E. coli

Read files

Function to read files

Read all files and arrange the data

Manually correction for problematic genes

Plot: reads without faetures

Plot: reads not aligned

Make DDS

Average samples

Pre-filtering

Compute DESeq2

estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
    Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
    sequence count data: removing the noise and preserving large differences.
    Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895

In green genes highly expressed in vesicles but not in cells.

In red genes highly expressed in cells but not in the vesicles.

Warning message:
In as.data.frame(readDcf(file.path(rsconnectDir, deploymentFile)),  :
  internal error -3 in R_decompress1

gene	average.ecoli	average.vesicles	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj	link
lpp	64646.667	273260.00	194665.64	2.7100867	0.1649231	16.4324271	0.0000000	0.0000000	1
dps	73352.333	104814.67	94271.77	1.1328300	0.3580515	3.1638748	0.0015568	0.0038089	2
ompA	53168.000	85668.33	74126.32	1.3032907	0.2221734	5.8660971	0.0000000	0.0000000	3
ompX	38285.333	64484.67	55041.29	1.3798479	0.1702291	8.1058276	0.0000000	0.0000000	4
rplY	24667.000	55314.33	44060.72	1.7882230	0.2599776	6.8783720	0.0000000	0.0000000	5
rpmE	19532.000	54047.67	41185.99	2.0924890	0.1869062	11.1953951	0.0000000	0.0000000	6
ahpC	50614.667	28372.00	37659.91	-0.2064387	0.1678389	-1.2299811	0.2187042	0.3008149	7
rpoH	48413.000	27183.67	36044.40	-0.2018743	0.2170226	-0.9301995	0.3522678	0.4483504	8
gapC	22412.333	39333.33	33102.31	1.4537857	0.2662692	5.4598332	0.0000000	0.0000002	9
hdeA	35736.000	28136.33	31837.50	0.2734231	0.2239510	1.2209056	0.2221218	0.3041469	10
rpmB	26267.000	33569.00	31222.96	0.9773197	0.2015746	4.8484267	0.0000012	0.0000053	11
cspA	45035.000	19674.33	30006.64	-0.5648464	0.2802290	-2.0156602	0.0438355	0.0748492	12
rpsA	26070.000	30255.67	28927.54	0.8657653	0.2383232	3.6327362	0.0002804	0.0007942	13
infC	36116.000	22705.33	28386.98	-0.0381180	0.1918163	-0.1987216	0.8424805	0.8896777	14
acpP	10326.333	37813.00	27502.08	2.5030348	0.1815764	13.7850216	0.0000000	0.0000000	15
rpoA	26073.333	27151.67	27089.49	0.6940296	0.2222330	3.1229814	0.0017903	0.0043354	16
dnaK	9451.667	37137.67	26708.84	2.5646188	0.2996609	8.5584021	0.0000000	0.0000000	17
ompC	26710.667	25223.33	26105.22	0.5602536	0.2305841	2.4297144	0.0151107	0.0290267	18
rpmI	31232.667	21938.00	25972.47	0.1207508	0.1920288	0.6288160	0.5294695	0.6219050	19
rpmF	15268.333	29648.67	24379.94	1.5913510	0.2436218	6.5320550	0.0000000	0.0000000	20
rpsB	34217.667	17078.67	24045.40	-0.3626045	0.2671686	-1.3572123	0.1747138	0.2478780	21
rplJ	33518.333	16151.67	23005.48	-0.3892700	0.3617770	-1.0759943	0.2819298	0.3703093	22
yfcZ	14178.667	27684.00	22828.00	1.5874138	0.2481763	6.3963145	0.0000000	0.0000000	23
rplT	24491.333	20243.67	22225.90	0.3576544	0.1923944	1.8589647	0.0630321	0.1031808	24
rplA	23908.000	20424.67	22028.18	0.4180488	0.2094002	1.9964105	0.0458893	0.0778589	25
rpsO	15127.333	24091.00	20886.93	1.3008786	0.2730182	4.7648053	0.0000019	0.0000078	26
pflB	33086.333	12756.33	20865.55	-0.7231451	0.3137060	-2.3051685	0.0211571	0.0391402	27
yjbJ	2789.333	31627.33	20661.11	4.1305047	0.3227783	12.7967225	0.0000000	0.0000000	28
rplE	13804.333	22364.00	19219.17	1.3461619	0.2689524	5.0052050	0.0000006	0.0000025	29
rplM	10910.000	23917.67	19109.55	1.7829314	0.3007764	5.9277629	0.0000000	0.0000000	30
lpxC	26566.333	13702.67	18978.60	-0.3169031	0.2403540	-1.3184846	0.1873415	0.2625815	31
secY	16037.667	20148.33	18806.45	0.9644556	0.1951182	4.9429303	0.0000008	0.0000034	32
gltX	22734.667	14189.67	17808.83	-0.0561086	0.1857645	-0.3020418	0.7626202	0.8221849	33
rplN	14334.333	19489.00	17680.15	1.0887234	0.2504251	4.3475004	0.0000138	0.0000498	34
rpsD	12110.667	20754.33	17638.29	1.4131124	0.2267058	6.2332443	0.0000000	0.0000000	35
ymdF	4693.000	25141.67	17394.24	3.0476376	0.2777163	10.9739234	0.0000000	0.0000000	36
fusA	16450.667	17531.67	17311.76	0.7235594	0.2388876	3.0288699	0.0024547	0.0057681	37
tsf	17230.333	16289.67	16913.20	0.5591520	0.2386217	2.3432566	0.0191162	0.0357667	38
rplK	16293.333	16950.00	16908.63	0.6984038	0.2004159	3.4847726	0.0004926	0.0013289	39
osmB	1949.333	25727.00	16648.43	4.3694682	0.2666722	16.3851670	0.0000000	0.0000000	40
rpsC	21929.333	12484.67	16355.46	-0.1716489	0.3144539	-0.5458635	0.5851598	0.6711066	41
rplL	22092.333	12108.33	16110.93	-0.2126371	0.3638662	-0.5843827	0.5589629	0.6482283	42
rpmG	11825.667	17451.33	15503.66	1.1856309	0.2249170	5.2714141	0.0000001	0.0000007	43
yceD	13425.000	16019.67	15205.98	0.8935798	0.2470912	3.6163963	0.0002987	0.0008371	44
sra	5984.333	20606.00	15110.77	2.4067048	0.2201034	10.9344274	0.0000000	0.0000000	45
cspE	10217.000	17109.00	14630.06	1.3753946	0.1949544	7.0549563	0.0000000	0.0000000	46
rpsI	8151.333	18204.33	14466.02	1.8088648	0.3101922	5.8314330	0.0000000	0.0000000	47
eno	13187.667	14456.33	14107.16	0.7879623	0.2730388	2.8858981	0.0039030	0.0087439	48
rpsU	12084.000	14889.00	13990.25	0.9289145	0.2083660	4.4580911	0.0000083	0.0000312	49
zapB	8985.667	15554.33	13199.38	1.4250514	0.1802939	7.9040447	0.0000000	0.0000000	50

# coli = read.xlsx("C:/Users/rotemh.WISMAIN/Google Drive/@מגדל השן/vesicles/@78/e_coli.xlsx",1)