Goal

Simulating a fullCyc control gradient
- Not simulating incorporation (all 0% isotope incorp.)
  - Don't know how much true incorporatation for emperical data
Using parameters inferred from TRIMMED emperical data (fullCyc seq data), or if not available, default SIPSim parameters
Determining whether simulated taxa show similar distribution to the emperical data

Input parameters

phyloseq.bulk file
taxon mapping file
list of genomes
fragments simulated for all genomes
bulk community richness

workflow

Creating a community file from OTU abundances in bulk soil samples
- phyloseq.bulk --> OTU table --> filter to sample --> community table format
Fragment simulation
- simulated_fragments --> parse out fragments for target OTUs
- simulated_fragments --> parse out fragments from random genomes to obtain richness of interest
- combine fragment python objects
Convert fragment lists to kde object
Add diffusion
Make incorp config file
Add isotope incorporation
Calculating BD shift from isotope incorp
Simulating gradient fractions
Simulating OTU table
Simulating PCR
Subsampling from the OTU table

Init



In [7]:

    
import os
import glob
import re
import nestly



In [8]:

    
%load_ext rpy2.ipython
%load_ext pushnote









    



The rpy2.ipython extension is already loaded. To reload it, use:
  %reload_ext rpy2.ipython
The pushnote extension is already loaded. To reload it, use:
  %reload_ext pushnote



In [9]:

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

BD min/max



In [10]:

    
%%R
## 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_BD = min_GC/100.0 * 0.098 + 1.66    
max_BD = max_GC/100.0 * 0.098 + 1.66    

max_BD = max_BD + max_13C_shift_in_BD

cat('Min BD:', min_BD, '\n')
cat('Max BD:', max_BD, '\n')









    





Min BD: 1.67323 
Max BD: 1.7744

Nestly

assuming fragments already simulated



In [16]:

    
workDir = '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/'
buildDir = os.path.join(workDir, 'rep3')
R_dir = '/home/nick/notebook/SIPSim/lib/R/'

fragFile= '/home/nick/notebook/SIPSim/dev/bac_genome1147/validation/ampFrags.pkl'

nreps = 3



In [17]:

    
# building tree structure
nest = nestly.Nest()

# varying params
nest.add('rep', [x + 1 for x in xrange(nreps)])


## set params
nest.add('abs', ['1e9'], create_dir=False)
nest.add('percIncorp', [0], create_dir=False)
nest.add('percTaxa', [0], create_dir=False)
nest.add('np', [2], create_dir=False)
nest.add('subsample_dist', ['lognormal'], create_dir=False)
nest.add('subsample_mean', [9.432], create_dir=False)
nest.add('subsample_scale', [0.5], create_dir=False)
nest.add('subsample_min', [10000], create_dir=False)
nest.add('subsample_max', [30000], create_dir=False)

### input/output files
nest.add('buildDir', [buildDir], create_dir=False)
nest.add('R_dir', [R_dir], create_dir=False)
nest.add('fragFile', [fragFile], create_dir=False)
nest.add('bandwidth', [0.6], create_dir=False)
nest.add('comm_params', ['mean:-7.6836085,sigma:0.9082843'], create_dir=False)

# building directory tree
nest.build(buildDir)

# bash file to run
bashFile = os.path.join(buildDir, 'SIPSimRun.sh')



In [18]:

    
%%writefile $bashFile
#!/bin/bash

export PATH={R_dir}:$PATH

echo '#-- SIPSim pipeline --#'

echo '# converting fragments to KDE'
SIPSim fragment_KDE \
    {fragFile} \
    > ampFrags_KDE.pkl
    
echo '# making a community file'
SIPSim KDE_info \
    -t ampFrags_KDE.pkl \
    > taxon_names.txt
SIPSim communities \
    --abund_dist_p {comm_params} \
    taxon_names.txt \
    > comm.txt
    
echo '# adding diffusion'    
SIPSim diffusion \
    ampFrags_KDE.pkl \
    --bw {bandwidth} \
    --np {np} \
    > ampFrags_KDE_dif.pkl    

echo '# adding DBL contamination'
SIPSim DBL \
    ampFrags_KDE_dif.pkl \
    --bw {bandwidth} \
    --np {np} \
    > ampFrags_KDE_dif_DBL.pkl
    
echo '# making incorp file'
SIPSim incorpConfigExample \
  --percTaxa {percTaxa} \
  --percIncorpUnif {percIncorp} \
  > {percTaxa}_{percIncorp}.config

echo '# adding isotope incorporation to BD distribution'
SIPSim isotope_incorp \
    ampFrags_KDE_dif_DBL.pkl \
    {percTaxa}_{percIncorp}.config \
    --comm comm.txt \
    --bw {bandwidth} \
    --np {np} \
    > ampFrags_KDE_dif_DBL_inc.pkl

echo '# simulating gradient fractions'
SIPSim gradient_fractions \
    comm.txt \
    > fracs.txt 

echo '# simulating an OTU table'
SIPSim OTU_table \
    ampFrags_KDE_dif_DBL_inc.pkl \
    comm.txt \
    fracs.txt \
    --abs {abs} \
    --np {np} \
    > OTU_abs{abs}.txt
    
#-- w/ PCR simulation --#
echo '# simulating PCR'
SIPSim OTU_PCR \
    OTU_abs{abs}.txt \
    > OTU_abs{abs}_PCR.txt    
    
echo '# subsampling from the OTU table (simulating sequencing of the DNA pool)'
SIPSim OTU_subsample \
    --dist {subsample_dist} \
    --dist_params mean:{subsample_mean},sigma:{subsample_scale} \
    --min_size {subsample_min} \
    --max_size {subsample_max} \
    OTU_abs{abs}_PCR.txt \
    > OTU_abs{abs}_PCR_sub.txt
        
echo '# making a wide-formatted table'
SIPSim OTU_wideLong -w \
    OTU_abs{abs}_PCR_sub.txt \
    > OTU_abs{abs}_PCR_sub_w.txt
    
echo '# making metadata (phyloseq: sample_data)'
SIPSim OTU_sampleData \
    OTU_abs{abs}_PCR_sub.txt \
    > OTU_abs{abs}_PCR_sub_meta.txt
    

#-- w/out PCR simulation --#    
echo '# subsampling from the OTU table (simulating sequencing of the DNA pool)'
SIPSim OTU_subsample \
    --dist {subsample_dist} \
    --dist_params mean:{subsample_mean},sigma:{subsample_scale} \
    --min_size {subsample_min} \
    --max_size {subsample_max} \
    OTU_abs{abs}.txt \
    > OTU_abs{abs}_sub.txt
        
echo '# making a wide-formatted table'
SIPSim OTU_wideLong -w \
    OTU_abs{abs}_sub.txt \
    > OTU_abs{abs}_sub_w.txt
    
echo '# making metadata (phyloseq: sample_data)'
SIPSim OTU_sampleData \
    OTU_abs{abs}_sub.txt \
    > OTU_abs{abs}_sub_meta.txt









    



Writing /home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/SIPSimRun.sh



In [19]:

    
!chmod 777 $bashFile
!cd $workDir; \
    nestrun  --template-file $bashFile -d rep3 --log-file log.txt -j 3









    



2016-02-17 19:08:18,886 * INFO * Template: ./SIPSimRun.sh
2016-02-17 19:08:18,888 * INFO * [7389] Started ./SIPSimRun.sh in rep3/3
2016-02-17 19:08:18,890 * INFO * [7390] Started ./SIPSimRun.sh in rep3/2
2016-02-17 19:08:18,891 * INFO * [7391] Started ./SIPSimRun.sh in rep3/1
2016-02-17 20:11:50,738 * INFO * [7390] rep3/2 Finished with 0
2016-02-17 20:18:41,624 * INFO * [7391] rep3/1 Finished with 0
2016-02-17 20:20:11,761 * INFO * [7389] rep3/3 Finished with 0



In [20]:

    
%pushnote SIPsim rep3 complete

Loading non-PCR subsampled OTU tables



In [21]:

    
OTU_files = !find $buildDir -name "OTU_abs1e9_sub.txt"
OTU_files









    Out[21]:





['/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/3/OTU_abs1e9_sub.txt',
 '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/2/OTU_abs1e9_sub.txt',
 '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/1/OTU_abs1e9_sub.txt']



In [22]:

    
%%R -i OTU_files
# loading files

df.SIM = list()
for (x in OTU_files){
    SIM_rep = gsub('/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/', '', x)
    SIM_rep = gsub('/OTU_abs1e9_sub.txt', '', SIM_rep)
    df.SIM[[SIM_rep]] = read.delim(x, sep='\t') 
    }
df.SIM = do.call('rbind', df.SIM)
df.SIM$SIM_rep = gsub('\\.[0-9]+$', '', rownames(df.SIM))
rownames(df.SIM) = 1:nrow(df.SIM)
df.SIM %>% head(n=3)









    





  library    fraction                          taxon BD_min BD_mid BD_max count
1       1  -inf-1.660 Acaryochloris_marina_MBIC11017   -Inf  1.659  1.659    88
2       1 1.660-1.663 Acaryochloris_marina_MBIC11017  1.660  1.661  1.663    72
3       1 1.663-1.665 Acaryochloris_marina_MBIC11017  1.663  1.664  1.665   104
    rel_abund SIM_rep
1 0.004565736       3
2 0.006066734       3
3 0.005859155       3

BD range where an OTU is detected

Do the simulated OTU BD distributions span the same BD range of the emperical data?



In [23]:

    
comm_files = !find $buildDir -name "comm.txt"
comm_files









    Out[23]:





['/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/3/comm.txt',
 '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/2/comm.txt',
 '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/1/comm.txt']



In [24]:

    
%%R -i comm_files

df.SIM.comm = list()
for (x in comm_files){
    SIM_rep = gsub('/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/', '', x)
    SIM_rep = gsub('/comm.txt', '', SIM_rep)
    df.SIM.comm[[SIM_rep]] = read.delim(x, sep='\t') 
    }

df.SIM.comm = do.call(rbind, df.SIM.comm)
df.SIM.comm$SIM_rep = gsub('\\.[0-9]+$', '', rownames(df.SIM.comm))
rownames(df.SIM.comm) = 1:nrow(df.SIM.comm)
df.SIM.comm = df.SIM.comm %>%
    rename('bulk_abund' = rel_abund_perc) %>%
    mutate(bulk_abund = bulk_abund / 100)
df.SIM.comm %>% head(n=3)









    





  library               taxon_name bulk_abund rank SIM_rep
1       1   Petrotoga_mobilis_SJ95 0.01698990    1       3
2       1   Streptococcus_mitis_B6 0.01069916    2       3
3       1 Prevotella_ruminicola_23 0.01067385    3       3



In [25]:

    
%%R -w 800 -h 400
# Plotting the pre-fractionation abundances of each taxon

df.SIM.comm.s = df.SIM.comm %>%
    group_by(taxon_name) %>%
    summarize(median_rank = median(rank),
              mean_abund = mean(bulk_abund),
              sd_abund = sd(bulk_abund))

df.SIM.comm.s$taxon_name = reorder(df.SIM.comm.s$taxon_name, -df.SIM.comm.s$mean_abund)

ggplot(df.SIM.comm.s, aes(taxon_name, mean_abund, 
                          ymin=mean_abund-sd_abund,
                          ymax=mean_abund+sd_abund)) +
    geom_linerange(alpha=0.4) +
    geom_point(alpha=0.6, size=1.2) +
    scale_y_log10() +
    labs(x='taxon', y='Relative abundance', title='Pre-fractionation abundance') +
    theme_bw() +
    theme(
        text = element_text(size=16),
        axis.text.x = element_blank()
    )



In [26]:

    
%%R

## joining SIP & comm (pre-fractionation)
df.SIM.j = inner_join(df.SIM, df.SIM.comm, c('library' = 'library',
                                             'taxon' = 'taxon_name',
                                             'SIM_rep' = 'SIM_rep')) %>%
    filter(BD_mid >= min_BD, 
           BD_mid <= max_BD)
    
df.SIM.j %>% head(n=3)









    





  library    fraction                          taxon BD_min BD_mid BD_max count
1       1 1.674-1.678 Acaryochloris_marina_MBIC11017  1.674  1.676  1.678    27
2       1 1.678-1.681 Acaryochloris_marina_MBIC11017  1.678  1.679  1.681     6
3       1 1.681-1.684 Acaryochloris_marina_MBIC11017  1.681  1.683  1.684     0
     rel_abund SIM_rep  bulk_abund rank
1 0.0012021371       3 0.005029363   15
2 0.0003171583       3 0.005029363   15
3 0.0000000000       3 0.005029363   15



In [27]:

    
%%R
# calculating BD range
df.SIM.j.f = df.SIM.j %>%
    filter(count > 0) %>%
    group_by(SIM_rep) %>%
    mutate(max_BD_range = max(BD_mid) - min(BD_mid)) %>%
    ungroup() %>%
    group_by(SIM_rep, taxon) %>%
    summarize(mean_bulk_abund = mean(bulk_abund),
              min_BD = min(BD_mid),
              max_BD = max(BD_mid),
              BD_range = max_BD - min_BD,
              BD_range_perc = BD_range / first(max_BD_range) * 100) %>%
    ungroup() 
    
df.SIM.j.f %>% head(n=3) %>% as.data.frame









    





  SIM_rep                                taxon mean_bulk_abund min_BD max_BD
1       1       Acaryochloris_marina_MBIC11017    0.0006275150  1.676  1.772
2       1       Acetobacterium_woodii_DSM_1030    0.0003909846  1.687  1.772
3       1 Acetobacter_pasteurianus_IFO_3283-03    0.0014681691  1.676  1.772
  BD_range BD_range_perc
1    0.096     100.00000
2    0.085      88.54167
3    0.096     100.00000



In [28]:

    
%%R -h 300 -w 550
## plotting
ggplot(df.SIM.j.f, aes(mean_bulk_abund, BD_range_perc, color=SIM_rep)) +
    geom_point(alpha=0.5, shape='O') +
    scale_x_log10() +
    scale_y_continuous() +
    labs(x='Pre-fractionation abundance', y='% of total BD range') +
    #geom_vline(xintercept=0.001, linetype='dashed', alpha=0.5) +
    theme_bw() +
    theme(
        text = element_text(size=16),
        panel.grid = element_blank(),
        legend.position = 'none'
        )

Assessing diversity

Asigning zeros



In [29]:

    
%%R
# giving value to missing abundances
min.pos.val = df.SIM.j %>%
    filter(rel_abund > 0) %>%
    group_by() %>%
    mutate(min_abund = min(rel_abund)) %>%
    ungroup() %>%
    filter(rel_abund == min_abund)

min.pos.val = min.pos.val[1,'rel_abund'] %>% as.numeric
imp.val = min.pos.val / 10


# convert numbers
df.SIM.j[df.SIM.j$rel_abund == 0, 'abundance'] = imp.val

# another closure operation
df.SIM.j = df.SIM.j %>%
    group_by(SIM_rep, fraction) %>%
    mutate(rel_abund = rel_abund / sum(rel_abund))


# status
cat('Below detection level abundances converted to: ', imp.val, '\n')









    





Below detection level abundances converted to:  3.349972e-06

Plotting Shannon diversity for each



In [30]:

    
%%R
shannon_index_long = function(df, abundance_col, ...){
    # calculating shannon diversity index from a 'long' formated table
    ## community_col = name of column defining communities
    ## abundance_col = name of column defining taxon abundances
    df = df %>% as.data.frame
    cmd = paste0(abundance_col, '/sum(', abundance_col, ')')
    df.s = df %>%
        group_by_(...) %>%
        mutate_(REL_abundance = cmd) %>%
        mutate(pi__ln_pi = REL_abundance * log(REL_abundance),
               shannon = -sum(pi__ln_pi, na.rm=TRUE)) %>%
        ungroup() %>% 
        dplyr::select(-REL_abundance, -pi__ln_pi) %>%
        distinct_(...) 
    return(df.s)
}



In [31]:

    
%%R -w 800 -h 300
# calculating shannon
df.SIM.shan = shannon_index_long(df.SIM.j, 'count', 'library', 'fraction') %>%
    filter(BD_mid >= min_BD, 
           BD_mid <= max_BD) 

df.SIM.shan.s = df.SIM.shan %>%
    group_by(BD_bin = ntile(BD_mid, 24)) %>%
    summarize(mean_BD = mean(BD_mid),
              mean_shannon = mean(shannon),
              sd_shannon = sd(shannon))

# plotting
p = ggplot(df.SIM.shan.s, aes(mean_BD, mean_shannon, 
                             ymin=mean_shannon-sd_shannon,
                             ymax=mean_shannon+sd_shannon)) +
    geom_pointrange() +
    labs(x='Buoyant density', y='Shannon index') +
    theme_bw() +
    theme( 
        text = element_text(size=16),
        legend.position = 'none'
    )
p

Plotting variance



In [32]:

    
%%R -w 800 -h 350
df.SIM.j.var = df.SIM.j %>%
    group_by(SIM_rep, fraction) %>%
    mutate(variance = var(rel_abund)) %>%
    ungroup() %>%
    distinct(SIM_rep, fraction) %>%
    select(SIM_rep, fraction, variance, BD_mid)

ggplot(df.SIM.j.var, aes(BD_mid, variance, color=SIM_rep)) +
    geom_point() +
    geom_line() +
    theme_bw() +
    theme(
        text = element_text(size=16)
    )

Notes

spikes at low & high G+C
- absence of taxa or presence of taxa at those locations?

Plotting absolute abundance distributions



In [33]:

    
OTU_files = !find $buildDir -name "OTU_abs1e9.txt"
OTU_files









    Out[33]:





['/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/3/OTU_abs1e9.txt',
 '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/2/OTU_abs1e9.txt',
 '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/1/OTU_abs1e9.txt']



In [34]:

    
%%R -i OTU_files
# loading files

df.abs = list()
for (x in OTU_files){
    SIM_rep = gsub('/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/', '', x)
    SIM_rep = gsub('/OTU_abs1e9.txt', '', SIM_rep)
    df.abs[[SIM_rep]] = read.delim(x, sep='\t') 
    }
df.abs = do.call('rbind', df.abs)
df.abs$SIM_rep = gsub('\\.[0-9]+$', '', rownames(df.abs))
rownames(df.abs) = 1:nrow(df.abs)
df.abs %>% head(n=3)









    





  library                          taxon    fraction BD_min BD_mid BD_max count
1       1 Acaryochloris_marina_MBIC11017  -inf-1.660   -Inf  1.659  1.659   341
2       1 Acaryochloris_marina_MBIC11017 1.660-1.663  1.660  1.661  1.663   130
3       1 Acaryochloris_marina_MBIC11017 1.663-1.665  1.663  1.664  1.665    65
    rel_abund SIM_rep
1 0.004596740       3
2 0.006662225       3
3 0.004791037       3



In [35]:

    
%%R -w 800 

ggplot(df.abs, aes(BD_mid, count, fill=taxon)) +
    geom_area(stat='identity', position='dodge', alpha=0.5) +
    labs(x='Buoyant density', y='Subsampled community\n(absolute abundance)') +
    facet_grid(SIM_rep ~ .) +
    theme_bw() +
    theme( 
        text = element_text(size=16),
        legend.position = 'none',
        axis.title.y = element_text(vjust=1),        
        axis.title.x = element_blank()
        )



In [36]:

    
%%R -w 800 

p1 = ggplot(df.abs %>% filter(BD_mid < 1.7), aes(BD_mid, count, fill=taxon, color=taxon)) +
    labs(x='Buoyant density', y='Subsampled community\n(absolute abundance)') +
    facet_grid(SIM_rep ~ .) +
    theme_bw() +
    theme( 
        text = element_text(size=16),
        legend.position = 'none',
        axis.title.y = element_text(vjust=1),        
        axis.title.x = element_blank()
        )

p2 = p1 + geom_line(alpha=0.25) + scale_y_log10()
p1 = p1 + geom_area(stat='identity', position='dodge', alpha=0.5) 

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



In [37]:

    
%%R -w 800 

p1 = ggplot(df.abs %>% filter(BD_mid > 1.72), aes(BD_mid, count, fill=taxon, color=taxon)) +
    labs(x='Buoyant density', y='Subsampled community\n(absolute abundance)') +
    facet_grid(SIM_rep ~ .) +
    theme_bw() +
    theme( 
        text = element_text(size=16),
        legend.position = 'none',
        axis.title.y = element_text(vjust=1),        
        axis.title.x = element_blank()
        )


p2 = p1 + geom_line(alpha=0.25) + scale_y_log10()
p1 = p1 + geom_area(stat='identity', position='dodge', alpha=0.5) 

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

Conclusions

DBL is a bit too permissive
- low abundant taxa are spread out a bit more than emperical
Variance spiking:
- abundance distributions are too tight
  - emperical data variance suggests some extra unevenness in heavy fractions
    - some taxon DNA seems to be 'smeared' out into the heavy fractions
- possible fixes:
  - more abundant, high G+C genomes
  - more diffusion
  - more 'smearing' into the heavy fractions
- TODO:
  - determine what's changing in emperical data between Days 1,3,6 & 14,30,48



In [ ]: