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fw_plot

A command line tool to take the output of fasta windows and make some fast plots. fw_plot stat [options] takes the _windows output from fasta_windows, and lets you plot any of the calculated variables. fw_plot heatmap takes one of the three kmer frequency matrices generated from fasta_windows and makes heatmaps of each of the chromosomes. Note this tool is built upon fasta_windows version 2.

Usage

Build as all rust (--bin) projects: cargo build --release. The executable is in ./target/release/fw_plot.

fw_plot 0.1.2
Max Brown <[email protected]>
Create fast and simple plots of fasta_windows output.

USAGE:
    fw_plot [SUBCOMMAND]

FLAGS:
    -h, --help       Prints help information
    -V, --version    Prints version information

SUBCOMMANDS:
    corr       Quickly plot correlations between fundamental sequence statistics.
    heatmap    Make a heatmap of the kmer frequencies across chromosomes.
    help       Prints this message or the help of the given subcommand(s)
    stat       Quickly plot fundamental sequence statistics across chromosomes.

Make a heatmap of kmer frequencies

fw_plot-heatmap 
Make a heatmap of the kmer frequencies across chromosomes.

USAGE:
    fw_plot heatmap --colour <colour> --outdir <outdir> --tsv <tsv>

FLAGS:
    -h, --help       Prints help information
    -V, --version    Prints version information

OPTIONS:
    -c, --colour <colour>    The colour scale that the heatmap uses. See https://docs.rs/colorous/1.0.5/colorous/.
                             [default: TURBO]  [possible values: TURBO, VIRIDIS, INFERNO, MAGMA, PLASMA, CIVIDIS, WARM,
                             COOL, CUBEHELIX]
    -o, --outdir <outdir>    The output directory. [default: .]
    -t, --tsv <tsv>          The TSV file (..._di/tri/tetranuc_windows.tsv).

Note that the y-axis of the heatmap runs from the lexicographically lowest kmers at the bottom of the axis, to the greatest at the top (i.e. AA(AA) will be at the bottom of the plot and TT(TT) at the top).

Examples

Run with fw_plot heatmap -t ilPie_tetranuc_windows.tsv -o ./images

SUPER_16 of Xestia xanthographa:

And the mitochondrion:

Plot output statistics from fasta_windows

fw_plot-stat 
Quickly plot fundamental sequence statistics across chromosomes.

USAGE:
    fw_plot stat [OPTIONS] --circle_size <circle_size> --delta <delta> --frac <frac> --loess <loess> --nsteps <nsteps> --outdir <outdir> --tsv <tsv> --variable <variable>

FLAGS:
    -h, --help       Prints help information
    -V, --version    Prints version information

OPTIONS:
    -c, --circle_size <circle_size>      Size of circles. Only relevant if loess == true. [default: 3]
    -d, --delta <delta>                  Smoothing parameter #3. Not sure what this does. [default: 0.2]
    -f, --frac <frac>                    Smoothing parameter #1. Lower values make more wiggly loess line. [default:
                                         0.07]
    -l, --loess <loess>                  Should a loess fit be added? [default: false]  [possible values: true, false]
    -n, --nsteps <nsteps>                Robustness iterations - larger values significantly slower runtime. [default:
                                         0]
    -o, --outdir <outdir>                The output directory. [default: .]
    -s, --stroke_width <stroke_width>    Stroke width of the loess line. [default: 2]
    -t, --tsv <tsv>                      The TSV file (..._windows.tsv).
    -v, --variable <variable>            The variable to plot. [possible values: gc_prop, gc_skew, shannon_entropy,
                                         prop_gs, prop_cs, prop_as, prop_ts, prop_ns, dinucleotide_shannon,
                                         trinucleotide_shannon, tetranucleotide_shannon]

Examples

Run with fw_plot stat -t ./data/ilPie_windows.tsv -v gc_prop -l true -o ./stats

SUPER_16 GC proportion in Pieris rapae:

And in the mitochondrion - the loess fit is not good here!

Plot correlations from fasta_windows

fw_plot-corr 
Quickly plot correlations between fundamental sequence statistics.

USAGE:
    fw_plot corr --outdir <outdir> --tsv <tsv> --x_variable <x_variable> --y_variable <y_variable>

FLAGS:
    -h, --help       Prints help information
    -V, --version    Prints version information

OPTIONS:
    -o, --outdir <outdir>            The output directory. [default: .]
    -t, --tsv <tsv>                  The TSV file (..._windows.tsv).
    -x, --x_variable <x_variable>    The x variable to plot. [possible values: gc_prop, gc_skew, shannon_entropy,
                                     prop_gs, prop_cs, prop_as, prop_ts, prop_ns, dinucleotide_shannon,
                                     trinucleotide_shannon, tetranucleotide_shannon]
    -y, --y_variable <y_variable>    The y variable to plot. [possible values: gc_prop, gc_skew, shannon_entropy,
                                     prop_gs, prop_cs, prop_as, prop_ts, prop_ns, dinucleotide_shannon,
                                     trinucleotide_shannon, tetranucleotide_shannon]

Examples

Run with fw_plot corr -t ilPie_windows.tsv -x gc_prop -y tetranucleotide_shannon -o ./corr

SUPER_16 GC proportion vs tetranucleotide shannon diversity across 1kb windows in Pieris rapae:

And in the W:

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Create simple plots of fasta_windows output.

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