The following analysis path is probaby the quickest to lead you from your MEG data to presentable source estimates, with some compromised with respect to accuracy and statistical analysis. The general idea is this:

1. interpolate your MEG data to a standardised sensory array
2. apply statistics in "signal space" (e.g. using SensorSPM), in order to detect significant contrasts and latency ranges
3. run source analysis using a standardised head model on the grand-averaged MEG data
4. possibly apply the same source analysis on individual subject data, e.g. for statistical analysis in "source space"

This closely resembles the analysis strategy employed for ERP analysis, where electrodes are usually placed at standardised positions (e.g. the "10/20 system"), such that data for different subjects are very easy to combine (e.g. for grand-averages, statistics on the same eletrodes etc.).

In MEG recordings, the position and orientation of the sensor array usually differs significantly across subjects (unless special precautions are taken). Until recently, it was not straightforward to interpolate MEG data from different subjects into a common sensor array (e.g. the average across subjects). It has therefore been the standard to analyse MEG data only in source space, i.e. after source estimation procedures have been applied on single-subject data.