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.
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.
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).
Run with fw_plot heatmap -t ilPie_tetranuc_windows.tsv -o ./images
SUPER_16 of Xestia xanthographa:
And the mitochondrion:
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]
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!
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]
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: