workDir = '/home/nick/notebook/SIPSim/dev/bac_genome1147/Meselson_diff/validation/'
genomeDir = '/var/seq_data/ncbi_db/genome/Jan2016/bac_complete_spec-rep1_rn/'
R_dir = '/home/nick/notebook/SIPSim/lib/R/'
#figureDir = '/home/nick/notebook/SIPSim/figures/bac_genome_n1147/'

bandwidth = 0.8
DBL_scaling = 0.5
subsample_dist = 'lognormal'
subsample_mean = 9.432
subsample_scale = 0.5
subsample_min = 10000
subsample_max = 30000

"""
Explanation: Goal

A basic, full run of the SIPSim pipeline with the whole bacterial genome dataset to see:
Is the pipeline functional?
Check of output at each stage of pipeline
Note: using diffusion method from Meselson etla., 1957

Setting variables
End of explanation
"""


import glob
from os.path import abspath
import nestly
from IPython.display import Image
import os
%load_ext rpy2.ipython
%load_ext pushnote

%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)

if not os.path.isdir(workDir):
    os.makedirs(workDir)
    
if not os.path.isdir(figureDir):
    os.makedirs(figureDir)    
    
%cd $workDir    

# Determining min/max BD that 
## min G+C cutoff
min_GC = 13.5
## max G+C cutoff
max_GC = 80
## max G+C shift
max_13C_shift_in_BD = 0.036


min_range_BD = min_GC/100.0 * 0.098 + 1.66    
max_range_BD = max_GC/100.0 * 0.098 + 1.66    

max_range_BD = max_range_BD + max_13C_shift_in_BD

print 'Min BD: {}'.format(min_range_BD)
print 'Max BD: {}'.format(max_range_BD)

"""
Explanation: Init
End of explanation
"""


!SIPSim communities \
    $genomeDir/genome_index.txt \
    --n_comm 2 \
    > comm.txt

"""
Explanation: Creating a community file

2 communities
control vs treatment
End of explanation
"""


%%R -w 750 -h 300


tbl = read.delim('comm.txt', sep='\t')

tbl$library = as.character(tbl$library)
tbl$library = ifelse(tbl$library == 1, 'Control', 'Treatment')

ggplot(tbl, aes(rank, rel_abund_perc, color=library, group=taxon_name)) +
    geom_point() +
    scale_y_log10() +
    scale_color_discrete('Community') +
    labs(x='Rank', y='Relative abundance (%)') +
    theme_bw() +
    theme(
        text=element_text(size=16)
        )

"""
Explanation: Plotting community rank abundances
End of explanation
"""


!SIPSim gradient_fractions \
    --BD_min $min_range_BD \
    --BD_max $max_range_BD \
    comm.txt \
    > fracs.txt    

"""
Explanation: Simulating gradient fractions
End of explanation
"""


%%R -w 600 -h 300

tbl = read.delim('fracs.txt', sep='\t')

ggplot(tbl, aes(fraction, fraction_size)) +
    geom_bar(stat='identity') +
    facet_grid(library ~ .) +
    labs(y='fraction size') +
    theme_bw() +
    theme(
        text=element_text(size=16)
        )

%%R -w 300 -h 250
tbl$library = as.character(tbl$library)

ggplot(tbl, aes(library, fraction_size)) +
    geom_boxplot() +
    labs(y='fraction size') +
    theme_bw() +
    theme(
        text=element_text(size=16)
        )

"""
Explanation: Plotting fractions
End of explanation
"""


# estimated coverage
mean_frag_size = 9000.0
mean_amp_len = 300.0
n_frags = 10000

coverage = round(n_frags * mean_amp_len / mean_frag_size, 1)
msg = 'Average coverage from simulating {} fragments: {}X'
print msg.format(n_frags, coverage)

!SIPSim fragments \
    $genomeDir/genome_index.txt \
    --fp $genomeDir \
    --fr ../../../515F-806R.fna \
    --fld skewed-normal,9000,2500,-5 \
    --flr None,None \
    --nf 10000 \
    --np 24 \
    2> ampFrags.log \
    > ampFrags.pkl        

"""
Explanation: Simulating fragments
End of explanation
"""


!grep "Number of amplicons: " ampFrags.log | \
    perl -pe 's/.+ +//' | hist

!printf "Number of taxa with >=1 amplicon: "
!grep "Number of amplicons: " ampFrags.log | \
    perl -ne "s/^.+ +//; print unless /^0$/" | wc -l

"""
Explanation: Number of amplicons per taxon
End of explanation
"""


!SIPSim fragment_KDE \
    ampFrags.pkl \
    > ampFrags_kde.pkl

"""
Explanation: Converting fragments to kde object
End of explanation
"""


!SIPSim KDE_info \
    -s ampFrags_kde.pkl \
    > ampFrags_kde_info.txt

%%R 
# loading
df = read.delim('ampFrags_kde_info.txt', sep='\t')
df.kde1 = df %>%
    filter(KDE_ID == 1)
df.kde1 %>% head(n=3)

BD_GC50 = 0.098 * 0.5 + 1.66

%%R -w 500 -h 250
# plotting
p.amp = ggplot(df.kde1, aes(median)) +
    geom_histogram(binwidth=0.001) +
    geom_vline(xintercept=BD_GC50, linetype='dashed', color='red', alpha=0.7) +
    labs(x='Median buoyant density') +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )
p.amp

"""
Explanation: Checking ampfrag info
End of explanation
"""


!SIPSim diffusion \
    --bw $bandwidth \
    --np 24 \
    -m Meselson \
    ampFrags_kde.pkl \
    > ampFrags_kde_dif.pkl \
    2> ampFrags_kde_dif.log

"""
Explanation: Adding diffusion
End of explanation
"""


!SIPSim DBL \
    --comm comm.txt \
    --commx $DBL_scaling \
    --np 24 \
    ampFrags_kde_dif.pkl \
    > ampFrags_kde_dif_DBL.pkl \
    2> ampFrags_kde_dif_DBL.log
    
# checking output    
!tail -n 5 ampFrags_kde_dif_DBL.log

"""
Explanation: Adding DBL 'contamination'

DBL = diffusive boundary layer
End of explanation
"""


# none
!SIPSim KDE_info \
    -s ampFrags_kde.pkl \
    > ampFrags_kde_info.txt
    
# diffusion
!SIPSim KDE_info \
    -s ampFrags_kde_dif.pkl \
    > ampFrags_kde_dif_info.txt
    
# diffusion + DBL    
!SIPSim KDE_info \
    -s ampFrags_kde_dif_DBL.pkl \
    > ampFrags_kde_dif_DBL_info.txt

%%R 

inFile = 'ampFrags_kde_info.txt'
df.raw = read.delim(inFile, sep='\t')
df.raw$stage = 'raw'

inFile = 'ampFrags_kde_dif_info.txt'
df.dif = read.delim(inFile, sep='\t')
df.dif$stage = 'diffusion'

inFile = 'ampFrags_kde_dif_DBL_info.txt'
df.DBL = read.delim(inFile, sep='\t')
df.DBL$stage = 'diffusion +\nDBL'

df = rbind(df.raw, df.dif, df.DBL)
df.dif = ''
df.DBL = ''
df %>% head(n=3)

%%R -w 350 -h 300

df$stage = factor(df$stage, levels=c('raw', 'diffusion', 'diffusion +\nDBL'))

ggplot(df, aes(stage)) +
    geom_boxplot(aes(y=min), color='red') +
    geom_boxplot(aes(y=median), color='darkgreen') +
    geom_boxplot(aes(y=max), color='blue') +
    scale_y_continuous(limits=c(1.3, 2)) +
    labs(y = 'Buoyant density (g ml^-1)') +
    theme_bw() +
    theme(
        text = element_text(size=16),
        axis.title.x = element_blank()
    )

"""
Explanation: Comparing DBL+diffusion to diffusion
End of explanation
"""


!SIPSim incorpConfigExample \
    --percTaxa 10 \
    --percIncorpUnif 100 \
    > PT10_PI100.config
    
# checking output
!head PT10_PI100.config

"""
Explanation: Making an incorp config file

10% of taxa with 100% atom excess 13C
End of explanation
"""


!SIPSim isotope_incorp \
    --comm comm.txt \
    --np 24 \
    --shift ampFrags_BD-shift.txt \
    ampFrags_kde_dif_DBL.pkl \
    PT10_PI100.config \
    > ampFrags_kde_dif_DBL_incorp.pkl \
    2> ampFrags_kde_dif_DBL_incorp.log
    
# checking log
!tail -n 5 ampFrags_kde_dif_DBL_incorp.log

"""
Explanation: Adding isotope incorporation to BD distribution
End of explanation
"""


%%R
inFile = 'ampFrags_BD-shift.txt'
df = read.delim(inFile, sep='\t') %>%
    mutate(library = library %>% as.character)

%%R -h 275 -w 375

inFile = 'ampFrags_BD-shift.txt'
df = read.delim(inFile, sep='\t') %>%
    mutate(library = library %>% as.character)

df.s = df %>% 
    mutate(incorporator = ifelse(min > 0.001, TRUE, FALSE),
           incorporator = ifelse(is.na(incorporator), 'NA', incorporator),
           library = ifelse(library == '1', 'control', 'treatment')) %>%
    group_by(library, incorporator) %>%
    summarize(n_incorps = n())

# summary of number of incorporators
df.s %>%
    filter(library == 'treatment') %>%
    mutate(n_incorps / sum(n_incorps)) %>% 
    as.data.frame %>% print

# plotting
ggplot(df.s, aes(library, n_incorps, fill=incorporator)) +
    geom_bar(stat='identity') +
    labs(y = 'Count', title='Number of incorporators\n(according to BD shift)') +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )

"""
Explanation: Plotting stats on BD shift from isotope incorporation
End of explanation
"""


!SIPSim OTU_table \
    --abs 1e9 \
    --np 20 \
    ampFrags_kde_dif_DBL_incorp.pkl \
    comm.txt \
    fracs.txt \
    > OTU_n2_abs1e9.txt \
    2> OTU_n2_abs1e9.log 
    
# checking log
!tail -n 5 OTU_n2_abs1e9.log 

"""
Explanation: Simulating an OTU table
End of explanation
"""


%%R
## BD for G+C of 0 or 100
BD.GCp0 = 0 * 0.098 + 1.66
BD.GCp50 = 0.5 * 0.098 + 1.66
BD.GCp100 = 1 * 0.098 + 1.66

%%R -w 700 -h 350
# plotting absolute abundances

# loading file
df = read.delim('OTU_n2_abs1e9.txt', sep='\t') 

df.s = df %>%
    group_by(library, BD_mid) %>%
    summarize(total_count = sum(count)) 

## plot
p = ggplot(df.s, aes(BD_mid, total_count)) +
    #geom_point() +
    geom_area(stat='identity', alpha=0.3, position='dodge') +
    geom_vline(xintercept=c(BD.GCp50), linetype='dashed', alpha=0.5) +
    labs(x='Buoyant density', y='Total abundance') +
    facet_grid(library ~ .) +
    theme_bw() +
    theme( 
        text = element_text(size=16) 
    )
p

%%R -w 700 -h 350
# plotting number of taxa at each BD

df.nt = df %>%
    filter(count > 0) %>%
    group_by(library, BD_mid) %>%
    summarize(n_taxa = n())

## plot
p = ggplot(df.nt, aes(BD_mid, n_taxa)) +
    #geom_point() +
    geom_area(stat='identity', alpha=0.3, position='dodge') +
    #geom_histogram(stat='identity') +
    geom_vline(xintercept=c(BD.GCp50), linetype='dashed', alpha=0.5) +
    labs(x='Buoyant density', y='Number of taxa') +
    facet_grid(library ~ .) +
    theme_bw() +
    theme( 
        text = element_text(size=16),
        legend.position = 'none'
    )
p

%%R -w 700 -h 350
# plotting relative abundances

## plot
p = ggplot(df, aes(BD_mid, count, fill=taxon)) +
    geom_vline(xintercept=c(BD.GCp50), linetype='dashed', alpha=0.5) +
    labs(x='Buoyant density', y='Absolute abundance') +
    facet_grid(library ~ .) +
    theme_bw() +
    theme( 
        text = element_text(size=16),
        legend.position = 'none'
    )
p + geom_area(stat='identity', position='dodge', alpha=0.5)

%%R -w 700 -h 350
p + 
    geom_area(stat='identity', position='fill') +
    labs(x='Buoyant density', y='Relative abundance')

"""
Explanation: Plotting taxon abundances
End of explanation
"""


!SIPSim OTU_PCR \
    OTU_n2_abs1e9.txt \
    --debug \
    > OTU_n2_abs1e9_PCR.txt

"""
Explanation: Simulating PCR bias
End of explanation
"""


%%R -w 800 -h 300
# loading file
F = 'OTU_n2_abs1e9_PCR.txt'
df.SIM = read.delim(F, sep='\t') %>%
    mutate(molarity_increase = final_molarity / init_molarity * 100)

p1 = ggplot(df.SIM, aes(init_molarity, final_molarity)) +
    geom_point(shape='O', alpha=0.5) +
    labs(x='Initial molarity', y='Final molarity') +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )

p2 = ggplot(df.SIM, aes(init_molarity, molarity_increase)) +
    geom_point(shape='O', alpha=0.5) +
    scale_y_log10() +
    labs(x='Initial molarity', y='% increase in molarity') +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )

grid.arrange(p1, p2, ncol=2)

%%R -w 800 -h 450
# plotting rank abundances

df.SIM = df.SIM %>%
    group_by(library, fraction) %>%
    mutate(rel_init_molarity = init_molarity / sum(init_molarity),
           rel_final_molarity = final_molarity / sum(final_molarity),
           init_molarity_rank = row_number(rel_init_molarity),
           final_molarity_rank = row_number(rel_final_molarity)) %>%
    ungroup() 
    

p1 = ggplot(df.SIM, aes(init_molarity_rank, rel_init_molarity, color=BD_mid, group=BD_mid)) +
    geom_line(alpha=0.5) +
    scale_y_log10(limits=c(1e-7, 0.1)) +
    scale_x_reverse() +
    scale_color_gradient('Buoyant\ndensity') +
    labs(x='Rank', y='Relative abundance', title='pre-PCR') +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )

p2 = ggplot(df.SIM, aes(final_molarity_rank, rel_final_molarity, color=BD_mid, group=BD_mid)) +
    geom_line(alpha=0.5) +
    scale_y_log10(limits=c(1e-7, 0.1)) +
    scale_x_reverse() +
    scale_color_gradient('Buoyant\ndensity') +
    labs(x='Rank', y='Relative abundance', title='post-PCR') +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )


grid.arrange(p1, p2, ncol=1)

"""
Explanation: Plotting change in relative abundances
End of explanation
"""


# PCR w/out --debug
!SIPSim OTU_PCR \
    OTU_n2_abs1e9.txt \
    > OTU_n2_abs1e9_PCR.txt

"""
Explanation: Notes

The PCR raises the relative abundances most for low-abundance taxa
Results in a more even rank-abundance distribution
End of explanation
"""


!SIPSim OTU_subsample \
    --dist $subsample_dist \
    --dist_params mean:$subsample_mean,sigma:$subsample_scale \
    --min_size $subsample_min \
    --max_size $subsample_max \
    OTU_n2_abs1e9_PCR.txt \
    > OTU_n2_abs1e9_PCR_subNorm.txt

"""
Explanation: Subsampling from the OTU table

simulating sequencing of the DNA pool
End of explanation
"""


%%R -w 300 -h 250

df = read.csv('OTU_n2_abs1e9_PCR_subNorm.txt', sep='\t')

df.s = df %>% 
    group_by(library, fraction) %>%
    summarize(total_count = sum(count)) %>%
    ungroup() %>%
    mutate(library = as.character(library))

ggplot(df.s, aes(library, total_count)) +
    geom_boxplot() +
    labs(y='Number of sequences\nper fraction') +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )

"""
Explanation: Plotting seq count distribution
End of explanation
"""


%%R 

# loading file
df.abs = read.delim('OTU_n2_abs1e9.txt', sep='\t')
df.sub = read.delim('OTU_n2_abs1e9_PCR_subNorm.txt', sep='\t')

lib.reval = c('1' = 'control',
              '2' = 'treatment')

df.abs = mutate(df.abs, library = plyr::revalue(as.character(library), lib.reval))
df.sub = mutate(df.sub, library = plyr::revalue(as.character(library), lib.reval))

%%R -w 700 -h 800
# plotting absolute abundances
## plot
p = ggplot(df.abs, aes(BD_mid, count, fill=taxon)) +
    geom_vline(xintercept=c(BD.GCp50), linetype='dashed', alpha=0.5) +
    labs(x='Buoyant density') +
    facet_grid(library ~ .) +
    theme_bw() +
    theme( 
        text = element_text(size=16),
        axis.title.y = element_text(vjust=1),        
        axis.title.x = element_blank(),
        legend.position = 'none',
        plot.margin=unit(c(1,1,0.1,1), "cm")
    )
p1 = p + geom_area(stat='identity', position='dodge', alpha=0.5) +
    labs(y='Total community\n(absolute abundance)')

# plotting absolute abundances of subsampled
## plot
p = ggplot(df.sub, aes(BD_mid, count, fill=taxon)) +
    geom_vline(xintercept=c(BD.GCp50), linetype='dashed', alpha=0.5) +
    labs(x='Buoyant density') +
    facet_grid(library ~ .) +
    theme_bw() +
    theme( 
        text = element_text(size=16),
        legend.position = 'none'
    )
p2 = p + geom_area(stat='identity', position='dodge', alpha=0.5) +
    labs(y='Subsampled community\n(absolute abundance)') +
    theme(
          axis.title.y = element_text(vjust=1),        
          axis.title.x = element_blank(),
          plot.margin=unit(c(0.1,1,0.1,1), "cm")
         )

# plotting relative abundances of subsampled
p3 = p + geom_area(stat='identity', position='fill') +
    geom_vline(xintercept=c(BD.GCp50), linetype='dashed', alpha=0.5) +
    labs(y='Subsampled community\n(relative abundance)') +
    theme(
          axis.title.y = element_text(vjust=1),
          plot.margin=unit(c(0.1,1,1,1.35), "cm")
        )

# combining plots
grid.arrange(p1, p2, p3, ncol=1)

"""
Explanation: Plotting abundance distributions (paper figure)
End of explanation
"""


!SIPSim OTU_wideLong -w \
    OTU_n2_abs1e9_PCR_subNorm.txt \
    > OTU_n2_abs1e9_PCR_subNorm_w.txt

"""
Explanation: Making a wide OTU table
End of explanation
"""


!SIPSim OTU_sampleData \
    OTU_n2_abs1e9_PCR_subNorm.txt \
    > OTU_n2_abs1e9_PCR_subNorm_meta.txt

"""
Explanation: Making metadata (phyloseq: sample_data)
End of explanation
"""


# making phyloseq object from OTU table
!SIPSimR phyloseq_make \
    OTU_n2_abs1e9_PCR_subNorm_w.txt \
    -s OTU_n2_abs1e9_PCR_subNorm_meta.txt \
    > OTU_n2_abs1e9_PCR_subNorm.physeq

## making ordination
!SIPSimR phyloseq_ordination \
    OTU_n2_abs1e9_PCR_subNorm.physeq \
    OTU_n2_abs1e9_PCR_subNorm_bray-NMDS.pdf    

## filtering phyloseq object to just taxa/samples of interest (eg., BD-min/max)
!SIPSimR phyloseq_edit \
    OTU_n2_abs1e9_PCR_subNorm.physeq \
    --BD_min 1.71 --BD_max 1.75 --occur 0.25 \
    > OTU_n2_abs1e9_PCR_subNorm_filt.physeq

## making ordination
!SIPSimR phyloseq_ordination \
    OTU_n2_abs1e9_PCR_subNorm_filt.physeq \
    OTU_n2_abs1e9_PCR_subNorm_filt_bray-NMDS.pdf
    
# making png figures
!convert OTU_n2_abs1e9_PCR_subNorm_bray-NMDS.pdf OTU_n2_abs1e9_PCR_subNorm_bray-NMDS.png
!convert OTU_n2_abs1e9_PCR_subNorm_filt_bray-NMDS.pdf OTU_n2_abs1e9_PCR_subNorm_filt_bray-NMDS.png    

Image(filename='OTU_n2_abs1e9_PCR_subNorm_bray-NMDS.png') 

Image(filename='OTU_n2_abs1e9_PCR_subNorm_filt_bray-NMDS.png')

"""
Explanation: Community analysis
Phyloseq
End of explanation
"""


## DESeq2
!SIPSimR phyloseq_DESeq2 \
    --log2 0.25 \
    --hypo greater \
    OTU_n2_abs1e9_PCR_subNorm_filt.physeq \
    > OTU_n2_abs1e9_PCR_subNorm_DESeq2
    
## Confusion matrix
!SIPSimR DESeq2_confuseMtx \
    --padj 0.1 \
    ampFrags_BD-shift.txt \
    OTU_n2_abs1e9_PCR_subNorm_DESeq2

%%R -w 500 -h 250

byClass = read.delim('DESeq2-cMtx_byClass.txt', sep='\t') %>%
    filter(library == 2) 

ggplot(byClass, aes(variables, values)) +
    geom_bar(stat='identity') +
    labs(y='Value') +
    theme_bw() +
    theme(
        text = element_text(size=16),
        axis.title.x = element_blank(),
        axis.text.x = element_text(angle=45, hjust=1)
    )

"""
Explanation: DESeq2
End of explanation
"""


%%R

clsfy = function(guess,known){
    if(is.na(guess) | is.na(known)){
        return(NA)
        }
    if(guess == TRUE){
        if(guess == known){
            return('True positive')
            } else {
            return('False positive')
            }
        } else
    if(guess == FALSE){
        if(guess == known){
            return('True negative')
            } else {
            return('False negative')
            }
        } else {
        stop('Error: true or false needed')
        }
    }

%%R 

df = read.delim('DESeq2-cMtx_data.txt', sep='\t')

df = df %>%
    filter(! is.na(log2FoldChange), library == 2) %>%
    mutate(taxon = reorder(taxon, -log2FoldChange),
           cls = mapply(clsfy, incorp.pred, incorp.known))

df %>% head(n=3)

%%R -w 800 -h 350

df.TN = df %>% filter(cls == 'True negative')
df.TP = df %>% filter(cls == 'True positive')
df.FP = df %>% filter(cls == 'False negative')

ggplot(df, aes(taxon, log2FoldChange, color=cls, 
                  ymin=log2FoldChange - lfcSE, ymax=log2FoldChange + lfcSE)) +
    geom_pointrange(size=0.4, alpha=0.5) +
    geom_pointrange(data=df.TP, size=0.4, alpha=0.3) +
    geom_pointrange(data=df.FP, size=0.4, alpha=0.3) +
    labs(x = 'Taxon', y = 'Log2 fold change') +
    theme_bw() +
    theme(
        text = element_text(size=16),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),        
        legend.title=element_blank(),
        axis.text.x = element_blank(),
        legend.position = 'bottom'
        )

"""
Explanation: Plotting results of DESeq2
End of explanation
"""


%%R
df.ds = read.delim('DESeq2-cMtx_data.txt', sep='\t') 
df.comm = read.delim('comm.txt', sep='\t')

df.j = inner_join(df.ds, df.comm, c('taxon' = 'taxon_name',
                                    'library' = 'library'))

df.ds = df.comm = NULL
df.j %>% head(n=3)

%%R -h 500 -w 600

df.j.f = df.j %>%
    filter(! is.na(log2FoldChange),
           library == 2) %>%
    mutate(cls = mapply(clsfy, incorp.pred, incorp.known)) 

y.lab = 'Pre-fractionation\nabundance (%)'
p1 = ggplot(df.j.f, aes(padj, rel_abund_perc, color=cls)) +
    geom_point(alpha=0.7) +
    scale_y_log10() +
    labs(x='P-value (adjusted)', y=y.lab) +
    theme_bw() +
    theme(
        text = element_text(size=16),
        legend.position = 'bottom'
    )

p2 = ggplot(df.j.f, aes(cls, rel_abund_perc)) +
    geom_boxplot() +
    scale_y_log10() +
    labs(y=y.lab) +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )

grid.arrange(p1, p2, ncol=1)

%%R -h 300
# plotting
ggplot(df.j.f, aes(log2FoldChange, rel_abund_perc, color=cls)) +
    geom_point(alpha=0.7) +
    scale_y_log10() +
    labs(x='log2 fold change', y=y.lab) +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )


"""
Explanation: Notes:


Red circles = true positives


False positives should increase with taxon GC 

Higher GC moves 100% incorporators too far to the right the gradient for the 'heavy' BD range of 1.71-1.75
Lines indicate standard errors.

sensitivity ~ pre-frac relative_abundance


Enrichment of TP for abundant incorporators?


What is the abundance distribution of TP and FP?

Are more abundant incorporators being detected more than low abundant taxa
End of explanation
"""


