Data processing with Swan
The following section was already run so you don’t have to wait for results, but you’re welcome to give it a try!
# data download
!wget http://crick.bio.uci.edu/freese/210830_ucd_workshop/data.tgz
!tar -xf data.tgz
import swan_vis as swan
# construct SwanGraph
annot_gtf = 'ref/gencode.vM21.annotation.gtf'
data_gtf = 'data/all_talon_observedOnly.gtf'
ab_file = 'data/all_talon_abundance_filtered.tsv'
meta = 'data/metadata.tsv'
sg = swan.SwanGraph()
sg.add_annotation(annot_gtf)
sg.add_transcriptome(data_gtf)
sg.add_abundance(ab_file)
sg.add_metadata(meta)
sg.save_graph('swan')
Saving graph as swan.p
# compare 0 and 72 hr time point
obs_col = 'time_point'
obs_conditions = ['0hr', '72hr']
_ = sg.de_gene_test(obs_col, obs_conditions=obs_conditions)
_ = sg.de_transcript_test(obs_col, obs_conditions=obs_conditions)
_ = sg.die_gene_test(obs_col, obs_conditions=obs_conditions)
test = sg.die_gene_test(kind='tss', obs_col=obs_col, obs_conditions=obs_conditions)
test = sg.die_gene_test(kind='tes', obs_col=obs_col, obs_conditions=obs_conditions)
# find novel exon-skipping and intron retention events
_ = sg.find_es_genes()
_ = sg.find_ir_genes()
# save graph again so we have access to all the results
sg.save_graph('swan')
Saving graph as swan.p
Figures generated using bulk C2C12 data for the presentation
import swan_vis as swan
import scanpy as sc
sg = swan.read('swan.p')
Read in graph from swan.p
sg.plot_graph('Tfdp1', indicate_novel=True)
sg.plot_transcript_path('TALONT000169332', indicate_novel=True)
sg.gen_report('Chp1',
'figures/chp1',
metadata_cols=['time_point'],
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
sg.gen_report('Chp1',
'figures/chp1',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
novelty=True,
layer='pi',
browser=True,
display_numbers=True)
sg.adata.var = sg.adata.var.merge(sg.t_df[['gname', 'tname']], left_index=True, right_index=True, how='left')
var_names = sg.adata.var.loc[sg.adata.var.gname == 'Chp1', 'tname'].tolist()
sc.pl.matrixplot(sg.adata, var_names,
gene_symbols='tname',
groupby='time_point', layer='pi',
cmap='magma', swap_axes=True)
... storing 'cell_line' as categorical
... storing 'time_point' as categorical
... storing 'gname' as categorical
sg.gen_report('Tnnt2',
'figures/tnnt2',
metadata_cols=['time_point'],
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
sg.gen_report('Tnnt2',
'figures/tnnt2',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
novelty=True,
layer='pi',
browser=True,
display_numbers=True)
var_names = sg.tss_adata.var.loc[sg.tss_adata.var.gname == 'Tnnt2', 'tss_name'].tolist()
sc.pl.matrixplot(sg.tss_adata, var_names,
gene_symbols='tss_name',
groupby='time_point', layer='pi',
cmap='magma', swap_axes=True)
... storing 'cell_line' as categorical
... storing 'time_point' as categorical
... storing 'gid' as categorical
... storing 'gname' as categorical
... storing 'tss_name' as categorical
sg.gen_report('Tpm1',
'figures/tpm1',
metadata_cols=['time_point'],
groupby='time_point',
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
sg.gen_report('Tpm1',
'figures/tpm1',
metadata_cols=['time_point'],
groupby='time_point',
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
var_names = sg.tes_adata.var.loc[sg.tes_adata.var.gname == 'Tpm1', 'tes_name'].tolist()
sc.pl.matrixplot(sg.tes_adata, var_names,
gene_symbols='tes_name',
groupby='time_point', layer='pi',
cmap='magma')
Your turn!
Install Swan
Run the block below to install from GitHub directly
!git clone git@github.com:mortazavilab/swan_vis.git
!cd swan_vis
!pip install .
Alternatively, install from PyPi (this will be exactly the same!)
!pip install swan_vis
Now we’ll take a look at some of the results from Swan and make some cool plots. Please check out the documentation to get a better idea of the stuff you can do!
# download the SwanGraph
!wget http://crick.bio.uci.edu/freese/210830_ucd_workshop/swan.p
# create a figures directory because there will be a lot of output figures!
!mkdir figures
# import swan
import swan_vis as swan
# load the SwanGraph
sg = swan.read('swan.p')
Read in graph from swan.p
What genes contain novel exon skipping events?
We can query the SwanGraph object for a table of exon skipping events. Here, we’ll do just that as well as visualize the exon skipping events in a few genes.
# get table of exon skipping events
es = sg.adata.uns['es']
# add gene names, which I should make automatic in a future update!
gnames = sg.t_df[['gid', 'gname']]
print(len(es.index))
es = es.merge(gnames, on='gid', how='left')
es = es.drop_duplicates()
es.head()
1303
| gid | tid | egde_id | gname | |
|---|---|---|---|---|
| 0 | SIRV3.1 | SIRV3.1-SIRV301 | 741285 | SIRV3.1 |
| 7 | SIRV3.1 | SIRV3.1-SIRV303 | 741285 | SIRV3.1 |
| 14 | SIRV5.1 | SIRV5.1-SIRV505 | 741375 | SIRV5.1 |
| 24 | SIRV5.1 | SIRV5.1-SIRV510 | 741375 | SIRV5.1 |
| 34 | SIRV6.1 | SIRV6.1-SIRV601 | 741415 | SIRV6.1 |
# unique genes
es_genes = es.gname.unique().tolist()
print(len(es_genes))
es_genes[5:10]
518
['Col3a1', 'Bzw1', 'Creb1', 'Ttll4', 'Stk16']
Let’s plot a few of these genes that have exon skipping events.
es.loc[es.gname == 'Srsf4'] # Srsf4 is a splicing factor!
| gid | tid | egde_id | gname | |
|---|---|---|---|---|
| 9450 | ENSMUSG00000028911.16 | TALONT000495077 | 732856 | Srsf4 |
sg.plot_graph('Srsf4', indicate_novel=True)
sg.plot_transcript_path('TALONT000495077', indicate_novel=True)
es.loc[es.gname == 'Tnnt3']
| gid | tid | egde_id | gname | |
|---|---|---|---|---|
| 12557 | ENSMUSG00000061723.18 | TALONT000560315 | 734894 | Tnnt3 |
sg.plot_graph('Tnnt3', indicate_novel=True)
sg.plot_transcript_path('TALONT000560315', indicate_novel=True)
es.loc[es.gname == 'Vim']
| gid | tid | egde_id | gname | |
|---|---|---|---|---|
| 7589 | ENSMUSG00000026728.9 | TALONT000162980 | 731211 | Vim |
sg.plot_graph('Vim', indicate_novel=True)
sg.plot_transcript_path('TALONT000162980', indicate_novel=True)
What genes contain novel intron retention events?
We’ll do the same thing for genes with novel intron retention events. We’ll pull them from the SwanGraph and plot a few examples.
# get table of intron retention events
ir = sg.adata.uns['ir']
# add gene names, which I should make automatic in a future update!
gnames = sg.t_df[['gid', 'gname']]
print(len(ir.index))
ir = ir.merge(gnames, on='gid', how='left')
ir = ir.drop_duplicates()
ir.head()
215
| gid | tid | egde_id | gname | |
|---|---|---|---|---|
| 0 | SIRV2.2 | SIRV2.2-SIRV206 | 741283 | SIRV2.2 |
| 1 | SIRV3.1 | SIRV3.1-SIRV302 | 741299 | SIRV3.1 |
| 8 | SIRV3.1 | SIRV3.1-SIRV303 | 741299 | SIRV3.1 |
| 15 | SIRV5.1 | SIRV5.1-SIRV506 | 741388 | SIRV5.1 |
| 25 | SIRV5.1 | SIRV5.1-SIRV511 | 741388 | SIRV5.1 |
# unique genes
ir_genes = ir.gname.unique().tolist()
print(len(ir_genes))
ir_genes[4]
117
'Plekhb2'
sg.plot_graph('Plekhb2', indicate_novel=True)
ir.loc[ir.gname == 'Plekhb2']
| gid | tid | egde_id | gname | |
|---|---|---|---|---|
| 189 | ENSMUSG00000001436.15 | TALONT000218169 | 736549 | Slc19a1 |
| 204 | ENSMUSG00000001436.15 | TALONT000218202 | 736549 | Slc19a1 |
sg.plot_transcript_path('TALONT000218169', indicate_novel=True)
sg.plot_transcript_path('TALONT000218169', browser=True)
ir_genes[10:15]
['Ptbp1', 'Oaz1', 'Apba3', 'TALONG000074151', 'Txnrd1']
sg.plot_graph('Oaz1', indicate_novel=True)
ir.loc[ir.gname == 'Oaz1']
| gid | tid | egde_id | gname | |
|---|---|---|---|---|
| 317 | ENSMUSG00000035242.15 | TALONT000269069 | 736630 | Oaz1 |
sg.plot_transcript_path('TALONT000269069', indicate_novel=True)
What genes are differentially-expressed between myotubes and myoblasts?
Now we’ll take a look at some of the results of statistical tests run between the myoblast and myotube conditions. We can access the differentially-expressed genes in the SwanGraph and provide q value and log fold change thresholds. Here we’ll use q < 0.05 and log2fc > 1.
obs_col = 'time_point'
obs_conditions = ['0hr', '72hr']
de_genes = sg.get_de_genes(obs_col, obs_conditions=obs_conditions,
q=0.05, log2fc=1)
de_genes.head()
| gid | pval | qval | log2fc | mean | zero_mean | grad | coef_mle | coef_sd | ll | gname | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 19866 | ENSMUSG00000070733.13 | 0.0 | 0.0 | 283.913085 | 0.561909 | False | 0.500002 | 283.913085 | 2.222759e-162 | -16.217098 | Fryl |
| 18848 | ENSMUSG00000065852.1 | 0.0 | 0.0 | 283.913085 | 0.407021 | False | 0.500000 | 283.913085 | 2.222759e-162 | -15.435992 | Gm22353 |
| 18852 | ENSMUSG00000065862.1 | 0.0 | 0.0 | 283.913085 | 0.407021 | False | 0.500000 | 283.913085 | 2.222759e-162 | -15.435992 | Gm24029 |
| 19949 | ENSMUSG00000071042.12 | 0.0 | 0.0 | 283.913085 | 0.203510 | False | 0.409290 | 283.913085 | 2.222759e-162 | -11.215066 | Rasgrp3 |
| 7466 | ENSMUSG00000029699.13 | 0.0 | 0.0 | 283.913085 | 0.578117 | False | 1.000000 | 283.913085 | 2.222759e-162 | -28.840319 | Ssc4d |
# look for myo-related genes (which are often implicated in the myogenic process)
de_genes.loc[de_genes.gname.str.contains('Myo')].head()
| gid | pval | qval | log2fc | mean | zero_mean | grad | coef_mle | coef_sd | ll | gname | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 19415 | ENSMUSG00000068697.7 | 0.0 | 0.0 | 283.913085 | 4.689763 | False | 1.000000 | 283.913085 | 2.222759e-162 | -38.567663 | Myoz1 |
| 6503 | ENSMUSG00000028116.13 | 0.0 | 0.0 | 283.913085 | 28.415941 | False | 0.999999 | 283.913085 | 2.222759e-162 | -45.865588 | Myoz2 |
| 20253 | ENSMUSG00000072720.9 | 0.0 | 0.0 | 283.913085 | 5.471390 | False | 1.000000 | 283.913085 | 2.222759e-162 | -39.200583 | Myo18b |
| 10601 | ENSMUSG00000037139.15 | 0.0 | 0.0 | 283.913085 | 16.414171 | False | 0.999999 | 283.913085 | 2.222759e-162 | -43.658559 | Myom3 |
| 8369 | ENSMUSG00000031461.4 | 0.0 | 0.0 | 283.913085 | 351.204727 | False | 0.999999 | 283.913085 | 2.222759e-162 | -55.932105 | Myom2 |
# number of differentially-expressed genes
len(de_genes.index)
5392
What transcripts are differentially-expressed between myoblasts and myotubes?
Now we’ll take a look at transcripts that are differentially-expressed between the time points. These can be accessed much in the same way that the differentially-expressed genes can be. We’ll also make a gene report showing the differentially-expressed transcript isoforms in one gene, Myo6.
# results from differential transcript expression test
de_transcripts = sg.get_de_transcripts(obs_col, obs_conditions=obs_conditions,
q=0.05, log2fc=1)
de_transcripts.head()
| tid | pval | qval | log2fc | mean | zero_mean | grad | coef_mle | coef_sd | ll | gid | gname | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 90372 | ENSMUST00000184224.1 | 0.0 | 0.0 | 283.913085 | 0.187303 | False | 0.374605 | 283.913085 | 2.222759e-162 | -10.301695 | ENSMUSG00000098716.1 | Gm28033 |
| 90689 | ENSMUST00000184591.1 | 0.0 | 0.0 | 283.913085 | 2.312467 | False | 0.999999 | 283.913085 | 2.222759e-162 | -35.526327 | ENSMUSG00000033182.12 | Kbtbd12 |
| 90287 | ENSMUST00000184135.1 | 0.0 | 0.0 | 283.913085 | 0.203510 | False | 0.409290 | 283.913085 | 2.222759e-162 | -11.215066 | ENSMUSG00000098761.1 | Gm18821 |
| 16680 | ENSMUST00000079773.13 | 0.0 | 0.0 | 283.913085 | 13.458757 | False | 0.999999 | 283.913085 | 2.222759e-162 | -42.859372 | ENSMUSG00000003345.16 | Csnk1g2 |
| 45913 | ENSMUST00000132080.1 | 0.0 | 0.0 | 283.913085 | 0.187303 | False | 0.374605 | 283.913085 | 2.222759e-162 | -10.301695 | ENSMUSG00000013858.14 | Tmem259 |
# look for myo-related transcripts (which are often implicated in the myogenic process)
de_transcripts.loc[de_transcripts.gname.str.contains('Myo')].head()
| tid | pval | qval | log2fc | mean | zero_mean | grad | coef_mle | coef_sd | ll | gid | gname | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 89607 | ENSMUST00000183377.1 | 0.0 | 0.0 | 283.913085 | 0.203510 | False | 0.409290 | 283.913085 | 2.222759e-162 | -11.215066 | ENSMUSG00000033577.18 | Myo6 |
| 49005 | ENSMUST00000135375.3 | 0.0 | 0.0 | 283.913085 | 1.563254 | False | 1.000001 | 283.913085 | 2.222759e-162 | -33.923097 | ENSMUSG00000000631.20 | Myo18a |
| 86409 | ENSMUST00000179759.2 | 0.0 | 0.0 | 283.913085 | 20.477194 | False | 0.999999 | 283.913085 | 2.222759e-162 | -44.551066 | ENSMUSG00000024049.15 | Myom1 |
| 45155 | ENSMUST00000131251.1 | 0.0 | 0.0 | 283.913085 | 0.390814 | False | 0.781626 | 283.913085 | 2.222759e-162 | -21.519703 | ENSMUSG00000031461.4 | Myom2 |
| 58333 | ENSMUST00000145304.7 | 0.0 | 0.0 | 283.913085 | 10.364664 | False | 1.000000 | 283.913085 | 2.222759e-162 | -41.799408 | ENSMUSG00000037139.15 | Myom3 |
# look for Pkm, which has isoforms with important differences in expression
# profile across myogenesis
print(de_transcripts.loc[de_transcripts.gname == 'Pkm'])
# same with Tpm2
print(de_transcripts.loc[de_transcripts.gname == 'Tpm2'])
tid pval qval log2fc mean zero_mean \
146970 TALONT000524508 0.000000 0.000000 283.913085 4.502460 False
146957 TALONT000523760 0.000002 0.000020 2.987293 13.641756 False
146953 TALONT000523686 0.006228 0.042511 2.183307 4.312392 False
146951 TALONT000523682 0.000022 0.000176 1.360462 14.967567 False
grad coef_mle coef_sd ll gid \
146970 0.999999 283.913085 2.222759e-162 -38.394349 ENSMUSG00000032294.17
146957 0.352200 2.987293 6.332595e-01 -4.011946 ENSMUSG00000032294.17
146953 0.000038 2.183307 7.981339e-01 -6.115639 ENSMUSG00000032294.17
146951 1.263405 1.360462 3.206211e-01 0.000000 ENSMUSG00000032294.17
gname
146970 Pkm
146957 Pkm
146953 Pkm
146951 Pkm
tid pval qval log2fc mean \
145514 TALONT000415087 0.000000 0.000000 283.913085 10.503345
145516 TALONT000415089 0.000000 0.000000 283.913085 6.155773
145517 TALONT000415177 0.000000 0.000000 283.913085 2.141371
26461 ENSMUST00000107913.9 0.000000 0.000000 3.570377 673.755588
145515 TALONT000415088 0.003188 0.022426 3.218529 5.673465
zero_mean grad coef_mle coef_sd ll \
145514 False 1.000000 283.913085 2.222759e-162 -41.851966
145516 False 1.000000 283.913085 2.222759e-162 -39.562596
145517 False 1.000004 283.913085 2.222759e-162 -35.287652
26461 False 5.430085 3.570377 1.180513e-01 0.000000
145515 False 0.091297 3.218529 1.091389e+00 0.000000
gid gname
145514 ENSMUSG00000028464.16 Tpm2
145516 ENSMUSG00000028464.16 Tpm2
145517 ENSMUSG00000028464.16 Tpm2
26461 ENSMUSG00000028464.16 Tpm2
145515 ENSMUSG00000028464.16 Tpm2
# how many differentially-expressed transcripts are there?
len(de_transcripts.index)
12232
We can use different colors to represent the two different time points in the dataset. Change them if you’d like! You can either use hex codes or named matplotlib colors.
# add some colors to represent the 2 different time points
green = '#019f73'
pink = '#cb79a7'
cmap = {'0hr': pink, '72hr': green}
sg.set_metadata_colors('time_point', cmap)
# make a plot showing significantly differentially expressed Myo6 isoforms
sg.gen_report('Myo6',
'figures/myo6',
metadata_cols=['time_point'],
cmap='viridis',
transcript_name=True,
include_qvals=True,
qval_obs_col='time_point',
qval_obs_conditions=['0hr', '72hr'])
Plotting transcripts for ENSMUSG00000033577.18
Saving transcript path graph for TALONT000562244 as figures/myo6_TALONT000562244_path.png
Saving transcript path graph for ENSMUST00000138650.2 as figures/myo6_ENSMUST00000138650.2_path.png
Saving transcript path graph for ENSMUST00000035889.14 as figures/myo6_ENSMUST00000035889.14_path.png
Saving transcript path graph for ENSMUST00000113266.7 as figures/myo6_ENSMUST00000113266.7_path.png
Saving transcript path graph for ENSMUST00000183377.1 as figures/myo6_ENSMUST00000183377.1_path.png
Generating report for ENSMUSG00000033577.18
What genes exhibit isoform switching between myoblasts and myotubes?
This is the most interesting stuff in my opinion. We can find genes that exhibit isoform switching (also called differential isoform expression, or DIE). These genes are characterized by a change in frequency of use of different constituent isoforms across the two conditions. The signficance thresholds for this test can be modulated by choosing a maximum p value threshold as well as a minimum change in percent isoform expression value, which is indicative of how substantial the isoform change is. Hopefully this will be better explained by making some figures though!
# results from differential transcript expression test
die_genes = sg.get_die_genes(obs_col=obs_col, obs_conditions=obs_conditions,
p=0.05, dpi=10)
die_genes.head()
| gid | p_val | dpi | adj_p_val | |
|---|---|---|---|---|
| 3 | ENSMUSG00000061689.15 | 1.443605e-19 | 67.565826 | 3.424032e-18 |
| 5 | ENSMUSG00000020152.7 | 6.042619e-11 | 19.780212 | 8.726454e-10 |
| 11 | ENSMUSG00000000326.13 | 9.814141e-04 | 28.234030 | 5.577975e-03 |
| 17 | ENSMUSG00000000420.15 | 1.257904e-29 | 37.495850 | 4.475362e-28 |
| 18 | ENSMUSG00000000440.12 | 9.107806e-03 | 44.185776 | 4.001738e-02 |
# add gene names, which I should make automatic in future releases!
gnames = sg.t_df[['gname', 'gid']].drop_duplicates()
die_genes = die_genes.merge(gnames, on='gid', how='left')
die_genes = die_genes.sort_values(by='dpi', ascending=False)
die_genes.head()
| gid | p_val | dpi | adj_p_val | gname | |
|---|---|---|---|---|---|
| 281 | ENSMUSG00000026414.13 | 5.312136e-237 | 98.948153 | 2.645927e-234 | Tnnt2 |
| 161 | ENSMUSG00000020836.15 | 1.787333e-64 | 97.932594 | 1.718034e-62 | Coro6 |
| 151 | ENSMUSG00000020439.17 | 5.141485e-75 | 97.710327 | 5.523568e-73 | Smtn |
| 946 | ENSMUSG00000039542.16 | 7.076545e-128 | 96.704330 | 1.436015e-125 | Ncam1 |
| 496 | ENSMUSG00000036918.16 | 3.520068e-06 | 95.447243 | 3.115743e-05 | Ttc7 |
Plotting some isoform-switching genes:
die_genes.loc[die_genes.gname == 'Dlgap4']
| gid | p_val | dpi | adj_p_val | gname | |
|---|---|---|---|---|---|
| 0 | ENSMUSG00000061689.15 | 1.443605e-19 | 67.565826 | 3.424032e-18 | Dlgap4 |
sg.gen_report('Dlgap4',
'figures/dlgap4',
metadata_cols=['time_point'],
cmap='viridis',
transcript_name=True,
browser=True)
Plotting transcripts for ENSMUSG00000061689.15
Saving transcript path graph for ENSMUST00000109566.8 as figures/dlgap4_browser_ENSMUST00000109566.8_path.png
Saving transcript path graph for ENSMUST00000099145.5 as figures/dlgap4_browser_ENSMUST00000099145.5_path.png
Saving transcript path graph for ENSMUST00000127944.8 as figures/dlgap4_browser_ENSMUST00000127944.8_path.png
Saving transcript path graph for ENSMUST00000131157.8 as figures/dlgap4_browser_ENSMUST00000131157.8_path.png
Generating report for ENSMUSG00000061689.15
sg.gen_report('Dlgap4',
'figures/dlgap4',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
layer='pi',
display_numbers=True)
Plotting transcripts for ENSMUSG00000061689.15
Saving transcript path graph for ENSMUST00000109566.8 as figures/dlgap4_ENSMUST00000109566.8_path.png
Saving transcript path graph for ENSMUST00000099145.5 as figures/dlgap4_ENSMUST00000099145.5_path.png
Saving transcript path graph for ENSMUST00000127944.8 as figures/dlgap4_ENSMUST00000127944.8_path.png
Saving transcript path graph for ENSMUST00000131157.8 as figures/dlgap4_ENSMUST00000131157.8_path.png
Generating report for ENSMUSG00000061689.15
die_genes.loc[die_genes.gname == 'Tnnt2']
| gid | p_val | dpi | adj_p_val | gname | |
|---|---|---|---|---|---|
| 281 | ENSMUSG00000026414.13 | 5.312136e-237 | 98.948153 | 2.645927e-234 | Tnnt2 |
sg.gen_report('Tnnt2',
'figures/tnnt2',
metadata_cols=['time_point'],
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
Plotting transcripts for ENSMUSG00000026414.13
Saving transcript path graph for TALONT000394811 as figures/tnnt2_novel_TALONT000394811_path.png
Saving transcript path graph for TALONT000394812 as figures/tnnt2_novel_TALONT000394812_path.png
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Saving transcript path graph for TALONT000394894 as figures/tnnt2_novel_TALONT000394894_path.png
Saving transcript path graph for ENSMUST00000191055.1 as figures/tnnt2_novel_ENSMUST00000191055.1_path.png
Saving transcript path graph for TALONT000394893 as figures/tnnt2_novel_TALONT000394893_path.png
Saving transcript path graph for TALONT000394956 as figures/tnnt2_novel_TALONT000394956_path.png
Saving transcript path graph for TALONT000394970 as figures/tnnt2_novel_TALONT000394970_path.png
Saving transcript path graph for TALONT000394838 as figures/tnnt2_novel_TALONT000394838_path.png
Generating report for ENSMUSG00000026414.13
sg.gen_report('Tnnt2',
'figures/tnnt2',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
layer='pi',
display_numbers=True,
novelty=True,
browser=True)
Plotting transcripts for ENSMUSG00000026414.13
Saving transcript path graph for TALONT000394811 as figures/tnnt2_browser_TALONT000394811_path.png
Saving transcript path graph for TALONT000394812 as figures/tnnt2_browser_TALONT000394812_path.png
Saving transcript path graph for TALONT000396016 as figures/tnnt2_browser_TALONT000396016_path.png
Saving transcript path graph for TALONT000396098 as figures/tnnt2_browser_TALONT000396098_path.png
Saving transcript path graph for TALONT000394818 as figures/tnnt2_browser_TALONT000394818_path.png
Saving transcript path graph for TALONT000394815 as figures/tnnt2_browser_TALONT000394815_path.png
Saving transcript path graph for TALONT000394848 as figures/tnnt2_browser_TALONT000394848_path.png
Saving transcript path graph for TALONT000394894 as figures/tnnt2_browser_TALONT000394894_path.png
Saving transcript path graph for ENSMUST00000191055.1 as figures/tnnt2_browser_ENSMUST00000191055.1_path.png
Saving transcript path graph for TALONT000394893 as figures/tnnt2_browser_TALONT000394893_path.png
Saving transcript path graph for TALONT000394956 as figures/tnnt2_browser_TALONT000394956_path.png
Saving transcript path graph for TALONT000394970 as figures/tnnt2_browser_TALONT000394970_path.png
Saving transcript path graph for TALONT000394838 as figures/tnnt2_browser_TALONT000394838_path.png
Generating report for ENSMUSG00000026414.13
die_genes.loc[die_genes.gname == 'Coro6']
| gid | p_val | dpi | adj_p_val | gname | |
|---|---|---|---|---|---|
| 161 | ENSMUSG00000020836.15 | 1.787333e-64 | 97.932594 | 1.718034e-62 | Coro6 |
sg.gen_report('Coro6',
'figures/coro6',
metadata_cols=['time_point'],
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
Plotting transcripts for ENSMUSG00000020836.15
Saving transcript path graph for TALONT000544061 as figures/coro6_novel_TALONT000544061_path.png
Saving transcript path graph for ENSMUST00000102493.7 as figures/coro6_novel_ENSMUST00000102493.7_path.png
Saving transcript path graph for TALONT000544046 as figures/coro6_novel_TALONT000544046_path.png
Saving transcript path graph for TALONT000544055 as figures/coro6_novel_TALONT000544055_path.png
Saving transcript path graph for TALONT000544048 as figures/coro6_novel_TALONT000544048_path.png
Saving transcript path graph for TALONT000544058 as figures/coro6_novel_TALONT000544058_path.png
Saving transcript path graph for TALONT000544052 as figures/coro6_novel_TALONT000544052_path.png
Saving transcript path graph for ENSMUST00000108391.8 as figures/coro6_novel_ENSMUST00000108391.8_path.png
Generating report for ENSMUSG00000020836.15
sg.gen_report('Coro6',
'figures/coro6',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
novelty=True,
layer='pi',
browser=True)
Plotting transcripts for ENSMUSG00000020836.15
Saving transcript path graph for TALONT000544061 as figures/coro6_browser_TALONT000544061_path.png
Saving transcript path graph for ENSMUST00000102493.7 as figures/coro6_browser_ENSMUST00000102493.7_path.png
Saving transcript path graph for TALONT000544046 as figures/coro6_browser_TALONT000544046_path.png
Saving transcript path graph for TALONT000544055 as figures/coro6_browser_TALONT000544055_path.png
Saving transcript path graph for TALONT000544048 as figures/coro6_browser_TALONT000544048_path.png
Saving transcript path graph for TALONT000544058 as figures/coro6_browser_TALONT000544058_path.png
Saving transcript path graph for TALONT000544052 as figures/coro6_browser_TALONT000544052_path.png
Saving transcript path graph for ENSMUST00000108391.8 as figures/coro6_browser_ENSMUST00000108391.8_path.png
Generating report for ENSMUSG00000020836.15
die_genes.loc[die_genes.gname == 'Smtn']
| gid | p_val | dpi | adj_p_val | gname | |
|---|---|---|---|---|---|
| 151 | ENSMUSG00000020439.17 | 5.141485e-75 | 97.710327 | 5.523568e-73 | Smtn |
sg.gen_report('Smtn',
'figures/smtn',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
novelty=True,
layer='pi',
browser=True)
Plotting transcripts for ENSMUSG00000020439.17
Saving transcript path graph for ENSMUST00000020721.14 as figures/smtn_browser_ENSMUST00000020721.14_path.png
Saving transcript path graph for ENSMUST00000020718.9 as figures/smtn_browser_ENSMUST00000020718.9_path.png
Saving transcript path graph for TALONT000451717 as figures/smtn_browser_TALONT000451717_path.png
Saving transcript path graph for TALONT000451783 as figures/smtn_browser_TALONT000451783_path.png
Saving transcript path graph for TALONT000451804 as figures/smtn_browser_TALONT000451804_path.png
Saving transcript path graph for ENSMUST00000170588.7 as figures/smtn_browser_ENSMUST00000170588.7_path.png
Generating report for ENSMUSG00000020439.17
die_genes.loc[die_genes.gname == 'Pkm']
| gid | p_val | dpi | adj_p_val | gname | |
|---|---|---|---|---|---|
| 428 | ENSMUSG00000032294.17 | 0.0 | 54.352844 | 0.0 | Pkm |
sg.gen_report('Pkm',
'figures/pkm',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
novelty=True,
layer='pi',
browser=True)
Plotting transcripts for ENSMUSG00000032294.17
Saving transcript path graph for ENSMUST00000034834.15 as figures/pkm_browser_ENSMUST00000034834.15_path.png
Saving transcript path graph for ENSMUST00000163694.3 as figures/pkm_browser_ENSMUST00000163694.3_path.png
Saving transcript path graph for TALONT000523649 as figures/pkm_browser_TALONT000523649_path.png
Saving transcript path graph for TALONT000523632 as figures/pkm_browser_TALONT000523632_path.png
Saving transcript path graph for TALONT000523613 as figures/pkm_browser_TALONT000523613_path.png
Saving transcript path graph for TALONT000523685 as figures/pkm_browser_TALONT000523685_path.png
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Saving transcript path graph for ENSMUST00000214571.1 as figures/pkm_browser_ENSMUST00000214571.1_path.png
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Saving transcript path graph for TALONT000523967 as figures/pkm_browser_TALONT000523967_path.png
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Saving transcript path graph for TALONT000536468 as figures/pkm_browser_TALONT000536468_path.png
Saving transcript path graph for TALONT000524508 as figures/pkm_browser_TALONT000524508_path.png
Saving transcript path graph for TALONT000525165 as figures/pkm_browser_TALONT000525165_path.png
Saving transcript path graph for TALONT000523658 as figures/pkm_browser_TALONT000523658_path.png
Saving transcript path graph for TALONT000523798 as figures/pkm_browser_TALONT000523798_path.png
Saving transcript path graph for TALONT000523622 as figures/pkm_browser_TALONT000523622_path.png
Saving transcript path graph for TALONT000524081 as figures/pkm_browser_TALONT000524081_path.png
Saving transcript path graph for TALONT000524865 as figures/pkm_browser_TALONT000524865_path.png
Generating report for ENSMUSG00000032294.17
More complex gene queries
Let’s try to see if there are any genes that are interesting in more than one way. For instance, are there any isoform-switching genes that also have novel exon skipping events? We can check by taking the intersection of these gene sets, and we’ll plot some of the results.
# get table of exon skipping events
es = sg.adata.uns['es']
# add gene names, which I should make automatic in a future update!
gnames = sg.t_df[['gid', 'gname']]
es = es.merge(gnames, on='gid', how='left')
es = es.drop_duplicates()
es_genes = es.gname.unique().tolist()
es_genes[5:10]
['Col3a1', 'Bzw1', 'Creb1', 'Ttll4', 'Stk16']
# get a table of isoform-switching genes
die = sg.get_die_genes(obs_col='time_point',
obs_conditions=['0hr', '72hr'],
p=0.05, dpi=10)
# add gene names, which I should make automatic in a future update!
gnames = sg.t_df[['gid', 'gname']].drop_duplicates()
die = die.merge(gnames, on='gid', how='left')
die = die.sort_values(by='dpi', ascending=False)
print(die.head())
die_genes = die.gname.unique().tolist()
print(len(die_genes))
die_genes[:5]
gid p_val dpi adj_p_val gname
281 ENSMUSG00000026414.13 5.312136e-237 98.948153 2.645927e-234 Tnnt2
161 ENSMUSG00000020836.15 1.787333e-64 97.932594 1.718034e-62 Coro6
151 ENSMUSG00000020439.17 5.141485e-75 97.710327 5.523568e-73 Smtn
946 ENSMUSG00000039542.16 7.076545e-128 96.704330 1.436015e-125 Ncam1
496 ENSMUSG00000036918.16 3.520068e-06 95.447243 3.115743e-05 Ttc7
987
['Tnnt2', 'Coro6', 'Smtn', 'Ncam1', 'Ttc7']
# get the intersection of novel exon skipping genes and isoform switching genes
genes = list(set(die_genes)&set(es_genes))
genes[:10]
['Tfdp1',
'Slc12a7',
'Bnip2',
'Tmem87b',
'Ap1s2',
'Klc1',
'Chp1',
'Tbcd',
'Bves',
'Lhfpl2']
sg.plot_graph('Chp1', indicate_novel=True)
sg.gen_report('Chp1',
'figures/chp1',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
novelty=True,
layer='pi',
browser=True,
display_numbers=True)
Plotting transcripts for ENSMUSG00000014077.13
Saving transcript path graph for ENSMUST00000014221.12 as figures/chp1_browser_ENSMUST00000014221.12_path.png
Saving transcript path graph for ENSMUST00000137585.2 as figures/chp1_browser_ENSMUST00000137585.2_path.png
Saving transcript path graph for TALONT000411159 as figures/chp1_browser_TALONT000411159_path.png
Saving transcript path graph for TALONT000411157 as figures/chp1_browser_TALONT000411157_path.png
Saving transcript path graph for ENSMUST00000154406.7 as figures/chp1_browser_ENSMUST00000154406.7_path.png
Generating report for ENSMUSG00000014077.13
sg.gen_report('Chp1',
'figures/chp1',
metadata_cols=['time_point'],
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
Plotting transcripts for ENSMUSG00000014077.13
Saving transcript path graph for ENSMUST00000014221.12 as figures/chp1_novel_ENSMUST00000014221.12_path.png
Saving transcript path graph for ENSMUST00000137585.2 as figures/chp1_novel_ENSMUST00000137585.2_path.png
Saving transcript path graph for TALONT000411159 as figures/chp1_novel_TALONT000411159_path.png
Saving transcript path graph for TALONT000411157 as figures/chp1_novel_TALONT000411157_path.png
Saving transcript path graph for ENSMUST00000154406.7 as figures/chp1_novel_ENSMUST00000154406.7_path.png
Generating report for ENSMUSG00000014077.13
# which transcript has the novel exon skipping event
es.loc[es.gname == 'Tfdp1']
| gid | tid | egde_id | gname | |
|---|---|---|---|---|
| 12800 | ENSMUSG00000038482.11 | TALONT000169332 | 735016 | Tfdp1 |
sg.plot_graph('Tfdp1', indicate_novel=True, prefix='figures/tfdpi1')
Saving summary graph for ENSMUSG00000038482.11 as figures/tfdpi1_novel_ENSMUSG00000038482.11_summary.png
sg.plot_transcript_path('TALONT000169332', indicate_novel=True, prefix='figures/tfdp1')
Saving transcript path graph for TALONT000169332 as figures/tfdp1_novel_TALONT000169332_path.png
sg.gen_report('Tfdp1',
'figures/tfdp1',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
novelty=True,
layer='pi',
browser=True,
display_numbers=True)
Plotting transcripts for ENSMUSG00000038482.11
Saving transcript path graph for TALONT000168710 as figures/tfdp1_browser_TALONT000168710_path.png
Saving transcript path graph for ENSMUST00000209885.1 as figures/tfdp1_browser_ENSMUST00000209885.1_path.png
Saving transcript path graph for ENSMUST00000170909.1 as figures/tfdp1_browser_ENSMUST00000170909.1_path.png
Saving transcript path graph for TALONT000168713 as figures/tfdp1_browser_TALONT000168713_path.png
Saving transcript path graph for TALONT000169332 as figures/tfdp1_browser_TALONT000169332_path.png
Saving transcript path graph for ENSMUST00000209396.1 as figures/tfdp1_browser_ENSMUST00000209396.1_path.png
Saving transcript path graph for TALONT000169373 as figures/tfdp1_browser_TALONT000169373_path.png
Saving transcript path graph for TALONT000169353 as figures/tfdp1_browser_TALONT000169353_path.png
Saving transcript path graph for TALONT000169095 as figures/tfdp1_browser_TALONT000169095_path.png
Saving transcript path graph for TALONT000169529 as figures/tfdp1_browser_TALONT000169529_path.png
Saving transcript path graph for TALONT000168688 as figures/tfdp1_browser_TALONT000168688_path.png
Saving transcript path graph for TALONT000168649 as figures/tfdp1_browser_TALONT000168649_path.png
Saving transcript path graph for TALONT000168665 as figures/tfdp1_browser_TALONT000168665_path.png
Generating report for ENSMUSG00000038482.11
sg.gen_report('Tfdp1',
'figures/tfdp1',
metadata_cols=['time_point'],
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
Plotting transcripts for ENSMUSG00000038482.11
Saving transcript path graph for TALONT000168710 as figures/tfdp1_novel_TALONT000168710_path.png
Saving transcript path graph for ENSMUST00000209885.1 as figures/tfdp1_novel_ENSMUST00000209885.1_path.png
Saving transcript path graph for ENSMUST00000170909.1 as figures/tfdp1_novel_ENSMUST00000170909.1_path.png
Saving transcript path graph for TALONT000168713 as figures/tfdp1_novel_TALONT000168713_path.png
Saving transcript path graph for TALONT000169332 as figures/tfdp1_novel_TALONT000169332_path.png
Saving transcript path graph for ENSMUST00000209396.1 as figures/tfdp1_novel_ENSMUST00000209396.1_path.png
Saving transcript path graph for TALONT000169373 as figures/tfdp1_novel_TALONT000169373_path.png
Saving transcript path graph for TALONT000169353 as figures/tfdp1_novel_TALONT000169353_path.png
Saving transcript path graph for TALONT000169095 as figures/tfdp1_novel_TALONT000169095_path.png
Saving transcript path graph for TALONT000169529 as figures/tfdp1_novel_TALONT000169529_path.png
Saving transcript path graph for TALONT000168688 as figures/tfdp1_novel_TALONT000168688_path.png
Saving transcript path graph for TALONT000168649 as figures/tfdp1_novel_TALONT000168649_path.png
Saving transcript path graph for TALONT000168665 as figures/tfdp1_novel_TALONT000168665_path.png
Generating report for ENSMUSG00000038482.11
What genes exhibit TSS switching between myoblasts and myotubes?
Just the same as the isoform switching genes, we can also query for transcription start site (TSS) switching genes. These can also be visualized with Swan, or Scanpy as the presentation demonstrated.
import scanpy as sc # we'll use scanpy to make a few plots here as well
tss_genes = sg.get_die_genes(kind='tss', obs_col=obs_col,
obs_conditions=obs_conditions,
p=0.05, dpi=10)
tss_genes = tss_genes.merge(sg.t_df[['gid', 'gname']].drop_duplicates(), on='gid', how='left')
tss_genes.sort_values(by='dpi', ascending=False)
| gid | p_val | dpi | adj_p_val | gname | |
|---|---|---|---|---|---|
| 277 | ENSMUSG00000026414.13 | 0.000000e+00 | 98.952271 | 0.000000e+00 | Tnnt2 |
| 139 | ENSMUSG00000020439.17 | 2.103395e-137 | 97.718636 | 4.216753e-135 | Smtn |
| 495 | ENSMUSG00000036918.16 | 4.249157e-10 | 95.652176 | 3.808244e-09 | Ttc7 |
| 445 | ENSMUSG00000033060.15 | 3.750830e-50 | 95.027420 | 1.930664e-48 | Lmo7 |
| 735 | ENSMUSG00000116358.1 | 9.559514e-13 | 95.000000 | 1.080480e-11 | Gm49450 |
| ... | ... | ... | ... | ... | ... |
| 326 | ENSMUSG00000028484.16 | 1.404916e-02 | 10.000000 | 4.281059e-02 | Psip1 |
| 93 | ENSMUSG00000015880.13 | 1.573873e-11 | 10.000000 | 1.602910e-10 | Ncapg |
| 87 | ENSMUSG00000015053.14 | 3.748807e-04 | 10.000000 | 1.654601e-03 | Gata2 |
| 428 | ENSMUSG00000032228.16 | 2.812052e-03 | 10.000000 | 1.028925e-02 | Tcf12 |
| 256 | ENSMUSG00000025792.9 | 7.227598e-14 | 10.000000 | 8.767490e-13 | Slc25a10 |
738 rows × 5 columns
sg.gen_report('Tnnt2',
'figures/tnnt2',
metadata_cols=['time_point'],
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
Plotting transcripts for ENSMUSG00000026414.13
Saving transcript path graph for TALONT000394811 as figures/tnnt2_novel_TALONT000394811_path.png
Saving transcript path graph for TALONT000394812 as figures/tnnt2_novel_TALONT000394812_path.png
Saving transcript path graph for TALONT000396016 as figures/tnnt2_novel_TALONT000396016_path.png
Saving transcript path graph for TALONT000396098 as figures/tnnt2_novel_TALONT000396098_path.png
Saving transcript path graph for TALONT000394818 as figures/tnnt2_novel_TALONT000394818_path.png
Saving transcript path graph for TALONT000394815 as figures/tnnt2_novel_TALONT000394815_path.png
Saving transcript path graph for TALONT000394848 as figures/tnnt2_novel_TALONT000394848_path.png
Saving transcript path graph for TALONT000394894 as figures/tnnt2_novel_TALONT000394894_path.png
Saving transcript path graph for ENSMUST00000191055.1 as figures/tnnt2_novel_ENSMUST00000191055.1_path.png
Saving transcript path graph for TALONT000394893 as figures/tnnt2_novel_TALONT000394893_path.png
Saving transcript path graph for TALONT000394956 as figures/tnnt2_novel_TALONT000394956_path.png
Saving transcript path graph for TALONT000394970 as figures/tnnt2_novel_TALONT000394970_path.png
Saving transcript path graph for TALONT000394838 as figures/tnnt2_novel_TALONT000394838_path.png
Generating report for ENSMUSG00000026414.13
sg.gen_report('Tnnt2',
'figures/tnnt2',
metadata_cols=['time_point'],
cmap='magma',
transcript_name=True,
novelty=True,
layer='pi',
browser=True,
display_numbers=True)
Plotting transcripts for ENSMUSG00000026414.13
Saving transcript path graph for TALONT000394811 as figures/tnnt2_browser_TALONT000394811_path.png
Saving transcript path graph for TALONT000394812 as figures/tnnt2_browser_TALONT000394812_path.png
Saving transcript path graph for TALONT000396016 as figures/tnnt2_browser_TALONT000396016_path.png
Saving transcript path graph for TALONT000396098 as figures/tnnt2_browser_TALONT000396098_path.png
Saving transcript path graph for TALONT000394818 as figures/tnnt2_browser_TALONT000394818_path.png
Saving transcript path graph for TALONT000394815 as figures/tnnt2_browser_TALONT000394815_path.png
Saving transcript path graph for TALONT000394848 as figures/tnnt2_browser_TALONT000394848_path.png
Saving transcript path graph for TALONT000394894 as figures/tnnt2_browser_TALONT000394894_path.png
Saving transcript path graph for ENSMUST00000191055.1 as figures/tnnt2_browser_ENSMUST00000191055.1_path.png
Saving transcript path graph for TALONT000394893 as figures/tnnt2_browser_TALONT000394893_path.png
Saving transcript path graph for TALONT000394956 as figures/tnnt2_browser_TALONT000394956_path.png
Saving transcript path graph for TALONT000394970 as figures/tnnt2_browser_TALONT000394970_path.png
Saving transcript path graph for TALONT000394838 as figures/tnnt2_browser_TALONT000394838_path.png
Generating report for ENSMUSG00000026414.13
var_names = sg.tss_adata.var.loc[sg.tss_adata.var.gname == 'Tnnt2', 'tss_name'].tolist()
sc.pl.matrixplot(sg.tss_adata, var_names,
gene_symbols='tss_name',
groupby='time_point', layer='pi',
cmap='magma', swap_axes=True)
What genes exhibit TES switching between myoblasts and myotubes?
And we’ll do the same thing with TES switching genes.
tes_genes = sg.get_die_genes(kind='tes', obs_col=obs_col,
obs_conditions=obs_conditions,
p=0.05, dpi=10)
tes_genes = tes_genes.merge(sg.t_df[['gid', 'gname']].drop_duplicates(), on='gid', how='left')
tes_genes.sort_values(by='dpi', ascending=False)
| gid | p_val | dpi | adj_p_val | gname | |
|---|---|---|---|---|---|
| 770 | ENSMUSG00000036779.12 | 2.430124e-12 | 95.348839 | 3.122969e-11 | Tent4b |
| 703 | ENSMUSG00000033060.15 | 2.111947e-47 | 95.027420 | 1.007065e-45 | Lmo7 |
| 243 | ENSMUSG00000021451.16 | 1.294532e-09 | 93.023262 | 1.291207e-08 | Sema4d |
| 429 | ENSMUSG00000026253.14 | 4.529658e-159 | 92.235542 | 9.470469e-157 | Chrng |
| 681 | ENSMUSG00000032366.15 | 0.000000e+00 | 90.557555 | 0.000000e+00 | Tpm1 |
| ... | ... | ... | ... | ... | ... |
| 133 | ENSMUSG00000015880.13 | 1.573873e-11 | 10.000000 | 1.913043e-10 | Ncapg |
| 404 | ENSMUSG00000025792.9 | 9.733678e-22 | 10.000000 | 2.232585e-20 | Slc25a10 |
| 308 | ENSMUSG00000023032.12 | 5.326164e-03 | 10.000000 | 1.966918e-02 | Slc4a8 |
| 122 | ENSMUSG00000015053.14 | 3.748807e-04 | 10.000000 | 1.830953e-03 | Gata2 |
| 724 | ENSMUSG00000034361.10 | 1.378576e-07 | 10.000000 | 1.116832e-06 | Cpne2 |
1115 rows × 5 columns
sg.gen_report('Tpm1',
'figures/tpm1',
metadata_cols=['time_point'],
groupby='time_point',
cmap='viridis',
transcript_name=True,
novelty=True,
indicate_novel=True)
Plotting transcripts for ENSMUSG00000032366.15
Saving transcript path graph for ENSMUST00000113707.8 as figures/tpm1_novel_ENSMUST00000113707.8_path.png
Saving transcript path graph for ENSMUST00000113685.9 as figures/tpm1_novel_ENSMUST00000113685.9_path.png
Saving transcript path graph for ENSMUST00000113695.7 as figures/tpm1_novel_ENSMUST00000113695.7_path.png
Saving transcript path graph for ENSMUST00000113697.7 as figures/tpm1_novel_ENSMUST00000113697.7_path.png
Saving transcript path graph for TALONT000543286 as figures/tpm1_novel_TALONT000543286_path.png
Saving transcript path graph for ENSMUST00000131279.1 as figures/tpm1_novel_ENSMUST00000131279.1_path.png
Saving transcript path graph for ENSMUST00000030185.4 as figures/tpm1_novel_ENSMUST00000030185.4_path.png
Saving transcript path graph for TALONT000543297 as figures/tpm1_novel_TALONT000543297_path.png
Saving transcript path graph for TALONT000543293 as figures/tpm1_novel_TALONT000543293_path.png
Saving transcript path graph for ENSMUST00000113705.7 as figures/tpm1_novel_ENSMUST00000113705.7_path.png
Saving transcript path graph for TALONT000544599 as figures/tpm1_novel_TALONT000544599_path.png
Saving transcript path graph for ENSMUST00000113687.7 as figures/tpm1_novel_ENSMUST00000113687.7_path.png
Saving transcript path graph for ENSMUST00000113696.7 as figures/tpm1_novel_ENSMUST00000113696.7_path.png
Saving transcript path graph for TALONT000544629 as figures/tpm1_novel_TALONT000544629_path.png
Saving transcript path graph for TALONT000544522 as figures/tpm1_novel_TALONT000544522_path.png
Saving transcript path graph for TALONT000543361 as figures/tpm1_novel_TALONT000543361_path.png
Saving transcript path graph for TALONT000543336 as figures/tpm1_novel_TALONT000543336_path.png
Saving transcript path graph for ENSMUST00000050905.15 as figures/tpm1_novel_ENSMUST00000050905.15_path.png
Saving transcript path graph for ENSMUST00000113686.7 as figures/tpm1_novel_ENSMUST00000113686.7_path.png
Saving transcript path graph for ENSMUST00000113701.7 as figures/tpm1_novel_ENSMUST00000113701.7_path.png
Saving transcript path graph for ENSMUST00000034928.11 as figures/tpm1_novel_ENSMUST00000034928.11_path.png
Saving transcript path graph for ENSMUST00000129466.7 as figures/tpm1_novel_ENSMUST00000129466.7_path.png
Saving transcript path graph for ENSMUST00000113693.7 as figures/tpm1_novel_ENSMUST00000113693.7_path.png
Generating report for ENSMUSG00000032366.15
sg.gen_report('Tpm1',
'figures/tpm1',
metadata_cols=['time_point'],
groupby='time_point',
cmap='magma',
transcript_name=True,
novelty=True,
layer='pi',
browser=True,
display_numbers=True)
Plotting transcripts for ENSMUSG00000032366.15
Saving transcript path graph for ENSMUST00000113707.8 as figures/tpm1_browser_ENSMUST00000113707.8_path.png
Saving transcript path graph for ENSMUST00000113685.9 as figures/tpm1_browser_ENSMUST00000113685.9_path.png
Saving transcript path graph for ENSMUST00000113695.7 as figures/tpm1_browser_ENSMUST00000113695.7_path.png
Saving transcript path graph for ENSMUST00000113697.7 as figures/tpm1_browser_ENSMUST00000113697.7_path.png
Saving transcript path graph for TALONT000543286 as figures/tpm1_browser_TALONT000543286_path.png
Saving transcript path graph for ENSMUST00000131279.1 as figures/tpm1_browser_ENSMUST00000131279.1_path.png
Saving transcript path graph for ENSMUST00000030185.4 as figures/tpm1_browser_ENSMUST00000030185.4_path.png
Saving transcript path graph for TALONT000543297 as figures/tpm1_browser_TALONT000543297_path.png
Saving transcript path graph for TALONT000543293 as figures/tpm1_browser_TALONT000543293_path.png
Saving transcript path graph for ENSMUST00000113705.7 as figures/tpm1_browser_ENSMUST00000113705.7_path.png
Saving transcript path graph for TALONT000544599 as figures/tpm1_browser_TALONT000544599_path.png
Saving transcript path graph for ENSMUST00000113687.7 as figures/tpm1_browser_ENSMUST00000113687.7_path.png
Saving transcript path graph for ENSMUST00000113696.7 as figures/tpm1_browser_ENSMUST00000113696.7_path.png
Saving transcript path graph for TALONT000544629 as figures/tpm1_browser_TALONT000544629_path.png
Saving transcript path graph for TALONT000544522 as figures/tpm1_browser_TALONT000544522_path.png
Saving transcript path graph for TALONT000543361 as figures/tpm1_browser_TALONT000543361_path.png
Saving transcript path graph for TALONT000543336 as figures/tpm1_browser_TALONT000543336_path.png
Saving transcript path graph for ENSMUST00000050905.15 as figures/tpm1_browser_ENSMUST00000050905.15_path.png
Saving transcript path graph for ENSMUST00000113686.7 as figures/tpm1_browser_ENSMUST00000113686.7_path.png
Saving transcript path graph for ENSMUST00000113701.7 as figures/tpm1_browser_ENSMUST00000113701.7_path.png
Saving transcript path graph for ENSMUST00000034928.11 as figures/tpm1_browser_ENSMUST00000034928.11_path.png
Saving transcript path graph for ENSMUST00000129466.7 as figures/tpm1_browser_ENSMUST00000129466.7_path.png
Saving transcript path graph for ENSMUST00000113693.7 as figures/tpm1_browser_ENSMUST00000113693.7_path.png
Generating report for ENSMUSG00000032366.15
var_names = sg.tes_adata.var.loc[sg.tes_adata.var.gname == 'Tpm1', 'tes_name'].tolist()
sc.pl.matrixplot(sg.tes_adata, var_names,
gene_symbols='tes_name',
groupby='time_point', layer='pi',
cmap='magma', swap_axes=True)
