In [1]:
import os
import glob
import re
import nestly
In [2]:
%load_ext rpy2.ipython
%load_ext pushnote
In [3]:
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(phyloseq)
In [4]:
%%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')
In [10]:
workDir = '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/'
buildDir = os.path.join(workDir, 'rep3_DBL-comm_bw2')
R_dir = '/home/nick/notebook/SIPSim/lib/R/'
fragFile = '/home/nick/notebook/SIPSim/dev/bac_genome1147/validation/ampFrags_kde_parsed.pkl'
commFile = '/home/nick/notebook/SIPSim/dev/fullCyc/fullCyc_12C-Con_trm_comm.txt'
nreps = 10
In [11]:
# building tree structure
nest = nestly.Nest()
# varying params
nest.add('DBL_scaling', [0.1, 0.2, 0.3])
nest.add('rep', [x + 1 for x in xrange(nreps)])
## set params
nest.add('bandwidth', [0.08], create_dir=False)
nest.add('abs', ['1e9'], create_dir=False)
nest.add('percIncorp', [0], create_dir=False)
nest.add('percTaxa', [0], create_dir=False)
nest.add('np', [10], 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('commFile', [commFile], create_dir=False)
# building directory tree
nest.build(buildDir)
# bash file to run
bashFile = os.path.join(buildDir, 'SIPSimRun.sh')
In [12]:
%%writefile $bashFile
#!/bin/bash
export PATH={R_dir}:$PATH
echo '#-- SIPSim pipeline --#'
echo '# shuffling taxa in comm file'
comm_shuffle_taxa.r {commFile} > comm.txt
echo '# adding diffusion'
SIPSim diffusion \
{fragFile} \
--bw {bandwidth} \
--np {np} \
> ampFrags_KDE_dif.pkl
echo '# adding DBL contamination; abundance-weighted smearing'
SIPSim DBL \
ampFrags_KDE_dif.pkl \
--comm comm.txt \
--commx {DBL_scaling} \
--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
In [13]:
!chmod 777 $bashFile
!cd $workDir; \
nestrun --template-file $bashFile -d rep3_DBL-comm_bw2 --log-file log.txt -j 2
In [131]:
OTU_files = !find $buildDir -name "OTU_abs1e9_sub.txt"
OTU_files
Out[131]:
In [132]:
%%R -i OTU_files
# loading files
df.SIM = list()
for (x in OTU_files){
df = read.delim(x, sep='\t')
xx = strsplit(x, '/')[[1]]
df$DBL_scale = xx[10] %>% as.numeric
#df$bw = xx[11] %>% as.numeric
df$SIM_rep = xx[11] %>% as.numeric
df.SIM[[x]] = df
}
df.SIM = do.call('rbind', df.SIM)
df.SIM$file = gsub('\\.[0-9]+$', '', rownames(df.SIM))
rownames(df.SIM) = 1:nrow(df.SIM)
df.SIM %>% head(n=3)
In [133]:
comm_files = !find $buildDir -name "comm.txt"
comm_files
Out[133]:
In [134]:
%%R -i comm_files
df.SIM.comm = list()
for (x in comm_files){
df = read.delim(x, sep='\t')
xx = strsplit(x, '/')[[1]]
df$DBL_scale = xx[10] %>% as.numeric
#df$bw = xx[11] %>% as.numeric
df$SIM_rep = xx[11] %>% as.numeric
df.SIM.comm[[x]] = df
}
df.SIM.comm = do.call(rbind, 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)
In [135]:
%%R
## joining SIP & comm (pre-fractionation)
df.SIM.j = inner_join(df.SIM, df.SIM.comm, c('library' = 'library',
'taxon' = 'taxon_name',
'DBL_scale' = 'DBL_scale',
'SIM_rep' = 'SIM_rep')) %>%
filter(BD_mid >= min_BD,
BD_mid <= max_BD)
df.SIM.j %>% head(n=3)
In [136]:
%%R
# calculating BD range
df.SIM.j.f = df.SIM.j %>%
filter(count > 0) %>%
group_by(DBL_scale, SIM_rep) %>%
mutate(max_BD_range = max(BD_mid) - min(BD_mid)) %>%
ungroup() %>%
group_by(DBL_scale, 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() %>%
mutate(SIM_rep = SIM_rep %>% as.character)
df.SIM.j.f %>% head(n=3) %>% as.data.frame
In [137]:
%%R -h 750 -w 600
## 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(limits=c(0.0001, 0.1)) +
scale_y_continuous() +
facet_grid(DBL_scale ~ .) +
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'
)
In [146]:
%%R -h 750 -w 600
## plotting
ggplot(df.SIM.j.f, aes(mean_bulk_abund, BD_range_perc)) +
geom_hex() +
scale_x_log10(limits=c(0.0001, 0.1)) +
scale_y_continuous(limits=c(0,105)) +
scale_fill_gradient(low='grey95', high='black') +
facet_grid(DBL_scale ~ .) +
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'
)
In [28]:
%%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(DBL_scale, SIM_rep, fraction) %>%
mutate(rel_abund = rel_abund / sum(rel_abund))
# status
cat('Below detection level abundances converted to: ', imp.val, '\n')
In [29]:
%%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 [30]:
%%R -w 800 -h 700
# calculating shannon
df.SIM.shan = shannon_index_long(df.SIM.j, 'count', 'library', 'fraction', 'DBL_scale') %>%
filter(BD_mid >= min_BD,
BD_mid <= max_BD)
df.SIM.shan.s = df.SIM.shan %>%
group_by(DBL_scale, 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') +
facet_grid(DBL_scale ~ .) +
theme_bw() +
theme(
text = element_text(size=16),
legend.position = 'none'
)
p
In [36]:
%%R -w 850 -h 700
df.SIM.j.var = df.SIM.j %>%
group_by(DBL_scale, SIM_rep, fraction) %>%
mutate(variance = var(rel_abund)) %>%
ungroup() %>%
distinct(DBL_scale, SIM_rep, fraction) %>%
select(DBL_scale, SIM_rep, fraction, variance, BD_mid) %>%
mutate(SIM_rep = SIM_rep %>% as.character)
ggplot(df.SIM.j.var, aes(BD_mid, variance, color=SIM_rep)) +
geom_point() +
geom_line() +
facet_grid(DBL_scale ~ .) +
theme_bw() +
theme(
text = element_text(size=16)
)
In [38]:
%%R -w 800 -h 600
ggplot(df.SIM.j.var %>% filter(BD_mid >= 1.75), aes(BD_mid, variance, color=SIM_rep)) +
geom_point() +
geom_line() +
facet_grid(DBL_scale ~ .) +
theme_bw() +
theme(
text = element_text(size=16)
)
In [42]:
%%R -w 850 -h 700
df.SIM.j.var.s = df.SIM.j.var %>%
group_by(DBL_scale, BD_bin = ntile(BD_mid, 24)) %>%
summarize(mean_BD = mean(BD_mid),
mean_var = mean(variance),
sd_var = sd(variance)) %>%
ungroup()
ggplot(df.SIM.j.var.s, aes(mean_BD, mean_var,
ymin=mean_var-sd_var,
ymax=mean_var+sd_var)) +
geom_pointrange() +
facet_grid(DBL_scale ~ .) +
theme_bw() +
theme(
text = element_text(size=16)
)
In [97]:
%%R
BD.diffs = function(df){
BDs = df$BD_mid %>% unique
df.BD = expand.grid(BDs, BDs)
df.BD$diff = df.BD %>% apply(1, diff) %>% abs %>% as.vector
df.BD = df.BD %>% spread(Var1, diff)
rownames(df.BD) = df.BD$Var2
df.BD$Var2 = NULL
dist.BD = df.BD %>% as.matrix
dist.BD[upper.tri(dist.BD, diag=TRUE)] = 0
dist.BD %>% as.dist
}
vegdist.by = function(df, ...){
df.w = df %>%
select(taxon, rel_abund, fraction, BD_mid) %>%
spread(taxon, rel_abund) %>%
as.data.frame()
rownames(df.w) = df.w$BD_mid %>% as.vector
df.w$BD_mid = NULL
df.w$fraction = NULL
vegan::vegdist(df.w, ...)
}
dist.match = function(X,D){
# making sure matrices match
X.m = X %>% as.matrix
D.m = D %>% as.matrix
d = setdiff(rownames(X.m), rownames(D.m))
if(length(d) > 0){
print(rownames(X.m))
print(rownames(D.m))
print(d)
stop('Distance matrices don\'t match')
}
D.m = D.m[rownames(X.m), colnames(X.m)]
D = D.m %>% as.dist
return(D)
}
m.corr = function(X, D, ...){
res = list()
for (i in 1:length(X)){
X.d = X[[i]]
X.d[is.na(X.d)] = 0
D.d = D[[i]]
D.d = dist.match(X.d, D.d)
tmp = vegan::mantel.correlog(X.d, D.d, ...)
tmp = tmp['mantel.res'][['mantel.res']] %>% as.data.frame
colnames(tmp) = c('class.index', 'n.dist', 'Mantel.corr', 'Pr', 'Pr.corr')
res[[i]] = tmp
}
return(res)
}
# running
df.SIM.j.d = df.SIM.j %>%
ungroup() %>%
filter(BD_mid >= min_BD, BD_mid <= max_BD) %>%
distinct(DBL_scale, SIM_rep, BD_mid) %>%
group_by(DBL_scale, SIM_rep) %>%
nest() %>%
mutate(dist.bray = lapply(data, vegdist.by),
dist.BD = lapply(data, BD.diffs),
mantel.corr = m.corr(dist.bray, dist.BD, n.class=24)) %>%
select(DBL_scale, SIM_rep, mantel.corr) %>%
unnest(mantel.corr %>% purrr::map(function(x) x))
df.SIM.j.d %>% head
In [99]:
%%R -w 800 -h 650
df.SIM.j.d.s = df.SIM.j.d %>%
filter(! is.na(Mantel.corr)) %>%
group_by(DBL_scale, bin = ntile(class.index, 12)) %>%
summarize(mean.class.index = mean(class.index),
mean.Mantel.corr = mean(Mantel.corr, na.rm=TRUE),
sd.Mantel.corr = sd(Mantel.corr, na.rm=TRUE),
max.Pr.corr = max(Pr.corr, na.rm=TRUE)) %>%
ungroup() %>%
mutate(significant = ifelse(max.Pr.corr <= 0.05, TRUE, FALSE))
ggplot(df.SIM.j.d.s, aes(mean.class.index, mean.Mantel.corr, color=significant,
ymin=mean.Mantel.corr-sd.Mantel.corr,
ymax=mean.Mantel.corr+sd.Mantel.corr)) +
geom_pointrange() +
scale_color_discrete('P_corr < 0.05') +
labs(x='Class index (binned; 12 bins)', y='Mean Mantel correlation\n(+/- stdev)') +
facet_grid(DBL_scale ~ .) +
theme_bw() +
theme(
text = element_text(size=16)
)
In [100]:
OTU_files = !find $buildDir -name "OTU_abs1e9.txt"
OTU_files
Out[100]:
In [101]:
%%R -i OTU_files
# loading files
df.abs = list()
for (x in OTU_files){
df = read.delim(x, sep='\t')
xx = strsplit(x, '/')[[1]]
df$DBL_scale = xx[10] %>% as.numeric
# df$bw = xx[11] %>% as.numeric
df$SIM_rep = xx[11] %>% as.numeric
df.abs[[x]] = df
}
df.abs = do.call('rbind', df.abs)
df.abs$file = gsub('\\.[0-9]+$', '', rownames(df.abs))
rownames(df.abs) = 1:nrow(df.abs)
df.abs %>% head(n=3)
In [104]:
%%R -w 1000 -h 500
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(DBL_scale ~ 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 [105]:
%%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 ~ DBL_scale) +
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)
p2
In [106]:
%%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 ~ DBL_scale) +
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)
p2
In [ ]: