Hinagaur - Bioinformatics Pathway

Certificates June 2020 Certificates Bioinformatics Workflow Self-Assessments
1

Concise overview of things learned
Technical area: Learned all the basics of R , installing and loading packages. Creating plots(Scatter plots, histogram, boxplots) using ggplot2 package, creating volcano plots and heatmaps, wrangling data for visualization, evaluating quality of samples using PCA, Hierarchial clustering of samples in dataset, Differntial expression analysis in context of DESeq2.

Tools: R programming language and introductory python.

Soft skills: I’m more comfortable in asking questions to the leads. Also, I have improved my communication skills with the help of interactive sessions conducted by the leads.

Three achievement highlights:
Learned a lot in R, I have learnt programming in context of bioinformatics , got to know real world applications of bioinformatics.

List of meetings/Training attended including social team events:
I have attented all the bioinformatics webinar that happened initially, all the R training webinar, Python webinar for beginners, Debaleena’s welcome session, Erin’s get-to-know meeting.

Goals for the upcoming week:
To improve my programming skills, complete all the tasks before deadline,and to interact more with my team members.

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2

Self Assessment (Week 3)

Concise overview of things learned
Technical area- I had learned Quality control, Background correction , normalization and visualization on microarray data. I also learned how to interpret the graphs we got from QCReport and how to interpret other plots generated from the the steps.
Tools- R programming , bioconductor
Soft skills- I have learned how to work in a team efficiently, and got more comfortable in interacting with my teammates and leads.

Three Achievement highlights

1- Submitted week 3 deliverable on time
2-Connected with many people on linkedin
3-Interacted with my teammated and helped each other out.

List of meetings/training attended-
Attended all the R trainings, Asana training, technical webinars and all the team meetings.

Goals for the upcoming week-
My goal for this week is to complete the week 4 deliverable and submit it on time, and to learn from the mistakes I did in week 3.

Detailed statement of tasks done. State each task, hurdles faced if any and how you solved the hurdle. You need to clearly mark whether the hurdles were solved with the help of training webinars, some help from project leads or significant help from project leads.
1-Completed all the steps in week 3 deliverables. I faced some problem in PCA plot and analysis, but it’s clear now with help of document provided by Yves.
2- Completed R exercises, joined slack, Asana, and connected with people related to my field of interest on linkedin.

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3

Final self assessment

Concise overview of things learned
Technical area
I have learned how to analyze and visualize microarray dataset. Also, I have learned to create various plots in R( heatmap, PCA, volcanoplot, boxplot) and to interpret them. Now, I have better understanding of python and R programming language and to work with datasets in RStudio.
Tools
Jupyter notebook, R, Python, Asana, Slack, GEO, GEO2R, Github and GSuite.
Soft skills
Communicated with my team members and team leads. I have connected with proffesionals from my field on Linkedin. Learned importance of time management.

Three achievement highlight

1- Better knowledge of python and R programming languages.
2- Learned more about bioinformatics and it’s real-world applications.
3- Learned steps to identify prognostic markers for colorectal cancer.

List of meetings attended
I have attended all the technical webinars, R training, python training , Asana training, Github training and team meetings.

Detailed statement of tasks done
Worked on deliverables for week 3,4 and 5. Downloaded the dataset GSE32323 from GEO, perform quality control analysis using affy QC package, Identifying and removing outlier samples, normalization of dataset using Gcrma package, Gene annotation using hgu133plus2.db package, gene filtration using quantile() function, limma analysis. I have also learned to visualize data using heatmaps, PCA, histograms, box plots with the help of ggplot2 and enhanced volcano package. I had some problem with with PCA plot which was then cleared with the help of mentors.

Thank you so much @egunduz, @yvesgaetan, @Isha and @Alex_Cook for all your help to make sure we had wonderful experience!

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