search.json

{
  "articles": [
    {
      "path": "about_asfar.html",
      "title": "Asfar Lathif",
      "author": [],
      "contents": "\n\n          \n          \n          \n          \n          \n          \n          \n          HOME\n          SYLLABUS\n          LECTURES\n          SEMINARS\n          \n          \n          ASSIGNMENTS\n           \n          ▾\n          \n          \n          ASSIGNMENTS OVERVIEW\n          TIMELINE\n          SUBMISSION GUIDE\n          GROUP PROJECT RUBRICS\n          PAPER CRITIQUE RUBRIC\n          \n          \n          \n          \n          PEOPLE\n           \n          ▾\n          \n          \n          Keegan Korthauer (Instructor)\n          Yongjin Park (Instructor)\n          Asfar Lathif (TA)\n          Ishika Luthra (TA)\n          \n          \n          FAQ\n          \n          \n          \n          ☰\n          \n          \n      \n        \n          \n            \n              \n            \n              Asfar Lathif\n            \n            \n              \n                \n                    \n                      \n                        Twitter\n                      \n                    \n                  \n                                    \n                    \n                      \n                        GitHub\n                      \n                    \n                  \n                                    \n                    \n                      \n                        Email\n                      \n                    \n                  \n                                  \n            \n          \n        \n        \n        \n          \n            \n            Teaching Assistant\n            Pronouns: He/Him/HisVirtual office hours: Weds 11 am-12 pm\n            (PST)\n            I am PhD student in the Genome Science and Technology\n            department at UBC. I did my undergraduate in Biotechnology\n            in southern India before moving to UBC for my graduate\n            studies at the de Boer lab. My area of interests include\n            high dimensional biology and data science. My thesis project\n            involves understanding how non coding variants in genome\n            affect gene regulation in Cancers. In my free time I enjoy\n            cooking and exploring Vancouver!\n            \n          \n        \n      \n    \n\n    \n      \n        \n          \n            \n              \n            \n              Asfar Lathif\n            \n            \n              \n                \n                                    \n                    \n                      Twitter\n                    \n                  \n                                    \n                    \n                      GitHub\n                    \n                  \n                                    \n                    \n                      Email\n                    \n                  \n                                  \n              \n            \n            \n              \n              Teaching Assistant\n              Pronouns: He/Him/HisVirtual office hours: Weds 11 am-12 pm\n              (PST)\n              I am PhD student in the Genome Science and Technology\n              department at UBC. I did my undergraduate in Biotechnology\n              in southern India before moving to UBC for my graduate\n              studies at the de Boer lab. My area of interests include\n              high dimensional biology and data science. My thesis\n              project involves understanding how non coding variants in\n              genome affect gene regulation in Cancers. In my free time\n              I enjoy cooking and exploring Vancouver!\n              \n            \n        \n      \n    \n\n    \n    \n    \n       All materials of the course are\n      licensed by the course instructors under the Attribution-NonCommercial\n      4.0 International license.\n      \n      \n\n  \n  ",
      "last_modified": "2024-03-27T16:13:18-07:00"
    },
    {
      "path": "about_ishika.html",
      "title": "Ishika Luthra",
      "author": [],
      "contents": "\n\n          \n          \n          \n          \n          \n          \n          \n          HOME\n          SYLLABUS\n          LECTURES\n          SEMINARS\n          \n          \n          ASSIGNMENTS\n           \n          ▾\n          \n          \n          ASSIGNMENTS OVERVIEW\n          TIMELINE\n          SUBMISSION GUIDE\n          GROUP PROJECT RUBRICS\n          PAPER CRITIQUE RUBRIC\n          \n          \n          \n          \n          PEOPLE\n           \n          ▾\n          \n          \n          Keegan Korthauer (Instructor)\n          Yongjin Park (Instructor)\n          Asfar Lathif (TA)\n          Ishika Luthra (TA)\n          \n          \n          FAQ\n          \n          \n          \n          ☰\n          \n          \n      \n        \n          \n            \n              \n            \n              Ishika Luthra\n            \n            \n              \n                \n                    \n                      \n                        Twitter\n                      \n                    \n                  \n                                    \n                    \n                      \n                        GitHub\n                      \n                    \n                  \n                                    \n                    \n                      \n                        Email\n                      \n                    \n                  \n                                  \n            \n          \n        \n        \n        \n          \n            \n            Teaching Assistant\n            Pronouns: She/Her/Hers\n            Virtual office hours: Friday 10-11am\n            (PST)\n            I am a PhD candidate in the Bioinformatics program at\n            UBC. I completed my undergraduate degree from Simon Fraser\n            University with a Bachelors of Applied Science in Biomedical\n            Engineering in 2018. I am interested in using machine\n            learning approaches to further our understanding of the\n            genome. My thesis project involves using neural network\n            models to predict gene expression from DNA sequence. In my\n            free time I enjoy yoga, baking, hiking and skiing.\n            \n          \n        \n      \n    \n\n    \n      \n        \n          \n            \n              \n            \n              Ishika Luthra\n            \n            \n              \n                \n                                    \n                    \n                      Twitter\n                    \n                  \n                                    \n                    \n                      GitHub\n                    \n                  \n                                    \n                    \n                      Email\n                    \n                  \n                                  \n              \n            \n            \n              \n              Teaching Assistant\n              Pronouns: She/Her/Hers\n              Virtual office hours: Friday 10-11am\n              (PST)\n              I am a PhD candidate in the Bioinformatics program at\n              UBC. I completed my undergraduate degree from Simon Fraser\n              University with a Bachelors of Applied Science in\n              Biomedical Engineering in 2018. I am interested in using\n              machine learning approaches to further our understanding\n              of the genome. My thesis project involves using neural\n              network models to predict gene expression from DNA\n              sequence. In my free time I enjoy yoga, baking, hiking and\n              skiing.\n              \n            \n        \n      \n    \n\n    \n    \n    \n       All materials of the course are\n      licensed by the course instructors under the Attribution-NonCommercial\n      4.0 International license.\n      \n      \n\n  \n  ",
      "last_modified": "2024-03-27T16:13:26-07:00"
    },
    {
      "path": "about_keegan.html",
      "title": "Keegan Korthauer",
      "author": [],
      "contents": "\n\n          \n          \n          \n          \n          \n          \n          \n          HOME\n          SYLLABUS\n          LECTURES\n          SEMINARS\n          \n          \n          ASSIGNMENTS\n           \n          ▾\n          \n          \n          ASSIGNMENTS OVERVIEW\n          TIMELINE\n          SUBMISSION GUIDE\n          GROUP PROJECT RUBRICS\n          PAPER CRITIQUE RUBRIC\n          \n          \n          \n          \n          PEOPLE\n           \n          ▾\n          \n          \n          Keegan Korthauer (Instructor)\n          Yongjin Park (Instructor)\n          Asfar Lathif (TA)\n          Ishika Luthra (TA)\n          \n          \n          FAQ\n          \n          \n          \n          ☰\n          \n          \n      \n        \n          \n            \n              \n            \n              Keegan Korthauer\n            \n            \n              \n                \n                    \n                      \n                        Website\n                      \n                    \n                  \n                                    \n                    \n                      \n                        Twitter\n                      \n                    \n                  \n                                    \n                    \n                      \n                        GitHub\n                      \n                    \n                  \n                                    \n                    \n                      \n                        Email\n                      \n                    \n                  \n                                  \n            \n          \n        \n        \n        \n          \n            \n            Instructor\n            Pronouns: She/Her/HersVirtual office hours: By appointment\n            I am an Assistant Professor in the Department of\n            Statistics at UBC and an Investigator at the BC Children’s\n            Hospital Research Institute. I grew up and studied in the\n            United States before moving to Vancouver in 2019. My\n            research is focused on developing and applying statistical\n            and computational tools for the analysis of high-throughput\n            sequencing data, with the ultimate goal of uncovering new\n            molecular signals in cancer, child health, and development.\n            My hobbies include board gaming and spending time outdoors\n            with my family. Please feel free to address me as “Keegan”\n            or “Dr. Korthauer”.\n            \n          \n        \n      \n    \n\n    \n      \n        \n          \n            \n              \n            \n              Keegan Korthauer\n            \n            \n              \n                \n                                    \n                    \n                      Website\n                    \n                  \n                                    \n                    \n                      Twitter\n                    \n                  \n                                    \n                    \n                      GitHub\n                    \n                  \n                                    \n                    \n                      Email\n                    \n                  \n                                  \n              \n            \n            \n              \n              Instructor\n              Pronouns: She/Her/HersVirtual office hours: By appointment\n              I am an Assistant Professor in the Department of\n              Statistics at UBC and an Investigator at the BC Children’s\n              Hospital Research Institute. I grew up and studied in the\n              United States before moving to Vancouver in 2019. My\n              research is focused on developing and applying statistical\n              and computational tools for the analysis of\n              high-throughput sequencing data, with the ultimate goal of\n              uncovering new molecular signals in cancer, child health,\n              and development. My hobbies include board gaming and\n              spending time outdoors with my family. Please feel free to\n              address me as “Keegan” or “Dr. Korthauer”.\n              \n            \n        \n      \n    \n\n    \n    \n    \n       All materials of the course are\n      licensed by the course instructors under the Attribution-NonCommercial\n      4.0 International license.\n      \n      \n\n  \n  ",
      "last_modified": "2024-03-27T16:13:33-07:00"
    },
    {
      "path": "about_yongjin.html",
      "title": "Yongjin Park",
      "author": [],
      "contents": "\n\n          \n          \n          \n          \n          \n          \n          \n          HOME\n          SYLLABUS\n          LECTURES\n          SEMINARS\n          \n          \n          ASSIGNMENTS\n           \n          ▾\n          \n          \n          ASSIGNMENTS OVERVIEW\n          TIMELINE\n          SUBMISSION GUIDE\n          GROUP PROJECT RUBRICS\n          PAPER CRITIQUE RUBRIC\n          \n          \n          \n          \n          PEOPLE\n           \n          ▾\n          \n          \n          Keegan Korthauer (Instructor)\n          Yongjin Park (Instructor)\n          Asfar Lathif (TA)\n          Ishika Luthra (TA)\n          \n          \n          FAQ\n          \n          \n          \n          ☰\n          \n          \n      \n        \n          \n            \n              \n            \n              Yongjin Park\n            \n            \n              \n                \n                    \n                      \n                        Website\n                      \n                    \n                  \n                                    \n                    \n                      \n                        Twitter\n                      \n                    \n                  \n                                    \n                    \n                      \n                        GitHub\n                      \n                    \n                  \n                                    \n                    \n                      \n                        Email\n                      \n                    \n                  \n                                  \n            \n          \n        \n        \n        \n          \n            \n            Instructor\n            Pronouns: He/Him/HisVirtual office hours: By appointment\n            I am an Assistant Professor in the Department of\n            Pathology and Statistics since Fall 2020. My research\n            primarily focuses on developing scalable probabilistic\n            inference methods to elucidate hidden causal mechanisms of\n            human diseases, such as cancer and other common/complex\n            disorders. Since I am a data-driven scientist, I become\n            excited when hidden patterns emerge, and data finally start\n            speaking back to me. I firmly believe in Don Knuth’s famous\n            quote, “best practice is inspired by theory,” and “best\n            theory is inspired by practice.”\n            \n          \n        \n      \n    \n\n    \n      \n        \n          \n            \n              \n            \n              Yongjin Park\n            \n            \n              \n                \n                                    \n                    \n                      Website\n                    \n                  \n                                    \n                    \n                      Twitter\n                    \n                  \n                                    \n                    \n                      GitHub\n                    \n                  \n                                    \n                    \n                      Email\n                    \n                  \n                                  \n              \n            \n            \n              \n              Instructor\n              Pronouns: He/Him/HisVirtual office hours: By appointment\n              I am an Assistant Professor in the Department of\n              Pathology and Statistics since Fall 2020. My research\n              primarily focuses on developing scalable probabilistic\n              inference methods to elucidate hidden causal mechanisms of\n              human diseases, such as cancer and other common/complex\n              disorders. Since I am a data-driven scientist, I become\n              excited when hidden patterns emerge, and data finally\n              start speaking back to me. I firmly believe in Don Knuth’s\n              famous quote, “best practice is inspired by theory,” and\n              “best theory is inspired by practice.”\n              \n            \n        \n      \n    \n\n    \n    \n    \n       All materials of the course are\n      licensed by the course instructors under the Attribution-NonCommercial\n      4.0 International license.\n      \n      \n\n  \n  ",
      "last_modified": "2024-03-27T16:13:40-07:00"
    },
    {
      "path": "assignments.html",
      "title": "Course work",
      "author": [],
      "contents": "\n\nContents\nSummary\nTimeline Overview\nAssignments Overview\nIntro assignment (5%)\nSeminar participation (20%)\nPaper critique (5%)\nAnalysis assignment (30%)\nFinal group project (40%)\n\n\nSummary\nYou will have three individual assignments, six seminar submissions, and one group project:\nOne intro assignment (5%)\nSeminar participation (20% total; 2% each after dropping the lowest score)\nOne paper review (5%)\nOne analysis assignment (30%)\nFinal group project (40%) which contains multiple parts.\nDeadlines are all by 11:59 pm (Pacific time) on the due date. Any submission or modification after the due date will not be graded unless you have requested an extension. If you anticipate having trouble meeting a deadline and need to request an extension/academic concession please reach out via email.\nFor detailed instructions on how to submit your work, see the submission guide.\nTimeline Overview\nFor a visual summary, click here.\n\nCategory\nAssignment\nDue Date\nSeminar\nSeminar 1\nFri Jan 12\nSeminar\nSeminar 2a & 2b\nFri Jan 19\nIndividual Assignment\nIntro Assignment\nThu Jan 25\nSeminar\nSeminar 3\nFri Jan 26\nGroup Project\nProposal Lightning Talks\nTue Jan 30\nSeminar\nSeminar 4\nFri Feb 02\nGroup Project\nWritten Proposal\nThu Feb 08\nSeminar\nSeminar 5\nFri Feb 09\nSeminar\nSeminar 6\nFri Feb 16\nPaper Critique\nPaper Critique\nThu Feb 22\nIndividual Assignment\nAnalysis Assignment\nThu Feb 29\nSeminar\nSeminar 7\nFri Mar 01\nGroup Project\nProgress Report\nThu Mar 07\nSeminar\nSeminar 8\nFri Mar 15\nSeminar\nSeminar 9\nFri Mar 22\nSeminar\nSeminar 10\nFri Mar 29\nGroup Project\nFinal Report\nTue Apr 02\nGroup Project\nPresentation Day 1\nThu Apr 04\nGroup Project\nPresentation Day 2\nTue Apr 09\nGroup Project\nPresentation Day 3\nThu Apr 11\nGroup Project\nIndividual & Group Evaluation\nFri Apr 12\nSeminar\nSeminar 11\nFri Apr 12\n\nAssignments Overview\nIntro assignment (5%)\nThis assignment is designed to give you independent practice in the workflow used for completing and submitting course work: committing and pushing files to GitHub, formatting an R Markdown document, using R to do simple analyses, and writing about your results. Grade point values are listed in the assignment.\nThe instructions and questions are available here.\nSeminar participation (20%)\nYou will submit short “deliverables” to demonstrate your participation for every seminar. These deliverables give practical experience applying the knowledge that will be helpful on the homework assignment, final project, and (hopefully) your future research. Each of Seminar session is weighted equally, but the lowest score will be dropped (so that the 10 seminars with highest score will each count for 2% of the final grade). Seminar deliverables are due on the Friday following the TA-led session for that seminar. See the Seminars page for the submission materials and schedule.\nPaper critique (5%)\nEach student will review and provide a 500-700 word critique a paper that will be posted on Canvas.\nPlease see critique rubric for detailed instructions on this task.\nAnalysis assignment (30%)\nThis assignment will assess your understanding of the seminar and lecture materials. Start early because this assignment will take time to fully work through. Use the issues in the Discussion repo and the seminar time to ask questions. You will find most of the analysis workflow of the assignment in the seminar materials.\nThe instruction and questions will be available here.\nThe grade point values for each question are listed right in the assignment.\nFinal group project (40%)\nGeneral principles\nIdentify a biological question of interest and a relevant dataset. Develop and apply a statistical approach that allows you to use the dataset to answer the question.\nWe assume the biological question and data fall in the general area of high-throughput, large-scale biological investigations targeted by the course. Beyond that, it is wide open: methylation, SNPs, miRNAs, CNVs, RNA-Seq, CHiP-Seq, gene networks, … it’s fair game. Avoid a dataset that doesn’t have any/much quantitative data, i.e. contains only sequence or discrete data. If you are using published data, it is critical to be clear about how your project differs from previous literature.\nNote that definitive answers are not necessarily expected. Rather, aim to provide a critical appraisal of the data, the analytical approach, and the results. You will have to handle the competing pressures to “get it right” and “get it done”. Shortcomings of the data, misfits between the data or the biological question and the statistical model, etc. are inevitable. Your goal is to identify such issues and discuss them critically, without becoming paralyzed. Demonstrate understanding of the statistical concepts and methods that are the foundation of your analytical approach.\nWe assume the analytical and computing task will have a substantial statistical component, probably enacted via R. So beware of a major analytical or computational undertaking that is, nonetheless, not statistical (example: constructing a database). Creating useful data visualizations can be absolutely vital and is arguably statistical, but your analysis should go beyond merely creating pretty pictures (but please do include some!). Key concepts, at least some of which should come up in your analysis:\nthe (hypothesized, probably artificial) data-generating model\nbackground variation, variance, signal to noise ratio, estimates and their associated standard error\nrelationship between biological factors and experimental factors, apparent relative importance in terms of “explaining” observed data\nattention to large-scale inference, e.g. control of family-wise error rate or false discovery rate\nData considerations\nAppropriate use of data\nIf your project involves using unpublished data, ensure your plans are known to the data providers (e.g., your supervisors), and think about implications for publishing - are you are bringing the project team in as collaborators in effect? Are you planning to publish the results of your project, and if so who will be the co-authors? It is best to deal with these questions at the outset of the project.\nPrivacy of project data\nThe projects are not made public (other than an oral presentation of your work in front of your classmates, which will be recorded and made available only to the teaching team and registered students in the course). The project report materials are loaded into GitHub, the secure site we use to manage the course. The course staff and instructors are the only people who have access to the project GitHub repo other than the other members of the project group. The data used can be uploaded to the project, but this can limited or omitted if there are special concerns about privacy etc. - it’s primarily the code and write-up about the results that needs to be provided for evaluation.\nYou can read Github’s security and privacy policies.\nGroup makeup\nGroups will be formed by the instructional team following the results of an in class survey, and posted in the Discussion repo. Groups will have a target size of 4 members. Groups will be formed with priority for diversity in terms of backgrounds. In practice, this probably means the team members come from a mix of programs/departments. We will try to honour requests for working with specific team mates, and you may come talk to us immediately after receiving group assignments if you’d like to make a change.\nDeliverables\nDetails and grading rubrics for each component of the final group project can be found on the Group project rubrics page.\nProject Proposal Lightning Talks (5%)\nWritten Proposal (5%)\nProgress report (5%)\nFinal report (10%)\nOral presentation (10%) - A Detailed rubric can be found here\nIndividual report (5%)\n\n\n\n",
      "last_modified": "2024-03-27T16:13:42-07:00"
    },
    {
      "path": "critique.html",
      "title": "Paper Critique Rubric",
      "author": [],
      "contents": "\nGoal\nTo practice your ability to go through a paper, identify the biological problem that the authors want to address and critique how they chose to approach this question. Since this course is based around statistical methods of high dimensional biology, we want you to review their methods of analyses and think about ways in which it could be improved/modified and the extent to which they are able to address their biological questions with their given data/analyses.\nPaper in question:\nTakahama, M., Patil, A., Richey, G. et al. A pairwise cytokine code explains the organism-wide response to sepsis. Nat Immunol (2024).\nDeliverables and Rubric:\nAim for a length of 500-700 words in your summary/report. Submission: You should write your paper critique in .md format (notice that you don’t need to write it in .Rmd since you are not going to have any R code in it).\nIn four sections, present:\n1) A brief review of the goal, findings and conclusion of the paper. (1 pt) \nWhat’s the biological problem (from abstract/introduction)? What did the authors do (introduction/methods)? Briefly, what are some of their key findings (results)? Taken together, what did the authors conclude from these results?\n2) A list (or mentioning) of the related datasets/databases and data types used in the study. In the case of datasets, provide some details of the data matrix and meta data. (0.5 pt)\nWhat kind of dataset was used/generated in this study? How was it generated? What does the dataset represent (i.e. groups, conditions, timepoints, cell types, mouse strains)?\n3) A brief review of the analytical steps in the paper with more details on some selected parts which are relevant to the course materials. You don’t need to understand all of the analysis, but should be able to identify the key analysis/method used to answer the question the paper is intended to answer. (1.5 pt)\nWhat kinds of statistical analyses were used on this dataset? How do they control for variation between/within groups?\n4) Some comments and critiques about the analytical steps, alternative suggestions or improvements. (2 pt)\nThis is where you add your own opinions on their methods. Do you agree with how they interpreted their findings? Do you see any gene function/network analyses? Do you see any limitations with their approach?\nGeneral Tips:\nWhere should I start?\nRead the title and abstract. Try to identify:\nThe biological problem that the authors are trying to address\nThe biological model they are using to address this problem\nThe datasets/methods they are using\nThe main finding of the paper\nRead through the introduction:\nWhat’s the biology of the system?\nWhat kind of model are they using?\nWhat’s the point of this study?\nWhat do they want to know? (e.g. expression of select genes, methylation, transcription factor binding sites?)\nBriefly look at the methods:\nIt will look like a jumble of text if you are not familiar with the techniques here. Try to understand what kind of models were being used (human, mouse, llama?), what technique was conducted to generate their dataset (ChIP-seq, RNA-seq, WGBS, microarray?), what statistical methods were used on this dataset (clustering, FDR-cutoffs, ANOVA?).\nRead through the results:\nWhat is being compared? (i.e. condition A vs condition B)\nWhat were some of the key results that were found? (ex. what genes were identified? What do these genes do?)\nHow does this relate back to the biology? (i.e. how does this relate to the question that was being asked?)\nBriefly understand the discussion:\nHow did the authors interpret these findings? Do you agree with this?\nHere’s another helpful resource on how to read a research paper.\n\n\n\n",
      "last_modified": "2024-03-27T16:13:44-07:00"
    },
    {
      "path": "faq.html",
      "title": "Frequently Asked Questions",
      "author": [],
      "contents": "\n\nContents\nRegistration\nWhen is the course offered?\nI am not in one of the three cross-listed grad programs (STAT/BIOF/GSAT), but I would like to take this course. Can you grant me permission to register?\nI am on the waitlist. Will I get a spot in the course?\nCan I audit the course?\nCan I just ‘sit in’ the course without enrolling?\n\nPrerequisites\nI have a strong background in biology, and am proficient in R, but have only had one university-level statistics course. Is STAT 540 for me?\nI have a strong background in statistics, am proficient in R, but haven’t taken any biology or genetics courses. Is STAT 540 for me?\nI have never used R before. Is STAT 540 for me?\n\n\nRegistration\nWhen is the course offered?\nJanuary-April of each academic year.\nI am not in one of the three cross-listed grad programs (STAT/BIOF/GSAT), but I would like to take this course. Can you grant me permission to register?\nThis course is primarily aimed at graduate students from three grad programs: STAT (Statistics), BIOF (Bioinformatics), and GSAT (Genome Sciences and Technology), and will not allow other students to register without permission. This is because the course is a recommended elective for these degree programs. As the course has become increasingly popular with students from other grad programs, we maintain a waitlist. We invite you to register for the waitlist as soon as possible, as we fill any available seats not occupied by STAT/BIOF/GSAT students from the waitlist (first-come, first-served) close to the start of the term.\nI am on the waitlist. Will I get a spot in the course?\nIf there are seats available, we will fill them from the waitlist (first-come, first-served) close to the start of the term. If there are no seats available, unfortunately we are not able to increase the class capacity.\nCan I audit the course?\nIf there’s a seat available for you, then yes! Unfortunately, you’ll still have to join the waitlist if you’re not a STAT/BIOF/GSAT student. Auditing still counts as a seat in the class, so auditing is not a way around the course capacity. Auditors are expected to complete all coursework (pass/fail) except the final project.\nCan I just ‘sit in’ the course without enrolling?\nUnfortunately not. Besides room capacity constraints, we just don’t have enough human resources (instructors, TAs) to accommodate students who are not registered. But you are still welcome to check out all the lecture notes, seminar worksheets, and resources freely available on this website!\nPrerequisites\nI have a strong background in biology, and am proficient in R, but have only had one university-level statistics course. Is STAT 540 for me?\nYes! None of us is an expert in all areas. This course is designed for those who have at least some entry-level experience in statistics, biology, and programming, but it is expected that each student will have differing degrees of strength across these areas. This is actually ideal for the group project, since we form teams with complementary areas of expertise.\nI have a strong background in statistics, am proficient in R, but haven’t taken any biology or genetics courses. Is STAT 540 for me?\nYes! See previous question :)\nI have never used R before. Is STAT 540 for me?\nIf you have some background in statistics and/or biology, then it could be! But be prepared for a steep learning curve with a lot of self-guided learning, especially if you don’t have experience with other programing languages.\n\n\n\n",
      "last_modified": "2024-03-27T16:13:45-07:00"
    },
    {
      "path": "group_project_rubrics.html",
      "title": "Group project instructions & rubrics",
      "author": [],
      "contents": "\n\nContents\nProject Proposal Lightning Talks (5 pts)\nWritten Proposal (5 pts)\nProgress report (5 pts)\nFinal report (10 pts)\nOral presentation (10 pts)\nIndividual report (5 pts)\n\nThis page contains more specific instructions for each project deliverable as well as their point values for grading. Each point represents 1 percent of total course grade; i.e. since the final project represents 40% of the final grade, you can think of each point as a percentage of the final grade. Ensure your repo is organized from the start so the TAs can easily find your work for marking.\nProject Proposal Lightning Talks (5 pts)\nYour group will prepare a short (5 minute, 5 slides) lightning talk presenting your project proposal. You will push a PDF of your slides to your group project repo, and give the oral presentation in class. Each group member should present at least one slide. Add a link to this file in your main group repo README.md file so the TAs can easily find your work for marking. This is a group-level deliverable (one submission per team).\nRubric: Marks will be based on effective communication of the main ideas of the following four aspects:\nBackground/Motivation (1 point)\nWhat is already known in the context of this study?\n\nQuestion/Hypothesis (2 points)\nWhat is the knowledge gap you are trying to fill?\nWhat do you expect to find?\n\nData (1 point)\nDescribe the content and source of the data you will use?\nIf using previously published data, how will your project differ from previous literature?\n\nAnalysis Plan (1 point)\nHow will you answer your main research question?\n\nWritten Proposal (5 pts)\nCreate a ~one-page proposal (as a Markdown file called final_project_proposal.md). This will be an expanded, more detailed description of your project. Please make sure that you have incorporated the feedback and comments by the professors/TAs on your proposal lightning talks. Add a link to this file in your main group repo README.md file so the TAs can easily find your work for marking. Provide references for any sections as needed.\nYour project proposal includes:\nMotivation and background (1 pt)\nWhat is the relevance and significance of your project?\nWhat is already known in the context of your project?\nQuestion/Hypothesis (1 pt)\nWhat is the knowledge gap you are trying to fill?\nWhat do you expect to find?\nDataset (1 pt)\nWhat kind of data are you working with? e.g. what technology was used to generate it?\nWhat is the general description and characteristics of the data? e.g. how many rows, how many columns, experimental design etc.\nHow will this data help you answer your research question?\nAims and methodology (1 pt)\nWhat are the specific aims that you will address in order to answer your research question?\nFor each of the aims, what computational/statistical approaches will you use? (Note: your methodology might change as you are progressing, exploring your data and reading through the literature)\nIf you are using data that is previously published, how will your project differ from previous literature?\nDivision of labour (1 pt)\nWho is going to do what? State assignment of tasks and projected contributions for each group members.\nPlease provide a table of group members with their background, degree, affiliations and job assignments.\nProgress report (5 pts)\nYour group progress report will be a Markdown document called progress_report.md. Add a link to this file in your main group repo README.md file so the TAs can easily find your work for marking.\nWhat has changed based on the final proposal (1 pt.)\nDid your dataset change? If so, why?\nHave you decided to do a different analysis than what was mentioned in your proposal? If so, Why?\nAre there any changes in task assignments of group members?\nWhat is the progress of the analyses (2 pts.)\nSince your initial proposal, you should have decided more concretely on what methods to use for each step of your analyses, and employed some of those methods.\nBriefly and concisely explain your methodology and progress for the aims you have investigated so far. Which parts were modified and which parts remained the same?\nWhat R packages or other tools are you using for your analyses? You do not need to provide your scripts in your report.\nProvide the links to any markdown reports within your repo to refer to the relevant analysis.\nProvide references.\nResults (2 pts.)\nWhat are your primary results?\nWere you able to answer your hypothesis? Did you have any positive results? If no, postulate a discussion as to why that may be. Provide plots and/or tables to present your results.\nList some challenges that you have encountered or anticipate. How will you address them?\nFinal report (10 pts)\nThis is the final state of your group project repo, including your final written report summarizing your results, all analysis code, and presentation slides. It should contain the materials (or associated live links) an instructor would need to evaluate your work and that a group member would need to reproduce/reuse/extend the work. This is a group-level deliverable.\nWritten report (5 pts)\nThink of it as a technical report of your presentation that includes analysis and results. This should generally be GitHub Markdown output of your key statistical results generated from R Markdown analyses. Be sure to:\nInclude text throughout explaining the aim and main conclusions of each part.\nMake sure your code is readable, documented, and could be run by instructors. Make sure you are addressing the important files, such as “inputs” (e.g. links to the dataset(s) you analyzed) and “outputs” (e.g. figures). Provide valid links for the files you use in your scripts. Link to the report in your main README file.\nYou can find the full detailed rubric for evaluation of the written report here.\nGitHub repository organization (5 pts.)\nDetailed rubric here. Organizing your project files logically and consistently will help your team and the instructors to keep track of them. One best practice is to create subdirectories for different parts of your analyses. For example:\nsrc: contains source code (e.g .R files)\nresults: contains summary figures and/or reports (e.g. .pdf or html files)\npresentation: contains presentation slides files\nreports: contains the project proposal and progress report deliverables\nThe README.md file in the root directory of your the group repo should contain the summary of your project content/main results, and links to all deliverables.\nProvide a README.md file for each subdirectory to summarize its contents and analyses. Your project instructors should be able to go through all of your work just by following your README.md file(s). You can include a flow chart if you find it useful. Keep it brief and concise.\nYou may include any other directories you find helpful.\nExample:\n\n\n\nFor lots more detail and discussion on project organization and research computing, see this article.\nPresentation slides submission\nYour presentation slides files (e.g. presentation.PPTX or presentation.pdf or presentation.html) should be placed in your group repo. If your slides require extra files (e.g. if you used Rmd or Latex), create a subdirectory for those materials. Please provide a link to your final presentation slides in your group repo README.md. This is a group-level deliverable and is due with your final report (but is marked as part of your oral presentation).\nOral presentation (10 pts)\nPresentation sessions\nEach group will prepare one 20 minute oral presentation, which will be followed by a Q&A period of 5-10 minutes. We will have oral presentation sessions on the last four scheduled lecture sessions. Assignments for presentation dates will be posted on Canvas. Groups may choose to present synchronously (live), or prerecord their presentation and play the video during their presentation slot. This is a group-level deliverable, and all group members should participate, and be present during the presentation even if prerecorded so they can answer questions. In addition, each individual will randomly be assigned 2 other group presentations to peer review (see Individual report), so be sure to be present during the other presentation days as well.\nResources for pre-recording presentations: If you choose to prerecord your group presentation, you may do so using a variety of tools. One option is to record during a group meeting using Microsoft Teams or Zoom. Another option is to record in Powerpoint. You could also use screencast software such as QuickTime for Mac. If you need advice (or have advice to share) on recording/editing software for your system, please post in the Discussion repo.\nPresentation evaluation\nYour oral presentation is evaluated in 4 major categories:\nBackground/introduction (2 points)\nStatistical analysis (3 points)\nQuality of presentation (2 points)\nScientific maturity (3 points)\nYou can find the full detailed rubric for presentation evaluation in each of these categories here.\nIndividual report (5 pts)\nA one-page individual report as individual_report.md is an individual deliverable and should be in an individual private repository (it will have its own invitation link on Canvas). The report includes:\nPeer evaluation (2 pts)\nYou will be randomly assigned to provide peer feedback for two (2) oral presentations from other teams.\nFollowing the presentations, please review and provide constructive feedback using the 4-component Reflect Inquire Suggest Elevate (RISE) model of peer feedback. Include a statement/question for each of the 4 RISE components.\nPlease copy and paste each of your peer reviews in the Piazza #peer-review channel (so that the teams can receive your feedback), and include links to these messages in this report.\nA concise summary of contributions of each group member (1 pts)\nDescribes the tasks and contributions of each of your group members\nDo you think the task assignments were fairly assigned and appropriate given each member’s background and skills? If no, how would you change it?\nYour specific contributions and comments (1 pts)\nExplain what are your contributions to the project?\nWhat worked well and what did not? What was the most challenging or rewarding moment during your group project?\nHow did you find having members of different backgrounds for a scientific project?\nWhat have you learned from this group project?\nAny other comments on how the group project could have been structured differently (i.e. delivery requirements, assessment)\nWould you like to share your presentation? (1 pt)\nThanks to your feedback, we would like to make project presentation slides available to future students with your consent. Indicate which of the following statements is true (mark in this section is for completion; you will earn 1 point as long as you include one of these statements):\n\nI consent to sharing of my team’s final presentation slides on the course website for the benefit of future enrolled students as well as potential students.\n\n\nI consent to sharing of my team’s final presentation slides via Canvas for the benefit of future enrolled students only.\n\n\nI do not consent to sharing of my team’s final presentation slides with anyone.\n\nNote that slides will only be shared on the course website if all individuals in your team provide consent level a. Likewise, slides will only be shared on Canvas if all individuals in your team provide consent level a or b. If at least one person in the team selects option c (or does include a statement at all), your slides will not be shared with anyone. Note also that if you have used data that is not publicly available, it is your responsibility to first obtain consent from the data custodian if you choose option a or b.\n\n\n\n",
      "last_modified": "2024-03-27T16:13:46-07:00"
    },
    {
      "path": "index.html",
      "title": "Statistical Methods for High Dimensional Biology",
      "description": "STAT 540 @ UBC <br>\nJan-Apr 2024\n",
      "author": [],
      "contents": "\nLearn how to:\nPerform exploratory data analysis and visualize genomic data\nApply tailored statistical methods to answer questions using high dimensional biological data\nMake your work reproducible, reusable, and shareable\nWork with real data in a collaborative model\nQuick links for current students:\nCanvas course page\nAdditional resources\n\n\n\n",
      "last_modified": "2024-03-27T16:13:47-07:00"
    },
    {
      "path": "lectures.html",
      "title": "Lectures",
      "author": [],
      "contents": "\nClass meetings and schedule\nTime : Tues Thurs 9:00 - 10:30am\nLocation: Building and room number listed on Canvas\n\n\nDate\n\n\nPre-reading\n\n\nTopic\n\n\nInstructor\n\n\nTue Jan 9\n\n\n1\n\n\nLecture 1: Course intro; Molecular biology primer\n\n\nKeegan\n\n\nThu Jan 11\n\n\n\n\nLecture 2: High-dimensional biology intro: RNA, DNA, methylation, ChIP-Seq\n\n\nKeegan\n\n\nTue Jan 16\n\n\n1\n\n\nLecture 3: Exploratory data analysis; confounding & batch effects\n\n\nKeegan\n\n\nThu Jan 18\n\n\n1,\n2,\n3\n\n\nLecture 4: Statistics & probability primer\n\n\nKeegan\n\n\nTue Jan 23\n\n\n1\n\n\nLecture 5: Statistical inference: two groups\n\n\nKeegan\n\n\nThu Jan 25\n\n\n1,\n2,\n3\n\n\nLecture 6: Statistical inference: linear regression & ANOVA\n\n\nKeegan\n\n\nTue Jan 30\n\n\n\n\nProject lightning talk\n\n\n\n\nThu Feb 1\n\n\n1\n\n\nLecture 7: Statistical inference: multiple linear regression\n\n\nKeegan\n\n\nTue Feb 6\n\n\n1\n\n\nLecture 8: Statistical inference: continuous model & limma\n\n\nKeegan\n\n\nThu Feb 8\n\n\n1\n\n\nLecture 9: Statistical inference: multiple testing\n\n\nKeegan\n\n\nTue Feb 13\n\n\n1\n\n\nLecture 10: Application of statistical inference to RNA-seq\n\n\nYongjin\n\n\nThu Feb 15\n\n\n\n\nLecture 11: Application of statistical inference to epigenetics\n\n\nYongjin\n\n\nTue Feb 20\n\n\n\n\nReading break (no class)\n\n\n\n\nThu Feb 22\n\n\n\n\nReading break (no class)\n\n\n\n\nTue Feb 27\n\n\n1\n\n\nLecture 12: Gene set enrichment\n\n\nKeegan\n\n\nThu Feb 29\n\n\n\n\nLecture 13: Supervised learning: GWAS and eQTL\n\n\nYongjin\n\n\nTue Mar 5\n\n\n\n\nLecture 14: Supervised learning: polygenic risk prediction\n\n\nYongjin\n\n\nThu Mar 7\n\n\n\n\nLecture 15: Supervised learning: post-GWAS & causal inference\n\n\nYongjin\n\n\nTue Mar 12\n\n\n\n\nLecture 16: Single-cell data analysis: technology and data normalization\n\n\nYongjin\n\n\nThu Mar 14\n\n\n\n\nLecture 17: Unsupervised learning: application of latent factor models to scRNA-seq\n\n\nYongjin\n\n\nTue Mar 19\n\n\n\n\nLecture 18: Unsupervised learning: recent approaches\n\n\nYongjin\n\n\nThu Mar 21\n\n\n\n\nLecture 19: Model-based data analysis\n\n\nYongjin\n\n\nTue Mar 26\n\n\n\n\nLecture 20: Spatial Transcriptomics\n\n\nYongjin\n\n\nThu Mar 28\n\n\n\n\nLecture 21: Multiomics data integration\n\n\nYongjin\n\n\nTue Apr 2\n\n\n\n\nPresentation Prep Time (no class)\n\n\n\n\nThu Apr 4\n\n\n\n\nFinal Project Presentations\n\n\n\n\nTue Apr 9\n\n\n\n\nFinal Project Presentations\n\n\n\n\nThu Apr 11\n\n\n\n\nFinal Project Presentations\n\n\n\n\nSupplemental Files:\nAdditional companion notes can be found in the STAT540-UBC/resources GitHub repo\nSource files for lectures can be found the STAT540-UBC/lectures GitHub repo\n\n\n\n",
      "last_modified": "2024-03-27T16:13:49-07:00"
    },
    {
      "path": "LICENSE.html",
      "author": [],
      "contents": "\nAttribution-NonCommercial 4.0 International\nOfficial translations of this license are available in other languages.\nCreative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. 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For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.\n    If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.\n    If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.\nSection 4 – Sui Generis Database Rights.\nWhere the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:\nfor the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;\nif You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and\nYou must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.\nFor the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.\nSection 5 – Disclaimer of Warranties and Limitation of Liability.\nUnless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. 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Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.\n\nThe disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.\nSection 6 – Term and Termination.\nThis Public License applies for the term of the Copyright and Similar Rights licensed here. 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If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.\nNo term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.\nNothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.\nCreative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” The text of the Creative Commons public licenses is dedicated to the public domain under the CC0 Public Domain Dedication. Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at creativecommons.org/policies, Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses.\nCreative Commons may be contacted at creativecommons.org.\n\n\n",
      "last_modified": "2024-03-27T16:13:50-07:00"
    },
    {
      "path": "seminars.html",
      "title": "Seminars",
      "author": [],
      "contents": "\nSeminars\nTime: Tuesday 12:30pm - 1:30pm\nLocation: Building and room number listed on Canvas\nWe strongly recommend reading the seminar materials prior to attending each seminar\n\n\nDate\n\n\nRepo\n\n\nTopic\n\n\nLead.TA\n\n\nTue Jan 9\n\n\nSeminar 1\n\n\nIntroduction to version control, Git, GitHub\n\n\nIshika\n\n\nTue Jan 16\n\n\nSeminar 2a\n\n\nIntroduction to Markdown, Knitr\n\n\nIshika\n\n\nTue Jan 16\n\n\nSeminar 2b\n\n\nR graphics – Intro to ggplot2\n\n\nIshika\n\n\nTue Jan 23\n\n\nSeminar 3\n\n\nProbability and simulations\n\n\nIshika\n\n\nTue Jan 30\n\n\nSeminar 4\n\n\nReading in data and wrangling\n\n\nAsfar\n\n\nTue Feb 6\n\n\nSeminar 5\n\n\nTwo group testing, fitting and interpreting linear models\n\n\nAsfar\n\n\nTue Feb 13\n\n\nSeminar 6\n\n\nRNA-seq: from beginning to end – BAM to count data\n\n\nIshika\n\n\nTue Feb 20\n\n\n\n\nNo seminar (reading break)\n\n\n\n\nTue Feb 27\n\n\nSeminar 7\n\n\nMethylation microarray analysis\n\n\nAsfar\n\n\nTue Mar 5\n\n\n\n\nNo seminar\n\n\n\n\nTue Mar 12\n\n\nSeminar 8\n\n\nGene set enrichment analysis\n\n\nAsfar\n\n\nTue Mar 19\n\n\nSeminar 9\n\n\nUnsupervised learning - clustering and dimension reduction\n\n\nAsfar\n\n\nTue Mar 26\n\n\nSeminar 10\n\n\nSupervised learning, cross-validation, variable selection\n\n\nIshika\n\n\nTue Apr 2\n\n\n\n\nNo seminar\n\n\n\n\nTue Apr 9\n\n\nSeminar 11\n\n\neQTL analysis\n\n\nIshika\n\n\n\n\n\n",
      "last_modified": "2024-03-27T16:13:56-07:00"
    },
    {
      "path": "submission_guide.html",
      "title": "Submission Guide",
      "author": [],
      "contents": "\nHow to start and submit assignments through GitHub and Gradescope\nYou need to have Git installed on your computer, and have a GitHub account. You can use command line Git or RStudio itself to push, pull, etc. to/from GitHub. See Seminar 1 for an illustrated setup guide and illustration of the full workflow from accepting an assignment to submission.\nStart a new assignment\nAccept: Click on the assignment invitation link for the assignment in Canvas. Each individual assignment/deliverable will have its own link, whereas the group project will only have one link at the start of the project (all deliverables will use the same shared repository). After you accept the assignment, a GitHub repository will be automatically be generated for you.\nClone the repository to your local machine (see Seminar 1 for illustration of this workflow). Now you can begin working on your submission files locally. See below for more detail on repositories and organizing files.\nHow to “turn in” your work: Save, Commit, Push, Submit\nFollow these steps carefully, and be sure they are complete before the deadline of each deliverable. Important Note: the deadline is automated, so you will not be able to perform a late submission (even by one second). Make sure to allow plenty of time for submission!\nPlease include a link to your assignment repository at the top of your main output file.\nSave all the files associated with your solution locally (i.e. for coding assignments R Markdown, GitHub Markdown, and any associated output files - see below for more detail).\nCommit those files to your local Git repository.\nPush the current state of your local repo to your private repo on GitHub. Double check that this was successful, by viewing your repo on GitHub to verify that the most recent commits are visible.\nSubmit the repository on Gradescope via the link in Canvas.\nThat’s it! You can commit/push to update your assignment as often as you’d like before the deadline (frequent commits are actually encouraged). A snapshot of your repo at the deadline will automatically be captured for grading.\n\nIf you’re concerned that something hasn’t gone right with the submission and the deadline is imminent, send the TAs an e-mail with your assignment attached. Note: this is only a last-minute emergency back-up plan, and penalties may apply. We will work with you until you get it submitted via GitHub/Gradescope.\n\nFiles & organization\nIndividual assignments\nUse the repositories that are automatically generated for you when you accept each assignment (e.g., the repo with the format STAT540-UBC-2024/seminar-01-yourGitHubID will contain your Seminar 1 deliverable). Do not use additional branches or any other repositories to submit your course work.\nAll your work for each deliverable should be nicely organized in its repository.\nYour repositories should include a README.md file with a clear description. These are like each assignment’s landing page.\nGroup project\nA shared team repository will be created for your group project; all group-level project deliverables will be submitted in this repo.\nInformation on organizing this repo can be found on the Group Project Rubrics Page\nMain required file types\nIn general for coding assignments, you need to commit and push your R Markdown, GitHub Markdown, and any associated figure/output files with your assignments (note: you should not to commit raw data to your repository - see below). Writing assignments are the exception (e.g. paper critique, project proposal): a Markdown file sufficient.\n\nThis workflow is specific to STAT 540 and may not necessarily reflect your workflow in other contexts.\n\nR Markdown (source file)\nWrite your homework in R Markdown. The file extension should be .Rmd.\nRecommendation: For coding assignments, start with the Markdown or R Markdown file that creates the assignment itself! You can take some things away (unnecessary detail) and add others (R chunks, explanatory text) to morph this into your homework solution.\nGitHub Markdown (output file)\nCompile your homework to GitHub Markdown (file extension should be .md).\nIn the YAML header, specify output: github_document as shown below\nRStudio’s “Knit” button will create the GitHub Markdown file (.md) when the output is specified in this way. The Github Markdown file is a file that can be rendered nicely on GitHub.\n\n---\ntitle: \"Homework assignment\"\nauthor: \"Your Name\"\noutput: github_document\n---\n\nNever ever edit the output Markdown “by hand”. Only edit the R Markdown source and then regenerate the downstream products from that.\nFigure files (output files)\nBy default, any figures created by your .Rmd are placed into a subdirectory titled [filename]_files where [filename] is the basename (without extension) of your .Rmd file.\nThe GitHub Markdown file links to these figure files and, therefore, requires these files to be present to in order to include the figures in your full report.\nFiles not to commit: Data files (input files)\nLocally, you may keep input data files in some logical place within the assignment’s directory. But list the names of such data files in your top-level .gitignore file, so that Git ignores it. Or better yet, include commands that download the data directly (if available and not too large). This is to reduce storage required on GitHub and when the TAs download your repositories for marking.\nGeneral tips for working on the assignments\nStart early. Even if you are already fluent with all the seminar materials, it will still take time to answer all the questions, and prepare files for submission.\nCommit early; commit often (and push). It’s helpful to have an earlier version of your work to return to (e.g. to debug if something suddenly throws an error and you can’t figure out why 🤯, or if your cat walks on your keyboard and deletes your repo 🤷).\nWhen you get stuck or when you run into an error, ask yourself these questions:\nAm I in the right working directory ?\nIs this material covered in one of the lectures or seminars?\nCan I google how to do this?\nIs there an R package that can more efficiently do what I’m attempting now?\nAm I using the right parameters for this function? (hint, type ?function_name() where function_name is the name of the function, to check if you’re using it right)\n\nYour Rmarkdown can’t be knitted? Is it because you didn’t define all the variables inside your Rmarkdown? Your code chunk might run in RStudio if all the variables are all defined in the RStudio environment, but it wouldn’t run as the Rmarkdown is being knitted if the variables aren’t defined in the Rmarkdown document.\nOverall presentation and mechanics refer to the fluency, neatness, easiness to read. For example:\nExplain what you’re doing to show your understanding, i.e. sandwiching your code and result with some explanations and interpretation. We don’t want to see just a graph or some R outputs standing alone in a question.\nUse headings/subheadings to distinguishes different sections/questions\nWell-structured dataframes and reusable functions can often lighten your work load and increase readability of code.\nUse inline R code whenever you refers to the value of a variable in a block of text.\nComment on your code so that everyone, including yourself, can easily follow through your steps. In R, comment follows the number sign. For example # What I did here\nConsider formating R output in a nice table instead of just printing it (e.g. see the kable() function from knitr package)\n\nYou might find cheatsheets from Rstudio useful, in terms of graphing, making an awesome R markdown, etc.\nMake it easy for others to run your code\nIn exactly one, very early R chunk, load any necessary packages, so your dependencies are obvious.\nIn exactly one, very early R chunk, import anything coming from an external file. This will make it easy for someone to see which data files are required, edit to reflect their locals paths if necessary, etc. There are situations where you might not keep data in the repo itself. Ideally, include commands to download if it is available and not too large.\nPretend you are someone else. Clone a fresh copy of your own repo from GitHub, fire up a new RStudio session and try to knit your R markdown file. Does it “just work”? It should!\n\n\n\n\n",
      "last_modified": "2024-03-27T16:13:57-07:00"
    },
    {
      "path": "syllabus.html",
      "title": "Syllabus",
      "description": "STAT 540: Statistical Methods for High Dimensional Biology\n",
      "author": [],
      "contents": "\n\nContents\nLand acknowledgement\nCourse-level learning objectives\nSelected topics\nTeaching Team\nSchedule\nCourse communication\nPrerequisites and Resources\nEvaluation\nAcademic Concession\nAcademic Integrity\nPrivacy\nLearning Environment & Support\nHealth and Safety in the Classroom\nExtreme Environmental Conditions Contingency Plan\n\n2023-2024 Winter Term 2 (January 8, 2024 - April 12, 2024)\nSTAT 540 is a 3 credit course with a mandatory computing seminar\nCross-listed as STAT 540, BIOF 540, GSAT 540\nLand acknowledgement\nWe respectfully recognize that the University of British Columbia Vancouver campus is located on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people. Please take a moment to learn about the territory you are occupying by visiting this interactive indigenous land map.\nCourse-level learning objectives\nPerform exploratory data analysis and visualize genomics data\nApply tailored statistical methods to answer questions using high dimensional biological data\nMake your work reproducible, reusable, and shareable\nWork with real data in a collaborative model\nSelected topics\nBasics of molecular genetics/genomic and data collection assays (methods)\nBasic probability and math foundations\nExploratory data analysis and data quality control\nNormalization, batch correction\nCausal inference and confounding effects\nBasic statistical inference (“one gene at a time”) – linear models\nLarge-scale inference (“genome-wide”) – multiple testing\nAnalysis of microarray, RNASeq, and epigenetics data\nPrincipal Component Analysis and clustering (unsupervised machine learning)\nClassification and cross validation (supervised machine learning)\nGene set analysis and gene networks\nGenome-wide association analysis (GWAS)\nSingle-cell genomics\nTeaching Team\nFor more info on the Teaching team, including brief bios, see the ‘People’ pages on this website (linked below). Links for connecting to recurring virtual office hours will be shared via Canvas.\nInstructors\nKeegan Korthauer, PhD (She/Her/Hers)\nEmail: keegan@stat.ubc.ca\nVirtual office hours: By appointment\nYongjin Park, PhD (He/Him/His)\nEmail: ypp@stat.ubc.ca\nVirtual office hours: By appointment\nTeaching Assistants\nAsfar Lathif (He/Him/His)\nEmail: asfar.lathif@ubc.ca\nVirtual office hours: Tuesday 4-5pm (PST)\nIshika Luthra\nEmail: ishika.luthra@ubc.ca\nVirtual office hours: Friday 10-11am (PST)\nSchedule\nLectures (Sec 201)\nTime: Tues Thurs 9:00 - 10:30am\nLocation: Building and room number listed on Canvas\nSee Lectures for lecture materials and schedule\nSeminars (Sec S2B)\nTime: Tues 12:30pm - 1:30pm\nLocation: Building and room number listed on Canvas\nSee Seminars for schedule and seminar materials\nCourse communication\nAnnouncements\nCourse announcements will be posted on Canvas. You are responsible for checking it regularly.\nGeneral questions\nPiazza (in Canvas) for posting questions (e.g. topics discussed in class, questions about course organization, assignment clarifications, if you are stuck on an assignment and need help). This ensures the message can be seen by the entire teaching team, and that others in the class who might have the same question can learn from responses. You are also welcome to share your input on questions posted by others.\nPrivate matters\nFor private matters (e.g. requesting an extension, scheduling appointment for office hours), please contact the Teaching team by email (listed above). Do not use email to ask general questions described above.\nGroup work\nIn your final project groups, we expect you to (1) arrange regular meetings either in person or virtually and (2) make use of your team’s Piazza group.\nPrerequisites and Resources\nThis interdisciplinary course requires a broad range of skills at the interface of statistics, molecular biology / genomics, and computing. While you may have some background in one or more of the following areas, you are not expected to be an expert in all. That said, to be successful in the course, you may need to spend a bit more time in the areas that you have less experience in. Although there are no official prerequisites for the course, here is a list of useful skills to bring into the course and/or learn along the way.\nStatistics:\nYou should have already taken a university level introductory statistics course.\nThis free online book “Modern Statistics for Modern Biology” by Susan Holmes and Wolfgang Huber is a great reference for introduction or review of many of the statistical concepts that are relevant for this course.\nThis free online book “Data Analysis for the Life Sciences” by Rafael Irizarry and Michael Love is another great resource for introduction or review of many of the statistical concepts relevant in this course, with an emphasis on implementation in R.\nBiology:\nNo requirements, but you are expected to learn things like, e.g. the difference between DNA and RNA, and the difference between a gene and a genome.\nSee this video and this slideset for some basic introductory material.\nGo through the (optional, not for a grade) molecular biology quiz on Canvas\nThis free online book “Concepts of Biology” by Fowler, Roush & Wise is a great resource for biological concepts, in particular chapters 6 and 9\nThis free online book “Biology” by Clark, Douglas & Choi goes more in-depth, see Chapters 14, 15, and 16 for material on genetics that is particularly relevant for this course.\nNo matter your prior experience, when you come across a new biological concept during the course or in your research, you might need to spend a bit of time ‘learning as you go’ - this is expected and something I still do regularly in my day-to-day research!\nR:\nNo experience required but be prepared to do a lot of self-guided learning if you haven’t taken other courses on R or used it in your research.\nStart now by installing R and the HIGHLY RECOMMENDED “integrated development environment” (IDE) RStudio - both are free and open source.\nYou should be able to run R on your own computer during each seminar session.\nIf you are new to R, check out this blog post on getting started with R.\nThis free online book “Introduction to Data Science” by Rafael Irizarry is also a great resource for getting more in-depth with R, programming basics, and the tidyverse. In particular see Chapters 1-5:\nChapter 1: Getting Started with R and R Studio\nChapter 2: R Basics\nChapter 3: Programming Basics\nChapter 4: The tidyverse\nChapter 5: Importing data\n\nGit/GitHub and R Markdown:\nIn this course we’ll be using the version control software Git and its web-based hosting and collaborative platform GitHub.\nThe online resource “Happy Git and GitHub for the useR” from Jenny Bryan is a great reference for these tools as we learn them.\nAnother helpful git resource is Hadley Wickham’s webinar “Collaboration and time travel- version control with git, github and RStudio”\nWe’ll learn about using R markdown to generate readable and reproducible reports with code and text, and you’ll be using that a lot in this course - see Chapter 18 of the ‘Happy Git’ resource: “Test drive R markdown”.\nEvaluation\nYou will have three individual assignments, six seminar submissions (one divided into two parts), and one group project. Deadlines are all by 11:59 pm (Pacific time) on the due date. Any submission or modification after the due date will not be graded unless you have requested an extension. If you anticipate having trouble meeting a deadline and need to request an extension/academic concession please reach out in advance via email to the instructors.\nFor more detail on each of these assignments, see the assignments page (the header of each assignment on this page points to the relevant section of the assignments page). Also refer to this visual overview of the timeline.\nFor detailed instructions on how to work and submit assignments for this course, please see the Submission Guide.\nIntro Assignment (5%)\nAn introductory assignment designed to assess basic knowledge of GitHub, R and Rmarkdown\n\nAssignment\nDue Date\nIntro Assignment\nThu 25 January 2024\n\nSeminar completion (20%)\nYou will submit short “deliverables” for each seminar session\nEach seminar session is weighted equally, but the lowest score will be dropped (so that the 10 seminars with highest score will each count for 2% of the final grade).\nThese deliverables give practical experience applying the knowledge that will be helpful on the homework assignment, final project, and (hopefully) your future research.\nEach deliverable is due on the Friday following the TA-led session for that seminar\n\nAssignment\nDue Date\nSeminar 1\nFri 12 January 2024\nSeminar 2a & 2b\nFri 19 January 2024\nSeminar 3\nFri 26 January 2024\nSeminar 4\nFri 02 February 2024\nSeminar 5\nFri 09 February 2024\nSeminar 6\nFri 16 February 2024\nSeminar 7\nFri 01 March 2024\nSeminar 8\nFri 15 March 2024\nSeminar 9\nFri 22 March 2024\nSeminar 10\nFri 29 March 2024\nSeminar 11\nFri 12 April 2024\n\nPaper critique (5%)\nSee here for detailed instructions and rubric.\n\nAssignment\nDue Date\nPaper Critique\nThu 22 February 2024\n\nAnalysis assignment (30%)\nInvolves detailed analysis of real data using R\nThis assignment will assess your ability to understand and apply the methods learned in class\n\nAssignment\nDue Date\nAnalysis Assignment\nThu 29 February 2024\n\nGroup project (40%)\nA semester-long data analysis group project where you will apply the techniques covered in class to a research question of your choosing\nGroups of target size of 4 students will be formed at the beginning of the course\nImportant checkpoints during the term (with deliverables):\nProject Proposal Lightning Talks\nWritten Project Proposal\nProgress Report\nOral presentation\nWritten Report & GitHub repository\nIndividual and Group Evaluation\n\nFor more details on the project components and how groups are selected see the assignments page\nFor detailed grading rubrics of the final project components, see the final project rubric page and the final project presentation rubric\n\nAssignment\nDue Date\nProposal Lightning Talks\nTue 30 January 2024\nWritten Proposal\nThu 08 February 2024\nProgress Report\nThu 07 March 2024\nFinal Report\nTue 02 April 2024\nPresentation Day 1\nThu 04 April 2024\nPresentation Day 2\nTue 09 April 2024\nPresentation Day 3\nThu 11 April 2024\nIndividual & Group Evaluation\nFri 12 April 2024\n\nAcademic Concession\nIf you anticipate having trouble meeting a deadline and need an academic concession, please reach out in advance via email to the instructors. Here is a template you can use for a self-declaration.\nIf you miss class, we suggest you to:\nConsult the class resources on the course website\nUse the class Piazza discussion board to discuss missed material\nCome to virtual office hours\nSeek academic concessions, if applicable\nAcademic Integrity\nNot only is academic integrity is essential to the successful functioning of our course, but adopting best practices will benefit you in your research practice. Make sure you understand UBC’s definitions of academic misconduct and its consequences. Policy guidelines can be found here.\nWhat does academic integrity look like in this course?\nDo your own work. All individual work that you submit should be completed by you and submitted by you. Do not receive or share completed coursework with students who take the course in another term.\nAcknowledge others’ ideas. Scholars build on the work of others, and give credit accordingly. This refers to both outside sources (e.g. from the literature, or AI tools), and inside sources (e.g from your peers).\nLearn to avoid unintentional plagiarism. Visit the Learning Commons’ guide to academic integrity to help you organize your writing as well as understand how to prevent unintentional plagiarism.\nAt any time: if you are unsure if a certain type of assistance is authorized, please ask us.\nPrivacy\nThis course requires students to work on github.com. Please be advised that the material and information you put on GitHub will be stored on servers outside of Canada. Data used for these tools may not be protected, as they have not gone through a Privacy Impact Assessment (PIA) and been identified as FIPPA compliant. When you access GitHub, you will be required to create an account. While this tool has a privacy policy, UBC cannot guarantee security of your private details on servers outside of Canada. Please exercise caution whenever providing personal information. Please feel free to contact UBC (access.and.privacy@ubc.ca) or the support team for GitHub if you have any questions.\nLearning Environment & Support\nWe strive to provide a learning environment where all students can succeed. Please join me in contributing to a classroom culture where everyone feels valued. If you encounter an issue that presents a barrier to your learning, please reach out to us. You can also contact the Ombudsperson for help: https://ombudsoffice.ubc.ca. If you have a documented disability that affects your learning, you may contact the UBC Centre for Acessibility: https://students.ubc.ca/about-student-services/centre-for-accessibility, and contact us as soon as possible if you think you may require accommodation options for course work. Use of class time\nDiscussions during our scheduled class time are intended to relate to the learning outcomes for the course.\nYour mental health and wellbeing impacts your academic performance. Sometimes it is possible to manage challenges on your own, while other times you may need support. UBC is committed to providing student mental health and wellbeing resources, strategies, and services that help you achieve your goals. Visit https://students.ubc.ca/health for more information.\nHealth and Safety in the Classroom\nPlease follow the current UBC Communicable Disease Prevention Guidelines regarding self-monitoring, and staying home if you are sick. Although masks are no longer required on campus, please respect the choices of your fellow students and the instructional team who may continue to wear masks.\nExtreme Environmental Conditions Contingency Plan\nIn-person, on campus activities may need to be cancelled due to issues such as weather conditions (e.g., snow). The most up-to-date information about cancellations will be posted on ubc.ca. Please check ubc.ca often during times when an extreme weather event could disrupt our course activities. Here is what you can expect in the event an in-person lecture or lab session is cancelled:\nDepending on the nature of the planned in-class activities, class may take place over Zoom (in this case, Zoom link will be posted on Canvas), or an alternate activity may be posted on Canvas for you to complete before the next scheduled class. We will communicate via Canvas to announce the specifics for each case that arises as soon as we can.\n\n\n\n",
      "last_modified": "2024-03-27T16:13:59-07:00"
    },
    {
      "path": "timeline.html",
      "title": "Timeline Visualization",
      "author": [],
      "contents": "\n Today’s date is indicated by the vertical red bar. Click the zoom buttons to zoom in on a date. Click and drag horizontally to slide date range and vertically to view all items. Reload the page to reset the view to include all due dates. \n\n\n\n+\n-\n\n\n\n\n\n\n",
      "last_modified": "2024-03-27T16:14:00-07:00"
    }
  ],
  "collections": []
}