Calour microbiome databases interface tutorial¶

Setup¶

In [1]:

import calour as ca

/Users/amnon/miniconda3/envs/calour/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

In [2]:

ca.set_log_level(11)

In [3]:

%matplotlib notebook

Load the data¶

We will use the Chronic faitigue syndrome data from:

Giloteaux, L., Goodrich, J.K., Walters, W.A., Levine, S.M., Ley, R.E. and Hanson, M.R., 2016.

Reduced diversity and altered composition of the gut microbiome in individuals with myalgic encephalomyelitis/chronic fatigue syndrome.

Microbiome, 4(1), p.30.

In [4]:

cfs=ca.read_amplicon('data/chronic-fatigue-syndrome.biom',
                     'data/chronic-fatigue-syndrome.sample.txt',
                     normalize=10000,min_reads=1000)

2018-07-26 13:09:44 INFO loaded 87 samples, 2129 features
2018-07-26 13:09:44 WARNING These have metadata but do not have data - dropped: {'ERR1331814'}
2018-07-26 13:09:44 INFO After filtering, 87 remaining

preprocess¶

remove non-interesting bacteria, cluster bacteria and sort samples by disease status

In [5]:

cfs=cfs.filter_abundance(10)

2018-07-26 13:09:45 INFO After filtering, 1100 remaining

In [6]:

cfs=cfs.cluster_features()

2018-07-26 13:09:45 INFO After filtering, 1100 remaining

In [7]:

cfs=cfs.sort_samples('Subject')

Viewing database annotations¶

in the interactive heatmap, when clicking on a bacteria, we get a list of all database results about the selected bacteria.

We can choose which databases to use by the databases=['dbbact',...] parameter. The possible databases depend on which database modules were installed.

Currently, supported microbiome database interfaces include:

dbBact - a community database for manual annotations about bacteria (interface installation instruction at dbbact-calour).
SpongeEMP - an automatic database for sea sponge samples (interface installation instruction at spongeworld-calour).
phenoDB - phenotypic information about selected bacteria (interface installation instruction at pheno-calour).

By default, calour uses the dbBact database for microbiome data

In [8]:

cfs.plot(sample_field='Subject',gui='jupyter')

Out[8]:

<calour.heatmap.plotgui_jupyter.PlotGUI_Jupyter at 0x1a10abebe0>

dbBact enrichment of selected bacteria¶

By selecting a set of bacteria (using the shift+click or ctrl+click) and choosing the “Enrichment” button, we can get a list of terms that are significantly enriched in the selected bacteria compared to the rest of the bacteria in the plot

Adding dbBact annotations¶

(Only possible using the gui='qt5' GUI)

To add a new annotation to the selected set of bacteria, choose the “Annotate” button.

Detailed instructions are available at the dbBact.org website.

Differential abundance¶

To find the bacteria significantly different between samples with ‘Control’ (healthy) and ‘Patient’ (sick) in the ‘Subject’ field.

In [9]:

dd=cfs.diff_abundance(field='Subject',val1='Control',val2='Patient', random_seed=2018)

2018-07-26 13:09:57 INFO 87 samples with both values
2018-07-26 13:09:57 INFO After filtering, 1100 remaining
2018-07-26 13:09:57 INFO 39 samples with value 1 (['Control'])
2018-07-26 13:09:58 INFO method meandiff. number of higher in ['Control'] : 38. number of higher in ['Patient'] : 16. total 54

Plot the significant bacteria¶

When clicking on a bacteria, we’ll get both dbBact, SpongeEMP, and phenoDB information

In [10]:

dd.plot(sample_field='Subject', gui='jupyter', databases=['dbbact','sponge'])

Out[10]:

<calour.heatmap.plotgui_jupyter.PlotGUI_Jupyter at 0x1a16f33f98>

dbBact term enrichment (`diff_abundance_enrichment`)¶

We can ask what is special in the bacteria significanly higher in the Control vs. the Patient group and vice versa.

Note since we need to get the per-feature annotations from dbBact, we need a live internet connection to run this command.

Default parameters¶

In [11]:

ax, enriched=dd.plot_diff_abundance_enrichment()

2018-07-26 13:10:03 INFO Getting dbBact annotations for 54 sequences, please wait...
2018-07-26 13:10:08 INFO Got 2328 annotations
2018-07-26 13:10:08 INFO Added annotation data to experiment. Total 705 annotations, 54 terms
2018-07-26 13:10:08 INFO removed 0 terms

The enriched terms are in a calour experiment class (terms are features, bacteria are samples), so we can see the list of enriched terms with the p-value (pval) and effect size (odif)

In [12]:

enriched.feature_metadata

Out[12]:

	odif	pvals	term
little physical activity {single exp 63}	-18.562500	0.000999	little physical activity {single exp 63}
LOWER IN physical activity {single exp 63}	-18.562500	0.000999	LOWER IN physical activity {single exp 63}
LOWER IN rural community	-18.518092	0.000999	LOWER IN rural community
LOWER IN control	-17.807566	0.000999	LOWER IN control
LOWER IN small village	-17.452303	0.000999	LOWER IN small village
LOWER IN tunapuco {single exp 276}	-16.430921	0.000999	LOWER IN tunapuco {single exp 276}
LOWER IN peru {single exp 276}	-16.430921	0.000999	LOWER IN peru {single exp 276}
crohn's disease	-15.587171	0.000999	crohn's disease
chronic fatigue syndrome {single exp 12}	-15.187500	0.000999	chronic fatigue syndrome {single exp 12}
LOWER IN adult	-14.254934	0.001998	LOWER IN adult
mus musculus	-13.588816	0.000999	mus musculus
age > 1 year	-12.478618	0.004995	age > 1 year
kingdom of denmark {single exp 273}	-12.434211	0.006993	kingdom of denmark {single exp 273}
mouse	-11.945724	0.000999	mouse
state of oklahoma	-11.501645	0.007992	state of oklahoma
age 1 year	-11.368421	0.000999	age 1 year
LOWER IN plant diet {single exp 74}	-11.368421	0.002997	LOWER IN plant diet {single exp 74}
stroke {single exp 333}	-11.235197	0.001998	stroke {single exp 333}
age one year {single exp 273}	-11.190789	0.003996	age one year {single exp 273}
research facility	-10.968750	0.005994	research facility
infant	-10.657895	0.018981	infant
LOWER IN male	-10.347039	0.002997	LOWER IN male
LOWER IN age 30-40 {single exp 330}	-10.125000	0.000999	LOWER IN age 30-40 {single exp 330}
msw {single exp 344}	-10.036184	0.007992	msw {single exp 344}
finland	-10.036184	0.024975	finland
heterosexual {single exp 344}	-10.036184	0.007992	heterosexual {single exp 344}
LOWER IN age <1 year {single exp 240}	-9.858553	0.019980	LOWER IN age <1 year {single exp 240}
age	-9.858553	0.031968	age
animal product diet	-9.769737	0.005994	animal product diet
obsolete_juvenile stage	-9.547697	0.038961	obsolete_juvenile stage
...	...	...	...
LOWER IN state of oklahoma	14.210526	0.000999	LOWER IN state of oklahoma
msm {single exp 344}	14.388158	0.000999	msm {single exp 344}
gay {single exp 344}	14.388158	0.000999	gay {single exp 344}
homosexual {single exp 344}	14.388158	0.000999	homosexual {single exp 344}
cron diet {single exp 293}	15.009868	0.000999	cron diet {single exp 293}
caloric restriction diet {single exp 293}	15.009868	0.000999	caloric restriction diet {single exp 293}
LOWER IN united states of america	15.631579	0.000999	LOWER IN united states of america
sus scrofa	15.942434	0.000999	sus scrofa
pig	15.942434	0.000999	pig
right colon {single exp 256}	16.164474	0.000999	right colon {single exp 256}
left colon {single exp 256}	16.164474	0.000999	left colon {single exp 256}
LOWER IN city	16.342105	0.000999	LOWER IN city
influent {single exp 53}	17.230263	0.000999	influent {single exp 53}
sewage {single exp 53}	17.230263	0.000999	sewage {single exp 53}
LOWER IN effluent	17.230263	0.000999	LOWER IN effluent
wastewater treatment plant	17.452303	0.000999	wastewater treatment plant
LOWER IN finland	17.629934	0.000999	LOWER IN finland
tanzania {single exp 190}	17.763158	0.000999	tanzania {single exp 190}
hadza {single exp 190}	17.763158	0.000999	hadza {single exp 190}
egypt {single exp 62}	18.118421	0.000999	egypt {single exp 62}
amerindian {single exp 75}	19.184211	0.000999	amerindian {single exp 75}
venezuela {single exp 75}	19.184211	0.000999	venezuela {single exp 75}
south america {single exp 53}	20.250000	0.000999	south america {single exp 53}
peru {single exp 276}	21.315789	0.000999	peru {single exp 276}
tunapuco {single exp 276}	21.315789	0.000999	tunapuco {single exp 276}
rural community	21.937500	0.000999	rural community
hunter gatherer	22.026316	0.000999	hunter gatherer
el salvador {single exp 53}	22.026316	0.000999	el salvador {single exp 53}
LOWER IN infant	23.269737	0.000999	LOWER IN infant
small village	23.758224	0.000999	small village

135 rows × 3 columns

We can plot the enriched terms heatmap to see the term scores for each bacteria.

Note now rows are the bacteria and columns are the terms

In [16]:

enriched.plot(gui='jupyter', databases=[], feature_field='term',sample_field='group',
              yticklabel_kwargs={'rotation': 0, 'size': 7})

Out[16]:

<calour.heatmap.plotgui_jupyter.PlotGUI_Jupyter at 0x1a1c678c50>

Look at the behavior of a single term¶

We want to see all the annotations where a given term appears, and see what bacteria from either group (CFS or healthy) appear in that annotations. To do this, we use dbbact.show_term_details_diff(). The output of this function is an experiment where each COLUMN is a bacteria, and each row is an annotation. We see whether each bacteria appears in the annotation. Color indicates the annotation type.

In [38]:

dbbact=ca.database._get_database_class('dbbact')

In [40]:

term_info_exp = dbbact.show_term_details_diff('small village',dd,gui='jupyter')

2018-07-26 13:24:01 INFO found 12 annotations with term
2018-07-26 13:24:01 WARNING Do you forget to normalize your data? It is required before running this function
2018-07-26 13:24:01 INFO After filtering, 12 remaining

getting enriched annotations instead of terms¶

Each annotation is coming from a single experiment (as opposed to terms that can come from annotations in multiple experiment)

In [17]:

ax, enriched=dd.plot_diff_abundance_enrichment(term_type='annotation')

2018-07-26 13:12:53 INFO removed 0 terms

In [18]:

enriched.feature_metadata

Out[18]:

79 rows × 3 columns

Getting both enriched terms and annotations¶

In [19]:

ax, enriched=dd.plot_diff_abundance_enrichment(term_type='combined')

2018-07-26 13:13:05 INFO removed 0 terms

In [20]:

enriched.feature_metadata

Out[20]:

	odif	pvals	term
higher in individuals with low physical activity ( high in little physical activity compared to physical activity in feces homo sapiens united states of america	-18.562500	0.000999	higher in individuals with low physical activi...
LOWER IN physical activity {single exp 63}	-18.562500	0.000999	LOWER IN physical activity {single exp 63}
little physical activity {single exp 63}	-18.562500	0.000999	little physical activity {single exp 63}
LOWER IN rural community	-18.518092	0.000999	LOWER IN rural community
LOWER IN control	-17.807566	0.000999	LOWER IN control
LOWER IN small village	-17.452303	0.000999	LOWER IN small village
LOWER IN peru {single exp 276}	-16.430921	0.000999	LOWER IN peru {single exp 276}
high in united states of america city state of oklahoma compared to peru small village tunapuco rural community in feces homo sapiens adult	-16.430921	0.000999	high in united states of america city state o...
LOWER IN tunapuco {single exp 276}	-16.430921	0.000999	LOWER IN tunapuco {single exp 276}
crohn's disease	-15.587171	0.000999	crohn's disease
high in children with Crohn's disease compared to healthy adult controls ( high in crohn's disease child obsolete_juvenile stage compared to control adult in feces homo sapiens glasgow	-15.187500	0.000999	high in children with Crohn's disease compared...
high in chronic fatigue syndrome compared to control in feces homo sapiens new york county	-15.187500	0.000999	high in chronic fatigue syndrome compared to...
high in female compared to male in feces homo sapiens united states of america	-15.187500	0.000999	high in female compared to male in feces ho...
chronic fatigue syndrome {single exp 12}	-15.187500	0.000999	chronic fatigue syndrome {single exp 12}
LOWER IN adult	-14.254934	0.001998	LOWER IN adult
mus musculus	-13.588816	0.000999	mus musculus
age > 1 year	-12.478618	0.000999	age > 1 year
kingdom of denmark {single exp 273}	-12.434211	0.001998	kingdom of denmark {single exp 273}
mouse	-11.945724	0.000999	mouse
state of oklahoma	-11.501645	0.007992	state of oklahoma
LOWER IN plant diet {single exp 74}	-11.368421	0.001998	LOWER IN plant diet {single exp 74}
age 1 year	-11.368421	0.000999	age 1 year
Higher in animal product diet compared to plant diet ( high in diet animal product diet compared to plant diet in feces homo sapiens united states of america	-11.368421	0.001998	Higher in animal product diet compared to plan...
stroke {single exp 333}	-11.235197	0.001998	stroke {single exp 333}
age one year {single exp 273}	-11.190789	0.004995	age one year {single exp 273}
research facility	-10.968750	0.011988	research facility
infant	-10.657895	0.014985	infant
LOWER IN male	-10.347039	0.001998	LOWER IN male
LOWER IN age 30-40 {single exp 330}	-10.125000	0.000999	LOWER IN age 30-40 {single exp 330}
common feces, homo sapiens, infant, kingdom of norway, oslo, age 1 year,	-10.125000	0.000999	common feces, homo sapiens, infant, kingdom o...
...	...	...	...
high in male compared to female in feces homo sapiens united states of america	15.631579	0.000999	high in male compared to female in feces ho...
pig	15.942434	0.001998	pig
sus scrofa	15.942434	0.001998	sus scrofa
left colon {single exp 256}	16.164474	0.000999	left colon {single exp 256}
right colon {single exp 256}	16.164474	0.000999	right colon {single exp 256}
lower in babies from finland compared to estonia ( high in estonia compared to finland in feces homo sapiens infant age < 3 years	16.342105	0.000999	lower in babies from finland compared to eston...
LOWER IN city	16.342105	0.000999	LOWER IN city
sewage {single exp 53}	17.230263	0.000999	sewage {single exp 53}
LOWER IN effluent	17.230263	0.000999	LOWER IN effluent
influent {single exp 53}	17.230263	0.000999	influent {single exp 53}
lower in wastewater plant effluent compared to influent and sewer in south america ( high in sewage influent compared to effluent in city wastewater treatment plant south america	17.230263	0.000999	lower in wastewater plant effluent compared to...
wastewater treatment plant	17.452303	0.000999	wastewater treatment plant
LOWER IN finland	17.629934	0.000999	LOWER IN finland
hadza {single exp 190}	17.763158	0.000999	hadza {single exp 190}
tanzania {single exp 190}	17.763158	0.000999	tanzania {single exp 190}
egypt {single exp 62}	18.118421	0.000999	egypt {single exp 62}
venezuela {single exp 75}	19.184211	0.000999	venezuela {single exp 75}
amerindian {single exp 75}	19.184211	0.000999	amerindian {single exp 75}
common feces, homo sapiens, venezuela, amerindian, hunter gatherer,	19.184211	0.000999	common feces, homo sapiens, venezuela, amerin...
common feces, homo sapiens, adult, peru, small village, tunapuco, rural community,	19.184211	0.000999	common feces, homo sapiens, adult, peru, smal...
south america {single exp 53}	20.250000	0.000999	south america {single exp 53}
common feces, homo sapiens, city, el salvador, small village,	20.605263	0.000999	common feces, homo sapiens, city, el salvador...
tunapuco {single exp 276}	21.315789	0.000999	tunapuco {single exp 276}
peru {single exp 276}	21.315789	0.000999	peru {single exp 276}
rural community	21.937500	0.000999	rural community
el salvador {single exp 53}	22.026316	0.000999	el salvador {single exp 53}
hunter gatherer	22.026316	0.000999	hunter gatherer
high in adult compared to infant age < 1 year in feces homo sapiens india	22.470395	0.000999	high in adult compared to infant age < 1 yea...
LOWER IN infant	23.269737	0.000999	LOWER IN infant
small village	23.758224	0.000999	small village

215 rows × 3 columns

Ignoring selected experiments already in dbBact¶

If our experiment is already in dbBact, or if there are other experiments in dbBact we do not want to include in the enrichment analysis, we can specify them using the ignore_exp=[expID,...] parameter.

In our case, the cfs experiment is already added to dbBact, so let’s ignore it’s annotations when doing the analysis. By looking at dbBact.org we know its experimentID is 12. Alternatively we can use ignore_exp=True to automatically detect the current experimentID if it exists in dbBact (using the data and mapping file md5 hash).

In [21]:

ax, enriched=dd.plot_diff_abundance_enrichment(term_type='combined', ignore_exp=[12])

2018-07-26 13:13:12 INFO removed 0 terms

Adding common dbBact terms to features (`add_terms_to_features`)¶

We can attach to each bacteria the most common dbBact term associated with it.

The terms are selected from all of the dbBact terms, or can be selected from a supplied list.

In [22]:

cfs=cfs.add_terms_to_features(dbname='dbbact',use_term_list=['feces','saliva','skin','mus musculus'])

2018-07-26 13:13:20 INFO Getting dbBact annotations for 1100 sequences, please wait...
2018-07-26 13:13:32 INFO Got 24053 annotations
2018-07-26 13:13:32 INFO Added annotation data to experiment. Total 2151 annotations, 1100 terms

In [23]:

tt=cfs.sort_by_metadata('common_term',axis='feature')

In [24]:

tt.plot(sample_field='Subject', feature_field='common_term', gui='jupyter')

Out[24]:

<calour.heatmap.plotgui_jupyter.PlotGUI_Jupyter at 0x1a1cbe0e48>

Get enriched terms using all bacteria¶

Instead of just comparing the bacteria enriched in the two groups (and then comparing terms between them), we can do a weighted term average for each group using all bacteria (weighing the terms of each bacteria by its’ frequency in the sample). This can work if we don’t have a strong set of bacteria separating between the two groups.

In [25]:

dbbact=ca.database._get_database_class('dbbact')

In [32]:

enriched=dbbact.sample_enrichment(cfs,'Subject','Control','Patient',
                                  term_type='combined',ignore_exp=[12])

2018-07-26 13:17:22 INFO 87 samples with both values
2018-07-26 13:17:22 WARNING Do you forget to normalize your data? It is required before running this function
2018-07-26 13:17:22 INFO After filtering, 2704 remaining
2018-07-26 13:17:22 INFO 39 samples with value 1 (['Control'])
2018-07-26 13:17:24 INFO method meandiff. number of higher in ['Control'] : 455. number of higher in ['Patient'] : 51. total 506

In [27]:

enriched.feature_metadata

Out[27]:

	term	num_features	_calour_diff_abundance_effect	_calour_diff_abundance_pval	_calour_diff_abundance_group
enzyme supplement	enzyme supplement	20	-1.467864	0.000999	Patient
-no enzyme supplement	-no enzyme supplement	20	-1.252388	0.000999	Patient
high in EPI dogs with enzyme supplement compared to no supplement ( high in enzyme supplement compared to no enzyme supplement in feces united states of america exocrine pancreatic insufficiency canis lupus familiaris dog	high in EPI dogs with enzyme supplement compar...	20	-1.252388	0.000999	Patient
-gastric bypass	-gastric bypass	4	-1.009475	0.000999	Patient
lower in people with Roux-en-Y gastric bypass compared to controls ( high in control compared to gastric bypass in feces homo sapiens united states of america	lower in people with Roux-en-Y gastric bypass ...	4	-1.009475	0.000999	Patient
-physical activity	-physical activity	49	-0.964541	0.001998	Patient
higher in individuals with low physical activity ( high in little physical activity compared to physical activity in feces homo sapiens united states of america	higher in individuals with low physical activi...	49	-0.964541	0.001998	Patient
little physical activity	little physical activity	49	-0.931222	0.002997	Patient
high in children with Crohn's disease compared to healthy adult controls ( high in crohn's disease child obsolete_juvenile stage compared to control adult in feces homo sapiens glasgow	high in children with Crohn's disease compared...	53	-0.874353	0.000999	Patient
-age 30-40	-age 30-40	16	-0.832852	0.000999	Patient
high in infant age 1 year compared to adult age 30-40 in feces homo sapiens kingdom of norway oslo	high in infant age 1 year compared to adult ...	16	-0.832852	0.000999	Patient
salmune vaccination	salmune vaccination	12	-0.821224	0.001998	Patient
-salmune vaccination	-salmune vaccination	28	-0.812220	0.001998	Patient
-vaccination	-vaccination	28	-0.812220	0.001998	Patient
higher in non-vaccinated chickens ( high in control compared to vaccination salmune vaccination in united states of america caecum gallus gallus chicken	higher in non-vaccinated chickens ( high in co...	28	-0.812220	0.001998	Patient
pulsed antibiotic treatment, macrolide tylosin tartrate	pulsed antibiotic treatment, macrolide tylosin...	9	-0.771024	0.004995	Patient
exocrine pancreatic insufficiency	exocrine pancreatic insufficiency	33	-0.728781	0.002997	Patient
highfreq feces, acinonyx jubatus, namibia,	highfreq feces, acinonyx jubatus, namibia,	7	-0.661063	0.000999	Patient
common united states of america, caecum, gallus gallus, chicken, age 14-28 days,	common united states of america, caecum, gall...	23	-0.626557	0.001998	Patient
higher in stroke patients compared to healthy controls ( high in stroke compared to control in feces homo sapiens china adult guangzhou city prefecture	higher in stroke patients compared to healthy ...	31	-0.610551	0.001998	Patient
high in old (14-28 days) compared to young (0-3 day) chickens ( high in age old age compared to young age in united states of america caecum gallus gallus chicken	high in old (14-28 days) compared to young (0-...	36	-0.601091	0.003996	Patient
high in control compared to diarrhea in feces felis catus state of california	high in control compared to diarrhea in fec...	10	-0.592252	0.006993	Patient
higher in babies from finland compared to estonia ( high in finland compared to estonia in feces homo sapiens infant age < 3 years	higher in babies from finland compared to esto...	26	-0.589219	0.012987	Patient
canis mesomelas	canis mesomelas	33	-0.569207	0.001998	Patient
smj: higher in female mice feces treated with antibiotics ( high in antibiotic pulsed antibiotic treatment, macrolide tylosin tartrate compared to control in feces united states of america female research facility mus musculoides nyulmc nod/shiltj (no. 001976, jackson labs)	smj: higher in female mice feces treated with ...	9	-0.563986	0.011988	Patient
common feces, namibia, canis mesomelas,	common feces, namibia, canis mesomelas,	33	-0.559916	0.002997	Patient
acinonyx jubatus	acinonyx jubatus	17	-0.553638	0.002997	Patient
-dust day	-dust day	21	-0.550642	0.012987	Patient
higher in dust storm compared to clear day in israel air ( high in clear day compared to dust day in air dust israel size < 10um	higher in dust storm compared to clear day in ...	21	-0.550642	0.012987	Patient
namibia	namibia	40	-0.538952	0.001998	Patient
...	...	...	...	...	...
peru	peru	234	0.851086	0.000999	Control
tunapuco	tunapuco	229	0.856866	0.000999	Control
-tibetan pig	-tibetan pig	26	0.866270	0.001998	Control
-tibetan swine	-tibetan swine	26	0.866270	0.001998	Control
high in sus scrofa pig compared to tibetan pig tibetan swine in china farm caecum tibet autonomous region cecal content	high in sus scrofa pig compared to tibetan p...	26	0.866270	0.001998	Control
high in male compared to female in feces homo sapiens united states of america	high in male compared to female in feces ho...	129	0.878053	0.000999	Control
lower in lean participants in human feces ( high in high bmi compared to low bmi in feces homo sapiens united states of america adult	lower in lean participants in human feces ( hi...	25	0.914211	0.001998	Control
lower in babies from finland compared to estonia ( high in estonia compared to finland in feces homo sapiens infant age < 3 years	lower in babies from finland compared to eston...	110	0.943118	0.000999	Control
lower in small intestine compared to colon in pigs ( high in caecum left colon right colon compared to duodenum jejunum ileum in sus scrofa united kingdom pig	lower in small intestine compared to colon in ...	137	0.962579	0.000999	Control
-irritable bowel syndrome	-irritable bowel syndrome	65	0.969604	0.000999	Control
high in control compared to irritable bowel syndrome in feces homo sapiens adult kingdom of spain	high in control compared to irritable bowel ...	65	0.969604	0.000999	Control
-united states of america	-united states of america	195	0.979835	0.002997	Control
-camp hukamako	-camp hukamako	6	0.993227	0.000999	Control
lower in Hadza camp Hukamako compared to hadza camp Sengeli ( high in camp sengeli compared to camp hukamako in feces homo sapiens tanzania hunter gatherer hadza	lower in Hadza camp Hukamako compared to hadza...	6	0.993227	0.000999	Control
common feces, ethiopia, monkey, theropithecus gelada,	common feces, ethiopia, monkey, theropithecus...	17	1.005345	0.001998	Control
plant based diet	plant based diet	4	1.033398	0.000999	Control
-little physical activity	-little physical activity	84	1.051961	0.001998	Control
higher in individuals with high physical activity ( high in physical activity compared to little physical activity in feces homo sapiens united states of america	higher in individuals with high physical activ...	84	1.051961	0.001998	Control
high in male compared to female in feces homo sapiens toronto	high in male compared to female in feces ho...	11	1.053019	0.000999	Control
physical activity	physical activity	84	1.072794	0.001998	Control
-city	-city	184	1.084257	0.000999	Control
highfreq caecum, left colon, right colon, sus scrofa, united kingdom, pig,	highfreq caecum, left colon, right colon, sus...	10	1.108316	0.000999	Control
hiv infection	hiv infection	67	1.112100	0.000999	Control
-state of oklahoma	-state of oklahoma	177	1.132083	0.000999	Control
high in peru small village tunapuco rural community compared to united states of america city state of oklahoma in feces homo sapiens adult	high in peru small village tunapuco rural com...	177	1.132083	0.000999	Control
camp sengeli	camp sengeli	6	1.177868	0.000999	Control
-msw	-msw	82	1.255350	0.000999	Control
-heterosexual	-heterosexual	82	1.255350	0.000999	Control
higher in gay (msm) individuals compared to heterosexual (msw) ( high in gay homosexual msm compared to heterosexual msw in feces homo sapiens united states of america state of colorado denver	higher in gay (msm) individuals compared to he...	82	1.255350	0.000999	Control
high in hiv infection compared to control in feces homo sapiens united states of america	high in hiv infection compared to control i...	65	1.518273	0.000999	Control

516 rows × 5 columns

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