inst/report_bin_file.md
In philliplab/MotifBinner: Bin reads by Motif
File bin processing report
Input File
Sys.time()
## [1] "2015-03-20 14:36:54 SAST"
A summary of the input sequences
print(seq_dat)
## A DNAStringSet instance of length 14575
## width seq
## [1] 352 TATGGGATCAAAGTCTAAAACCATGTGTA...TGCTTACCTAACTGAGCGTGTGGCAAGGC
## [2] 352 TATGGGACGAAAGCCTCAAGCCATGTGTA...TGTTAGATGTTCTGAGCGTGTGGCAAGGC
## [3] 361 TATGGGACGAAAGTCTAAAGCCATGTGTA...TGTATTGGGGCCTGAGCGTGTGGCAAGGC
## [4] 419 AATGTCAGCACAGTACAATGTACACATGG...ACTCATGCAACCTGAGCGTGTGGCAAGGC
## [5] 351 TATGGGACCAAAGTCTAAAGCCATGTGTA...GTGCTATCGAACTGAGCGTGTGGCAAGGC
## ... ... ...
## [14571] 352 TATGGGACGAAAGTCTAAAGCCATGTGTA...TGGTACACAAGCTGAGCGTGTGGCAAGGC
## [14572] 352 TATGGGACGAAAGCCTCAAGCTATGTGTA...TGCATTTAAAACTGAGCGTGTGGCAAGGC
## [14573] 352 TATGGGACGAAAGTCTCAAACCATGTGTA...TGGCTCAAGTACTGAGCGTGTGGCAAGGC
## [14574] 361 TATGGGATCAAAGTCTAAAACCATGTGTA...TGAATCCTCTACTGAGCGTGTGGCAAGGC
## [14575] 352 TATGGGACGAAAGTCTAAAGCCATGTGTA...TGTAATTGCGACCGAGCGTGTGGCAAGGC
The letter frequencies
Counts of the letters in the input sequences.
x <- apply(consensusMatrix(seq_dat), 1, sum)
x <- x[x != 0]
kable(data.frame(letter = names(x),
count = x,
count_per_seq = round(x/length(seq_dat),2),
row.names = 1:length(x)))
|letter | count| count_per_seq|
|:------|-------:|-------------:|
|A | 1889682| 129.65|
|C | 1028655| 70.58|
|G | 973999| 66.83|
|T | 1394116| 95.65|
|N | 12| 0.00|
Normalized counts of the letters in the input sequences.
x_freq <- round(x/sum(x), 4)
kable(data.frame(letter = names(x_freq),
count = x_freq,
row.names = 1:length(x_freq)))
|letter | count|
|:------|------:|
|A | 0.3575|
|C | 0.1946|
|G | 0.1842|
|T | 0.2637|
|N | 0.0000|
barplot(x/sum(x),
main = 'Normalized counts of the letters\nin the input sequences')
The sequence lengths
hist(width(seq_dat),
main = "Histogram of the lengths of the input sequences",
xlab = "Sequence Length",
ylab = "Number of Sequences")
Motif Searches
Sys.time()
## [1] "2015-03-20 14:36:54 SAST"
Input parameters for the searches
print(prefix)
## [1] "CCAGCTGGTTATGCGATTCTMARGTG"
print(suffix)
## [1] "CTGAGCGTGTGGCAAGGCCC"
print(motif_length)
## [1] 9
Matched vs Unmatched sequences
n_matched <- length(motif_dat$matched_seq)
n_unmatched <- length(motif_dat$unmatched_seq)
n_total <- n_matched + n_unmatched
kable(data.frame(seq_matched_count = n_matched,
seq_matched_perc = 100*round(n_matched / n_total, 4),
seq_unmatched_count = n_unmatched,
seq_unmatched_perc = 100*round(n_unmatched / n_total, 4)))
| seq_matched_count| seq_matched_perc| seq_unmatched_count| seq_unmatched_perc|
|-----------------:|----------------:|-------------------:|------------------:|
| 11561| 79.32| 3014| 20.68|
Matched Sequences
Summary of sequences
print(motif_dat$matched_seq)
## A DNAStringSet instance of length 11561
## width seq names
## [1] 299 TATGGGATCAAAGTCTAAA...CCTATACATTATTGTGCT CTTACCTAA
## [2] 299 TATGGGACGAAAGCCTCAA...CCTATACATTATTGTGCT TTAGATGTT
## [3] 308 TATGGGACGAAAGTCTAAA...CCTATACATTATTGTGCT TATTGGGGC
## [4] 299 TATGGGACGAAAGCCTCAA...CCTATACATTATTGTGCT ATTGTGCCA
## [5] 299 TATGGGATGAAAGCCTCAA...CCTATACATTATTGTGCT GTAGACTGT
## ... ... ...
## [11557] 299 TATGGGACGAAAGTCTAAA...CCTATACATTATTGTGCT GTACACAAG
## [11558] 299 TATGGGACGAAAGCCTCAA...CCTATACATTATTGTGCT CATTTAAAA
## [11559] 299 TATGGGACGAAAGTCTCAA...CCTATACATTATTGTGCT GCTCAAGTA
## [11560] 308 TATGGGATCAAAGTCTAAA...CCTATACATTATTGTGCT AATCCTCTA
## [11561] 299 TATGGGACGAAAGTCTAAA...CCTATACATTATGGTGCT TAATTGCGA
The letter frequencies
Counts of the letters in the matched sequences.
x <- apply(consensusMatrix(motif_dat$matched_seq), 1, sum)
x <- x[x != 0]
kable(data.frame(letter = names(x),
count = x,
count_per_seq = round(x/length(motif_dat$matched_seq),2),
row.names = 1:length(x)))
|letter | count| count_per_seq|
|:------|-------:|-------------:|
|A | 1296178| 112.12|
|C | 675110| 58.40|
|G | 536220| 46.38|
|T | 978310| 84.62|
|+ | 2| 0.00|
Normalized counts of the letters in the matched sequences.
x_freq <- round(x/sum(x), 4)
kable(data.frame(letter = names(x_freq),
count = x_freq,
row.names = 1:length(x_freq)))
|letter | count|
|:------|------:|
|A | 0.3718|
|C | 0.1937|
|G | 0.1538|
|T | 0.2807|
|+ | 0.0000|
barplot(x/sum(x),
main = 'Normalized counts of the letters\nin the matched sequences')
The sequence lengths
hist(width(motif_dat$matched_seq),
main = "Histogram of the lengths of the matched sequences",
xlab = "Sequence Length",
ylab = "Number of Sequences")
Unmatched Sequences
Summary of sequences
print(motif_dat$unmatched_seq)
## A DNAStringSet instance of length 3014
## width seq names
## [1] 419 AATGTCAGCACAGTACAAT...CCTGAGCGTGTGGCAAGGC seq_4
## [2] 351 TATGGGACCAAAGTCTAAA...ACTGAGCGTGTGGCAAGGC seq_5
## [3] 353 TATGGGACGAAAGCCTCAA...CCTGAGCGTGTGGCAAGGC seq_10
## [4] 351 TATGGGACGAAAGCCTCAA...CCTGAGCGTGTGGCAAGGC seq_15
## [5] 343 TATGGGATGAAAGCCTAAA...CCTGAGCGTGTGGCAAGGC seq_17
## ... ... ...
## [3010] 419 AATGTCAGTACAGTACAAT...ACTGAGCGTGTGGCAAGGC seq_14561
## [3011] 419 AATGTCAGTACAGTACAAT...ACTGAGCGTGTGGCAAGGC seq_14562
## [3012] 418 AATGTCAGTACAGTACAAT...CCTGAGCGTGTGGCAAGGC seq_14563
## [3013] 419 AATGTAAGCACAGTACAAT...CCTGAGCGTGTGGCAAGGC seq_14564
## [3014] 419 AATGTCAGTACAGTACAAT...GCTGAGCTTGTGGCAAGGC seq_14566
The letter frequencies
Counts of the letters in the unmatched sequences.
x <- apply(consensusMatrix(motif_dat$unmatched_seq), 1, sum)
x <- x[x != 0]
kable(data.frame(letter = names(x),
count = x,
count_per_seq = round(x/length(motif_dat$unmatched_seq),2),
row.names = 1:length(x)))
|letter | count| count_per_seq|
|:------|------:|-------------:|
|A | 466241| 154.69|
|C | 213997| 71.00|
|G | 241376| 80.08|
|T | 266341| 88.37|
|+ | 8| 0.00|
Normalized counts of the letters in the unmatched sequences.
x_freq <- round(x/sum(x), 4)
kable(data.frame(letter = names(x_freq),
count = x_freq,
row.names = 1:length(x_freq)))
|letter | count|
|:------|------:|
|A | 0.3925|
|C | 0.1801|
|G | 0.2032|
|T | 0.2242|
|+ | 0.0000|
barplot(x/sum(x),
main = 'Normalized counts of the letters\nin the unmatched sequences')
The sequence lengths
hist(width(motif_dat$unmatched_seq),
main = "Histogram of the lengths of the unmatched sequences",
xlab = "Sequence Length",
ylab = "Number of Sequences")
The unprocessed bins produced
Sys.time()
## [1] "2015-03-20 14:36:55 SAST"
The number of bins
print(length(bin_seqs))
## [1] 998
The sizes of the bins
bin_sizes <- unlist(lapply(bin_seqs, length))
hist(bin_sizes)
kable(data.frame(bin_size = as.numeric(names(table(bin_sizes))),
num_bins = as.numeric(table(bin_sizes))))
| bin_size| num_bins|
|--------:|--------:|
| 1| 482|
| 2| 76|
| 3| 35|
| 4| 32|
| 5| 24|
| 6| 20|
| 7| 9|
| 8| 13|
| 9| 9|
| 10| 17|
| 11| 15|
| 12| 10|
| 13| 8|
| 14| 6|
| 15| 6|
| 16| 10|
| 17| 9|
| 18| 8|
| 19| 11|
| 20| 9|
| 21| 5|
| 22| 8|
| 23| 5|
| 24| 5|
| 25| 4|
| 26| 6|
| 27| 6|
| 28| 4|
| 29| 8|
| 30| 8|
| 31| 5|
| 32| 4|
| 33| 4|
| 34| 4|
| 35| 5|
| 36| 3|
| 37| 7|
| 38| 3|
| 39| 5|
| 40| 4|
| 41| 3|
| 43| 3|
| 44| 5|
| 45| 2|
| 46| 5|
| 47| 1|
| 49| 4|
| 51| 1|
| 52| 1|
| 53| 1|
| 54| 1|
| 55| 6|
| 56| 1|
| 57| 3|
| 58| 1|
| 59| 1|
| 60| 3|
| 61| 1|
| 62| 2|
| 63| 1|
| 65| 5|
| 66| 3|
| 67| 3|
| 68| 3|
| 70| 1|
| 73| 4|
| 75| 1|
| 76| 1|
| 77| 1|
| 80| 1|
| 81| 2|
| 82| 1|
| 85| 1|
| 86| 2|
| 87| 1|
| 94| 2|
| 96| 1|
| 97| 2|
| 100| 1|
| 101| 1|
| 103| 1|
| 113| 1|
| 119| 1|
scatter and log log plot of the bin sizes
loglog <- data.frame(bin_size = as.numeric(names(table(bin_sizes))),
num_bins = as.numeric(table(bin_sizes)))
plot(num_bins ~ bin_size, data = loglog)
plot(log10(num_bins) ~ log10(bin_size), data = loglog)
Categories and count frequencies
cats <- hist(bin_sizes, breaks = c(0,1,2,3,4,10,20,400,1000000), plot=FALSE)
counts <- cats$counts
names(counts) <- paste('(', cats$breaks[1:(length(cats$breaks)-1)],
'-', cats$breaks[2:(length(cats$breaks))], ']',
sep='')
names(counts)[length(counts)] <- paste(cats$breaks[length(cats$breaks)-1], '+',
sep = '')
barplot(counts, main = 'Histogram of Bin Sizes')
kable(data.frame(category = names(counts),
count = counts,
row.names = 1:length(counts)))
|category | count|
|:--------|-----:|
|(0-1] | 482|
|(1-2] | 76|
|(2-3] | 35|
|(3-4] | 32|
|(4-10] | 92|
|(10-20] | 92|
|(20-400] | 189|
|400+ | 0|
Basic report on the processed bins
Arguments
x <- pb_dat
x$pb_out <- NULL
x$seqs <- NULL
print(x)
## $classification_technique
## [1] "absolute"
##
## $classification_params
## $classification_params$threshold
## [1] 0.01333333
##
## $classification_params$start_threshold
## [1] 0.01333333
##
## $classification_params$max_sequences
## [1] 100
##
##
## $alignment_technique
## [1] "muscle"
##
## $alignment_params
## list()
##
## $consensus_technique
## [1] "mostConsensusString"
##
## $consensus_params
## list()
##
## $remove_gaps
## [1] TRUE
Compute metrics
Effects of outlier removal
Before and after bin sizes
hist(metrics$input_size)
hist(metrics$output_size)
plot(metrics$output_size ~ metrics$input_size)
abline(a=0, b=1)
Where are the small bins coming from?
small_bins <- subset(metrics, output_size < 6)
plot(jitter(small_bins$output_size) ~ jitter(small_bins$input_size))
abline(a=0, b=1)
Before and after bin distances
hist(metrics$in_max_dist)
hist(metrics$out_max_dist)
hist(metrics$min_out_dist)
hist(metrics$in_max_dist - metrics$out_max_dist)
Alignment step
Number of gaps inserted
hist(metrics$gaps)
kable(data.frame(num_gaps = as.numeric(names(table(metrics$gaps))),
num_alignments = as.numeric(table(metrics$gaps))))
| num_gaps| num_alignments|
|--------:|--------------:|
| 0| 356|
| 1| 75|
| 2| 26|
| 3| 7|
| 4| 5|
| 5| 2|
| 6| 3|
| 7| 1|
| 8| 1|
| 14| 1|
| 15| 1|
| 17| 1|
| 18| 1|
| 19| 1|
| 20| 1|
| 21| 1|
| 24| 1|
| 25| 1|
| 26| 1|
| 27| 1|
| 28| 1|
| 32| 1|
| 35| 2|
| 38| 1|
| 42| 1|
| 44| 1|
| 45| 2|
| 47| 1|
| 51| 2|
| 54| 1|
| 118| 1|
Number positions with mismatches in the alignment
hist(metrics$pos_no_mismatch)
hist(metrics$pos_mismatch)
Total number of mismatches
hist(metrics$total_mismatches)
NON-ACGT characters in consensus
hist(metrics$non_acgt)
kable(data.frame(num_non_acgt = as.numeric(names(table(metrics$non_acgt))),
num_con_seq = as.numeric(table(metrics$non_acgt))))
| num_non_acgt| num_con_seq|
|------------:|-----------:|
| 0| 324|
| 1| 70|
| 2| 46|
| 3| 18|
| 4| 18|
| 5| 15|
| 6| 10|
philliplab/MotifBinner documentation built on Sept. 2, 2020, 11:41 a.m.
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File bin processing report
Input File
Sys.time()
## [1] "2015-03-20 14:36:54 SAST"
A summary of the input sequences
print(seq_dat)
## A DNAStringSet instance of length 14575
## width seq
## [1] 352 TATGGGATCAAAGTCTAAAACCATGTGTA...TGCTTACCTAACTGAGCGTGTGGCAAGGC
## [2] 352 TATGGGACGAAAGCCTCAAGCCATGTGTA...TGTTAGATGTTCTGAGCGTGTGGCAAGGC
## [3] 361 TATGGGACGAAAGTCTAAAGCCATGTGTA...TGTATTGGGGCCTGAGCGTGTGGCAAGGC
## [4] 419 AATGTCAGCACAGTACAATGTACACATGG...ACTCATGCAACCTGAGCGTGTGGCAAGGC
## [5] 351 TATGGGACCAAAGTCTAAAGCCATGTGTA...GTGCTATCGAACTGAGCGTGTGGCAAGGC
## ... ... ...
## [14571] 352 TATGGGACGAAAGTCTAAAGCCATGTGTA...TGGTACACAAGCTGAGCGTGTGGCAAGGC
## [14572] 352 TATGGGACGAAAGCCTCAAGCTATGTGTA...TGCATTTAAAACTGAGCGTGTGGCAAGGC
## [14573] 352 TATGGGACGAAAGTCTCAAACCATGTGTA...TGGCTCAAGTACTGAGCGTGTGGCAAGGC
## [14574] 361 TATGGGATCAAAGTCTAAAACCATGTGTA...TGAATCCTCTACTGAGCGTGTGGCAAGGC
## [14575] 352 TATGGGACGAAAGTCTAAAGCCATGTGTA...TGTAATTGCGACCGAGCGTGTGGCAAGGC
The letter frequencies
Counts of the letters in the input sequences.
x <- apply(consensusMatrix(seq_dat), 1, sum)
x <- x[x != 0]
kable(data.frame(letter = names(x),
count = x,
count_per_seq = round(x/length(seq_dat),2),
row.names = 1:length(x)))
|letter | count| count_per_seq| |:------|-------:|-------------:| |A | 1889682| 129.65| |C | 1028655| 70.58| |G | 973999| 66.83| |T | 1394116| 95.65| |N | 12| 0.00|
Normalized counts of the letters in the input sequences.
x_freq <- round(x/sum(x), 4)
kable(data.frame(letter = names(x_freq),
count = x_freq,
row.names = 1:length(x_freq)))
|letter | count| |:------|------:| |A | 0.3575| |C | 0.1946| |G | 0.1842| |T | 0.2637| |N | 0.0000|
barplot(x/sum(x),
main = 'Normalized counts of the letters\nin the input sequences')
The sequence lengths
hist(width(seq_dat),
main = "Histogram of the lengths of the input sequences",
xlab = "Sequence Length",
ylab = "Number of Sequences")
Motif Searches
Sys.time()
## [1] "2015-03-20 14:36:54 SAST"
Input parameters for the searches
print(prefix)
## [1] "CCAGCTGGTTATGCGATTCTMARGTG"
print(suffix)
## [1] "CTGAGCGTGTGGCAAGGCCC"
print(motif_length)
## [1] 9
Matched vs Unmatched sequences
n_matched <- length(motif_dat$matched_seq)
n_unmatched <- length(motif_dat$unmatched_seq)
n_total <- n_matched + n_unmatched
kable(data.frame(seq_matched_count = n_matched,
seq_matched_perc = 100*round(n_matched / n_total, 4),
seq_unmatched_count = n_unmatched,
seq_unmatched_perc = 100*round(n_unmatched / n_total, 4)))
| seq_matched_count| seq_matched_perc| seq_unmatched_count| seq_unmatched_perc| |-----------------:|----------------:|-------------------:|------------------:| | 11561| 79.32| 3014| 20.68|
Matched Sequences
Summary of sequences
print(motif_dat$matched_seq)
## A DNAStringSet instance of length 11561
## width seq names
## [1] 299 TATGGGATCAAAGTCTAAA...CCTATACATTATTGTGCT CTTACCTAA
## [2] 299 TATGGGACGAAAGCCTCAA...CCTATACATTATTGTGCT TTAGATGTT
## [3] 308 TATGGGACGAAAGTCTAAA...CCTATACATTATTGTGCT TATTGGGGC
## [4] 299 TATGGGACGAAAGCCTCAA...CCTATACATTATTGTGCT ATTGTGCCA
## [5] 299 TATGGGATGAAAGCCTCAA...CCTATACATTATTGTGCT GTAGACTGT
## ... ... ...
## [11557] 299 TATGGGACGAAAGTCTAAA...CCTATACATTATTGTGCT GTACACAAG
## [11558] 299 TATGGGACGAAAGCCTCAA...CCTATACATTATTGTGCT CATTTAAAA
## [11559] 299 TATGGGACGAAAGTCTCAA...CCTATACATTATTGTGCT GCTCAAGTA
## [11560] 308 TATGGGATCAAAGTCTAAA...CCTATACATTATTGTGCT AATCCTCTA
## [11561] 299 TATGGGACGAAAGTCTAAA...CCTATACATTATGGTGCT TAATTGCGA
The letter frequencies
Counts of the letters in the matched sequences.
x <- apply(consensusMatrix(motif_dat$matched_seq), 1, sum)
x <- x[x != 0]
kable(data.frame(letter = names(x),
count = x,
count_per_seq = round(x/length(motif_dat$matched_seq),2),
row.names = 1:length(x)))
|letter | count| count_per_seq| |:------|-------:|-------------:| |A | 1296178| 112.12| |C | 675110| 58.40| |G | 536220| 46.38| |T | 978310| 84.62| |+ | 2| 0.00|
Normalized counts of the letters in the matched sequences.
x_freq <- round(x/sum(x), 4)
kable(data.frame(letter = names(x_freq),
count = x_freq,
row.names = 1:length(x_freq)))
|letter | count| |:------|------:| |A | 0.3718| |C | 0.1937| |G | 0.1538| |T | 0.2807| |+ | 0.0000|
barplot(x/sum(x),
main = 'Normalized counts of the letters\nin the matched sequences')
The sequence lengths
hist(width(motif_dat$matched_seq),
main = "Histogram of the lengths of the matched sequences",
xlab = "Sequence Length",
ylab = "Number of Sequences")
Unmatched Sequences
Summary of sequences
print(motif_dat$unmatched_seq)
## A DNAStringSet instance of length 3014
## width seq names
## [1] 419 AATGTCAGCACAGTACAAT...CCTGAGCGTGTGGCAAGGC seq_4
## [2] 351 TATGGGACCAAAGTCTAAA...ACTGAGCGTGTGGCAAGGC seq_5
## [3] 353 TATGGGACGAAAGCCTCAA...CCTGAGCGTGTGGCAAGGC seq_10
## [4] 351 TATGGGACGAAAGCCTCAA...CCTGAGCGTGTGGCAAGGC seq_15
## [5] 343 TATGGGATGAAAGCCTAAA...CCTGAGCGTGTGGCAAGGC seq_17
## ... ... ...
## [3010] 419 AATGTCAGTACAGTACAAT...ACTGAGCGTGTGGCAAGGC seq_14561
## [3011] 419 AATGTCAGTACAGTACAAT...ACTGAGCGTGTGGCAAGGC seq_14562
## [3012] 418 AATGTCAGTACAGTACAAT...CCTGAGCGTGTGGCAAGGC seq_14563
## [3013] 419 AATGTAAGCACAGTACAAT...CCTGAGCGTGTGGCAAGGC seq_14564
## [3014] 419 AATGTCAGTACAGTACAAT...GCTGAGCTTGTGGCAAGGC seq_14566
The letter frequencies
Counts of the letters in the unmatched sequences.
x <- apply(consensusMatrix(motif_dat$unmatched_seq), 1, sum)
x <- x[x != 0]
kable(data.frame(letter = names(x),
count = x,
count_per_seq = round(x/length(motif_dat$unmatched_seq),2),
row.names = 1:length(x)))
|letter | count| count_per_seq| |:------|------:|-------------:| |A | 466241| 154.69| |C | 213997| 71.00| |G | 241376| 80.08| |T | 266341| 88.37| |+ | 8| 0.00|
Normalized counts of the letters in the unmatched sequences.
x_freq <- round(x/sum(x), 4)
kable(data.frame(letter = names(x_freq),
count = x_freq,
row.names = 1:length(x_freq)))
|letter | count| |:------|------:| |A | 0.3925| |C | 0.1801| |G | 0.2032| |T | 0.2242| |+ | 0.0000|
barplot(x/sum(x),
main = 'Normalized counts of the letters\nin the unmatched sequences')
The sequence lengths
hist(width(motif_dat$unmatched_seq),
main = "Histogram of the lengths of the unmatched sequences",
xlab = "Sequence Length",
ylab = "Number of Sequences")
The unprocessed bins produced
Sys.time()
## [1] "2015-03-20 14:36:55 SAST"
The number of bins
print(length(bin_seqs))
## [1] 998
The sizes of the bins
bin_sizes <- unlist(lapply(bin_seqs, length))
hist(bin_sizes)
kable(data.frame(bin_size = as.numeric(names(table(bin_sizes))),
num_bins = as.numeric(table(bin_sizes))))
| bin_size| num_bins| |--------:|--------:| | 1| 482| | 2| 76| | 3| 35| | 4| 32| | 5| 24| | 6| 20| | 7| 9| | 8| 13| | 9| 9| | 10| 17| | 11| 15| | 12| 10| | 13| 8| | 14| 6| | 15| 6| | 16| 10| | 17| 9| | 18| 8| | 19| 11| | 20| 9| | 21| 5| | 22| 8| | 23| 5| | 24| 5| | 25| 4| | 26| 6| | 27| 6| | 28| 4| | 29| 8| | 30| 8| | 31| 5| | 32| 4| | 33| 4| | 34| 4| | 35| 5| | 36| 3| | 37| 7| | 38| 3| | 39| 5| | 40| 4| | 41| 3| | 43| 3| | 44| 5| | 45| 2| | 46| 5| | 47| 1| | 49| 4| | 51| 1| | 52| 1| | 53| 1| | 54| 1| | 55| 6| | 56| 1| | 57| 3| | 58| 1| | 59| 1| | 60| 3| | 61| 1| | 62| 2| | 63| 1| | 65| 5| | 66| 3| | 67| 3| | 68| 3| | 70| 1| | 73| 4| | 75| 1| | 76| 1| | 77| 1| | 80| 1| | 81| 2| | 82| 1| | 85| 1| | 86| 2| | 87| 1| | 94| 2| | 96| 1| | 97| 2| | 100| 1| | 101| 1| | 103| 1| | 113| 1| | 119| 1|
scatter and log log plot of the bin sizes
loglog <- data.frame(bin_size = as.numeric(names(table(bin_sizes))),
num_bins = as.numeric(table(bin_sizes)))
plot(num_bins ~ bin_size, data = loglog)
plot(log10(num_bins) ~ log10(bin_size), data = loglog)
Categories and count frequencies
cats <- hist(bin_sizes, breaks = c(0,1,2,3,4,10,20,400,1000000), plot=FALSE)
counts <- cats$counts
names(counts) <- paste('(', cats$breaks[1:(length(cats$breaks)-1)],
'-', cats$breaks[2:(length(cats$breaks))], ']',
sep='')
names(counts)[length(counts)] <- paste(cats$breaks[length(cats$breaks)-1], '+',
sep = '')
barplot(counts, main = 'Histogram of Bin Sizes')
kable(data.frame(category = names(counts),
count = counts,
row.names = 1:length(counts)))
|category | count| |:--------|-----:| |(0-1] | 482| |(1-2] | 76| |(2-3] | 35| |(3-4] | 32| |(4-10] | 92| |(10-20] | 92| |(20-400] | 189| |400+ | 0|
Basic report on the processed bins
Arguments
x <- pb_dat
x$pb_out <- NULL
x$seqs <- NULL
print(x)
## $classification_technique
## [1] "absolute"
##
## $classification_params
## $classification_params$threshold
## [1] 0.01333333
##
## $classification_params$start_threshold
## [1] 0.01333333
##
## $classification_params$max_sequences
## [1] 100
##
##
## $alignment_technique
## [1] "muscle"
##
## $alignment_params
## list()
##
## $consensus_technique
## [1] "mostConsensusString"
##
## $consensus_params
## list()
##
## $remove_gaps
## [1] TRUE
Compute metrics
Effects of outlier removal
Before and after bin sizes
hist(metrics$input_size)
hist(metrics$output_size)
plot(metrics$output_size ~ metrics$input_size)
abline(a=0, b=1)
Where are the small bins coming from?
small_bins <- subset(metrics, output_size < 6)
plot(jitter(small_bins$output_size) ~ jitter(small_bins$input_size))
abline(a=0, b=1)
Before and after bin distances
hist(metrics$in_max_dist)
hist(metrics$out_max_dist)
hist(metrics$min_out_dist)
hist(metrics$in_max_dist - metrics$out_max_dist)
Alignment step
Number of gaps inserted
hist(metrics$gaps)
kable(data.frame(num_gaps = as.numeric(names(table(metrics$gaps))),
num_alignments = as.numeric(table(metrics$gaps))))
| num_gaps| num_alignments| |--------:|--------------:| | 0| 356| | 1| 75| | 2| 26| | 3| 7| | 4| 5| | 5| 2| | 6| 3| | 7| 1| | 8| 1| | 14| 1| | 15| 1| | 17| 1| | 18| 1| | 19| 1| | 20| 1| | 21| 1| | 24| 1| | 25| 1| | 26| 1| | 27| 1| | 28| 1| | 32| 1| | 35| 2| | 38| 1| | 42| 1| | 44| 1| | 45| 2| | 47| 1| | 51| 2| | 54| 1| | 118| 1|
Number positions with mismatches in the alignment
hist(metrics$pos_no_mismatch)
hist(metrics$pos_mismatch)
Total number of mismatches
hist(metrics$total_mismatches)
NON-ACGT characters in consensus
hist(metrics$non_acgt)
kable(data.frame(num_non_acgt = as.numeric(names(table(metrics$non_acgt))),
num_con_seq = as.numeric(table(metrics$non_acgt))))
| num_non_acgt| num_con_seq| |------------:|-----------:| | 0| 324| | 1| 70| | 2| 46| | 3| 18| | 4| 18| | 5| 15| | 6| 10|
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