DE analysis between tissue samples¶
Tissue comparison AP-axis
Notebook for performing the DESeq2 analysis
Here we perform DEseq2 analysis for each tissue comparison, i.e. to identify the geens that change between the tissues.
This is performed on each condition separately.
print(filepath)
Error in print(filepath) : object 'filepath' not foundRun each of the supplimentary DEseq 2 experiments
runDeseq2BetweenTissue(paste('merged_df_wt_fb-hb_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_wt_fb-hb_', date, '.csv', sep=''), condition_pos="fb")
[1] "../data/results/deseq2/merged_df_wt_fb-hb_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_wt_fb-hb_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15189 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15189 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4570, 30%
LFC < 0 (down) : 4257, 28%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15189 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
18504-Pax2 2581.57 -7.72159 0.2014535 -38.3294 0.00000e+00 0.00000e+00
21375-Tbr1 5888.69 9.09244 0.2237556 40.6356 0.00000e+00 0.00000e+00
207667-Skor1 2280.05 -5.08910 0.1509651 -33.7104 4.06787e-249 2.05956e-245
54713-Fezf2 2137.53 8.85889 0.2646784 33.4704 1.30009e-245 4.93679e-242
18053-Ngfr 2133.24 -2.26346 0.0686299 -32.9807 1.53415e-238 4.66044e-235
... ... ... ... ... ... ...
71583-9130008F23Rik 22.0738 -0.000634352 0.416585 -1.52274e-03 0.998785 0.999048
57431-Dnajc4 465.6501 0.000146053 0.141234 1.03412e-03 0.999175 0.999372
107986-Ddb2 324.7783 -0.000114246 0.143105 -7.98338e-04 0.999363 0.999495
23972-Papss2 184.6896 -0.000199504 0.499253 -3.99605e-04 0.999681 0.999747
69398-Cdhr4 63.5716 0.000030796 0.327945 9.39061e-05 0.999925 0.999925runDeseq2BetweenTissue(paste('merged_df_ko_fb-mb_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_ko_fb-mb_', date, '.csv', sep=''), condition_pos="fb")[1] "../data/results/deseq2/merged_df_ko_fb-mb_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_ko_fb-mb_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15419 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15419 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1974, 13%
LFC < 0 (down) : 2223, 14%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15419 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
22326-Vax1 363.997 2.44168 0.1245576 19.6028 1.46343e-85 2.25647e-81
13162-Slc6a3 186.021 -2.68403 0.1406397 -19.0844 3.40073e-81 2.62179e-77
13195-Ddc 754.877 -1.52192 0.0816047 -18.6500 1.26357e-77 6.49434e-74
17260-Mef2c 1526.872 1.46683 0.0841923 17.4223 5.58448e-68 2.15268e-64
72961-Slc17a7 357.829 2.71418 0.1592171 17.0470 3.67790e-65 1.13419e-61
... ... ... ... ... ... ...
59005-Trappc2l 1391.6255 4.29446e-05 0.0617906 6.95002e-04 0.999445 0.999686
330627-Trim66 410.4278 7.34667e-05 0.1152983 6.37188e-04 0.999492 0.999686
97086-Slc9b2 77.4063 -9.30080e-05 0.1799592 -5.16828e-04 0.999588 0.999717
50762-Fbxo6 437.1020 -3.30580e-05 0.0848144 -3.89769e-04 0.999689 0.999754
118453-Mmp28 403.3344 9.80980e-06 0.1182736 8.29416e-05 0.999934 0.999934runDeseq2BetweenTissue(paste('merged_df_ko_fb-hb_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_ko_fb-hb_', date, '.csv', sep=''), condition_pos="fb")[1] "../data/results/deseq2/merged_df_ko_fb-hb_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_ko_fb-hb_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15470 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15470 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4120, 27%
LFC < 0 (down) : 3573, 23%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 8)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15470 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
20618-Sncg 2565.85 -4.98818 0.1265364 -39.4209 0.00000e+00 0.00000e+00
19132-Prph 2867.79 -6.31069 0.1624458 -38.8480 0.00000e+00 0.00000e+00
13390-Dlx1 1415.58 4.52381 0.1169688 38.6754 0.00000e+00 0.00000e+00
20473-Six3 1653.11 2.74427 0.0734462 37.3644 1.48099e-305 5.72774e-302
19713-Ret 2378.77 -2.22423 0.0725912 -30.6405 3.53246e-206 1.09294e-202
... ... ... ... ... ... ...
12370-Casp8 373.9910 1.11391e-04 0.1496048 0.000744566 0.999406 0.999664
22446-Xlr3c 15.1539 3.02849e-04 0.5760056 0.000525774 0.999580 0.999717
114674-Gtf2ird2 1463.2548 2.16752e-05 0.0419401 0.000516812 0.999588 0.999717
66172-Med11 727.3440 1.73760e-05 0.0615357 0.000282373 0.999775 0.999839
66647-Nsmce3 1098.2838 4.73089e-06 0.0507149 0.000093284 0.999926 0.999926runDeseq2BetweenTissue(paste('merged_df_wt_fb-sc_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_wt_fb-sc_', date, '.csv', sep=''), condition_pos="fb")[1] "../data/results/deseq2/merged_df_wt_fb-sc_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_wt_fb-sc_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15211 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15211 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4564, 30%
LFC < 0 (down) : 4259, 28%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 8)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15211 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
15228-Foxg1 12655.63 12.07355 0.290568 41.5516 0.00000e+00 0.00000e+00
22139-Ttr 13816.93 9.34008 0.248094 37.6473 0.00000e+00 0.00000e+00
21375-Tbr1 5778.35 9.22174 0.179648 51.3323 0.00000e+00 0.00000e+00
11878-Arx 3468.81 5.39362 0.119295 45.2124 0.00000e+00 0.00000e+00
16874-Lhx6 1971.34 5.01579 0.138666 36.1718 1.68924e-286 5.13901e-283
... ... ... ... ... ... ...
66917-Chordc1 3290.53999 -0.000142244 0.0679287 -0.002094018 0.998329 0.998592
19733-Rgn 9.04286 -0.000899893 0.4574050 -0.001967387 0.998430 0.998627
56709-Dnajb12 957.83962 -0.000143522 0.0821821 -0.001746388 0.998607 0.998738
14598-Ggt1 59.80753 -0.000370048 0.2752292 -0.001344509 0.998927 0.998993
66847-Hint3 249.49728 -0.000107992 0.1122038 -0.000962465 0.999232 0.999232runDeseq2BetweenTissue(paste('merged_df_ko_fb-sc_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_ko_fb-sc_', date, '.csv', sep=''), condition_pos="fb")[1] "../data/results/deseq2/merged_df_ko_fb-sc_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_ko_fb-sc_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15453 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15453 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4354, 28%
LFC < 0 (down) : 3972, 26%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 8)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15453 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
19132-Prph 7891.89 -7.76541 0.1555271 -49.9296 0.00000e+00 0.00000e+00
13390-Dlx1 1368.89 5.26154 0.1104935 47.6185 0.00000e+00 0.00000e+00
19713-Ret 4056.98 -3.13184 0.0710962 -44.0508 0.00000e+00 0.00000e+00
18039-Nefl 30598.93 -2.74064 0.0797271 -34.3753 5.89706e-259 2.27818e-255
20618-Sncg 4134.94 -5.65586 0.1663144 -34.0070 1.75365e-253 5.41982e-250
... ... ... ... ... ... ...
74182-Gpcpd1 4202.3692 4.24146e-05 0.0557002 7.61481e-04 0.999392 0.999619
100041151-Gm3636 190.3273 9.13524e-05 0.1267739 7.20593e-04 0.999425 0.999619
381810-Lpar5 22.1253 1.51774e-04 0.3298917 4.60072e-04 0.999633 0.999762
218236-Fam120a 7176.6258 5.78174e-06 0.0311130 1.85830e-04 0.999852 0.999916
12021-Bard1 273.5795 -6.66996e-07 0.1807347 -3.69047e-06 0.999997 0.999997runDeseq2BetweenTissue(paste('merged_df_wt_mb-hb_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_wt_mb-hb_', date, '.csv', sep=''), condition_pos="mb")[1] "../data/results/deseq2/merged_df_wt_mb-hb_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_wt_mb-hb_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15168 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15168 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2788, 18%
LFC < 0 (down) : 2764, 18%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15168 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
21416-Tcf7l2 10776.285 4.77109 0.1109733 42.9931 0.00000e+00 0.00000e+00
15402-Hoxa5 6975.547 -10.93360 0.2542923 -42.9962 0.00000e+00 0.00000e+00
15413-Hoxb5 5628.256 -11.06147 0.2988927 -37.0082 8.46723e-300 4.28103e-296
18040-Nefm 19559.473 -1.80678 0.0504091 -35.8424 2.41611e-281 9.16188e-278
20508-Slc18a3 687.587 -3.30223 0.1043722 -31.6390 1.07469e-219 3.26019e-216
... ... ... ... ... ... ...
399603-Lratd2 898.919 -2.06779e-04 0.2031491 -0.001017870 0.999188 0.999406
192157-Socs7 6726.739 -7.95107e-05 0.0862370 -0.000922002 0.999264 0.999406
26394-Lypla2 5312.284 5.70722e-05 0.0627347 0.000909738 0.999274 0.999406
20447-St6galnac3 716.853 -6.76108e-05 0.1038571 -0.000650998 0.999481 0.999546
20229-Sat1 860.341 1.51078e-05 0.1076705 0.000140315 0.999888 0.999888runDeseq2BetweenTissue(paste('merged_df_ko_mb-hb_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_ko_mb-hb_', date, '.csv', sep=''), condition_pos="mb")[1] "../data/results/deseq2/merged_df_ko_mb-hb_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_ko_mb-hb_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15455 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15455 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2100, 14%
LFC < 0 (down) : 1847, 12%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15455 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
75209-Sv2c 1137.375 -1.12658 0.103268 -10.9093 1.04078e-27 1.60578e-23
68659-Gask1b 538.569 -1.44205 0.132954 -10.8462 2.07800e-27 1.60578e-23
18424-Otx2 1602.344 1.53460 0.142873 10.7410 6.53284e-27 3.36550e-23
13599-Ecel1 2913.654 -1.07481 0.102737 -10.4617 1.29455e-25 5.00181e-22
110648-Lmx1a 755.590 1.28084 0.123046 10.4095 2.24428e-25 6.93707e-22
... ... ... ... ... ... ...
76482-Rmc1 1007.089 6.01905e-05 0.0563605 1.06795e-03 0.999148 0.999407
71726-Smug1 992.662 4.68197e-05 0.0524263 8.93058e-04 0.999287 0.999481
66302-Rmdn1 574.932 -5.84485e-05 0.0762691 -7.66346e-04 0.999389 0.999518
77048-Cep83 1347.812 -1.25148e-05 0.0477668 -2.61998e-04 0.999791 0.999856
93841-Uchl4 109.100 -1.05960e-05 0.1623343 -6.52725e-05 0.999948 0.999948runDeseq2BetweenTissue(paste('merged_df_wt_mb-sc_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_wt_mb-sc_', date, '.csv', sep=''), condition_pos="mb")[1] "../data/results/deseq2/merged_df_wt_mb-sc_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_wt_mb-sc_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15220 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15220 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4029, 26%
LFC < 0 (down) : 3381, 22%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15220 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
16876-Lhx9 4520.863 4.93418 0.137513 35.8816 5.91356e-282 9.00043e-278
13799-En2 3503.790 9.80198 0.283247 34.6057 2.07241e-262 1.57711e-258
110648-Lmx1a 581.398 3.92737 0.116220 33.7925 2.54406e-250 1.29069e-246
14462-Gata3 1907.172 3.58233 0.107524 33.3165 2.22392e-243 8.46200e-240
231044-Gbx1 475.348 -5.61318 0.187681 -29.9081 1.54535e-196 4.70403e-193
... ... ... ... ... ... ...
13709-Elf1 591.3987 -2.68991e-04 0.1091934 -0.002463432 0.998034 0.998297
16535-Kcnq1 52.6146 4.69871e-04 0.2142520 0.002193078 0.998250 0.998447
69930-Zfp715 1706.9609 -6.34196e-05 0.0562283 -0.001127896 0.999100 0.999231
56526-Septin6 5952.2199 -3.90871e-05 0.0488340 -0.000800408 0.999361 0.999427
16826-Ldb2 1795.5646 -4.11876e-05 0.0817559 -0.000503788 0.999598 0.999598runDeseq2BetweenTissue(paste('merged_df_ko_mb-sc_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_ko_mb-sc_', date, '.csv', sep=''), condition_pos="mb")[1] "../data/results/deseq2/merged_df_ko_mb-sc_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_ko_mb-sc_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15438 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15438 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2797, 18%
LFC < 0 (down) : 2637, 17%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15438 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
18424-Otx2 1285.675 3.24715 0.0935820 34.6984 8.33070e-264 1.28609e-259
15417-Hoxb9 5082.155 -4.25565 0.1559455 -27.2893 5.67667e-164 4.38182e-160
13599-Ecel1 3844.974 -1.67415 0.0652495 -25.6577 3.46657e-145 1.78390e-141
319764-A730046J19Rik 438.478 -3.72426 0.1470220 -25.3313 1.44307e-141 5.56952e-138
18423-Otx1 577.358 4.98515 0.2343268 21.2744 1.96152e-100 6.05638e-97
... ... ... ... ... ... ...
107723-Slc12a6 4150.81 2.11463e-05 0.0539244 0.000392147 0.999687 0.999930
20017-Polr1b 1253.78 2.45220e-05 0.0738857 0.000331891 0.999735 0.999930
100019-Mdn1 4249.81 1.56321e-05 0.0742060 0.000210658 0.999832 0.999961
69524-Esam 1216.60 -9.56169e-06 0.0882814 -0.000108309 0.999914 0.999978
66884-Appbp2 2330.35 1.19663e-06 0.0773064 0.000015479 0.999988 0.999988runDeseq2BetweenTissue(paste('merged_df_wt_hb-sc_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_wt_hb-sc_', date, '.csv', sep=''), condition_pos="hb")[1] "../data/results/deseq2/merged_df_wt_hb-sc_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_wt_hb-sc_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15252 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15252 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2393, 16%
LFC < 0 (down) : 2081, 14%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15252 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
74561-Nkx6-3 219.762 5.77647 0.289990 19.9196 2.75385e-88 4.20017e-84
15567-Slc6a4 435.900 3.95559 0.206299 19.1740 6.09959e-82 4.65155e-78
16870-Lhx2 1144.983 2.28457 0.133638 17.0952 1.61089e-65 8.18976e-62
15399-Hoxa2 1836.224 1.93196 0.120068 16.0905 2.97243e-58 1.13339e-54
20429-Shox2 1168.891 2.05792 0.131967 15.5942 7.96904e-55 2.43088e-51
... ... ... ... ... ... ...
27364-Srr 1447.007 -2.11829e-05 0.0634633 -3.33782e-04 0.999734 0.999971
100705-Acacb 270.097 3.98149e-05 0.1412191 2.81937e-04 0.999775 0.999971
77519-Zfp266 6531.182 1.33894e-05 0.0821211 1.63044e-04 0.999870 0.999971
20338-Sel1l 8661.133 -7.82597e-06 0.0661583 -1.18292e-04 0.999906 0.999971
170826-Ppargc1b 211.500 -2.08963e-06 0.1570731 -1.33035e-05 0.999989 0.999989runDeseq2BetweenTissue(paste('merged_df_ko_hb-sc_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_ko_hb-sc_', date, '.csv', sep=''), condition_pos="hb")[1] "../data/results/deseq2/merged_df_ko_hb-sc_FEATURE_COUNTS_20210124.csv"
[1] "========================== RUNNING merged_df_ko_hb-sc_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15477 12"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: time + condition_id"
out of 15477 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1243, 8%
LFC < 0 (down) : 1398, 9%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15477 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
319764-A730046J19Rik 490.247 -2.621006 0.1394109 -18.8006 7.46948e-79 1.15605e-74
19713-Ret 5626.669 -0.905542 0.0615947 -14.7016 6.29212e-49 4.86915e-45
246086-Onecut3 966.592 1.174591 0.0900294 13.0467 6.63184e-39 3.42137e-35
14275-Folr1 210.550 3.425958 0.2672890 12.8174 1.30966e-37 5.06741e-34
14462-Gata3 2179.087 1.583849 0.1259300 12.5772 2.81758e-36 8.72154e-33
... ... ... ... ... ... ...
20318-Sdf4 6243.471 2.49669e-05 0.0432589 0.000577149 0.999540 0.999752
93702-Pcdhgb5 383.688 4.71794e-05 0.0931547 0.000506463 0.999596 0.999752
17210-Mcl1 5847.782 -2.66354e-05 0.0565347 -0.000471133 0.999624 0.999752
83814-Nedd4l 8419.468 3.16319e-05 0.0807421 0.000391764 0.999687 0.999752
26399-Map2k6 875.334 3.06799e-05 0.1081848 0.000283588 0.999774 0.999774Print session info
sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] DESeq2_1.28.1 SummarizedExperiment_1.18.2 DelayedArray_0.14.1 matrixStats_0.58.0 Biobase_2.48.0
[6] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2 IRanges_2.22.2 S4Vectors_0.26.1 BiocGenerics_0.34.0
loaded via a namespace (and not attached):
[1] sass_0.4.0 bit64_4.0.5 jsonlite_1.7.2 splines_4.0.3 StanHeaders_2.21.0-7 RcppParallel_5.1.4 bslib_0.2.5
[8] assertthat_0.2.1 blob_1.2.1 GenomeInfoDbData_1.2.3 yaml_2.2.1 pillar_1.6.1 RSQLite_2.2.7 lattice_0.20-44
[15] glue_1.4.2 digest_0.6.27 RColorBrewer_1.1-2 XVector_0.28.0 colorspace_2.0-1 htmltools_0.5.1.1 Matrix_1.3-3
[22] XML_3.99-0.6 pkgconfig_2.0.3 sircle_0.0.0.9000 rstan_2.21.2 genefilter_1.70.0 zlibbioc_1.34.0 purrr_0.3.4
[29] xtable_1.8-4 scales_1.1.1 processx_3.5.2 BiocParallel_1.22.0 tibble_3.1.0 annotate_1.66.0 generics_0.1.0
[36] ggplot2_3.3.3 ellipsis_0.3.2 withr_2.4.2 cachem_1.0.5 cli_2.5.0 survival_3.2-11 magrittr_2.0.1
[43] crayon_1.4.1 ps_1.6.0 memoise_2.0.0 evaluate_0.14 fansi_0.4.2 pkgbuild_1.2.0 rsconnect_0.8.17
[50] loo_2.4.1 prettyunits_1.1.1 tools_4.0.3 lifecycle_1.0.0 stringr_1.4.0 V8_3.4.2 munsell_0.5.0
[57] locfit_1.5-9.4 callr_3.7.0 AnnotationDbi_1.50.3 compiler_4.0.3 jquerylib_0.1.4 rlang_0.4.11 grid_4.0.3
[64] RCurl_1.98-1.3 bitops_1.0-7 rmarkdown_2.8 codetools_0.2-18 gtable_0.3.0 curl_4.3.1 inline_0.3.18
[71] DBI_1.1.1 R6_2.5.0 gridExtra_2.3 knitr_1.33 dplyr_1.0.6 fastmap_1.1.0 bit_4.0.4
[78] utf8_1.2.1 stringi_1.5.3 Rcpp_1.0.6 vctrs_0.3.8 geneplotter_1.66.0 tidyselect_1.1.1 xfun_0.23 References:
Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi: 10.1186/s13059-014-0550-8.
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