analyzeBasesPlotOnly: Plots the number of reads at all previously defined positions
In BBCAnalyzer: BBCAnalyzer: an R/Bioconductor package for visualizing base counts

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/BBCAnalyzer.R

To allow for a quick change in the way the analysis of the bases, deletions and insertions at defined positions in sequence alignment data is visualized, the function analyzeBasesPlotOnly may be used. It solely performs the last step of the whole analysis pipeline – the plotting of the results.

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analyzeBasesPlotOnly(sample_names, vcf_input, output, known_file, 
                     output_list, qual_lower_bound, qual_upper_bound, 
                     marks, relative, per_sample)

`sample_names`	The file containing the names of the samples to be analyzed.
`vcf_input`	The folder containing the vcf files or a VcfFileList of the samples to be analyzed or a multiple-sample vcf file. The argument may be left blank.
`output`	The folder to write the output (plots) into. The argument may be left blank. In this case, the plots are returned to the workspace.
`known_file`	The name of a tabix file containing e.g. known polymorphisms or mutations (dbSNP). The argument may be left blank.
`output_list`	The name of the list that is returned by the function `analyzeBases`.
`qual_lower_bound`	The lower bound for the mean quality that shall be color-coded in the plots. All bases with a mean quality below `qual_lower_bound` are colored with the lightest color defined for the corresponding base. If the bases shall not be color-coded according to their mean quality, `qual_lower_bound` has to be identical compared to `qual_upper_bound`.
`qual_upper_bound`	The upper bound for the mean quality that shall be color-coded in the plots. All bases with a mean quality above `qual_upper_bound` are colored with the darkest color defined for the corresponding base. If the bases shall not be color-coded according to their mean quality, `qual_upper_bound` has to be identical compared to `qual_lower_bound`.
`marks`	A vector of floats [0,1] defining the levels at which marks shall be drawn in the plot.
`relative`	A boolean defining whether the relative (`true`) or the absolute (`false`) number of reads shall be plotted.
`per_sample`	A boolean defining whether one plot per sample (`true`) or one plot per target (`false`) shall be created.

About the input:

Names of the samples to be analyzed have to be provided by a file (sample_names). There has to be one sample name per line without the ".bam"-suffix.

If vcf files shall be considered, the corresponding files of the samples to be analyzed have to be provided in a folder (vcf_input). There has to be one file per sample. The names of the files have to match the sample names provided by sample_names. If a vcf file is not available for one or more samples, empty vcf files may be used instead.

The value that gets returned by the function analyzeBases has to be provided (output_list). Otherwise, analyzeBasesPlotOnly will not be able to use the previously generated output.

About plotting the results:

The absolute number of the detected bases, deletions and insertions for each sample at each targeted position is plotted if relative==FALSE. Otherwise the relative frequencies of the detected bases, deletions and insertions for each sample at each targeted position get plotted. The bars are colored according to the base (adenine: green; cytosine: blue; guanine: yellow; thymine: red; deletion: black; insertion: lilac edge) and their mean quality (high mean quality: dark color; low mean quality: light color). The reference bases (using the defined package ref_genome) are plotted on the negative y-axis below each position. If a file containing known variants or mutations is provided (known_file), more than one reference base is plotted at the corresponding position. For each position to be analyzed, lines are drawn at the heights of the ratios defined in marks. Every targeted position is labelled according to the chromosome and the position. The function copes with different inserted bases at one position (stacked bars) and insertions $>$1bp, even if these are not covered by all samples. If the analysis shall consider vcf files as well (vcf_input not blank), the expected number of detected bases, deletions and insertions – according to the vcf file – is added to the plot using dashed lines.

Plot per sample: One barplot per sample is created. The output is saved as [Sample].png.

Plot per target: One barplot per targeted position is created. The output is saved as chr[number];[position].png.

No value is returned.

Sarah Sandmann <sarah.sandmann@uni-muenster.de>

db SNP – Short Genetic variations: http://www.ncbi.nlm.nih.gov/SNP/

BBCAnalyzer, analyzeBases

library("BSgenome.Hsapiens.UCSC.hg19")
ref_genome<-BSgenome.Hsapiens.UCSC.hg19

output<-analyzeBases(sample_names=system.file("extdata","SampleNames_small.txt",package="BBCAnalyzer"),
                     bam_input=system.file("extdata",package="BBCAnalyzer"),
                     target_regions=system.file("extdata","targetRegions_small.txt",package="BBCAnalyzer"),
                     vcf_input="",
                     output=system.file("extdata",package="BBCAnalyzer"),
                     output_pictures=system.file("extdata",package="BBCAnalyzer"),
                     known_file="",
                     genome=ref_genome,
                     MQ_threshold=60,
                     BQ_threshold=50,
                     frequency_threshold=0.01,
                     qual_lower_bound=58,
                     qual_upper_bound=63,
                     marks=c(0.01),
                     relative=TRUE,
                     per_sample=TRUE)
                     
analyzeBasesPlotOnly(sample_names=system.file("extdata","SampleNames_small.txt",package="BBCAnalyzer"),
                     vcf_input="",
                     output=system.file("extdata",package="BBCAnalyzer"),
                     known_file="",
                     output_list=output,
                     qual_lower_bound=58,
                     qual_upper_bound=63,
                     marks=c(0.25,0.5,0.75,1),
                     relative=FALSE,
                     per_sample=TRUE)