260 1880 Meeting Password: UEuBV2P2Z$3

Abstract and Outline:

  

Date

Title

Speaker

Affiliation

Slides

4/12

Cancer stem cell model and evolutionary dynamics

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

4/26

Cell fate decision determined by Gene Regulatory Network (GRN) 

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

5/10

Waddington’s epigenetic landscape quantified with quasi-potential

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

5/24

Network motifs and dynamics of cellular states

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

6/7

Network motifs for desensitization and history of the treatment exposure

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

6/21

Drug-tolerant persister (DTP) and cancer dynamics

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR




2020 Fall/Winter Series – Clustering Methods: from k-means to Gaussian mixture model and Louvain algorithm

Maxwell Lee will review some basic concepts of clustering analyses that are commonly used in cancer research, including 1) traditional methods of hierarchical clustering and k-means; 2) Gaussian mixture model (GMM) and latent Dirichlet allocation (LDA); 3) graph-based approaches, which are the state-of-the-art technologies used in the single cell data analysisMax will provide practical examples to illustrate how each method works, how to interpret the results of clustering analysis and explain the pros and cons of each method.  His talk abstract, outline, the topic of each week are listed below for your reference.  Slides will be posted the weekend before the talk and the video recording will be available afterwards.

Monday 10:00AM-11:00AM

WebEx Link: https://cbiit.webex.com/cbiit/j.php?MTID=m593aca29c65d0e9f7be43bfc2ad1b092

Meeting number: 172 414 1612 Meeting Password: AmWdZgu*775 

Abstract and Outline:

  

Date

Title

Speaker

Affiliation

Slides

Video Recording Link

9/21

Introduction and k-means clustering

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

10/5

Gaussian mixture model (GMM) and Latent Dirichlet Allocation (LDA)

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

10/19

Non-negative matrix factorization (NMF) and its connection to k-means clustering

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR


11/2

Hierarchical clustering

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

11/16

Spectral clustering and its connection to Laplacian Eigenmaps

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

12/21

Louvain clustering and its application to single cell RNAseq data analysis

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

1/25

Latent Dirichlet Allocation (LDA) and its application to sequence motif analysis

Maxwell Lee

Laboratory of Cancer Biology and Genetics, NCI/CCR

LDA video link click here 




2020 Spring/Summer Series

12:00 PM - 01:00 PM, Building 37, Room 2041 (second floor conference room) - please note the location and time may be different in some cases

Date

Title

Speaker(s)

Affiliation

Slides

WebEx Link, Video Recording Link and Abstract

04/16

Dimension Reduction Methods: from PCA to TSNE and UMAP - Part I


Maxwell
Lee
Laboratory of Cancer Biology and Genetics, NCI/CCR

Recording link 

Principal Component Analysis (PCA) provides a foundation to understanding various dimension reduction methods.  Principal components are the linear combinations of the original variables with the 1st principal component captures the largest variance, followed by a descending order for the rest of the principal components and subject to the principal components being orthonormal.  A geometric perspective of PCA is the rotation of coordinate system.

04/23

Dimension Reduction Methods: from PCA to TSNE and UMAP - Part II

Maxwell
Lee
Laboratory of Cancer Biology and Genetics, NCI/CCR

Recording link

Multidimensional Scaling (MDS) provides an alternative strategy for dimension reduction.  The data matrix is pairwise distance matrix.  The distance matrix is converted to Gram matrix (similarity matrix).  Eigen decomposition of Gram matrix generates eigen vectors and eigen values.  The product of eigen vector matrix and square root of eigen value matrix is identical to principal component matrix.

04/30

Dimension Reduction Methods: from PCA to TSNE and UMAP - Part III

Maxwell
Lee
Laboratory of Cancer Biology and Genetics, NCI/CCR

Recording link

Non-linear dimension reduction is essential for the analysis of data that lie in non-Euclidian space.  Geodesic distance is better measurement for points on a curve or curved surface.  Isomap is similar to MDS except that pairwise distance matrix uses geodesic distance instead of Euclidian distance.

 

05/07Dimension Reduction Methods: from PCA to TSNE and UMAP - Part IVMaxwell
Lee
Laboratory of Cancer Biology and Genetics, NCI/CCR

Recording link

TSNE models data in high-dimension with Gaussian distribution and data in low-dimension with t-distribution. Covariance matrix adjusted distance matrix is converted to similarity matrix through Gaussian distribution.  The cost function is KL divergence to minimize the two probability distributions. Seurat package was used to analyze RNAseq data.  Effect of mitochondria gene percentage cutoff was analyzed 

05/14Dimension Reduction Methods: from PCA to TSNE and UMAP - Part VMaxwell
Lee
Laboratory of Cancer Biology and Genetics, NCI/CCR

Recording link

TSNE and UMAP were compared.  UMAP uses k-nearest neighbor (KNN) and calculates weight matrix.  The cost function is binary cross-entropy.  It is much fast than TSNE and also achieves better separation between clusters.


05/28Dimension Reduction Methods: from PCA to TSNE and UMAP - Part VI Maxwell
Lee
Laboratory of Cancer Biology and Genetics, NCI/CCR

Recording link

More detailed analyses of scRNAseq data with UMAP were performed.  The effect of filtering out cells with high mitochondrial genes was discussed.  Algorithm of UMAP was explained.


06/04

Dimension Reduction Methods: from PCA to TSNE and UMAP - Part VII

Maxwell
Lee
Laboratory of Cancer Biology and Genetics, NCI/CCR

Recording link

Trajectory analysis was performed using Monocle 2 and 3 packages.  Two datasets were analyzed, Human Skeletal Muscle Myoblasts (HSMM) and neural crest cells from Mouse Organogenesis Cell Atlas (MOCA).  Monocle 2 uses minimal spanning tree model where Monocle 3 uses general graph and produces trajectory analysis for multiple subgraphs.

06/18Dimension Reduction Methods: from PCA to TSNE and UMAP - Part VIII and Q&AMaxwell
Lee
Laboratory of Cancer Biology and Genetics, NCI/CCR

Webex
Thursday, Jun 18, 12noon - 1pm

https://cbiit.webex.com/cbiit/j.php?MTID=md38682248ab766611823beee46acab18

Meeting number (access code): 476 578 641
Meeting password:c3wEha9Y5h$

 

Summary of comparison between Monocle 2 and 3 analysis.  Monocle 2 generates plot with coordinates resulted from the reversed graph embedding.  Monocle 3 plot uses UMAP data and shows trajectory for multiple subgraphs.  The objective function Monocle 2 has 3 parts: Pearson residuals in high-dimension, cost function for the minimal spanning tree, and k-means like clustering.

07/15

What Should Staff Scientists & Staff Clinicians (SSSCs) Know About Technology Transfer?

CCR SSSC Professional Development Committee

Aida Cremesti

Laura Prestia

Special Seminar from the CCR SSSC Professional Development Committee and NCI’s Technology Transfer Center (TTC)

 

Webex
Wed, July 15, 12noon - 1pm

https://cbiit.webex.com/cbiit/j.php?MTID=m610dac8f56d3c290997f2bf6c97


The following topics will be covered in an interactive Webex: 

  • Overview of the technology transfer process
  • Material Transfer Agreements (MTAs)
  • Cooperative Research and Development Agreements (CRADAs)
  • Technology transfer training/programs and discussion for future opportunities 






 




2019 Series

12:00 PM - 01:00 PM, Building 37, Room 2041 (second floor conference room) - please note the location and time may be different in some cases

Date

Title

Speaker(s)

Affiliation

Slides

Location

01/04

Five reasons to pay attention to SAXS-assisted protein structure prediction

Susan Tsutakawa

Lawrence Berkeley National Laboratory,
University of California at Berkeley

3-4pm
37/4041

01/25

Principles of cancer modeling in miceChi-Ping DayLaboratory of Cancer Biology
and Genetics, NCI/CCR

12-1pm

37/2041

03/01

The Advanced Imaging & Microscopy Resource:
a new trans-NIH resource for next generation microscopy 

Harsh VishwasraoDirector, Advanced Imaging and Microscopy Resource, NIH

1-2 pm

37/6041

03/28The Genome Modification Core at the Frederick National Lab for Cancer Research – a collaborative resource for the NCI CCR

Raj Chari

Director, Genome Modification Core, NCI/FNLCR

12-1pm

37/2041

2018 Series

12:00 PM - 01:00 PM, Building 37, Room 2041 (second floor conference room)

Date

Title

Speaker(s)

Affiliation

Slides

02/15

Single Cell Genomics - CCR new initiative and practical tips

Liz Conner
Mike Kelly

CCR Genomics Core
Single Cell Analysis Facility

03/01

Mass Spectrometry – How it works, what it does, and how it can help you

Lisa Jenkins

Mass Spectrometry Program,
Laboratory of Cell Biology, NCI/CCR

04/27

Statistical analysis: concept, practice, and interpretation.  Part IMaxwell LeeLaboratory of Cancer Biology
and Genetics, NCI/CCR

05/03

Statistical analysis: application to NGS data with DESeq2 and GSEA. Part IIHoward YangLaboratory of Cancer Biology
and Genetics, NCI/CCR

05/31High-Throughput Imaging for System Cell BiologyGianluca PegararoLaboratory of Receptor Biology
and Gene Expression, NCI/CCR

09/27

Cutting edge protein analysis technologies - advancing quantitative proteomic research,
biomarker assessment, and molecular profiling

Jinqiu (Jessie) Chen
Noemi Kedei

Collaborative Protein Technology Resource,
NCI/CCR

2017 Series

12:00 PM - 01:00 PM, Building 37, Room 2041 (second floor conference room)

Date

Title

Speaker(s)

Affiliation

04/06

Innovative Sequencing Resources in the CCR Sequencing Facility

Bao Tran

CCR, Sequencing Facility

05/18

ONC201 Kills Breast Cancer Cells by Inhibiting Mitochondrial Respiration

Yoshimi Endo Greer

Women's Malignancies Branch

06/01

Kicking Lymphoma When It’s Down - How we can exploit the apoptotic threshold therapeutically in Diffuse Large B Cell LymphomaArt SchafferLymphoid Malignancies Branch
07/06Leukocyte adhesion deficiency in a cat due to a deletion in the CD18 geneThomas BauerExperimental Transplantation and Immunology Branch

12/01

Predicting targets of T cell responses in cancer: Evaluating algorithms and lessons learned from the Immune Epitope DatabaseBjorn PetersLa Jolla Institute for Allergy and Immunology
12/07CCR Collaborative Bioinformatics Resource: Our NGS Pipeline Tools (Introduction by Maggie Cam)Justin LackCCR Collaborative Bioinformatics Resource (CCBR)



Last updated by Tai, Chin-Hsien (NIH/NCI) [E] on Apr 22, 2022