Additional utilities

Swan now comes with several utilities that can be used fo compute and output various metrics using data in the SwanGraph.

Table of contents

• Calculating TPM values

• Calculating pi values

• Obtaining edge abundance information

• Obtaining TSS/TES abundance information

We'll be using the same SwanGraph as the rest of the tutorial pages to demonstrate these utilities. Load it using the following code:

import swan_vis as swan

# code to download this data is in the Getting started tutorial
sg = swan.read('../tutorials/data/swan.p')

Read in graph from ../tutorials/data/swan.p

Calculating TPM values

Swan allows for users to calculate the TPM of their data using various groupby metrics using the calc_tpm() function. You can use this to calculate TPM of any of the AnnData SwanGraph objects (SwanGraph.adata for transcripts, SwanGraph.tss_adata for TSSs, SwanGraph.tes_adata for TESs, and SwanGraph.edge_adata for edges; see the Data structure FAQ page for more information on these tables.

First, we'll calculate the TPM for each transcript in each dataset:

df = swan.calc_tpm(sg.adata)
df.head()

tid	ENST00000000233.9	ENST00000000412.7	ENST00000000442.10	ENST00000001008.5	ENST00000001146.6	ENST00000002125.8	ENST00000002165.10	ENST00000002501.10	ENST00000002596.5	ENST00000002829.7	...	TALONT000482711	TALONT000482903	TALONT000483195	TALONT000483284	TALONT000483315	TALONT000483322	TALONT000483327	TALONT000483978	TALONT000484004	TALONT000484796
hepg2_1	196.138474	86.060760	8.005652	46.032497	0.0	16.011305	258.182281	60.042389	0.0	0.0	...	0.000000	4.002826	2.001413	12.008478	0.000000	0.000000	4.002826	14.009891	8.005652	0.000000
hepg2_2	243.975174	77.789185	7.071744	61.288448	0.0	12.964864	380.695557	64.824318	0.0	0.0	...	1.178624	14.143488	4.714496	7.071744	2.357248	8.250368	2.357248	11.786240	10.607616	1.178624
hffc6_1	131.320969	194.355042	0.000000	107.683197	0.0	6.566049	278.400452	0.000000	0.0	0.0	...	6.566049	13.132097	9.192468	1.313210	6.566049	9.192468	6.566049	0.000000	15.758516	1.313210
hffc6_2	137.061584	242.395935	0.000000	124.370689	0.0	8.883621	219.552338	0.000000	0.0	0.0	...	15.229064	10.152709	6.345443	8.883621	1.269089	10.152709	15.229064	0.000000	16.498154	8.883621
hffc6_3	147.986496	273.205841	3.252450	172.379868	0.0	9.757351	200.025696	1.626225	0.0	0.0	...	14.636026	11.383576	8.131125	8.131125	11.383576	11.383576	6.504900	0.000000	24.393377	9.757351

5 rows × 208306 columns

We can swap out the first argument with the different AnnData structures in the SwanGraph. For instance, say we want to calculate the TPM of each TSS:

df = swan.calc_tpm(sg.tss_adata)
df.head()

tss_id	ENSG00000000003.14_1	ENSG00000000003.14_2	ENSG00000000003.14_3	ENSG00000000003.14_4	ENSG00000000005.5_1	ENSG00000000005.5_2	ENSG00000000419.12_1	ENSG00000000419.12_2	ENSG00000000457.13_1	ENSG00000000457.13_2	...	TALONG000085596_1	TALONG000085799_1	TALONG000085978_1	TALONG000086022_1	TALONG000086057_1	TALONG000086218_1	TALONG000086443_1	TALONG000086539_1	TALONG000086553_1	TALONG000086766_1
hepg2_1	0.0	232.163910	0.0	0.0	0.0	0.0	0.0	54.038151	0.0	0.000000	...	0.000000	0.000000	0.000000	60.042389	0.000000	0.000000	6.004239	0.000000	0.000000	8.005652
hepg2_2	0.0	276.976654	0.0	0.0	0.0	0.0	0.0	103.718910	0.0	2.357248	...	0.000000	0.000000	0.000000	95.468544	0.000000	0.000000	31.822847	0.000000	1.178624	10.607616
hffc6_1	0.0	45.962341	0.0	0.0	0.0	0.0	0.0	101.117149	0.0	0.000000	...	2.626419	6.566049	9.192468	0.000000	7.879258	11.818888	233.751328	9.192468	6.566049	15.758516
hffc6_2	0.0	53.301723	0.0	0.0	0.0	0.0	0.0	85.028938	0.0	1.269089	...	6.345443	1.269089	12.690886	0.000000	8.883621	20.305418	119.294334	12.690886	2.538177	16.498154
hffc6_3	0.0	68.301460	0.0	0.0	0.0	0.0	0.0	89.442383	0.0	1.626225	...	8.131125	8.131125	11.383576	0.000000	11.383576	17.888477	134.976685	27.645828	8.131125	24.393377

5 rows × 130176 columns

And finally, we can use an alternative metadata column to compute TPM on. For instance, we can use the cell_line column:

df = swan.calc_tpm(sg.adata, obs_col='cell_line')
df.head()

tid	ENST00000000233.9	ENST00000000412.7	ENST00000000442.10	ENST00000001008.5	ENST00000001146.6	ENST00000002125.8	ENST00000002165.10	ENST00000002501.10	ENST00000002596.5	ENST00000002829.7	...	TALONT000482711	TALONT000482903	TALONT000483195	TALONT000483284	TALONT000483315	TALONT000483322	TALONT000483327	TALONT000483978	TALONT000484004	TALONT000484796
hepg2	226.245346	80.854897	7.417881	55.634102	0.0	14.093972	335.288177	63.051983	0.0	0.0	...	0.741788	10.385033	3.70894	8.901457	1.483576	5.192516	2.967152	12.610396	9.643245	0.741788
hffc6	138.145737	234.247116	0.924052	132.139404	0.0	8.316465	234.709137	0.462026	0.0	0.0	...	12.012672	11.550647	7.85444	6.006336	6.006336	10.164569	9.702543	0.000000	18.481035	6.468362

2 rows × 208306 columns

Calculating pi values

You can use the calc_pi() function to calculate percent isoform use (pi) per gene in nearly the exact same way that you can use calc_tpm(): you can run it on either the transcript, edge, TSS, or TES level, and you can choose the metadata variable to groupby. The only difference is that for calc_pi() you must also provide an additional DataFrame object as the second argument that tells Swan what gene each entry comes from. Below the corresponding DataFrame that must be provided is listed for each AnnData:

AnnData	DataFrame
SwanGraph.adata	SwanGraph.t_df
SwanGraph.tss_adata	SwanGraph.tss_adata.var
SwanGraph.tes_adata	SwanGraph.tes_adata.var

First, we'll calculate the pi value for each transcript in each dataset:

df, sums = swan.calc_pi(sg.adata, sg.t_df)
df.head()

tid	ENST00000000233.9	ENST00000000412.7	ENST00000000442.10	ENST00000001008.5	ENST00000001146.6	ENST00000002125.8	ENST00000002165.10	ENST00000002501.10	ENST00000002596.5	ENST00000002829.7	...	TALONT000482711	TALONT000482903	TALONT000483195	TALONT000483284	TALONT000483315	TALONT000483322	TALONT000483327	TALONT000483978	TALONT000484004	TALONT000484796
hepg2_1	100.000000	100.0	100.000000	100.0	0.0	100.000000	100.0	93.750000	0.0	0.0	...	0.000000	1.904762	6.666667	13.043478	0.000000	0.000000	1.333333	100.0	100.0	0.000000
hepg2_2	99.519226	100.0	60.000004	100.0	0.0	100.000000	100.0	80.882355	0.0	0.0	...	5.263158	3.225806	13.793103	8.695652	0.884956	3.097345	0.884956	100.0	100.0	2.380952
hffc6_1	98.039215	100.0	0.000000	100.0	0.0	100.000000	100.0	0.000000	0.0	0.0	...	2.604167	2.092050	16.279070	1.428571	0.854701	1.196581	0.854701	0.0	100.0	1.886792
hffc6_2	99.082573	100.0	0.000000	100.0	0.0	77.777779	100.0	0.000000	0.0	0.0	...	4.285715	2.144772	11.627908	14.893617	0.166667	1.333333	2.000000	0.0	100.0	9.859155
hffc6_3	100.000000	100.0	100.000000	100.0	0.0	85.714287	100.0	100.000000	0.0	0.0	...	4.326923	2.536232	15.151516	10.638298	1.711491	1.711491	0.977995	0.0	100.0	13.636364

5 rows × 208306 columns

As a note, the calc_pi() function outputs not only a table of pi values but of counts per isoform per condition, which is used as an intermediate during DIE testing. To avoid recalculation, it is output here.

sums.head()

	ENST00000000233.9	ENST00000000412.7	ENST00000000442.10	ENST00000001008.5	ENST00000001146.6	ENST00000002125.8	ENST00000002165.10	ENST00000002501.10	ENST00000002596.5	ENST00000002829.7	...	TALONT000482711	TALONT000482903	TALONT000483195	TALONT000483284	TALONT000483315	TALONT000483322	TALONT000483327	TALONT000483978	TALONT000484004	TALONT000484796
hepg2_1	98.0	43.0	4.0	23.0	0.0	8.0	129.0	30.0	0.0	0.0	...	0.0	2.0	1.0	6.0	0.0	0.0	2.0	7.0	4.0	0.0
hepg2_2	207.0	66.0	6.0	52.0	0.0	11.0	323.0	55.0	0.0	0.0	...	1.0	12.0	4.0	6.0	2.0	7.0	2.0	10.0	9.0	1.0
hffc6_1	100.0	148.0	0.0	82.0	0.0	5.0	212.0	0.0	0.0	0.0	...	5.0	10.0	7.0	1.0	5.0	7.0	5.0	0.0	12.0	1.0
hffc6_2	108.0	191.0	0.0	98.0	0.0	7.0	173.0	0.0	0.0	0.0	...	12.0	8.0	5.0	7.0	1.0	8.0	12.0	0.0	13.0	7.0
hffc6_3	91.0	168.0	2.0	106.0	0.0	6.0	123.0	1.0	0.0	0.0	...	9.0	7.0	5.0	5.0	7.0	7.0	4.0	0.0	15.0	6.0

5 rows × 208306 columns

We can also calculate the pi value for the TSSs and TESs in each dataset:

df, sums = swan.calc_pi(sg.tss_adata, sg.tss_adata.var)
print(df.head())
print()

df, sums = swan.calc_pi(sg.tes_adata, sg.tes_adata.var)
print(df.head())
print()

tss_id   ENSG00000000003.14_1  ENSG00000000003.14_2  ENSG00000000003.14_3  \
hepg2_1                   0.0                 100.0                   0.0   
hepg2_2                   0.0                 100.0                   0.0   
hffc6_1                   0.0                 100.0                   0.0   
hffc6_2                   0.0                 100.0                   0.0   
hffc6_3                   0.0                 100.0                   0.0   

tss_id   ENSG00000000003.14_4  ENSG00000000005.5_1  ENSG00000000005.5_2  \
hepg2_1                   0.0                  0.0                  0.0   
hepg2_2                   0.0                  0.0                  0.0   
hffc6_1                   0.0                  0.0                  0.0   
hffc6_2                   0.0                  0.0                  0.0   
hffc6_3                   0.0                  0.0                  0.0   

tss_id   ENSG00000000419.12_1  ENSG00000000419.12_2  ENSG00000000457.13_1  \
hepg2_1                   0.0                 100.0                   0.0   
hepg2_2                   0.0                 100.0                   0.0   
hffc6_1                   0.0                 100.0                   0.0   
hffc6_2                   0.0                 100.0                   0.0   
hffc6_3                   0.0                 100.0                   0.0   

tss_id   ENSG00000000457.13_2  ...  TALONG000085596_1  TALONG000085799_1  \
hepg2_1                   0.0  ...                0.0                0.0   
hepg2_2                 100.0  ...                0.0                0.0   
hffc6_1                   0.0  ...              100.0              100.0   
hffc6_2                 100.0  ...              100.0              100.0   
hffc6_3                 100.0  ...              100.0              100.0   

tss_id   TALONG000085978_1  TALONG000086022_1  TALONG000086057_1  \
hepg2_1                0.0              100.0                0.0   
hepg2_2                0.0              100.0                0.0   
hffc6_1              100.0                0.0              100.0   
hffc6_2              100.0                0.0              100.0   
hffc6_3              100.0                0.0              100.0   

tss_id   TALONG000086218_1  TALONG000086443_1  TALONG000086539_1  \
hepg2_1                0.0              100.0                0.0   
hepg2_2                0.0              100.0                0.0   
hffc6_1              100.0              100.0              100.0   
hffc6_2              100.0              100.0              100.0   
hffc6_3              100.0              100.0              100.0   

tss_id   TALONG000086553_1  TALONG000086766_1  
hepg2_1                0.0              100.0  
hepg2_2              100.0              100.0  
hffc6_1              100.0              100.0  
hffc6_2              100.0              100.0  
hffc6_3              100.0              100.0  

[5 rows x 130176 columns]

tes_id   ENSG00000000003.14_1  ENSG00000000003.14_2  ENSG00000000003.14_3  \
hepg2_1                   0.0                 100.0                   0.0   
hepg2_2                   0.0                 100.0                   0.0   
hffc6_1                   0.0                 100.0                   0.0   
hffc6_2                   0.0                 100.0                   0.0   
hffc6_3                   0.0                 100.0                   0.0   

tes_id   ENSG00000000003.14_4  ENSG00000000003.14_5  ENSG00000000005.5_1  \
hepg2_1                   0.0                   0.0                  0.0   
hepg2_2                   0.0                   0.0                  0.0   
hffc6_1                   0.0                   0.0                  0.0   
hffc6_2                   0.0                   0.0                  0.0   
hffc6_3                   0.0                   0.0                  0.0   

tes_id   ENSG00000000005.5_2  ENSG00000000419.12_1  ENSG00000000419.12_2  \
hepg2_1                  0.0             92.592590                   0.0   
hepg2_2                  0.0             98.863640                   0.0   
hffc6_1                  0.0             98.701302                   0.0   
hffc6_2                  0.0             95.522385                   0.0   
hffc6_3                  0.0             98.181824                   0.0   

tes_id   ENSG00000000419.12_3  ...  TALONG000085596_1  TALONG000085799_1  \
hepg2_1              7.407407  ...                0.0                0.0   
hepg2_2              1.136364  ...                0.0                0.0   
hffc6_1              1.298701  ...              100.0              100.0   
hffc6_2              4.477612  ...              100.0              100.0   
hffc6_3              1.818182  ...              100.0              100.0   

tes_id   TALONG000085978_1  TALONG000086022_1  TALONG000086057_1  \
hepg2_1                0.0              100.0                0.0   
hepg2_2                0.0              100.0                0.0   
hffc6_1              100.0                0.0              100.0   
hffc6_2              100.0                0.0              100.0   
hffc6_3              100.0                0.0              100.0   

tes_id   TALONG000086218_1  TALONG000086443_1  TALONG000086539_1  \
hepg2_1                0.0              100.0                0.0   
hepg2_2                0.0              100.0                0.0   
hffc6_1              100.0              100.0              100.0   
hffc6_2              100.0              100.0              100.0   
hffc6_3              100.0              100.0              100.0   

tes_id   TALONG000086553_1  TALONG000086766_1  
hepg2_1                0.0              100.0  
hepg2_2              100.0              100.0  
hffc6_1              100.0              100.0  
hffc6_2              100.0              100.0  
hffc6_3              100.0              100.0  

[5 rows x 187454 columns]

And we can also choose to calculate pi values using a different metadata column, here shown on the cell_line column:

df, sums = swan.calc_pi(sg.adata, sg.t_df, obs_col='cell_line')
df.head()

tid	ENST00000000233.9	ENST00000000412.7	ENST00000000442.10	ENST00000001008.5	ENST00000001146.6	ENST00000002125.8	ENST00000002165.10	ENST00000002501.10	ENST00000002596.5	ENST00000002829.7	...	TALONT000482711	TALONT000482903	TALONT000483195	TALONT000483284	TALONT000483315	TALONT000483322	TALONT000483327	TALONT000483978	TALONT000484004	TALONT000484796
hepg2	99.673203	100.0	71.428574	100.0	0.0	100.000000	100.0	85.0	0.0	0.0	...	4.545455	2.935011	11.363637	10.434782	0.531915	1.861702	1.06383	100.0	100.0	1.785714
hffc6	99.006622	100.0	100.000000	100.0	0.0	85.714287	100.0	100.0	0.0	0.0	...	3.823529	2.218279	14.285715	7.926829	0.815558	1.380176	1.31744	0.0	100.0	8.333334

2 rows × 208306 columns

Obtaining edge abundance information

In case you're interested in doing outside analyses on the level (For instance, using intron counting to assess alternative splicing), Swan provides a tool to output a DataFrame with edge abundance on the dataset level.

If we just want to get access to the edge abundance DataFrame, just use the get_edge_abundance() function:

df = sg.get_edge_abundance()
df.head()

	strand	edge_type	annotation	chrom	start	stop	hepg2_1	hepg2_2	hffc6_1	hffc6_2	hffc6_3
0	+	exon	True	chr1	11869	12227	0.0	0.0	0.0	0.0	0.0
1	+	exon	True	chr1	12010	12057	0.0	0.0	0.0	0.0	0.0
2	+	intron	True	chr1	12057	12179	0.0	0.0	0.0	0.0	0.0
3	+	exon	True	chr1	12179	12227	0.0	0.0	0.0	0.0	0.0
4	+	intron	True	chr1	12227	12613	0.0	0.0	0.0	0.0	0.0

You can also specify if you want the data to be output in raw counts (kind='counts') or TPM (kind='tpm). By default, this function returns counts. Here's an example with TPM:

df = sg.get_edge_abundance(kind='tpm')
df.head()

	strand	edge_type	annotation	chrom	start	stop	hepg2_1	hepg2_2	hffc6_1	hffc6_2	hffc6_3
0	+	exon	True	chr1	11869	12227	0.0	0.0	0.0	0.0	0.0
1	+	exon	True	chr1	12010	12057	0.0	0.0	0.0	0.0	0.0
2	+	intron	True	chr1	12057	12179	0.0	0.0	0.0	0.0	0.0
3	+	exon	True	chr1	12179	12227	0.0	0.0	0.0	0.0	0.0
4	+	intron	True	chr1	12227	12613	0.0	0.0	0.0	0.0	0.0

And finally, if you wish, you can provide the function with a prefix value which will indicate that you want the output DataFrame to be saved in TSV form.

df = sg.get_edge_abundance(kind='tpm', prefix='test')
df.head()

	strand	edge_type	annotation	chrom	start	stop	hepg2_1	hepg2_2	hffc6_1	hffc6_2	hffc6_3
0	+	exon	True	chr1	11869	12227	0.0	0.0	0.0	0.0	0.0
1	+	exon	True	chr1	12010	12057	0.0	0.0	0.0	0.0	0.0
2	+	intron	True	chr1	12057	12179	0.0	0.0	0.0	0.0	0.0
3	+	exon	True	chr1	12179	12227	0.0	0.0	0.0	0.0	0.0
4	+	intron	True	chr1	12227	12613	0.0	0.0	0.0	0.0	0.0

The results will be saved in '{prefix}_edge_abundance.tsv'.

Obtaining TSS/TES abundance information

Similarly, if you wish to do analysis involving your TSS or TES data, you can also output these using the get_tss_abundance() and get_tes_abundance() functions respectively. These have identical options to get_edge_abundance() so they can either output counts or TPM and optionally save to an output file.

First, let's output the TSS TPM to a file:

df = sg.get_tss_abundance(kind='tpm', prefix='test')
df.head()

	tss_id	gid	gname	vertex_id	tss_name	chrom	coord	hepg2_1	hepg2_2	hffc6_1	hffc6_2	hffc6_3
0	ENSG00000000003.14_1	ENSG00000000003.14	TSPAN6	926111	TSPAN6_1	chrX	100636191	0.00000	0.000000	0.000000	0.000000	0.00000
1	ENSG00000000003.14_2	ENSG00000000003.14	TSPAN6	926112	TSPAN6_2	chrX	100636608	232.16391	276.976654	45.962341	53.301723	68.30146
2	ENSG00000000003.14_3	ENSG00000000003.14	TSPAN6	926114	TSPAN6_3	chrX	100636793	0.00000	0.000000	0.000000	0.000000	0.00000
3	ENSG00000000003.14_4	ENSG00000000003.14	TSPAN6	926117	TSPAN6_4	chrX	100639945	0.00000	0.000000	0.000000	0.000000	0.00000
4	ENSG00000000005.5_1	ENSG00000000005.5	TNMD	926077	TNMD_1	chrX	100585066	0.00000	0.000000	0.000000	0.000000	0.00000

Now we'll get the counts of each TES without saving to a file:

df = sg.get_tes_abundance(kind='counts')
df.head()

	tes_id	gid	gname	vertex_id	tes_name	chrom	coord	hepg2_1	hepg2_2	hffc6_1	hffc6_2	hffc6_3
0	ENSG00000000003.14_1	ENSG00000000003.14	TSPAN6	926092	TSPAN6_1	chrX	100627109	0.0	0.0	0.0	0.0	0.0
1	ENSG00000000003.14_2	ENSG00000000003.14	TSPAN6	926093	TSPAN6_2	chrX	100628670	116.0	235.0	35.0	42.0	42.0
2	ENSG00000000003.14_3	ENSG00000000003.14	TSPAN6	926097	TSPAN6_3	chrX	100632063	0.0	0.0	0.0	0.0	0.0
3	ENSG00000000003.14_4	ENSG00000000003.14	TSPAN6	926100	TSPAN6_4	chrX	100632541	0.0	0.0	0.0	0.0	0.0
4	ENSG00000000003.14_5	ENSG00000000003.14	TSPAN6	926103	TSPAN6_5	chrX	100633442	0.0	0.0	0.0	0.0	0.0