There are subtle issues involving the difference between designed comparisons and post-hoc comparisons, of which this likely is an example.
If, before collecting the data, you anticipated this kind of pattern, you could employ a simple nonparametric test. The null hypothesis would be that all changes are due to chance with the alternative being that a specified category was increasing and the other eight categories were decreasing. Under the null, positive changes have a 50% chance of occurring, implying the chance of the alternative is $(0.50)^8(1 - 0.50)^1$ = $0.002$: highly significant evidence for the alternative.
The analysis for a post-hoc observation is difficult because we can't even get started with describing the situation. Exactly what kind of pattern would you happen to notice and considered worthy of testing? So many are possible, with no accurate description available, that all we can say (from experience) is that (a) it is highly likely that any interested investigator would notice some pattern in the data and (b) a post-hoc hypothesis test could be constructed to "demonstrate" the "high significance" of that pattern, exactly as I did above. For these reasons, applying hypothesis tests after the fact to support claims of "statistical validity" for exploratory results is frowned upon. (Among statisticians, who should know better, it is called "data snooping" or worse.)
One way out is to conduct your analysis with c. half the data, randomly selected. Look for any patterns you like. Construct an appropriate suite of hypothesis tests for those patterns and then apply them to the held-out data only. This is in the spirit of the scientific requirement for replication. If you don't do this, then you would be obliged to repeat your experiment to confirm whatever you're seeing in the data you currently have.