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DATA3888 Assignment

创建日期：2024-05-21 15:38:51

所属分类：统计学statistics

标签： DATA3888

Visualising data

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DATA3888 Assignment

Instructions
1. Your assignment submission needs to be a HTML document that you have compiled using R Markdown
or Quarto. Name your file as SIDXXX_Assignment.html” where XXX is your Student ID.
2. Under author, put your Student ID at the top of the Rmd file (NOT your name).
3. For your assignment, please use set.seed(3888) at the start of each chunk (where required).
4. Do not upload the code file (i.e. the Rmd or qmd file).
5. You must use code folding so that the marker can inspect your code where required.
6. Your assignment should make sense and provide all the relevant information in the text when the code
is hidden. Don’t rely on the marker to understand your code.
7. Any output that you include needs to be explained in the text of the document. If your code chunk
generates unnecessary output, please suppress it by specifying chunk options like message = FALSE.
8. Start each of the 3 questions in a separate section. The parts of each question should be in the same
section.
9. You may be penalised for excessive or poorly formatted output.
Question 1 - Case Study 1 (Reef): Visualising data
Sully and colleagues have curated a public dataset containing characteristics linked to coral bleaching over the
last two decades. The data is in the file Reef_Check_with_cortad_variables_with_annual_rate_of_SST_change.csv,
and the authors curated coral bleaching events at 3351 locations in 81 countries from 1998 to 2017. The full
description of the variables can be found in the supplementary table of the study.
a. In the paper, the authors claim “the highest probability of coral bleaching occurred at tropical mid-
latitude sites (15–20 degrees north and south of the Equator)”. Create an informative map visualisation
to explore this claim and comment on what you can learn from your visualisation.
b. A researcher wants to investigate coral bleaching events around the world as they occurred
from 1998 to 2017. Create an interactive map visualisation, representing the information you think
would be important. Justify your choice of visualisation, and comment on what you can learn from
your visualisation.
1
Question 2 - Case Study 2 (Kidney): Blood vs Biopsy Biomarker
for classification
In the data GSE46474, we estimated the accuracy for our predictive model in graft rejection from peripheral
blood gene expression dataset. However, rejection is a very active process that occurs in the kidney itself.
Here we will look at a similar kidney microarray dataset. Therefore, instead of genes being isolated and
sequenced from blood, we examine another dataset GSE138043 where the samples have been sequenced from
a kidney biopsy.
a. In each of the GSE46474 and GSE138043 datasets, use the topTable function in the limma package
to output the most differentially expressed genes between patients that experience graft rejection and
stable patients. Which genes are overlapped between the top 300 differentially expressed genes for each
dataset? In other words, which genes can be found in the top 300 differentially expressed genes for
BOTH datasets?
Hint. In the GSE46474 dataset, the outcome is found in the title column of the featureData and the gene
symbols are found the in Gene Symbol column of the featureData. In the GSE138043 dataset, the outcome
is found in the characteristics_ch1 column of the featureData and the gene symbols are found the in
gene_assignment column of the featureData, between the first and second // symbols.
b. Consider the following framework for cross-validation for a support vector machine (SVM) classifier.
Framework 1. Identify the 50 most differentially expressed genes from the entire dataset. Subset the entire
dataset to the 50 most differentially expressed genes. Randomly split the data into training and testing sets
(80:20 split). Build a SVM classifier on the training set. Calculate the accuracy of the classifier when applied
on the testing set.
For each of the GSE46474 and GSE138043 datasets, use repeated 5-fold cross validation (with 50 repeats),
following the framework above, to estimate the accuracy of graft survival prediction (rejection or stable).
Show your results in a visualisation and comment on the result.
c. Consider the following framework for cross-validation for a support vector machine (SVM) classifier.
Framework 2. Randomly split the entire dataset into training and testing sets (80:20 split). Identify the 50
most differentially expressed genes from the training data. Subset both the training and testing data to the
50 most differentially expressed genes. Build a SVM classifier on the training set. Calculate the accuracy of
the classifier when applied on the testing set.
For each of the GSE46474 and GSE138043 datasets, use repeated 5-fold cross validation (with 50 repeats),
following the framework above, to estimate the accuracy of graft survival prediction (rejection or stable).
Show your results in a visualisation and comment on the result.
d. Compare all the results from b and c using an appropriate graphic. Which of framework 1 or framework
2 is more valid? Is using blood or biopsy more accurate? Justify your answers.
2
Question 3: Case Study 3 (Brain): Streaming classifier for Brain-box
A physics instructor Zoe has created a data set stored under zoe_spiker.zip that contains brain signal
series (each series is a file) which corresponds to sequences of eye movements of varying lengths. The file
name corresponds to the true eye movement. For example the file LRL_z.wav corresponds to left-right-left
eye movements; the file LLRLRLRL_z.wav corresponds to left-left-right-left-right-left-right-left eye movements.
There are a total of 31 files.
a. Build a classification rule for detecting a series of {L, R} under a streaming condition where the function
will take a sequence of signals as an input. Explain how your classification rule works.
Note. Your function should take the entire .wav file as an input, but should run through the .wav file under
streaming conditions (e.g., by considering overlapping/rolling windows in the signal).
b. Create a metric to estimate the accuracy of your classifier on the length 3 wave files, justifying your
choice. Comment on the performance of your classifier (ie. is it reasonable for this context?).
c. Compare at least four different classification rules on the length 3 wave files, using the metric you
created. (This may include changing the parameters, different rules to identify events from non-events,
or different rules to identify left-movement from right-movement). What is your best model? Justify
your answer with appropriate visualisations.
d. For the best model that you found in part c, evaluate its performance on sequences of varying lengths.
Does the length of the sequence have an impact on the classification accuracy? Justify your answer
with appropriate visualisations.

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FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 MATH 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CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 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