LBMC » NucleoMiner

Arabic to Roman pair list.

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

Description

~~~~~~~~~~~

Utility to convert Arabic numbers to Roman numbers

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

~~~~~~~~~~~

Utility to convert Roman numbers into Arabic numbers

Usage

~~~~~

::

    ROM2ARAB()

Author(s)

~~~~~~~~~

Florent Chuffart

R: Aggregate replicated sample's nucleosomes.

Aggregate replicated sample's nucleosomes.

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

Description

~~~~~~~~~~~

This function aggregates nucleosomes from 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 (dyad). A chain of nucleosomes is builts across all

replicates. Adjacent nucleosomes of the chain are compared two by two.

Comparison is based on a log likelihood ratio (LLR1). depending on the

LLR1 value nucleosomes are merged (low LLR) or separated (high LLR).

Finally the function returns a list of clusters and all computed

llr\_scores. Each cluster ows an attribute wp for "well positioned".

This attribute is set to TRUE if the cluster is composed of exactly one

nucleosome of each sample.

Usage

~~~~~

::

    aggregate_intra_strain_nucs(samples, llr_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=...)*.

``llr_thres``

Log likelihood ratio threshold to decide between merging and separating

``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 llr 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 nucleosomes 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, llr_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.

``llr_thres``

Log likelihood ratio threshold to decide between merging and separating

``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 llr scores.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

::

        # Define new translate_cur function...

        translate_cur = function(roi, strain2, big_cur=NULL, config=NULL) {

          return(roi)

        }

        # Binding it by uncomment follwing lines.

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

        unlockBinding("translate_cur", getNamespace("nucleominer"))

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

        assign("translate_cur", translate_cur, getNamespace("nucleominer"))

        lockBinding("translate_cur", getNamespace("nucleominer"))

        lockBinding("translate_cur", 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_cur(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: 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

~~~~~

::

    analyse_count_table(marker, combi, form, all_samples, 

        FDR = 1e-04, config = NULL)

Arguments

~~~~~~~~~

``marker``

The marker involved.

``combi``

The strain combination involved.

``form``

the nuc form involved.

``all_samples``

Global list of samples.

``FDR``

``config``

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

::

    marker = "H3K4me1"

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

    form = "wpunr" # "wp" | "unr" | "wpunr"

    # foo = analyse_count_table(marker, combi, form)

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

R: Build count table for a set of samples.

Build count table for a set of samples.

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

Description

~~~~~~~~~~~

This function build a count table for a set of sample.

Usage

~~~~~

::

    build_count_table(marker, combi, form, curs, all_samples, 

        config = NULL)

Arguments

~~~~~~~~~

``marker``

The marker that we want to build the count table.

``combi``

The combinations of strains that we want to build the count table.

``form``

The nucleosome that we want to observe: "wp" for sel;l position and

"unr" for UNR.

``curs``

The list of CURs

``all_samples``

A table that describe all our samples.

``config``

GLOBAL config variable.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Extract maps from TemplateFilter outputs

Extract maps from TemplateFilter outputs

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

Description

~~~~~~~~~~~

This function extracts from TemplateFilter outputs./ This is from there

that aggregate\_intra\_strain\_nucs and align\_inter\_strain\_nucs

fucntions are calles. This fucntion write well positionned, fuzzy and

both maps in the config$RESULTS\_DIR directory.

Usage

~~~~~

::

    build_maps(strains, combis, all_samples, curs, config = NULL)

Arguments

~~~~~~~~~

``strains``

The strains for which we want to extract intra strain information.

``combis``

The combinations of strains for which we want to extract inter strain

information.

``all_samples``

A table that describe all our samples.

``curs``

The list of CURs

``config``

GLOBAL config variable.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Stage replicates data

Stage replicates data

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

Description

~~~~~~~~~~~

This function loads in memory the data corresponding to the given

experiments.

Usage

~~~~~

::

    build_replicates(expe, roi, only_fetch = FALSE, get_genome = FALSE, 

        all_samples, config = NULL)

Arguments

~~~~~~~~~

``expe``

a list of vectors corresponding to replicates.

``roi``

the region that we are interested in.

``only_fetch``

filter or not inputs.

``get_genome``

Load or not corresponding genome.

``all_samples``

Global list of samples.

``config``

GLOBAL config variable.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

::

    # library(rjson)

    # library(nucleominer)

    #

    # # Read config file

    # json_conf_file = "nucleominer_config.json"

    # config = fromJSON(paste(readLines(json_conf_file), collapse=""))

    # # Read sample file

    # all_samples = read.cvs(config$CSV_SAMPLE_FILE, sep=";", header=TRUE, stringsAsFactors=FALSE)

    # # here are the sample ids in a list

    # expes = list(c(1))

    # # here is the region that we wnt to see the coverage

    # cur = list(chr="8", begin=472000, end=474000, strain_ref="BY")

    # # it displays the corverage

    # replicates = build_replicates(expes, cur, all_samples=all_samples, config=config)

    # out = watch_samples(replicates, config$READ_LENGTH,

    #       plot_coverage = TRUE,

    #       plot_squared_reads = FALSE,

    #       plot_ref_genome = FALSE,

    #       plot_arrow_raw_reads = FALSE,

    #       plot_arrow_nuc_reads = FALSE,

    #       plot_gaussian_reads = FALSE,

    #       plot_gaussian_unified_reads = FALSE,

    #       plot_ellipse_nucs = FALSE,

    #       plot_wp_nucs = FALSE,

    #       plot_wp_nuc_model = FALSE,

    #       plot_common_nucs = FALSE,

    #       height = 50)

R: Extract a sub part of the corresponding c2c file

Extract a sub part of the corresponding c2c file

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

Description

~~~~~~~~~~~

This fonction allows to access to a specific part of the c2c file.

Usage

~~~~~

::

    c2c_extraction(strain1, strain2, chr = NULL, lower_bound = NULL, 

        upper_bound = NULL, config = NULL)

Arguments

~~~~~~~~~

``strain1``

the key strain

``strain2``

the target strain

``chr``

if defined, the c2c will be filtered according to the chromosome value

``lower_bound``

if defined, the c2c will be filtered for part of the genome upper than

lower\_bound

``upper_bound``

if defined, the c2c will be filtered for part of the genome lower than

upper\_bound

``config``

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

R: reformat an "apply manipulated" list of regions

reformat an "apply manipulated" list of regions

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

Description

~~~~~~~~~~~

Utils to reformat an "apply manipulated" list of regions

Usage

~~~~~

::

    collapse_regions(regions)

Arguments

~~~~~~~~~

+---------------+----+

| ``regions``   |    |

+---------------+----+

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_curs(diff_allowed = 30, min_cur_width = 4000, 

        combis = list(c("BY", "RM"), c("BY", "YJM"), c("RM", 

            "YJM")), config = NULL)

Arguments

~~~~~~~~~

``diff_allowed``

the maximum indel width allowe din a CUR

``min_cur_width``

The minimum width of a CUR

``combis``

list of strain than will be tested as uninterrupted regions

``config``

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

R: count reads cur

count reads cur

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

Usage

~~~~~

::

    count_reads_cur(...)

Arguments

~~~~~~~~~

+-----------+----+

| ``...``   |    |

+-----------+----+

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\_cur" bug in

translate\_cur 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 maps

extract maps

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

Usage

~~~~~

::

    extract_maps(...)

Arguments

~~~~~~~~~

+-----------+----+

| ``...``   |    |

+-----------+----+

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, filter_for_coverage = 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.

``filter_for_coverage``

Does it filter for plot coverage?

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 overlapping

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: to flat aggregate\_intra\_strain\_nucs function output

to flat aggregate\_intra\_strain\_nucs function output

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

Description

~~~~~~~~~~~

This function builds a dataframe of all clusters obtain from

aggregate\_intra\_strain\_nucs function.

Usage

~~~~~