n a 384-well plate luciferase assay, we drugged 4 receptors (A, B, C, D) with their endogenous agonists at a range of doses. Below is a subset of the resulting data frame (please use the full data frame attached to the original email as a CSV file). Each row of this data frame corresponds to a single well of the 384 well plate. The columns are as follows: receptor: the receptor tested in the well agonist: the endogenous agonist used for a receptor agonist_nM: the dose of the endogenous compound (in nanomolar) ● RLU: raw luminescence of the well (our measure of receptor activity) To answer this 3 part question, we’ll ask you to submit a PDF file containing all of your source code, plots, and written interpretation. (Using something like a Jupyter Notebook with Python or Rmarkdown is recommended, but not required.) You should provide brief explanations of your thought process at each step. Part A: Different receptors have different basal rates of activity. To better facilitate comparisons across between receptors, normalize these raw luciferase values by converting them into fold changes. For our purposes, we'll define fold change like so: fold_change = RLU / basal_RLU (where basal_rlu is the mean of the RLU values of control condition for that receptor, agonist_nM = 0) Briefly, explain your approach. Part B: Using the fold change values you computed in part A, plot the fold change of each receptor as a function of drug concentration (agonist_nM). (Hint: you should log-transform the x-axis values for this.) If you did not use Python or R for Part A, use graphing software of your choice. Otherwise, if you did use Python or R, we recommend using a library from their respective ecosystems (e.g. matplotlib or ggplot). Provide your plot in your report. Part C: Based on your plots in Part B and any other relevant statistics, which receptor has the highest relative induction when treated with its cognate agonist? Which receptor is the noisiest? Justify your answer in under 200 words. receptor compound agonist_nM RLU receptor_A compound_W 0 2470885.497 receptor_A compound_W 0 1789270.197 receptor_A compound_W 0 2104799.913 receptor_A compound_W 0.01 599090.4224 receptor_A compound_W 0.01 2275783.289 receptor_A compound_W 0.01 2164458.042 receptor_A compound_W 0.1 2142907.291 receptor_A compound_W 0.1 1329518.086 receptor_A compound_W 0.1 2266304.973 receptor_A compound_W 1 2448767.109 receptor_A compound_W 1 2601586.346 receptor_A compound_W 1 3248795.427 receptor_A compound_W 10 5065685.193 receptor_A compound_W 10 3550676.845 receptor_A compound_W 10 5068490.021 receptor_A compound_W 100 7079432.72 receptor_A compound_W 100 4569885.878 receptor_A compound_W 100 5224916.085 receptor_A compound_W 1000 6135018.325 receptor_A compound_W 1000 5677260.712 receptor_A compound_W 1000 6640906.347 receptor_B compound_X 0 265583.4984 receptor_B compound_X 0 134430.8882 receptor_B compound_X 0 223424.6271 receptor_B compound_X 0.01 171236.9694 receptor_B compound_X 0.01 101314.32 receptor_B compound_X 0.01 105333.8029 receptor_B compound_X 0.1 250599.4841 receptor_B compound_X 0.1 45726.39807 receptor_B compound_X 0.1 252500.7411 receptor_B compound_X 1 285226.504 receptor_B compound_X 1 427706.8518 receptor_B compound_X 1 493377.1034 receptor_B compound_X 10 1257457.597 receptor_B compound_X 10 1158986.806 receptor_B compound_X 10 1135125.955 receptor_B compound_X 100 1709292.623 receptor_B compound_X 100 1873679.589 receptor_B compound_X 100 2219088.884 receptor_B compound_X 1000 2083068.448 receptor_B compound_X 1000 2305974.292 receptor_B compound_X 1000 1941301.16 receptor_C compound_Y 0 659961.292 receptor_C compound_Y 0 777977.8438 receptor_C compound_Y 0 197378.4805 receptor_C compound_Y 0.01 741371.5868 receptor_C compound_Y 0.01 600253.4706 receptor_C compound_Y 0.01 522800.5522 receptor_C compound_Y 0.1 313671.2273 receptor_C compound_Y 0.1 1090044.126 receptor_C compound_Y 0.1 508716.6103 receptor_C compound_Y 1 1386562.236 receptor_C compound_Y 1 1128907.338 receptor_C compound_Y 1 715454.5016 receptor_C compound_Y 10 1315053.313 receptor_C compound_Y 10 3654814.694 receptor_C compound_Y 10 3218782.999 receptor_C compound_Y 100 3488266.691 receptor_C compound_Y 100 1756467.408 receptor_C compound_Y 100 3809924.457 receptor_C compound_Y 1000 6514460.483 receptor_C compound_Y 1000 5134059.146 receptor_C compound_Y 1000 210592.151 receptor_D compound_Z 0 1218379.796 receptor_D compound_Z 0 921714.6684 receptor_D compound_Z 0 943014.2516 receptor_D compound_Z 0.01 1370294.663 receptor_D compound_Z 0.01 1047448.337 receptor_D compound_Z 0.01 864027.7288 receptor_D compound_Z 0.1 2673525.891 receptor_D compound_Z 0.1 2640751.178 receptor_D compound_Z 0.1 2363862.94 receptor_D compound_Z 1 5252432.909 receptor_D compound_Z 1 4309527.511 receptor_D compound_Z 1 4394526.863 receptor_D compound_Z 10 8179025.739 receptor_D compound_Z 10 8044751.04 receptor_D compound_Z 10 6614506.353 receptor_D compound_Z 100 6600225.426 receptor_D compound_Z 100 5454973.734 receptor_D compound_Z 100 8783141.624 receptor_D compound_Z 1000 9747975.333 receptor_D compound_Z 1000 7616401 receptor_D compound_Z 1000 6124908.904
In a 384-well plate luciferase assay, we drugged 4 receptors (A, B, C, D) with their endogenous agonists at a range of doses. Below is a subset of the resulting data frame (please use the full data frame attached to the original email as a CSV file).
Each row of this data frame corresponds to a single well of the 384 well plate. The columns are as follows:
- receptor: the receptor tested in the well
- agonist: the endogenous agonist used for a receptor
- agonist_nM: the dose of the endogenous compound (in nanomolar) ● RLU: raw luminescence of the well (our measure of receptor activity)
To answer this 3 part question, we’ll ask you to submit a PDF file containing all of your source code, plots, and written interpretation. (Using something like a Jupyter Notebook with Python or Rmarkdown is recommended, but not required.) You should provide brief explanations of your thought process at each step.
Part A:
Different receptors have different basal rates of activity. To better facilitate comparisons across between receptors, normalize these raw luciferase values by converting them into fold changes. For our purposes, we'll define fold change like so:
fold_change = RLU / basal_RLU
(where basal_rlu is the mean of the RLU values of control condition for that receptor, agonist_nM = 0)
Briefly, explain your approach.
Part B:
Using the fold change values you computed in part A, plot the fold change of each receptor as a function of drug concentration (agonist_nM). (Hint: you should log-transform the x-axis values for this.) If you did not use Python or R for Part A, use graphing software of your choice. Otherwise, if you did use Python or R, we recommend using a library from their respective ecosystems (e.g. matplotlib or ggplot).
Provide your plot in your report.
Part C:
Based on your plots in Part B and any other relevant statistics, which receptor has the highest relative induction when treated with its cognate agonist? Which receptor is the noisiest? Justify your answer in under 200 words.
|
receptor |
compound |
agonist_nM |
RLU |
|
receptor_A |
compound_W |
0 |
2470885.497 |
|
receptor_A |
compound_W |
0 |
1789270.197 |
|
receptor_A |
compound_W |
0 |
2104799.913 |
|
receptor_A |
compound_W |
0.01 |
599090.4224 |
|
receptor_A |
compound_W |
0.01 |
2275783.289 |
|
receptor_A |
compound_W |
0.01 |
2164458.042 |
|
receptor_A |
compound_W |
0.1 |
2142907.291 |
|
receptor_A |
compound_W |
0.1 |
1329518.086 |
|
receptor_A |
compound_W |
0.1 |
2266304.973 |
|
receptor_A |
compound_W |
1 |
2448767.109 |
|
receptor_A |
compound_W |
1 |
2601586.346 |
|
receptor_A |
compound_W |
1 |
3248795.427 |
|
receptor_A |
compound_W |
10 |
5065685.193 |
|
receptor_A |
compound_W |
10 |
3550676.845 |
|
receptor_A |
compound_W |
10 |
5068490.021 |
|
receptor_A |
compound_W |
100 |
7079432.72 |
|
receptor_A |
compound_W |
100 |
4569885.878 |
|
receptor_A |
compound_W |
100 |
5224916.085 |
|
receptor_A |
compound_W |
1000 |
6135018.325 |
|
receptor_A |
compound_W |
1000 |
5677260.712 |
|
receptor_A |
compound_W |
1000 |
6640906.347 |
|
receptor_B |
compound_X |
0 |
265583.4984 |
|
receptor_B |
compound_X |
0 |
134430.8882 |
|
receptor_B |
compound_X |
0 |
223424.6271 |
|
receptor_B |
compound_X |
0.01 |
171236.9694 |
|
receptor_B |
compound_X |
0.01 |
101314.32 |
|
receptor_B |
compound_X |
0.01 |
105333.8029 |
|
receptor_B |
compound_X |
0.1 |
250599.4841 |
|
receptor_B |
compound_X |
0.1 |
45726.39807 |
|
receptor_B |
compound_X |
0.1 |
252500.7411 |
|
receptor_B |
compound_X |
1 |
285226.504 |
|
receptor_B |
compound_X |
1 |
427706.8518 |
|
receptor_B |
compound_X |
1 |
493377.1034 |
|
receptor_B |
compound_X |
10 |
1257457.597 |
|
receptor_B |
compound_X |
10 |
1158986.806 |
|
receptor_B |
compound_X |
10 |
1135125.955 |
|
receptor_B |
compound_X |
100 |
1709292.623 |
|
receptor_B |
compound_X |
100 |
1873679.589 |
|
receptor_B |
compound_X |
100 |
2219088.884 |
|
receptor_B |
compound_X |
1000 |
2083068.448 |
|
receptor_B |
compound_X |
1000 |
2305974.292 |
|
receptor_B |
compound_X |
1000 |
1941301.16 |
|
receptor_C |
compound_Y |
0 |
659961.292 |
|
receptor_C |
compound_Y |
0 |
777977.8438 |
|
receptor_C |
compound_Y |
0 |
197378.4805 |
|
receptor_C |
compound_Y |
0.01 |
741371.5868 |
|
receptor_C |
compound_Y |
0.01 |
600253.4706 |
|
receptor_C |
compound_Y |
0.01 |
522800.5522 |
|
receptor_C |
compound_Y |
0.1 |
313671.2273 |
|
receptor_C |
compound_Y |
0.1 |
1090044.126 |
|
receptor_C |
compound_Y |
0.1 |
508716.6103 |
|
receptor_C |
compound_Y |
1 |
1386562.236 |
|
receptor_C |
compound_Y |
1 |
1128907.338 |
|
receptor_C |
compound_Y |
1 |
715454.5016 |
|
receptor_C |
compound_Y |
10 |
1315053.313 |
|
receptor_C |
compound_Y |
10 |
3654814.694 |
|
receptor_C |
compound_Y |
10 |
3218782.999 |
|
receptor_C |
compound_Y |
100 |
3488266.691 |
|
receptor_C |
compound_Y |
100 |
1756467.408 |
|
receptor_C |
compound_Y |
100 |
3809924.457 |
|
receptor_C |
compound_Y |
1000 |
6514460.483 |
|
receptor_C |
compound_Y |
1000 |
5134059.146 |
|
receptor_C |
compound_Y |
1000 |
210592.151 |
|
receptor_D |
compound_Z |
0 |
1218379.796 |
|
receptor_D |
compound_Z |
0 |
921714.6684 |
|
receptor_D |
compound_Z |
0 |
943014.2516 |
|
receptor_D |
compound_Z |
0.01 |
1370294.663 |
|
receptor_D |
compound_Z |
0.01 |
1047448.337 |
|
receptor_D |
compound_Z |
0.01 |
864027.7288 |
|
receptor_D |
compound_Z |
0.1 |
2673525.891 |
|
receptor_D |
compound_Z |
0.1 |
2640751.178 |
|
receptor_D |
compound_Z |
0.1 |
2363862.94 |
|
receptor_D |
compound_Z |
1 |
5252432.909 |
|
receptor_D |
compound_Z |
1 |
4309527.511 |
|
receptor_D |
compound_Z |
1 |
4394526.863 |
|
receptor_D |
compound_Z |
10 |
8179025.739 |
|
receptor_D |
compound_Z |
10 |
8044751.04 |
|
receptor_D |
compound_Z |
10 |
6614506.353 |
|
receptor_D |
compound_Z |
100 |
6600225.426 |
|
receptor_D |
compound_Z |
100 |
5454973.734 |
|
receptor_D |
compound_Z |
100 |
8783141.624 |
|
receptor_D |
compound_Z |
1000 |
9747975.333 |
|
receptor_D |
compound_Z |
1000 |
7616401 |
|
receptor_D |
compound_Z |
1000 |
6124908.904 |
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