Self Assesment: BioInformatics- Maria Crisan- Self Assessment

Self assessment for week 3 (07/19 - 07/25)

Technical skills: After reading and learning how to do a QC, normalization (mas5, rma), batch correction, PCA, heatmap for microarray datasets, I was able to write my own code and understand why is so important when we have datasets to start with QC before moving forward.
One struggle was to learn R from zero and how to read datasets in R.

Tools used: STEM- Away forum, Slack, RStudio, R, GEO DB, GitHub.

Soft skills:
I’ve actively reached out to team members and mentors to make meaningful connections and hopefully to be able to bring my experience in clincal field at the table.

Achievements:
Learnt to read datasets in R
Learnt to create PCA, Heatmaps, data visualization graphics and to interpret it.

List of training and meetings that I attended:
Biology webinar (07/21), Group meeting(07/20, 07/23), Happy Hour (07/24)

Tasks done:
Did all the deliverables from week 3.
Had problems in the beginning with memory allocation but I fixed it.
PCA plot was hard in the beginning but after I talk with one of my mentors I’ve got it on my own.

Goals for the upcoming week:

Work as an participant for the week 4 deliverables.
Study more about data handling and microarrays.
Attend office hours and group meetings.
Prepare some cool games for happy hour.
Connect with other participants and leaders from STEM-Away Internship.

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Self assessment for week 4 (07/27 - 07/31)

Technical skills: Learnt how to do DGE analysis annotation, gene filtering, gene-matrix, enhanced volcano plot and to interpret data from heat maps and volcano plots.
One struggle was to und3rstand why I need to do some of the steps before moving to Limma package implementation.

Tools used: STEM- Away forum, Slack, RStudio, R, GEO DB, GitHub, Gene DB, Limma Pkg.

Soft skills:
Communication skills: hosted happy hour on Friday,
Collaboration: co-presented with my team our deliverables from week 4.

Achievements:
Learnt to read volcano plots and interpret heat maps.
Learnt to filter genes, annotate genes, delete duplicates and create gene matrix.
Upload my work on github

List of training and meetings that I attended:
webinar (07/28), Group meeting(07/27, 07/31), Happy Hour (07/31)

Tasks done:
Did all the deliverables from week 4.
For gene filtering and annotation I asked questions at the office hours where Alicia, my team lead helped me to figure it out how to do the annotation.

Goals for the upcoming week:

Work as an participant for the week 5 deliverables.
Study more about microarrays and how to interpret data.
Attend office hours and team meeting.
Connect with other participants and leaders from STEM-Away Internship.

Self Assessment Week 5(08/03-08/09)

Technical skills:
Learn how to do Functional Gene Analysis, especially GO, KEGG and Gene concept network in R working with specific packages.
Also interpreting data after technical part is one thing that I liked the most.

Tools used: R, Bioconductor, Slack, Github., STEM-away forum

Soft Skills:
Learnt to communicate better with my colleagues from different teams or paths.

Achievements:
Troubleshoot most of my errors alone and I understood why we have to make all this technical parts to get some real results.
Learn to do gene ontology and KEGG

Meetings:
Group Meeting, Webinar,

Tasks done:
Still working on my deliverables for this week as I did just 60% of the work already, Functional Enrichment Analysis.

Goals for the upcoming week.
To do my final project on time and to be able to troubleshoot my errors.
Also to collaborate more with my peers as part of the network.

SELF ASSESSMENT FOR WEEK 6 and 7

Things Learned

Technical area: Perfomed QC, DGE analysis- Limma Package, EnhancedVolcano, Functional Analysis: GO, KEGG, Gene network, StringDB, TFA, GSEA.
Tools: R, GitHub, G suite, Slack, GEPIA, StringDB.
Soft skills: Improved public speaking and presentation skills for the final project.

Achievements:
Complete final project presentation 100% learning how to work with different datasets and different functions or packages from Bioconductor.
Analysed datasets on Renal cell carcinoma but also on Alzheimer disease and Parkinson. all datasets with different type of datasets.

Meetings Attendee:
8/10 Team meeting, 8/11 Office Hours, 8/17 Team Meeting and Office Hours, 8/18 Webinar on professional presentation, 8/21 Final Presentation

Future Goals
Work more on analysing different sets of datasets on R using Bioconductor packages and different types of functional analysis.

Tasks and Challenges

  • Choosing Parkinson disease as my final project was a challenge because in the end I had to change my project and have another disease as my final presentation. Having to start all over again on another datasets was hard but definitely doable.

  • Functional analysis was not a hard task to do but the interpretation part was were I had to learn in depth to be able to interpret those graphs and plots in detail.

FINAL SELF ASSESSMENT:

This internship for me was an incredible experience, the whole team of mentors and leads (@ddas @Sarahrp @aliciarepka @yvesgaetan @Stephanie) and also my team members made this journey unforgettable and most of all an educative, networking and inspirational process.
I’m in process of starting residency in Neurology as a international med student and I’ve always been passionate about programming and how can I make an impact in health through tech. I found this program on a post on LinkedIn (thanks Stephanie) and I knew right away that this is something that I want to be part of and to learn more about genetics and bioinformatics. In the beginning it was hard to keep up with my study program for USMLE and also this internship deliverables but with the help of mentors and my team I started to learn how to troubleshoot myself and how to do things without extra help. All the office hours and webinars provided during this internship were really helpful and it was so nice to see everyone on Mondays in Team Meeting. This internship was not just learning how to code in R or how to do functional analysis on some genetic data, was also about people, how we can connect with each other and lift up each other. With all the guidance and help, after 7 weeks of internship I was ready to do my own dataset analysis, in addition create new graphics with additional packages and functions in R.
Thank you for empowering us all to learn more about biostatistics.

Technical Skills:

  • R programming
  • Data pipeline from microarray datasets
  • Quality Control
  • mas5 normalization
  • Heatmap & PCA & Volcano Plot (EnhancedVolcano)
  • Gene expression: Annotation,Duplicates, Omit. Gene Filtering
  • Differential Gene Expression Analysis
  • Functional analysis: GO, KEGG, REVIGO, StringDB, Gene Network,

Tools

  • GEO database, Bioconductor, R Studio, LIMMA, REVIGO, STRINGDB
  • STEMAway, GitHub, G Suite, Slack

Soft skills:

  • presenting difficult data in an easier way
  • importance of collaboration & networking
  • working with people with different background and in different time zones.

Achievement highlights:
Learned to program in R and to do a data pipeline also to use different packages and libraries to visualise my data better. Also I have done my final project by myself and it was quite an achievement since I had to change y final project 5 days before the final presentation.

Future Goals:
In the future I want to implement more data pipeline for different sets of data and also to do an algorithm to create data pipelines for people that don’t know how to use R for this and for clinicians to use this data for better treatments and who knows better diagnosis.

My final project was focused on renal cancer and one of the key take home messages from my presentation are:

#self-assessment #gene-filtering #final-project #bioinformatics-summer2020 #bi-team3-july2020 #gsea #limma-analysis #dge-analysis #functional-analysis #quality-control