LBMC » NucleoMiner

Arabic to Roman pair list.

--------------------------

Description

~~~~~~~~~~~

Util to convert Arabicto Roman

Usage

~~~~~

::

    ARAB2ROM()

Author(s)

~~~~~~~~~

Florent Chuffart

R: False Discovery Rate

False Discovery Rate

--------------------

Description

~~~~~~~~~~~

From a vector x of independent p-values, extract the cutoff

corresponding to the specified FDR. See Benjamini & Hochberg 1995 paper

Usage

~~~~~

::

    FDR(x, FDR)

Arguments

~~~~~~~~~

``x``

A vector x of independent p-values.

``FDR``

The specified FDR.

Value

~~~~~

Return the the corresponding cutoff.

Author(s)

~~~~~~~~~

Gael Yvert, Florent Chuffart

Examples

~~~~~~~~

::

    print("example")

R: Roman to Arabic pair list.

Roman to Arabic pair list.

--------------------------

Description

~~~~~~~~~~~

Util to convert Roman to Arabic

Usage

~~~~~

::

    ROM2ARAB()

Author(s)

~~~~~~~~~

Florent Chuffart

R: Aggregate replicated sample's nucleosomes.

Aggregate replicated sample's nucleosomes.

------------------------------------------

Description

~~~~~~~~~~~

This function aggregates nucleosome for replicated samples. It uses

TemplateFilter ouput of each sample as replicate. Each sample owns a set

of nucleosomes computed using TemplateFilter and ordered by the position

of their center. Adajacent nucleosomes are compared two by two.

Comparison is based on a log likelihood ratio score. The issue of

comparison is adjacents nucleosomes merge or separation. Finally the

function returns a list of clusters and all computed *lod\_scores*. Each

cluster ows an attribute *wp* for "well positionned". This attribute is

set as *TRUE* if the cluster is composed of exactly one nucleosomes of

each sample.

Usage

~~~~~

::

    aggregate_intra_strain_nucs(samples, lod_thres = -20, coord_max = 2e+07)

Arguments

~~~~~~~~~

``samples``

A list of samples. Each sample is a list like *sample = list(id=...,

marker=..., strain=..., roi=..., inputs=..., outputs=...)* with *roi =

list(name=..., begin=..., end=..., chr=..., genome=...)*.

``lod_thres``

Log likelihood ration threshold.

``coord_max``

A too big value to be a coord for a nucleosome lower bound.

Value

~~~~~

Returns a list of clusterized nucleosomes, and all computed lod scores.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

::

    # Dealing with a region of interest

    roi =list(name="example", begin=1000,  end=1300, chr="1", genome=rep("A",301))

    samples = list()

    for (i in 1:3) {

        # Create TF output

        tf_nuc = list("chr"=paste("chr", roi$chr, sep=""), "center"=(roi$end + roi$begin)/2, "width"= 150, "correlation.score"= 0.9)

        outputs = dfadd(NULL,tf_nuc)

        outputs = filter_tf_outputs(outputs, roi$chr, roi$begin, roi$end)

        # Generate corresponding reads

        nb_reads = round(runif(1,170,230))

        reads = round(rnorm(nb_reads, tf_nuc$center,20))

        u_reads = sort(unique(reads))

        strands = sample(c(rep("R",ceiling(length(u_reads)/2)),rep("F",floor(length(u_reads)/2))))

        counts = apply(t(u_reads), 2, function(r) { sum(reads == r)})

        shifts = apply(t(strands), 2, function(s) { if (s == "F") return(-tf_nuc$width/2) else return(tf_nuc$width/2)})

        u_reads = u_reads + shifts

        inputs = data.frame(list("V1" = rep(roi$chr, length(u_reads)), 

                                 "V2" = u_reads, 

                                                         "V3" = strands, 

                                                         "V4" = counts), stringsAsFactors=FALSE)

        samples[[length(samples) + 1]] = list(id=1, marker="Mnase_Seq", strain="strain_ex", total_reads = 10000000, roi=roi, inputs=inputs, outputs=outputs)

    }

    print(aggregate_intra_strain_nucs(samples))

R: Aligns nucleosomes between 2 strains.

Aligns nucleosomes between 2 strains.

-------------------------------------

Description

~~~~~~~~~~~

This function aligns nucs between two strains for a given genome region.

Usage

~~~~~

::

    align_inter_strain_nucs(replicates, wp_nucs_strain_ref1 = NULL, 

        wp_nucs_strain_ref2 = NULL, corr_thres = 0.5, lod_thres = -100, 

        config = NULL, ...)

Arguments

~~~~~~~~~

``replicates``

Set of replicates, ideally 3 per strain.

``wp_nucs_strain_ref1``

List of aggregates nucleosome for strain 1. If it's null this list will

be computed.

``wp_nucs_strain_ref2``

List of aggregates nucleosome for strain 2. If it's null this list will

be computed.

``corr_thres``

Correlation threshold.

``lod_thres``

LOD cut off.

``config``

GLOBAL config variable

``...``

A list of parameters that will be passed to

*aggregate\_intra\_strain\_nucs* if needed.

Value

~~~~~

Returns a list of clusterized nucleosomes, and all computed lod scores.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

::

        # Define new translate_roi function...

        translate_roi = function(roi, strain2, big_roi=NULL, config=NULL) {

          return(roi)

        }

        # Binding it by uncomment follwing lines.

        unlockBinding("translate_roi", as.environment("package:nucleominer"))

        unlockBinding("translate_roi", getNamespace("nucleominer"))

        assign("translate_roi", translate_roi, "package:nucleominer")

        assign("translate_roi", translate_roi, getNamespace("nucleominer"))

        lockBinding("translate_roi", getNamespace("nucleominer"))

        lockBinding("translate_roi", as.environment("package:nucleominer"))  

    # Dealing with a region of interest

    roi =list(name="example", begin=1000,  end=1300, chr="1", genome=rep("A",301), strain_ref1 = "STRAINREF1")

    roi2 = translate_roi(roi, roi$strain_ref1)

    replicates = list()

    for (j in 1:2) {

        samples = list()

        for (i in 1:3) {

            # Create TF output

            tf_nuc = list("chr"=paste("chr", roi$chr, sep=""), "center"=(roi$end + roi$begin)/2, "width"= 150, "correlation.score"= 0.9)

            outputs = dfadd(NULL,tf_nuc)

            outputs = filter_tf_outputs(outputs, roi$chr, roi$begin, roi$end)

            # Generate corresponding reads

            nb_reads = round(runif(1,170,230))

            reads = round(rnorm(nb_reads, tf_nuc$center,20))

            u_reads = sort(unique(reads))

            strands = sample(c(rep("R",ceiling(length(u_reads)/2)),rep("F",floor(length(u_reads)/2))))

            counts = apply(t(u_reads), 2, function(r) { sum(reads == r)})

            shifts = apply(t(strands), 2, function(s) { if (s == "F") return(-tf_nuc$width/2) else return(tf_nuc$width/2)})

            u_reads = u_reads + shifts

            inputs = data.frame(list("V1" = rep(roi$chr, length(u_reads)), 

                                     "V2" = u_reads, 

                                                             "V3" = strands, 

                                                             "V4" = counts), stringsAsFactors=FALSE)

            samples[[length(samples) + 1]] = list(id=1, marker="Mnase_Seq", strain=paste("strain_ex",j,sep=""), total_reads = 10000000, roi=roi, inputs=inputs, outputs=outputs)

        }

        replicates[[length(replicates) + 1]] = samples

    }

    print(align_inter_strain_nucs(replicates))

R: Launch deseq methods.

Launch deseq methods.

---------------------

Description

~~~~~~~~~~~

This function is based on deseq example. It mormalizes data, fit data to

GLM model with and without interaction term and compare the two

l;=models.

Usage

~~~~~

::

    analyse_design(snep_design, reads)

Arguments

~~~~~~~~~

``snep_design``

The design to considere.

``reads``

The data to considere.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Compute Common Uninterrupted Regions (CUR)

Compute Common Uninterrupted Regions (CUR)

------------------------------------------

Description

~~~~~~~~~~~

CURs are regions that can be aligned between the genomes

Usage

~~~~~

::

    compute_inter_all_strain_curs(diff_allowed = 10, min_cur_width = 200, 

        config = NULL, plot = FALSE)

Arguments

~~~~~~~~~

``diff_allowed``

the maximum indel width allowe din a CUR

``min_cur_width``

The minimum width of a CUR

``config``

GLOBAL config variable

``plot``

Plot CURs or not

Author(s)

~~~~~~~~~

Florent Chuffart

R: Crop bound of regions according to region of interest bound

Crop bound of regions according to region of interest bound

-----------------------------------------------------------

Description

~~~~~~~~~~~

The fucntion is no more necessary since we remove "big\_roi" bug in

translate\_roi function.

Usage

~~~~~

::

    crop_fuzzy(tmp_fuzzy_nucs, roi, strain, config = NULL)

Arguments

~~~~~~~~~

``tmp_fuzzy_nucs``

the regiuons to be croped.

``roi``

The region of interest.

``strain``

The strain to consider.

``config``

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

R: Adding list to a dataframe.

Adding list to a dataframe.

---------------------------

Description

~~~~~~~~~~~

Add a list *l* to a dataframe *df*. Create it if *df* is *NULL*. Return

the dataframe *df*.

Usage

~~~~~

::

    dfadd(df, l)

Arguments

~~~~~~~~~

``df``

A dataframe

``l``

A list

Value

~~~~~

Return the dataframe *df*.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

::

    ## Here dataframe is NULL

    print(df)

    df = NULL

    # Initialize df

    df = dfadd(df, list(key1 = "value1", key2 = "value2"))

    print(df)

    # Adding elements to df

    df = dfadd(df, list(key1 = "value1'", key2 = "value2'"))

    print(df)

R: Extract wp nucs from nuc map.

Extract wp nucs from nuc map.

-----------------------------

Description

~~~~~~~~~~~

Function based on common wp nuc index and roi\_index.

Usage

~~~~~

::

    extract_wp(strain_maps, roi_index, strain, tmp_common_nucs)

Arguments

~~~~~~~~~

``strain_maps``

Nuc maps.

``roi_index``

The region of interest index.

``strain``

The strain to consider.

``tmp_common_nucs``

the list of wp nucs.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Prefetch data

Prefetch data

-------------

Description

~~~~~~~~~~~

Fetch and filter inputs and outpouts per region of interest. Organize it

per replicates.

Usage

~~~~~

::

    fetch_mnase_replicates(strain, roi, all_samples, config = NULL, 

        only_fetch = FALSE, get_genome = FALSE, get_ouputs = TRUE)

Arguments

~~~~~~~~~

``strain``

The strain we want mnase replicatesList of replicates. Each replicates

is a vector of sample ids.

``roi``

Region of interest.

``all_samples``

Global list of samples.

``config``

GLOBAL config variable

``only_fetch``

If TRUE, only fetch and not filtering. It is used tio load sample files

into memory before forking.

``get_genome``

If TRUE, load corresponding genome sequence.

``get_ouputs``

If TRUE, get also ouput corresponding TF output files.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Filter TemplateFilter inputs

Filter TemplateFilter inputs

----------------------------

Description

~~~~~~~~~~~

This function filters TemplateFilter inputs according genome area

observed properties. It takes into account reads that are at the

frontier of this area and the strand of these reads.

Usage

~~~~~

::

    filter_tf_inputs(inputs, chr, x_min, x_max, nuc_width = 160, 

        only_f = FALSE, only_r = FALSE)

Arguments

~~~~~~~~~

``inputs``

TF inputs to be filtered.

``chr``

Chromosome observed, here chr is an integer.

``x_min``

Coordinate of the first bp observed.

``x_max``

Coordinate of the last bp observed.

``nuc_width``

Nucleosome width.

``only_f``

Filter only F reads.

``only_r``

Filter only R reads.

Value

~~~~~

Returns filtred inputs.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Filter TemplateFilter outputs

Filter TemplateFilter outputs

-----------------------------

Description

~~~~~~~~~~~

This function filters TemplateFilter outputs according, not only genome

area observerved properties, but also correlation and overlap threshold.

Usage

~~~~~

::

    filter_tf_outputs(tf_outputs, chr, x_min, x_max, nuc_width = 160, 

        ol_bp = 59, corr_thres = 0.5)

Arguments

~~~~~~~~~

``tf_outputs``

TemplateFilter outputs.

``chr``

Chromosome observed, here chr is an integer.

``x_min``

Coordinate of the first bp observed.

``x_max``

Coordinate of the last bp observed.

``nuc_width``

Nucleosome width.

``ol_bp``

Overlap Threshold.

``corr_thres``

Correlation threshold.

Value

~~~~~

Returns filtered TemplateFilter Outputs

Author(s)

~~~~~~~~~

Florent Chuffart

R: flat reads

flat reads

----------

Description

~~~~~~~~~~~

Extract reads coordinates from TempleteFilter input sequence

Usage

~~~~~

::

    flat_reads(reads, nuc_width)

Arguments

~~~~~~~~~

``reads``

TemplateFilter input reads

``nuc_width``

Width used to shift F and R reads.

Value

~~~~~

Returns a list of F reads, R reads and joint/shifted F and R reads.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Retrieve Reads

Retrieve Reads

--------------

Description

~~~~~~~~~~~

Retrieve reads for a given marker, combi, form.

Usage

~~~~~

::

    get_all_reads(marker, combi, form = "wp")

Arguments

~~~~~~~~~

``marker``

The marker to considere.

``combi``

The starin combination to considere.

``form``

The nuc form to considere.

Author(s)

~~~~~~~~~

Florent Chuffart

R: get comp strand

get comp strand

---------------

Description

~~~~~~~~~~~

Compute the complementatry strand.

Usage

~~~~~

::

    get_comp_strand(strand)

Arguments

~~~~~~~~~

``strand``

The original strand.

Value

~~~~~

Returns the complementatry strand.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Build the design for deseq

Build the design for deseq

--------------------------

Description

~~~~~~~~~~~

This function build the design according sample properties.

Usage

~~~~~

::

    get_design(marker, combi, all_samples)

Arguments

~~~~~~~~~

``marker``

The marker to considere.

``combi``

The starin combination to considere.

``all_samples``

Global list of samples.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Compute the fuzzy nucs.

Compute the fuzzy nucs.

-----------------------

Description

~~~~~~~~~~~

This function aggregate non common wp nucs for each strain and substract

common wp nucs. It does not take care about the size of the resulting

fuzzy regions. It will be take into account in the count read part og

the pipeline.

Usage

~~~~~

::

    get_fuzzy(combi, roi, roi_index, strain_maps, common_nuc_results, 

        config = NULL)

Arguments

~~~~~~~~~

``combi``

The strain combination to consider.

``roi``

The region of interest.

``roi_index``

The region of interest index.

``strain_maps``

Nuc maps.

``common_nuc_results``

Common wp nuc maps

``config``

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

R: Compute the list of SNEPs for a given set of marker, strain...

Compute the list of SNEPs for a given set of marker, strain combination and nuc form.

-------------------------------------------------------------------------------------

Description

~~~~~~~~~~~

This function uses

Usage

~~~~~

::

    get_sneps(marker, combi, form, all_samples)

Arguments

~~~~~~~~~

``marker``

The marker involved.

``combi``

The strain combination involved.

``form``

the nuc form involved.

``all_samples``

Global list of samples.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

::

    marker = "H3K4me1"

    combi = c("BY", "YJM") 

    form = "wpfuzzy" # "wp" | "fuzzy" | "wpfuzzy"

    # foo = get_sneps(marker, combi, form)

    # foo = get_sneps("H4K12ac", c("BY", "RM"), "wp")

R: Likelihood ratio

Likelihood ratio

----------------

Description

~~~~~~~~~~~

Compute the likelihood log of two set of value from two models Vs. a

unique model.

Usage

~~~~~

::

    lod_score_vecs(x, y)

Arguments

~~~~~~~~~

``x``

First vector.

``y``

Second vector.

Value

~~~~~

Returns the likelihood ratio.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

::

    # LOD score for 2 set of values

    mean1=5; sd1=2; card2 = 250

    mean2=6; sd2=3; card1 = 200

    x1 = rnorm(card1, mean1, sd1)

    x2 = rnorm(card2, mean2, sd2)  

    min = floor(min(c(x1,x2)))

    max = ceiling(max(c(x1,x2)))

    hist(c(x1,x2), xlim=c(min, max), breaks=min:max)

    lines(min:max,dnorm(min:max,mean1,sd1)*card1,col=2)

    lines(min:max,dnorm(min:max,mean2,sd2)*card2,col=3)

    lines(min:max,dnorm(min:max,mean(c(x1,x2)),sd(c(x1,x2)))*card2,col=4)

    lod_score_vecs(x1,x2)

R: nm

nm

--

Description

~~~~~~~~~~~

It provides a set of useful functions allowing to perform quantitative

analysis of nucleosomal epigenome.