Tests for checking Batch Effects
Batch 180305 | |
---|---|
Condition crowned | 7 |
Condition worker | 4 |
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. | 1.457 | 1.457 | 0 |
Median | 5.821 | 5.821 | 0 |
Mean | 10.69 | 10.69 | 0 |
3rd Qu. | 15.19 | 15.19 | 0 |
Max. | 84.69 | 84.69 | 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 | 5.946e-05 | 0.236 | 0.4748 | 0.4818 | 0.7237 | 1 | 0.05893 |
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 | |
---|---|---|---|---|---|---|
LPGAT1 | 141.4 | 612 | 7.048 | 4.217e-05 | 0.2929 | -4.045 |
KCNA1 | 54.21 | 151 | 6.881 | 5.134e-05 | 0.2929 | -4.05 |
PIK3R4 | 207.5 | 1651 | 6.817 | 5.538e-05 | 0.2929 | -4.052 |
RABL3 | 52.64 | 258 | 6.361 | 9.661e-05 | 0.3832 | -4.068 |
ZDHHC21 | 73.5 | 199.7 | 6.08 | 0.000138 | 0.4133 | -4.08 |
RBM12 | 276.2 | 1375 | 5.983 | 0.0001563 | 0.4133 | -4.084 |
ST6GALNAC5 | 177.3 | 310.9 | 5.715 | 0.0002226 | 0.5046 | -4.096 |
TMEM245 | 161.6 | 532.9 | 5.417 | 0.0003333 | 0.6162 | -4.111 |
OGFOD2 | -276.9 | 1982 | -5.382 | 0.0003496 | 0.6162 | -4.113 |
ACTN1 | 136 | 384.7 | 5.119 | 0.0005055 | 0.6466 | -4.128 |
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 | 28.09 | 28.09 | 6.7 | 6.7 | 0.4427 | 0 | 1 |
PC2 | 17.14 | 45.23 | 10.7 | 10.7 | 0.3253 | 0 | 1 |
PC3 | 13.17 | 58.39 | 29.3 | 29.3 | 0.0852 | 0 | 1 |
PC4 | 9.157 | 67.55 | 14.6 | 14.6 | 0.2457 | 0 | 1 |
PC5 | 7.275 | 74.83 | 4 | 4 | 0.5538 | 0 | 1 |
PC6 | 6.485 | 81.31 | 0 | 0 | 0.9937 | 0 | 1 |
PC7 | 5.305 | 86.62 | 2 | 2 | 0.6754 | 0 | 1 |
PC8 | 5.25 | 91.87 | 1.9 | 1.9 | 0.6825 | 0 | 1 |
PC9 | 4.39 | 96.26 | 18.1 | 18.1 | 0.1925 | 0 | 1 |
PC10 | 3.744 | 100 | 12.5 | 12.5 | 0.2856 | 0 | 1 |
PC11 | 1.421e-28 | 100 | 15.3 | 15.3 | 0.2348 | 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