Score each cell for how much their transcripts change their goodness-of-fit over space.
Usage
score_cell_segmentation_error(
chosen_cells,
transcript_df,
cellID_coln = "CellId",
transID_coln = "transcript_id",
score_coln = "score",
spatLocs_colns = c("x", "y", "z"),
model_cutoff = 50
)Arguments
- chosen_cells
the cell_ID of chosen cells
- transcript_df
the data.frame of transcript_ID, cell_ID, score, spatial coordinates
- cellID_coln
the column name of cell_ID in transcript_df
- transID_coln
the column name of transcript_ID in transcript_df
- score_coln
the column name of score in transcript_df
- spatLocs_colns
column names for 1st, 2nd and optional 3rd dimension of spatial coordinates in transcript_df
- model_cutoff
the cutoff of transcript number to do spatial modeling (default = 50)
Value
data.frame with columns for
cell_ID, cell id
transcript_num, number of transcripts in given cell
modAlt_rsq, summary(mod_alternative)$r.squared
lrtest_ChiSq, lrtest chi-squared value
lrtest_Pr, lrtest probability larger than chi-squared value, p-value
Details
For tLLRv2 score of transcripts within each cell, run a quadratic model: mod_alternative = lm(tLLRv2 ~ x + y + x2 + y2 +xy) for 2D, lm(tLLRv2 ~ x + y + z + x2 + y2 +z2 +xy + xz + yz) for 3D and a null model: mod_null = lm(tLLRv2 ~ 1); then run lmtest::lrtest(mod_alternative, mod_null). Return statistics for mod_alternative$fitted.values (standard deviation and minimal value), summary(mod_alternative)$r.squared and as well as lrtest chi-squared value.