SIStemA

Summary :

Poster - JOBIM2019 (pdf)

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Log10 Conversion

y = log10(x + 1)
Example: log10(100 + 1) ~ 2.004321

x = 10^(y) - 1
Example : 10^(2.004321) - 1 ~ 100

- Between 0 and 1: correspond to counts between 1 and 10
- Between 1 and 2: correspond to counts between 10 and 100
- Between 2 and 3: correspond to counts between 100 and 1000
- ...

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Update 2019/07 :

There are 400 samples and 21080 genes (8 432 000 rows) (NCBI GRCh38.92 07/2018).
On these counts:

- Minimum = 0
- Maximum = 110 069 (log10(x+1) = 6.04 )
- Median = 4.39 (log10(x+1) = 0.73 )
- Average = 49.16 (log10(x+1) = 1.70 )

Only 7 rows of the counting table, i.e. 5 genes (PARP2, RMRP, RPPH1, TTR & TUBA1C) on 4 samples (4 NGS_Number NGS23, NGS8, NGS28 & NGS12), have a tpm count above 50000 (log10(x+1) = 4.69).
There are :
- 2076 rows of this same table that have a count greater than 10000 tpm (log10(x+1) = 4.00 ).
- 46263 rows above 1000 tpm (log10(x+1) = 3.00 ).
- 2 145 533 rows = 0 tpm.

There are 6 284 390 rows with a rpm count between 1 and 9999.

We use the pearson correlation method to make the correlation matrix (cor R function) and the complete linkage method to make the dendrogram. (package R heatmaply)
Download Correlation matrix

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Update 2019/02 :

There are 43 samples and 21080 genes (906440 rows) (NCBI GRCh38.92 07/2018).
On these counts:

- Minimum = 0
- Maximum = 40979
- Median = 4.09
- Average = 49.15

Only 20 rows of the counting table, i.e. 5 genes (ACTB, PARP2, RMRP, RPPH1 & TUBA1C) on 9 samples (4 NGS_Number NGS13, NGS14, NGS23 & NGS43), have a tpm count above 20000.
There are :
- 202 rows of this same table that have a count greater than 10000 tpm.
- 5525 rows above 1000 tpm.
- 223858 rows = 0 tpm.

There are 668754 rows with a rpm count between 1 and 9999.

1	hIPSC 4603c45 Mef P49
2	hIPSC 4603c45 Mef P48
3	hIPSC 4603c45 Mef P51
4	hIPSC 4603c45 CS P47-4
5	hIPSC 4603c45 CS P47-5 n3
6	hIPSC 4603c45 CS P47-5 n2
7	hIPSC 4603c45 L7 P47-4
8	hIPSC 4603c45 L7 P47-5
10	hESC RC9 CS P35
11	hESC RC9 CS P40
12	hESC RC9 CS P41
13	hESC RC9 L7 P35-6
15	hESC RC9 L7 P35-5
16	hESC SA001 Mef P73
17	hESC SA001 Mef P75
18	hESC SA001 Mef P76
19	hESC SA001 CS P71-3
20	hESC SA001 CS P71-4
21	hESC SA001 CS P71-5
22	hESC SA001 L7 P71-5
23	hESC SA001 L7 P71-4
24	hESC SA001 L7 P71-3
25	hESC SA001
26	hESC SA001 P54
27	hESC SA001 P67
28	hESC SA001 P60 n1
29	hESC SA001 P60 n2
30	hESC SA001 P60 n3
31	hIPSC 1869c07
32	hIPSC PB12c03
33	hESC SA001 mTeser n1
34	hESC SA001 mTeser n3
35	hESC SA001 mTeser n2
36	hIPSC VM N2A 180 D3C
37	hIPSC VM N1A 197 D3C
38	hIPSC VM N2A 398 D3C
39	hESC RC9
40	hESC derived melanocyte D32 n1
41	hESC derived melanocyte D32 n2
42	hESC derived melanocyte D32 n3
43	hIPSC derived motoneurons WT 14-5 D10
44	hIPSC derived motoneurons WT 1427 D10
45	hIPSC derived motoneurons WT 56c02 D10

We use the pearson correlation method to make the correlation matrix (cor R function) and the complete linkage method to make the dendrogram.(package R heatmaply)
Download Correlation matrix