Tests for checking Batch Effects
Batch 180212 | |
---|---|
Condition crowned | 12 |
Condition worker | 10 |
Standardized Pearson Correlation Coefficient | Cramer’s V | |
---|---|---|
Confounding Coefficients (0=no confounding, 1=complete confounding) | NA | NA |
Full (Condition+Batch) | Condition | Batch | |
---|---|---|---|
Min. | 0 | 0 | 0 |
1st Qu. | 0.413 | 0.413 | 0 |
Median | 1.912 | 1.912 | 0 |
Mean | 3.675 | 3.675 | 0 |
3rd Qu. | 5.364 | 5.364 | 0 |
Max. | 47.78 | 47.78 | 0 |
Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | Ps<0.05 | |
---|---|---|---|---|---|---|---|
Batch P-values | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
Condition P-values | 0.0003672 | 0.2997 | 0.5394 | 0.536 | 0.7762 | 1 | 0.01954 |
Boxplots for all values for each of the samples and are colored by batch membership.
Condition: worker (logFC) | AveExpr | t | P.Value | adj.P.Val | B | |
---|---|---|---|---|---|---|
TAGLN | 354.3 | 968.7 | 3.663 | 0.001497 | 1 | -4.593 |
GDF9 | 10.23 | 24.32 | 3.545 | 0.001972 | 1 | -4.593 |
ZNF568 | 8.517 | 19.95 | 3.528 | 0.002051 | 1 | -4.593 |
NRXN3 | 6.6 | 8.5 | 3.524 | 0.00207 | 1 | -4.593 |
MTHFD2 | 22.12 | 45.14 | 3.427 | 0.002597 | 1 | -4.593 |
NRXN1 | 8.717 | 9.545 | 3.423 | 0.002624 | 1 | -4.593 |
GPM6B | 73.7 | 147.5 | 3.291 | 0.00356 | 1 | -4.594 |
PTGS1 | 38.2 | 73.36 | 3.18 | 0.004603 | 1 | -4.594 |
ENTPD1 | 53.7 | 156.4 | 3.12 | 0.005286 | 1 | -4.594 |
ELOVL6 | 13.27 | 22.36 | 3.075 | 0.005853 | 1 | -4.594 |
This plot helps identify outlying samples.
This is a heatmap of the given data matrix showing the batch effects and variations with different conditions.
This is a heatmap of the correlation between samples.
This is a Circular Dendrogram of the given data matrix colored by batch to show the batch effects.
This is a plot of the top two principal components colored by batch to show the batch effects.
Proportion of Variance (%) | Cumulative Proportion of Variance (%) | Percent Variation Explained by Either Condition or Batch | Percent Variation Explained by Condition | Condition Significance (p-value) | Percent Variation Explained by Batch | Batch Significance (p-value) | |
---|---|---|---|---|---|---|---|
PC1 | 55.27 | 55.27 | 1.1 | 1.1 | 0.6447 | 0 | 1 |
PC2 | 8.1 | 63.37 | 20.7 | 20.7 | 0.03329 | 0 | 1 |
PC3 | 5.389 | 68.76 | 0 | 0 | 0.9811 | 0 | 1 |
PC4 | 5.196 | 73.95 | 1.8 | 1.8 | 0.5497 | 0 | 1 |
PC5 | 3.013 | 76.97 | 4 | 4 | 0.3705 | 0 | 1 |
PC6 | 2.479 | 79.45 | 17.8 | 17.8 | 0.05016 | 0 | 1 |
PC7 | 2.142 | 81.59 | 3.4 | 3.4 | 0.4131 | 0 | 1 |
PC8 | 2.01 | 83.6 | 0.5 | 0.5 | 0.7485 | 0 | 1 |
PC9 | 1.826 | 85.42 | 0.1 | 0.1 | 0.9065 | 0 | 1 |
PC10 | 1.562 | 86.99 | 13.9 | 13.9 | 0.08724 | 0 | 1 |
PC11 | 1.471 | 88.46 | 3.2 | 3.2 | 0.4281 | 0 | 1 |
PC12 | 1.424 | 89.88 | 2.8 | 2.8 | 0.4603 | 0 | 1 |
PC13 | 1.366 | 91.25 | 3.8 | 3.8 | 0.3845 | 0 | 1 |
PC14 | 1.319 | 92.57 | 1.1 | 1.1 | 0.6371 | 0 | 1 |
PC15 | 1.228 | 93.8 | 1.4 | 1.4 | 0.6021 | 0 | 1 |
PC16 | 1.158 | 94.95 | 11.2 | 11.2 | 0.1277 | 0 | 1 |
PC17 | 1.103 | 96.06 | 3.6 | 3.6 | 0.3986 | 0 | 1 |
PC18 | 1.087 | 97.14 | 2.4 | 2.4 | 0.4918 | 0 | 1 |
PC19 | 1.038 | 98.18 | 3.4 | 3.4 | 0.4124 | 0 | 1 |
PC20 | 0.9797 | 99.16 | 3.7 | 3.7 | 0.3888 | 0 | 1 |
PC21 | 0.8387 | 100 | 0.1 | 0.1 | 0.917 | 0 | 1 |
PC22 | 3.021e-29 | 100 | 0.9 | 0.9 | 0.6679 | 0 | 1 |
This is a heatmap plot showing the variation of gene expression mean, variance, skewness and kurtosis between samples grouped by batch to see the batch effects variation
## Note: Sample-wise p-value is calculated for the variation across samples on the measure across genes. Gene-wise p-value is calculated for the variation of each gene between batches on the measure across each batch. If the data is quantum normalized, then the Sample-wise measure across genes is same for all samples and Gene-wise p-value is a good measure.
This is a plot showing whether parametric or non-parameteric prior is appropriate for this data. It also shows the Kolmogorov-Smirnov test comparing the parametric and non-parameteric prior distribution.
## Warning in combatPlot(shinyInput$lcounts, batch = shinyInput$batch, mod = mod): There is no batch
## Number of Surrogate Variables found in the given data: 2