Import ipyrad and remove previous test files if they are already present
## import modules
import ipyrad as ip ## for RADseq assembly
print ip.__version__ ## print versionAssembly and Sample objects are used by ipyrad to access data stored on disk and to manipulate it. Each biological sample in a data set is represented in a Sample object, and a set of Samples is stored inside an Assembly object. The Assembly object has functions to assemble the data, and stores a log of all steps performed and the resulting statistics of those steps. Assembly objects can be copied or merged to allow branching events where different parameters can subsequently be applied to different Assemblies going forward. Examples of this are shown below.
To create an Assembly object call ip.Assembly() and pass a name for the data set. An Assembly object does not initially contain Samples, they will be created either by linking fastq files to the Assembly object if data are already demultiplexed, or by running step1() to demultiplex raw data files, as shown below.
## create an Assembly object called data1.
data1 = ip.Assembly("data1")
## The object will be saved to disk using its assigned name
print "Assembly object named", data1.nameAssembly object named data1
All of the parameter settings are linked to an Assembly object, which has a set of default parameters when it is created. These can be viewed using the get_params() function. To get more detailed information about all parameters use ip.get_params_info() or to select a single parameter use ip.get_params_info(3). Assembly objects have a function set_params() that can be used to modify parameters.
## modify parameters for this Assembly object
data1.set_params(1, "./test_rad")
data1.set_params(2, "./data/rad_example_R1_.fastq.gz")
data1.set_params(3, "./data/rad_example_barcodes.txt")
#data1.set_params(2, "~/Dropbox/UO_C353_1.fastq.part-aa.gz")
#data1.set_params(3, "/home/deren/Dropbox/Viburnum_revised.barcodes")
data1.set_params(7, 3)
data1.set_params(10, 'rad')
## print the new parameters to screen
data1.get_params()1 project_dir ./test_rad 2 raw_fastq_path ./data/rad_example_R1.fastq.gz 3 barcodes_path ./data/rad_example_barcodes.txt 4 sorted_fastq_path 5 restriction_overhang ('TGCAG', '') 6 max_low_qual_bases 5 7 N_processors 3 8 mindepth_statistical 6 9 mindepth_majrule 6 10 datatype rad 11 clust_threshold 0.85 12 minsamp 4 13 max_shared_heterozygosity 0.25 14 prefix_outname data1 15 phred_Qscore_offset 33 16 max_barcode_mismatch 1 17 filter_adapters 0 18 filter_min_trim_len 35 19 ploidy 2 20 max_stack_size 1000 21 max_Ns_consens 5 22 max_Hs_consens 8 23 max_SNPs_locus (100, 100) 24 max_Indels_locus (5, 99) 25 trim_overhang (1, 2, 2, 1) 26 hierarchical_clustering 0
If the data are already demultiplexed then fastq files can be linked directly to the Data object, which in turn will create Sample objects for each fastq file (or pair of fastq files for paired data). The files may be gzip compressed. If the data are not demultiplexed then you will have to run the step1 function below to demultiplex the raw data.
## This would link fastq files from the 'sorted_fastq_path' if present
## Here it does nothing b/c there are no files in the sorted_fastq_path
data1.link_fastqs()0 new Samples created in data1. 0 fastq files linked to Samples.
This uses the barcodes information to demultiplex reads in data files found in the 'raw_fastq_path'. It will create a Sample object for each sample that will be stored in the Assembly object.
## run step 1 to demultiplex the data
data1.step1()
## print the results for each Sample in data1
print data1.stats.head()state reads_raw reads_filtered clusters_total clusters_kept
1A_0 1 20099 NaN NaN NaN 1B_0 1 19977 NaN NaN NaN 1C_0 1 20114 NaN NaN NaN 1D_0 1 19895 NaN NaN NaN 2E_0 1 19928 NaN NaN NaN
hetero_est error_est reads_consens
1A_0 NaN NaN NaN 1B_0 NaN NaN NaN 1C_0 NaN NaN NaN 1D_0 NaN NaN NaN 2E_0 NaN NaN NaN
If for some reason we wanted to execute on just a subsample of our data, we could do this by selecting only certain samples to call the step2 function on. Because step2 is a function of data, it will always execute with the parameters that are linked to data.
%%time
## example of ways to run step 2 to filter and trim reads
#data1.step2("1B_0") ## run on a single sample
#data1.step2(["1B_0", "1C_0"]) ## run on one or more samples
data1.step2(force=True) ## run on all samples, skipping finished ones
## print the results
print data1.stats.head()Let's imagine at this point that we are interested in clustering our data at two different clustering thresholds. We will try 0.90 and 0.85. First we need to make a copy the Assembly object. This will inherit the locations of the data linked in the first object, but diverge in any future applications to the object. Thus, they can share the same working directory, and will inherit shared files, but create divergently linked files within this directory. You can view the directories linked to an Assembly object with the .dirs argument, shown below. The prefix_outname (param 14) of the new object is automatically set to the Assembly object name.
## run step 3 to cluster reads within samples using vsearch
data1.step3(force=True)
## print the results
print data1.stats.head()And you can see below that the two Assembly objects are now working with several shared directories (working, fastq, edits) but with different clust directories (clust_0.85 and clust_0.9).
## create a branch of our Assembly object
data2 = data1.branch(newname="data2")
## set clustering threshold to 0.90
data2.set_params(11, 0.90)
## look at inherited parameters
data2.get_params()## run step 3 to cluster reads within samples using vsearch
data2.step3(force=True) # ["2H_0", "2G_0"])
## print the results
print data2.statsprint "data1 directories:"
for (i,j) in data1.dirs.items():
print "{}\t{}".format(i, j)
print "\ndata2 directories:"
for (i,j) in data2.dirs.items():
print "{}\t{}".format(i, j)## TODO, just make a [name]_stats directory in [work] for each data obj
data1.statsfilesdata1.stats.to_csv("data1_results.csv", sep="\t")
data1.stats.to_latex("data1_results.tex")There are a a few simple plotting functions in ipyrad useful for visualizing results. These are in the module ipyrad.plotting. Below is an interactive plot for visualizing the distributions of coverages across the 12 samples in the test data set.
import ipyrad.plotting as iplot
## plot for one or more selected samples
iplot.depthplot(data1, ["1A_0", "1B_0"])
## plot for all samples in data1
#iplot.depthplot(data1)
## save plot as pdf and html
iplot.depthplot(data1, outprefix="testfig")import ipyrad as ip
data1 = ip.load_json("test_rad/data1.json")## run step 4
data1.step4("1A_0", force=True)
## print the results
print data1.stats#import ipyrad as ip
## reload autosaved data. In case you quit and came back
#data1 = ip.load_dataobj("test_rad/data1.assembly")## run step 5
data1.step5()
## print the results
print data1.statsdata1.samples["1A_0"].statsip.get_params_info(10)A common problem after struggling through an analysis is that you find you've completely forgotten what parameters you used at what point, and when you changed them. The log history time stamps all calls to set_params(), as well as calls to step methods. It also records copies/branching of data objects.
for i in data1.log:
print iAssembly objects can be saved and loaded so that interactive analyses can be started, stopped, and returned to quite easily. The format of these saved files is a serialized 'dill' object used by Python. Individual Sample objects are saved within Assembly objects. These objects to not contain the actual sequence data, but only link to it, and so are not very large. The information contained includes parameters and the log of Assembly objects, and the statistics and state of Sample objects. Assembly objects are autosaved each time an assembly step function is called, but you can also create your own checkpoints with the save command.
## save assembly object
#ip.save_assembly("data1.p")
## load assembly object
#data = ip.load_json("data1.p.json")
#print data.name