patrickCNMartin/ChIPanalyser
ChIPanalyser: Predicting Transcription Factor Binding Sites

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In patrickCNMartin/ChIPanalyser: ChIPanalyser: Predicting Transcription Factor Binding Sites

Introduction - What is this package?

ChIPanalyser provides a quick an easy method to predict and explain TF binding. The package uses a statistical thermodynamic framework to model the bidning of proteins to DNA. ChIPanalyser makes the assumption that the probability that any given sites along the genome is bound by a TF can be explained by four main factors: DNA accessibility, Binding energy, a scaling factor modulating binding energy ($\lambda$) and number of TF bound ($N$) to DNA. Both $N$ and $\lambda$ are inferred internally by maximisng (or minimising) a goodness of fit metric between predictions and actual ChIP-data. The package will produce a ChIP like profile at a base pair level. As opposed to machine learning type frameworks, if these paramters are known by other means (experimentally), ChIPanalyser does require any training to produce ChIP like profiles. Estimated values can directly be plugged into the model. Furthermore, ChIPanalyser helps gain an understanding of the mechanims behind TF binding as described by (Zabet et al 2015 & Martin and Zabet 2019).

Methods - The Model

As described above, ChIPAnalyser is based on an approximation of statistical thermodynamics. The core formula describing TF binding is given by : $$P(N,a,\lambda,\omega){j} = \frac{N \cdot a{j} \cdot e^{(\frac{1}{\lambda} \cdot \omega_{j})}}{N \cdot a_{j} \cdot e^{(\frac{1}{\lambda} \cdot \omega_{j})}+ L \cdot n \cdot [a_{i} \cdot e^{(\frac{1} \lambda \cdot \omega_j)}]_ {i}} $$ with

Work Flow

The next section will be split between the following subsections

Loading Data

Before going through the inner workings of the package and the work flow, this section will quickly demonstrate how to load example datasets stored in the package. This data represents a minimal workable examples for the different functions. All data is derived from real biological data in Drosophila melanogaster (The Drosophila melanogaster genome can be found as a BSgenome ).

library(ChIPanalyser)

#Load data
data(ChIPanalyserData)


# Loading DNASequenceSet from BSgenome object
# We recommend using the latest version of the genome 
# Please ensure that all your data is aligned to the same version of the genome

library(BSgenome.Dmelanogaster.UCSC.dm6)

DNASequenceSet <- getSeq(BSgenome.Dmelanogaster.UCSC.dm6)



#Loading Position Frequency Matrix

PFM <- file.path(system.file("extdata",package="ChIPanalyser"),"BEAF-32.pfm")

#Checking if correctly loaded
ls()

The global environment should now contain a few new variables: DNASequenceSet, PFM, Access, geneRef, top, chip.

IMPORTANT: Data sets provided in the package have been currated to meet the size requirements for Bioconductor packages. Please read the instruction below carefully as we will describe how to incorportate your own data into the pipe line.

NOTE You can download gene reference from (Genome UCSC website)[https://genome.ucsc.edu/]

Quick Start

Step 1 - Extracting Normalised ChIP scores from ChIP-seq datasets

ChIPAnalyser requires ChIP data in order to infer the optimal set of values that will be assigned to bound Molecules and lambda. The package will maximise (or minimise) a goodness of fit metric between the predictions and ChIP data.

If you have inferred or approximated the values to be assigned to $N$ and $\lambda$ by other means, skip this step and go directly to Advanced Work.

chip can be a connection to your ChIP data, a GRanges of your ChIP or data frame (bed format style) loci is a GRanges object containing loci of interest. Default set as NULL (see Advanced Work)

chip <- processingChIP(profile = chip,
                      loci = top,
                      cores = 1)
chip

The output of processingChIP returns a ChIPScore object containing extracted and normalised ChIP scores, your loci of interest and internal parameters associated to the ChIP extractions process.

Step 2 - Computing a PWMs

The model relies on a Postion Weight Matrix. genomicProfiles serve as a way of storing important paramters. More importantly, this object stores intermediate results as you make your way through the analysis pipeline.

From a Position Frequency Matrix:

# PFMs are automatically converted to PWM when build genomicProfiles
GP <- genomicProfiles(PFM = PFM, PFMFormat = "JASPAR")
GP

From a Position Weight Matrix:

GP <- genomicProfiles(PWM=PositionWeightMatrix)

Step 3 - Computing Optimal Parameters

As described above, ChIPanalyser infers the optimal combination of bound molecules and lambda that will maximise (or minimise) a goodness of fit metric. The following function computes the optimal set of parameters and returns intermediate steps of the analysis pipeline. To do so, we will be parsing the follwing: a DNA sequence, a Position Weight Matrix (contained in a genomicProfiles), chromatin States (Accessible DNA - This is optional), and extracted/normalised experimental ChIP score (result of the processingChIP function).

## surpress dependency warnings
optimal <- suppressWarnings(computeOptimal(genomicProfiles = GP,
                        DNASequenceSet = DNASequenceSet,
                        ChIPScore = chip,
                        chromatinState = Access))

NOTE Default Optimal parameters are provided internally as the following:

 ## Lambda Values
 seq(0.25,5,by=0.25)

 ## Bound Molecule Values
 c(1, 10, 20, 50, 100,
    200, 500,1000,2000, 5000,10000,20000,50000, 100000,
    200000, 500000, 1000000)

NOTE To change these parameters see Advanced Work

Step 4 - Extracting Optimal Paramters (Prelim)

Once computed, we will extract the optimal set of parameters.

optimalParam <- optimal$Optimal
optimalParam$OptimalParameters

We can see the opitmal set of parameters suggested by ChIPanalyser.

Step 5 - Plotting Optimal Set of Parameters

Despite ChIPanalyser returning suggested optimal parameters, you may wish to visualise the optimal parameters for each paramter combination and choose your own set of parameters. To this effect, we have implemented an Optimal parameter plotting fucntion.

# Plotting Optimal heat maps
par(oma=c(0,0,3,0))
layout(matrix(1:8,ncol=4, byrow=T),width=c(6,1.5,6,1.5),height=c(1,1))
plotOptimalHeatMaps(optimalParam,layout=FALSE)

Step 6 - Extracting Optimal Set of Parameters with associated data

Once satisfied with your choice of optimal parameters, you can extracted the data associated to those parameters. You can select more that one parameter however the values assigned to each argument must be one of the values used for computing the optimal set of parameters. 

optimalParam <- searchSites(optimal,lambdaPWM = 1.25,BoundMolecules = 10000)

Step 7 - Plotting ChIP_seq like profiles

The final step is to plot the computed predicted profiles. We provide a plotting function to produce predicted profile plots.

plotOccupancyProfile(predictedProfile = optimalParam$ChIPProfiles,
                     ChIPScore = chip,
                     chromatinState = Access,
                     occupancy = optimalParam$Occupancy,
                     goodnessOfFit = optimalParam$goodnessOfFit,
                     geneRef = geneRef)

Advanced Work

In this section we will describe a more in depth work flow. This will include parameter tweakin, annexe functions and some insights into the outputs of each functions.

Step 1 - Parameter Set Up

Some parameters can be changed between each step of the pipeline. These parameters will enable you to tweak and improve the quality of your analysis.

There are many parameters to choose from. These parameters already have default value assigned to them.

## Suggested Parameters to start with.
parameterOptions()

## Changing parameters
PO <- parameterOptions(noiseFilter="sigmoid",chipSd=150,chipMean=150,lociWidth=30000)

NOTE If you wish to do so, you can change all your parameters at this step. These parameters will be parsed through the different steps of the pipeline as long as you parse this object to each step of the analysis.

Step 2 - Extracting Normalised ChIP scores.

In some case you will not have a pre-determined idea of which loci you wish to look at. The processingChIP function offers a few possibilities to work around this issue.

## Top 50 loci based on ChIP score
processingChIP(profile = "/path/to/ChIP",
               loci = NULL,
               reduce = 50,
               parameterOptions = PO)

## Top 50 loci ALSO containing peaks
processingChIP(profile = "/path/to/ChIP",
               loci = NULL,
               reduce = 50,
               peaks = "/path/to/peaks",
               parameterOptions=PO)

## Top 50 loci containing BOTH peaks and Accessible DNA
processingChIP(profile = "/path/to/ChIP",
               loci = NULL,
               reduce = 50,
               peaks = "/path/to/peaks",
               chromatinState = "/path/to/chromatinState",
               parameterOptions = PO)

Loci will be computed internally based on the ChIP score provided. ChIP Scores will be tilled into bins of width equals to the value assigned to the lociWidth argument in the parameterOptions object (see above). The default loci width is set at 20 kbp. Top regions are selected based on ordered ChIP scores.

Most genomic formats are supported by ChIPanalyser (wig, bed, bedGraph, bigWig, bigBed). The "path/to/file" may also be a GRanges object.

processingChIP function returns extracted/Normalised ChIP scores (list of numeric vectors), the loci of inetrest (GRanges), and associated paramters that have been extracted (such as maxSignal and backgroundSignal). The loci are the top n regions as selected by the reduce argument. Using the loci() accessor applied on a ChIPScore object will return a GRanges of selected loci. The scores() accessors applied on a ChIPScore object will return the normalised scores associated to each Locus.

NOTE This function also supports multi core computing.

Step 3 - Position Weight Matrix and Associated Paramters

Computing the PWM from PFMs can be tweaked by using some additional parameters. PWMs depend on Base Pair Frequency in the genome of interest. Either you can provide a vector containing the base pair frequency (A C T G in order) or the genomicProfiles object will compute it internally if you provide a BSgenome/DNAStringSet.

str(genomicProfiles())

GP <- genomicProfiles(PFM=PFM, PFMFormat="JASPAR", BPFrequency=DNASequenceSet)
GP

The genomicProfiles object also contains all parameters described in parameterOptions. This makes the parsing of parameters straight forward between each step of the analysis pipeline. The genomicProfiles object will be updated internally as you provided More parameters. This object will also be the main object that you will parse through each step of the analysis. There are a few different ways to add new paramters:

## Parsing pre computed parameters (processingChIP function)
GP <- genomicProfiles(PFM = PFM, 
                     PFMFormat = "JASPAR",
                     BPFrequency = DNASequenceSet,
                     ChIPScore = ChIPScore)

## Parsing pre assigned function (parameterOptions)
parameterOptions <- parameterOptions(lambdaPWM = c(1,2,3),
                                     boundMolecules = c(5,50,500))
GP <- genomicProfiles(PFM = PFM, 
                     PFMFormat = "JASPAR", 
                     BPFrequency = DNASequenceSet,
                     parameterOptions = parameterOptions)

## Direct parameter assignement

GP <- genomicProfiles(PFM = PFM, 
                     PFMFormat = "JASPAR", 
                     BPFrequency = DNASequenceSet, 
                     lambdaPWM = c(1,2,3),
                     boundMolecules = c(4,500,8000))

NOTE parameterOptions object can be parsed to any function of the analysis pipeline if parameters need to be changed along the way.

Step 4 - Computing Optimal Set of Parameters

The optimal set of parameters can be computed on custom set of values for $N$ and $\lambda$. As described above, there a few ways to modify parameter Options. If you were to assign more than two values to both of these slots, these new values will be used as "Optimal Parameters Combinations". NOTE parameterOptions is inherited by genomicProfiles hence why you can also assign those paramters to the genomicProfiles contructor.

The computeOptimal function offers a few more options that we will briefly describe here.

## Setting custom parameters
OP <- parameterOptions(lambdaPWM = seq(1,10,by=0.5), 
                      boundMolecules = seq(1,100000, length.out=20))

## Computing ONLY Optimal Parameters and MSE as goodness Of Fit metric
optimal <- computeOptimal(genomicProfiles = GP,
                         DNASequenceSet = DNASequenceSet,
                         ChIPScore = chip,
                         chromatinState = Access,
                         parameterOptions = OP,
                         optimalMethod = "MSE",
                         returnAll = FALSE)

### Computing ONLY Optimal Parameters and using Rank slection method
optimal <- computeOptimal(genomicProfiles = GP,
                         DNASequenceSet = DNASequenceSet,
                         ChIPScore = chip,
                         chromatinState = Access,
                         parameterOptions = OP,
                         optimalMethod = "all",
                         rank=TRUE)

When the returnAll argument is set to FALSE, only the optimal parameters Will be return. No internal Data will be returned.

Optimal Parameters are determined by selecting the best perfomring combination of paremeters. The goodness of fit score for each combination is averaged over all regions considered. When the rank argument is set to TRUE, the optimal parameters will be based on which combination of parameters showed the best performance for each regions individually. Parameter combinations are ranked based on how many individual regions performed best with that specific set of parameters.

Finally, optimalMethod argument will enbale you to selected the goodness Of Fit method you wish to use.

Step 5 - Extracting and Plotting Optimal Parameters

Now that you have selected custom parameters, you will want to plot the associated heat maps.

## Extracted Optimal Parameters
optimalParam <- optimal$Optimal

## Plotting heat maps
plotOptimalHeatMaps(optimalParam,overlay=TRUE)

It is possible to plot an overlay of the optimal set of paramters of all goodness Of Fit methods. Using the overlay argument in the plotting function will plot overlay the top 10% of optimal parameters as selected by each Goodness of fit metric.

Step 6 - Computing individual parameter combinations

Let's imagine that when looking at the optimal parameter heat maps, you would like to run a combination of parameters that is not in the ones that had been provided but you do not want to re-compute optimal parameters. Or Let us imagine that you have already an estimate of number of bound molecules. ChIPanalyser provides functions that will enable you to run the piple line on individual parameter combinations. The steps are described as following:

## Creating genomic Profiles object with PFMs and associated parameters
GP <- genomicProfiles(PFM = PFM,
                      PFMFormat = "JASPAR",
                      BPFrequency = DNASequenceSet,
                      lambdaPWM = 1, 
                      boundMolecules = 58794)

## Computing Genome Wide Score required
GW <- computeGenomeWideScores(genomicProfiles = GP,
                              DNASequenceSet = DNASequenceSet,
                              chromatinState = Access)
GW
## Computing PWM score above threshold
pwm <- computePWMScore(genomicProfiles = GW,
                       DNASequenceSet = DNASequenceSet,
                       loci = chip,
                       chromatinState = Access)
pwm
## Computing Occupancy of sites above threshold

occup <- computeOccupancy(genomicProfiles = pwm)
occup
## Compute ChIP seq like profiles

pred <- computeChIPProfile(genomicProfiles = occup,
                          loci = chip)
pred
## Compute goodness Of Fit of model
accu <- profileAccuracyEstimate(genomicProfiles = pred,
                                ChIPScore = chip)
accu

NOTE These functions can compute multiple parameter combinations if needed. computeOptimal is essentially a combination of these functions with a little more magic to it.

For more information on these functions see Parameters Discription

Step 7 - Plotting Single combination

Finally, you can plot your newly computed profiles.

plotOccupancyProfile(predictedProfile=pred,
                     ChIPScore=chip,
                     chromatinState=Access,
                     occupancy=occup,
                     goodnessOfFit=accu,
                     geneRef=geneRef)

In this case we have also added a gene reference object. This object is a GRanges object containing the postion of various genetic elements such as 3'UTR, 5'UTR, introns , etc

NOTE plotOccupancyProfile offers the possibility to customise graphical parameters. Unfortunately, plotOptimalHeatMaps offers limited graphical parameter customisation.

Parameter Description

In the following section, we will describe the different parameters present in both parameterOptions and genomicProfiles. Information concerning arguments to functions are described in the manual pages for each function.

As a reminder, here are the parameter options for the parameterOptions object. Parameters are divided into different categories depending on when they are required internally.

parameterOptions()

As you can see, some of these parameters are used during multiple steps of the analysis. If these paremeters have been changed in either a parameterOptions object or genomicProfiles object, please ensure that you parse these objects to each function. Each function will extract the values you have assigned to each parameter and use those values for analysis. It is possible to update, these parameters between each step of the analysis however, we recommend to set all parameters beforehand to avoid unwanted parameter mismatch.

Next, we will describe the content of genomicProfiles objects. As a reminder, genomicProfiles object have the following structure:

str(genomicProfiles())

As you can see, we are using the structure function to show all internal slots. The genomicProfiles object inherit from parameterOptions, contain slots that are not user updatetable and finally the show method applied to genomicProfiles varies with each step of the analysis. This is intended to reduce information overload when "looking" at an object.

All other parameters have either been explained above or are part of the internal working of the package. These parameters are mainly used to keep track of the advancement of the analysis between each step. They should not be changed by user.

Finally, we provide a list of setter/getter functions for each slot:

    ## Accessors and Setters for parameterOptions and genomicProfiles
    avrageExpPWMScore(obj)
    backgroundSignal(obj)
    backgroundSignal(obj)<-value
    boundMolecules(obj)
    boundMolecules(obj)<-value
    BPFrequency(obj)
    BPFrequency(obj)<-value
    chipMean(obj)
    chipMean(obj)<-value
    chipSd(obj)
    chipSd(obj)<-value
    chipSmooth(obj)
    chipSmooth(obj)<-value
    DNASequenceLength(obj)
    drop(obj)
    lambdaPWM(obj)
    lambdaPWM(obj)<-value
    lociWidth(obj)
    lociWidth(obj)<-value
    maxPWMScore(obj)
    maxSignal(obj)
    maxSignal(obj)<-value
    minPWMScore(obj)
    naturalLog(obj)
    naturalLog(obj)<-value
    noiseFilter(obj)
    noiseFilter(obj)<-value
    noOfSites(obj)
    noOfSites(obj)<-value
    PFMFormat(obj)
    PFMFormat(obj)<-value
    ploidy(obj)
    ploidy(obj)<-value
    PositionFrequencyMatrix(obj)
    PositionFrequencyMatrix(obj)<-value
    PositionWeightMatrix(obj)
    PositionWeightMatrix(obj)<-value
    profiles(obj)
    PWMpseudocount(obj)
    PWMpseudocount(obj)<-value
    PWMThreshold(obj)
    PWMThreshold(obj)<-value
    removeBackground(obj)
    removeBackground(obj)<-value
    stepSize(obj)
    stepSize(obj)<-value
    strandRule(obj)
    strandRule(obj)<-value
    whichstrand(obj)
    whichstrand(obj)<-value
    ## ChIPScore slots accessors 
    loci(obj)
    scores(obj)

Session Info

sessionInfo()

References

Zabet NR, Adryan B (2015) Estimating binding properties of transcription factors from genome-wide binding profiles. Nucleic Acids Res., 43, 84–94.



patrickCNMartin/ChIPanalyser documentation built on Nov. 24, 2022, 12:02 a.m.

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