analysis of drug-gene interaction data florian ganglberger sebastian nijman lab
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Analysis of Drug-Gene Interaction Data
Florian GanglbergerSebastian Nijman Lab
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Nijman Lab
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Nijman Lab
• Working on specialised target-oriented cancer therapies
• Cancer = cell mutation
Drug
Mutation
Mutation
Mutation
Mutation
Mutation
Mutation
Drug
Drug
Drug
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Motivation
• Testing various drugs on various mutated cells
• 100 drugs vs 100 mutations = 10.000 interactions
• Analyse the generated data to find new treatments
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Overview
• Background– Biological Background– Technical Procedure– Initial State– Special Aspects– Previous Approach
• Analysis– Explorative Data Analysis– Drug Noisiness
Data generation
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Overview
• Hit detection– Statistical Methods– Filtering Methods– The Algorithm– Evaluation of the result
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Biological Background
• Idea behind cancer treatment– Kill cancer cells while leaving normal cells
alive
• Common chemotherapies– Kill cells with higher division rate– Problem: moth-, throat-, bowel-mucosa and
hair cells– Feel sick, loosing hair etc.
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Biological Background
• Synthetic lethality approach– Some biochemical process which are
necessary for cell growth are redundant– e.g. DNA repair– Biochemical processes are chained
= “protein pathway”
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Protein pathways
Protein A
Protein B
Protein C
Cell growth
Drug Gene
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Synthetic lethality
• Choose a cancer which has a mutation of a gene in one of that pathways
• Find a drug which inhibits the other pathway
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Synthetic lethality
• Produce cells with mutations which are normally present in cancer
• Find drug• Possible that this will work in real cancer
– Tumours have more than one mutation can influence each other
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Technical Procedure
• Standard dataset consists of 38.400 interactions
• 96 drugs x 100 mutations x 4 • Testing would be inefficient
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Technical Procedure
• Idea: Testing different cell lines in one well
384 wells
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Before the experiment
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Before the experiment
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After the experiment
• Copy the barcodes of the cells by a polymerase chain reaction (PCR) amplifies the signal
• Adding a vitamin to the barcode which can stick on a dye-containing protein
• Amount of barcode correlates with the amount of remaining cells
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After the experiment
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Allocation
• Red and infrared emitted light barcode mutation
• Green reflected light cell amount – Arbitrary unit which correlates with the cell
amount– Called “Reporter”
• Drug because of the used well
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Initial state
• Because drugs are dissolved in a dilution, we can use wells without drugs use as control
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Back to statistics....
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Special Aspects
• Biological and technical factors cause noisy and not directly usable data Inter- and intraindividual variability
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Interindividual Variability
• Variability between observation units• Cells with the same mutation = one
observation unit = “one virtual cancer patient”
• Variation among different mutated cells• Reasons
– Mutations can be toxic itself – Characteristics of the technical process
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Interindividual Variability
• Average amount of remaining mutations
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Variability of Technical Procedure
• Limited precision– Precision of drug dosing– Precision of cell amount– Quality of the measurement equipment
• Decreased sensitivity to a lower signal– Detection limit– Killed cells don’t get a zero signal
background noise with different variability
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Variability of Technical Procedure
• Amplification problems– Copying the barcodes by PCR needs material – If some cell lines are completely killed
more material for other cell lines higher amplification of survived cells
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Amplification Problems
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Previous Approach
• Visual method, based on scatter plots• Identify outliers visually
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Previous Approach
1. Calculating the effect1. Median normalization of drugs
2. Calculate a relative ratio
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• Plotting the ratio against the median of a mutation
Previous Approach
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There are some problems....
• If two lines overlap, hits can be obscured• No comparable value that estimates the
significance of outliers• Intraindividual variability referred to
replicates is ignored• Human errors outlier-detection is
subjective• Slow, not automatable method
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Overview
• Background– Biological Background– Technical Procedure– Initial State– Special Aspects– Previous Approach
• Analysis– Explorative Data Analysis– Drug Noisiness
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Explorative Data Analysis
• Necessary for hit detection• Analysis of the behaviour of the data• Closer look at
– Distribution of mutations– Variability of mutations and replicates– Skewness of mutations– Noisiness of Drugs
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Distribution of Mutations
• Choosing the right statistical test• Test will be applied on mutations to see
which drug works best• Effect is point of interest Matrix of
relative ratios
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Variability of Mutations
• Decreased sensitivity to lower signal• Maybe a detection limit• Spread vs Level plot
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Replicate Variability
• Important factor is the multiple testing of cells by the same drugs.
• Indicator for accurateness and reproducibility of the technical procedure.
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Skewness of Mutations
• Another indicator for different behaviour below the threshold
• Right skewed distributions because of background noise in lower signal
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Drug Noisiness
• Nothing to do with background noise• Caused by technical procedure
– Overdosing of cells or drugs– Toxicity (“Dosis facit venenum“)
• Different effect– Strong resistance– Strong sensitivity
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Amplification Problems
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Strong Noisiness
• Easy to identify• Dedicated outliers• High amount of false positive hits• Idea: Noisiness causes weak correlation
to the control
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Weak Noisiness
• Also numerous differences in sensitivity or resistance
• Contrast to normal drugs is not well defined
• Visual methods failed• Also a lot of false positive hits
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Strong Noisiness vs Weak Noisiness
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Overview
• Hit detection– Statistical Methods– Filtering Methods– The Algorithm– Evaluation of the result
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Hit detection
• Definition of a Hit– Indicate synthetic lethality – Resistance is also interesting from a
biological point of view– Not noisy
• 2 Stages:1.Finding potential hits 2.Filtering false-positive hits and incomparable
data
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Statistical Test
• Mutations not normally distributed• Compare the 4 replicates to their
mutation• Mann-Whithney u-test
– Compares two medians – Needs approximately identical distribution
form of random variables X and Y– No symmetry or normal distribution needed
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Statistical Test
• Disadvantages– Rank-sum tests are based on the order, not
on the magnitudes– Weak outlying interactions get the same p-
values as strong outliers– P-values are not interindividual comparable,
but the significance is an indicator for it.– Strong noisy drugs are usually extreme
outliers reduce the significance
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Multiple testing
• Multiple testing of interactions against their mutations
• Increases the error• 100 different interactions• =
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Multiple testing
• Bonferroni correction needed• How to achieve significant results?
– Calculate the median of replicates– Testing just the upper and lower 10% of the
data
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Filtering Drugs
• Filtering strong noisy drugs by correlation coefficient
• Filter before the test to increase the significance
• Note: Drugs shouldn’t be filtered automatically, just identified. If drugs are toxic or not is the decision of a biologist
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Filtering strong noisy drugs
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Filtering weak noisy drugs
• Much harder to identify • Idea: Weak noisy drugs producing many
false-positive hits with high significance– Calculating p-value– Order by significance– Frequency of drugs in the top hits is an
indicator for weak noisiness
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Top Drugs
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Filter Mutations
Filter data below a detection limitIdeas• Filter by threshold: 30% of the data
just one dataset no universal validity of the threshold about 250
• Filter by skewness: 17% of the data• Filter by variationcoefficient 12%
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Threshold Estimation
• Idea: Modification of skewness filter method
• Outliers of skewness are below the threshold
• Last non-outlier above the skewness outliers are normal data
• Threshold should be approximately in the middle of these points
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The Algorithm
• R-Demo
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Results