Matt Suntay’s jump into the PINC computing program

Matt Suntay is one of the students in the PINC program and also a research student in my lab in the E. coli / drug resistance / machine learning team. A few days ago he gave a speech at our PINC/GOLD/gSTAR graduation event. I thought it was a great speech and Matt was kind enough to let me share it here both as a video and the text for those of you who prefer reading.

“To those of you who may know me, you all know I’m pretty adventurous. For those of you who may not know me, first off, my name is Matthew Suntay, and I have jumped off planes, cliffs, and bridges – and each time was just as exhilarating as the last. But, let me tell you about my most favorite jump: the leap of faith I took for the PINC program.

I call it a leap of faith because when I first heard about the PINC program, and specifically CSC 306, I thought, “Ain’t no way this could be for me. I may be stupid because I can barely understand the English in o-chem and now I gotta understand the English in Python? Maaaan, English isn’t even my first language… But they said I don’t need any prior computer science knowledge, so why not? It’s Spring ‘21, new year, new me, right?”

And let me tell you, it definitely made me a new me. I went from printing “Hello World!” to finding genes in Salmonella to constructing machine-learning models to study Alzheimer’s Disease and antibiotic resistance in E. coli. These are some pretty big jumps–my favorite, right?–and they weren’t easy to make. However, I was never scared to make any one of those jumps because of the PINC program.

When I think PINC, I don’t only see lines of code across my screen or cameras turned off on Zoom. I see friends, colleagues, mentors, and teachers. I see a community.

I see a community willing to support me in my efforts to develop myself as a scientist. I see a community providing me the platform and opportunities to grow as a researcher. And most importantly, I see a community that shared hardships, tears, laughter, and success with me.

I can confidently say that the PINC program was, and still is, monumental to my journey through science. Thanks to the PINC program, many doors have been opened to me and one of those doors I’m always happy to walk through each time is the one in Hensill Hall, Room 406 – or the CoDE lab. It was here in this lab that I met some of the most amazing people who want to do nothing but help me reach new heights. I’m so grateful and lucky to have them. So thank you, Dr. Pennings, for believing in me and continuing to believe in me. Thank you to everyone in the CoDE lab for supporting me and laughing at my terrible jokes – and real talk, please keep doing so, I don’t know how to handle the embarrassment that comes after a bad joke.

If I haven’t said it enough already, thank you so much to the PINC program. If you were to ask the me from a year ago what his plans were for the future, he would tell you, “Slow down, dude, I don’t even know I’m trying to eat for breakfast tomorrow.” But now if you were to ask me what my plans for the future are, I’d still tell you I don’t know what I’m trying to eat for breakfast tomorrow because I’m too busy writing code to solve my most current research question, whatever it may be.

For many students, including myself, one of the biggest causes of an existential crisis is, “What am I gonna do after I graduate?” To be honest, I’m still thinking that same thought, but without the dread of an existential crisis. One of the coolest parts of the PINC program is the exposure to research and the biotechnology industry, and learning that research == me and not just != the stereotype of a scientist.

Dr. Yoon, thank you for taking the time and effort to push me and my teammates forward, because even though our projects were difficult, we learned a lot about machine-learning and ourselves, like who knew we had it in us this whole time? You definitely did and you helped us see that. Professor Kulkarni, you also helped us realize that we should give ourselves more credit. 601 and 602 showed us we can be competitive and that we’re worth so much more than we make ourselves out to be. Also, I would like to give a quick shoutout to Chris Davies and Chun-Wan Yan for the wonderful seminars because those talks gave me hope and inspiration for the future. Knowing that there’s something out there for me makes going into the future a lot less scary and a lot more exciting because who knows what awesome opportunity is waiting for me?

And one last honorable mention I would like to make is to Professor Milo Johnson. He was my CSC 306 professor, and I don’t know if he is here today, but he was an amazing teacher in more ways than one. He helped me turn my ideas into possibilities and I have him to thank for helping kick start my journey through PINC. When I thought “I couldn’t do it, this isn’t for me,” he said “Don’t worry, you got this.”

So, once again, to wrap things up, thank you to everyone who’s helped me out this far and continues to help me out. Thank you to all my friends, mentors, and teachers that I’ve met along the way. And thank you to the PINC program, the best jump I’ve ever made.

Matthew Suntay – PINC graduate 2022

Meet Francisca Catalan, SFSU PINC alum and research associate at UCSF (spotlight)

Francisca Catalan, SFSU PINC alum and research associate at UCSF

1. How did you get into coding? 

I took a regular CS class my second year at SF state. I thought it would be a good skill to have as an aspiring researcher and saw that it fulfilled one of my major requirements. It was a PowerPoint-heavy 8 am class three times a week. I didn’t talk to anyone else in the class and by the end of the semester I found it very difficult to show up. I passed the class but was really devastated about my experience. I thought I could never learn to program, though I never gave up completely. A couple semesters went by and I saw a friendly flier announcing PINC, SFSU’s program that promotes inclusivity in computing for biologist and other non-computer science majors. I eagerly signed up and started the “Intro to Python” class soon after. Then, with some more programming under my belt, I joined Dr. Rohlfs’ lab and began doing research in the dry lab for the remainder of my undergraduate career.

2. What kind of work do you do now? 

I currently work at UCSF as a dry lab research associate. Our lab focuses on an aggressive form of brain cancer, glioblastoma. We try to find gene targets for new drug treatments and research the cell type of these cancerous cells in order to fight drug resistance. My main duties now include creating pipelines for our single cell, RNA-Seq, and Whole Genome Sequencing data. You can read about our lab’s latest study in our new publication on cancer discovery! DOI: 10.1158/2159-8290.

https://cancerdiscovery.aacrjournals.org/content/candisc/early/2019/09/25/2159-8290.CD-19-0329.full.pdf

3. How did learning coding skills impact your career?

Coding has opened so many pathways for me. I was able to find a great job at UCSF soon after graduating with my Bachelor’s of Science in cell and molecular biology and minor in Computing Applications. It has also given be a giant boost of confidence! As a woman of color in STEM, I often felt underrepresented and out of place, but those feelings now quickly subside when I can help my colleagues answer coding questions! It’s motivating to feel like a necessary component of your community when often time you feel pushed out. It’s also impacted my career choices! I know now I want to be a professor in the future, I want to provide access to programming to others in hopes it will open pathways like it did for me!

4. Do you have any advice for students who are just starting? 

Yes! Don’t give up! It can be really difficult to learn coding, but know that it’s not you, talking to a computer can just be hard sometimes! Continue practicing and ask questions, google your heart out. Take breaks when necessary, remember to breathe, and keep in mind all the amazing science you will be able to do once you have these skills under your belt!

No programming background? No problem! Learn R

Guest post by Rosana Callejas

Rosana Callejas

Can someone with no programming knowledge learn “R”? The answer is yes! My name is Rosana Callejas. I am a Physiology major, and recent graduate from San Francisco State University. I began to learn the programming language “R” at the beginning of February of this year. Despite not having any previous programming experience , I analyzed my first data set of more than 20,000 data points in only a couple of months. Would you like to learn how I did it? Stay tuned.

The power of “R”

So what exactly is “R”? It is a programming language used by many data analysts, scientists, and statisticians, to analyze data, and perform statistical analysis with graphs and figures. “R” is a great tool when analyzing large data sets. It has many additional packages that can be downloaded, which allow the user to expand or simplify commands when analyzing data.

How R coded its way into my heart

Dr. Pleuni Pennings, an evolutionary biologist, and Professor at SFSU, introduced me to this wonderful tool. “I do all my research on my computer,” Dr. Pennings said, as she showed me the open program. At first, the idea puzzled me. In all my years as a biology student, I had never met a biologist like Dr. Pennings, who has made many discoveries from analyzing HIV DNA sequences using R. She explained to me that there is an accumulation of data collected by scientists everyday waiting to be analyzed. Therefore, there is a need for scientists with the skills to interpret, and draw conclusions from such large data sets. This interested me as biologist. I imagined all the new findings that could be made if all the data collected was analyzed. It would definitely contribute to the advancement of science. With this in mind, I embarked myself in the adventure of learning R.

One command at a time

I began by taking the online course “Exploratory Data Analysis with R” on Udacity.com. The course is composed of 6 lessons, in which I first learned the basics of R, a few basic commands, followed by the analysis of one variable, and how to make simple plots. In my learning, I used R, and R studio, which can be downloaded free online. I also used data sets provided by Udacity to analyze. In addition, R comes with other data sets I practiced with. My first graphing assignment was a simple bar plot (Figure 1), that represented friend count for Facebook users of different ages. This task required the package “ggplot2”, which allows graphing.

Figure 1. Friend count as function of age.

As I learned more, I began to work with different packages, new commands, and to make better graphs. I discovered how to add color to the graphs. I learned how to order variables, make subsets, group variables, add a new columns to my data sets, work with multiple variables, run correlation tests, and much more. The following are some figures that followed that first one, and show the progress of my learning as I added more detail to that first plot throughout the course.

Figure 2. Median friend count as function of age by gender.

Figure 3. Friend count as function of age.  In the green graph each point represents 20 data points in the data set. The black line represents the mean friend count. The blue line represents with the 50th quantile. The dotted lines represent the 90th and 10th quantiles.

 

Figure 4. The top graph represents friend count as function of age in months, with the blue line representing the mean. The middle graph represents friend count as a function of age with blue line represents the mean. The bottom graph represents friend count vs. age in moths rounded, multiplied, and divided by 5.

Patience is the mother of all virtues

Learning R was definitely a challenge. Commands that in theory should work, sometimes did not work. As a new user, it was difficult to know exactly what had gone wrong. Fortunately, I had the guidance of Dr. Pennings who helped me through the process. I also looked for resources outside of Udacity. One great package to use along with R is “swirl,” which is a teaching package. With swirl, I learned commands not taught in the Udacity course. It has multiple lessons that give the user immediate feedback. Patience and persistence are key to learning R. Now I have seen what R can do, I know it was worth learning.

The possibilities are endless

My favorite feature of R is that the code used in a previous analysis can be saved, and reused. R users can also share pieces of code with one another, which helps expand the knowledge among users. If changes need to be made in the middle of analysis, this is rather simple, and there is no need to reanalyze the data. R can be used to study many different types of data of any size or background. Scientists such a Dr. Pennings make major findings in Biology using R.

Although new to R, I was able to begin the analysis of my own data set [1] within only a few months of learning about it. Below is a figure which resulted from the question: Which HIV regimens are most common and in what years? In order to answer this question, many hours of work were invested in preparing the data set, excluding undesired data points, sub setting, color coding, etc., ending up with 6255 HIV data points, which included only the 26 most common unique regimens as a function of time. The graph represents the most common regimens of HIV treatments taken by patients in different years. It is also organized in order of increasing number of drugs per regimen. Each regimen was color coded to include a NNRTI drug, a PI drug, or consist of nRTIs.

Figure 5. The graph represents the most common regimens of HIV treatments taken by patients in different years belonging either to NNRTI, nRTI, or PI.

As the graph shows in 1989, and early 1990s, the HIV treatment consisted of the single drug AZT, and later in 1997, NVP. As the years progressed, regimens composed of two drugs became more common. It isn’t until 1996 that we begin to see regimens composed of three drugs. Regimens composed of three drugs are the most abundant and continue to be taken by patients up to 2013, while the single drug treatments seemed to have ceased in 2008. In 2002, we first observe regimens composed of four drugs (although RTV is often not counted as a drug, so these regimens may be considered 3-drug regimens as well), which also continue to be used along with the three drugs regimens.

R is a great program for data analysis. I believe that anyone who would like to learn it, with persistence can definitely do it. I will continue learning R, and analyzing my data set. I hope to use it as a useful tool for future investigations in my career.

[1] Thanks to Dr Robert Shafer from Stanford University for sharing the data with us!