Correlation Analysis of a particular gene and the DE expressed genes between two conditions

Question

[dzisis1986]

I have a count matrix like that :

Genes   1_conA  2_conA  3_conA  4_conB  5_conB  6_conB
SNCA    2754.51468026414    2330.52372350378    2098.4507693163 493.840280542602    519.951931272473    739.283699887331
ACAP3   6674.36606862323    5409.85793263052    4323.59396601816    2835.88756309508    3140.25429738641    3896.43656225971
ADAM11  699.592200296909    461.280799487677    494.726262249319    183.26821707438 192.823381215176    307.040197251065
ANO8    2616.29181950827    2303.33914627923    1963.0501501436 1256.38797123759    1322.61352696467    1727.46307913068
ARHGAP33    4745.22335002185    3240.42700839725    2587.43328935726    1259.44145060271    1387.25369716501    1750.81995136913
ARHGEF25    2514.71835790986    1732.19279036597    1749.15256711928    954.396853986755    1101.64564134203    1273.47148785137
ATE1    681.226078895356    689.637999819787    557.304649328032    1337.15024965092    1390.46381978227    1039.91237064104
BLOC1S5 253.527233897061    234.056980445019    191.04859397678 490.761612501422    407.221389190741    514.030987914712
C8orf82 1087.38670986473    914.926707564202    937.432348227618    682.65905217546 653.544038903608    696.834295089135
CABLES2 779.731441395388    858.397991894595    986.471631919686    364.142546296198    369.280971509118    509.072980419863
CACNG8  712.444925820586    727.489752432312    969.411665067064    402.421625005241    378.143770666438    437.624092121583
CAMK2B  1606.34018505634    1563.46349434595    1293.10585539382    769.233241222907    1058.47936327409    786.98152040651
CAMKV   1571.66050327696    1399.56665570775    1181.14616689206    951.763777049311    816.53641659271 843.734731015618
CARMIL3 1973.93192405357    1544.7386129963 1345.01170227751    767.675003825552    771.270966376434    1113.39852140762

I want to perform a correlation analysis to see the levels (increases or decreases ) of SNCA gene with the DE genes. I would like to say if is more or less in 2 different conditions. How a correlation matrix be calculated in R for 3 samples in each condition?

I want to be able to measure the values of SNCA and how they are formed so that we can measure the expression in each experiment.

[dthorbur]

It sounds like you want an ANOVA, which can be invoked with the aov() function. You may need to melt your dataset using the melt() function. It's hard to tell as you don't explain your column names or what the conditions are.

[dzisis1986]

No anova is not what i need. i need to see how the SNCA is correlated to to other genes across conA and conB. The correlation is done with spearman core test but still I don't see the separation between the two conditions

test_cor
t=transpose(test_cor)
colnames(t) <- rownames(test_cor)
rownames(t) <- colnames(test_cor)
t


SNCA ACAP3 ADAM11 ANO8 ARHGAP33 ARHGEF25 ATE1 BLOC1S5 C8orf82 CABLES2 CACNG8 CAMK2B CAMKV CARMIL3
iPSC_1_conA 2754  6674    699 2616     4745     2514  681     253    1087     779    712   1606  1571    1973
iPSC_2_conA 2330  5409    461 2303     3240     1732  689     234     914     858    727   1563  1399    1544
iPSC_3_conA 2098  4323    494 1963     2587     1749  557     191     937     986    969   1293  1181    1345
iPSC_4_conB  493  2835    183 1256     1259      954 1337     490     682     364    402    769   951     767
iPSC_5_conB  519  3140    192 1322     1387     1101 1390     407     653     369    378   1058   816     771
iPSC_6_conB  739  3896    307 1727     1750     1273 1039     514     696     509    437    786   843    1113

rstatix::cor_test(t, SNCA, method = "spearman")

# A tibble: 13 × 6
   var1  var2       cor statistic       p method  
   <chr> <chr>    <dbl>     <dbl>   <dbl> <chr>   
 1 SNCA  ACAP3     1         0    0.00278 Spearman
 2 SNCA  ADAM11    0.94      2    0.0167  Spearman
 3 SNCA  ANO8      1         0    0.00278 Spearman
 4 SNCA  ARHGAP33  1         0    0.00278 Spearman
 5 SNCA  ARHGEF25  0.94      2    0.0167  Spearman
 6 SNCA  ATE1     -0.77     62    0.103   Spearman
 7 SNCA  BLOC1S5  -0.6      56    0.242   Spearman
 8 SNCA  C8orf82   0.89      4.00 0.0333  Spearman
 9 SNCA  CABLES2   0.77      8    0.103   Spearman
10 SNCA  CACNG8    0.71     10    0.136   Spearman
11 SNCA  CAMK2B    0.94      2    0.0167  Spearman
12 SNCA  CAMKV     0.83      6    0.0583  Spearman
13 SNCA  CARMIL3   1         0    0.00278 Spearman

How can I go further with this in order to see also the iPSC_1_conA iPSC_2_conA iPSC_3_conA etc in the result matrix ?? I that way I will be able to see that SNCA has higher correlation and raises in iPSC_2_conA and not in iPSC_5_conB ?