.. _resdk-tables: ============ ReSDK Tables ============ ReSDK tables are helper classes for aggregating collection data in tabular format. Currently, we have five flavours: - :ref:`rna-tables` - :ref:`methylation-tables` - :ref:`microarray-tables` - :ref:`variant-tables` - :ref:`qc-tables` .. _rna-tables: RNATables ========= Imagine you are modelling gene expression data from a given collection. Ideally, you would want all expression values organized in a table where rows represents samples and columns represent genes. Class ``RNATables`` gives you just that (and more). We will present the functionality of ``RNATables`` through an example. We will: - Create an instance of ``RNATables`` and examine it's attributes - Fetch raw expressions and select `TIS signature genes`_ with sufficient coverage - Normalize expression values (log-transform) and visualize samples in a simple PCA plot .. _`TIS signature genes`: https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-019-2100-3 First, connect to a Resolwe server, pick a collection and create and instance of ``RNATables``:: import resdk from resdk.tables import RNATables res = resdk.Resolwe(url='https://app.genialis.com/') res.login() collection = res.collection.get("sum149-fresh-for-rename") sum149 = RNATables(collection) Object ``sum149`` is an instance of ``RNATables`` and has many attributes. For a complete list see the :ref:`reference`, here we list the most commonly used ones:: # Expressions raw counts sum149.rc # Expressions normalized counts sum149.exp # See normalization method sum149.exp.attrs["exp_type"] # Sample metadata sum149.meta # Dictionary that maps gene ID's into gene symbols sum149.readable_columns # This is handy to rename column names (gene ID's) to gene symbols sum149.rc.rename(columns=sum149.readable_columns) .. note:: Expressions and metadata are cached in memory as well as on disk. At each time they are re-requested a check is made that local and server side of data is synced. If so, cached data is used. Otherwise, new data will be pulled from server. In our example we will only work with a set of `TIS signature genes`_:: TIS_GENES = ["CD3D", "IDO1", "CIITA", "CD3E", "CCL5", "GZMK", "CD2", "HLA-DRA", "CXCL13", "IL2RG", "NKG7", "HLA-E", "CXCR6", "LAG3", "TAGAP", "CXCL10", "STAT1", "GZMB"] We will identify low expressed genes and only keep the ones with average raw expression above 20:: tis_rc = sum149.rc.rename(columns=sum149.readable_columns)[TIS_GENES] mean = tis_rc.mean(axis=0) high_expressed_genes = mean.loc[mean > 20].index Now, lets select TPM normalized expressions and keep only highly expressed tis genes. We also transform to ``log2(TPM + 1)``:: import numpy as np tis_tpm = sum149.exp.rename(columns=sum149.readable_columns)[high_expressed_genes] tis_tpm_log = np.log(tis_tpm + 1) Finally, we perform PCA and visualize the results:: from sklearn.decomposition import PCA pca = PCA(n_components=2, whiten=True) Y = pca.fit_transform(tis_tpm_log) import matplotlib.pyplot as plt for ((x, y), sample_name) in zip(Y, tis_tpm.index): plt.plot(x, y, 'bo') plt.text(x, y, sample_name) plt.xlabel(f"PC1 ({pca.explained_variance_ratio_[0]})") plt.ylabel(f"PC2 ({pca.explained_variance_ratio_[1]})") plt.show() .. _methylation-tables: MethylationTables ================= Similar as ``RNATables`` provide access to raw counts and normalized expression values of RNA data, ``MethylationTables`` allow for fast access of beta and m-values of methylation data:: meth = resdk.tables.MethylationTables() # Methylation beta-values meth.beta # Methylation m-values meth.mval .. _microarray-tables: MATables ======== Similar as ``RNATables`` provide access to raw counts and normalized expression values of RNA data, ``MATables`` allow for fast access of expression values per probe of microarray:: ma = resdk.tables.MATables() # Microarray expressions values (columns are probe ID's) ma.exp .. _variant-tables: VariantTables ============= Similar as ``RNATables`` provide access to raw counts and normalized expression values of RNA data, ``VariantTables`` allow for fast access of variant data present in Data of type ``data:mutationstable``:: vt = resdk.tables.VariantTables() vt.variants The output of the above would look something like this: ========= ============ ============ sample_id chr1_123_C>T chr1_126_T>C ========= ============ ============ 101 2 NaN 102 0 2 ========= ============ ============ In rows, there are sample ID's. In columns there are variants where each variant is given as: ``__``. Values in table can be: - 0 (wild-type / no mutation) - 1 (heterozygous mutation), - 2 (homozygous mutation) - NaN (QC filters are failing - mutation status is unreliable) Inspecting depth ---------------- The reason for NaN values may be that the read depth on certain position is too low for GATK to reliably call a variant. In such case, it is worth inspecting the depth or depth per base:: # Similar as above but one gets depth on particular variant / sample vt.depth # One can also get depth for specific base vt.depth_a vt.depth_c vt.depth_t vt.depth_g Filtering mutations ------------------- Process ``mutations-table`` on Genialis Platform accepts either ``mutations`` or ``geneset`` input which specifies the genes of interest. It restricts the scope of mutation search to just a few given genes. However, it can happen that not all the samples have the same ``mutations`` or ``geneset`` input. In such cases, it makes little sense to merge the information about mutations from multiple samples. By default, ``VariantTables`` checks that all Data is computed with same ``mutations`` / ``geneset`` input. If this is not true, it will raise an error. But if you provide additional argument ``geneset`` it will limit the mutations to only those in the given geneset. An example:: # Sample 101 has mutations input "FHIT, BRCA2" # Sample 102 has mutations input "BRCA2" # This would cause error, since the mutations inputs are not the same vt = resdk.tables.VariantTables() vt.variants # This would limit the variants to just the ones in BRCA2 gene. vt = resdk.tables.VariantTables(, geneset=["BRCA2"]) vt.variants .. _qc-tables: QCTables ============= ``QCTables`` provides a tabular format access to quality control metrics of samples present in Data of type ``data:multiqc``. It parses the relevant ``.txt`` files generated by the MultiQC tool and provides a convenient way to access the metrics. The individual sets of metrics can be accessed as attributes of the object:: qt = resdk.tables.QCTables() # Alignment metrics qt.general_alignment qt.samtools_flagstat # ... # One can also access relevant metrics that are reported during a Resolwe pipeline run qt.rnaseq qt.wgs # ...