%load_ext autoreload
%autoreload 2

Minimal access to .gef files#

import gefslim
import matplotlib.pyplot as plt

PROJ_DIR = "../STT1_image_alignment/pipeline output/result/"

gef = gefslim.GEF(result_dir=PROJ_DIR)

04.tissuecut derived data#

gef.get_gene_stats(name="C01337B3.gef")
gene MIDcount E10
0 Gm42418 3939830 40.404686
1 Fth1 430715 38.508060
2 Tmsb4x 426657 34.661098
3 Cdk8 398340 34.563690
4 Calm1 323043 35.500229
... ... ... ...
25303 Gm15444 1 0.000000
25304 Gm15412 1 0.000000
25305 Gm15395 1 0.000000
25306 Gm15353 1 0.000000
25307 Zscan4d 1 0.000000

25308 rows × 3 columns

genecounts_per_spot = gef.get_genecounts_per_spot(name="C01337B3.gef", binsize=10)
genecounts_per_spot
x y gene counts
0 0 4080 Phactr1 2
1 0 4080 Tmsb4x 2
2 0 4080 Zer1 1
3 0 7100 Hgs 1
4 0 8060 Dnaja4 1
... ... ... ... ...
66694294 26450 16360 Sst 1
66694295 26450 22920 Snx27 1
66694296 26450 23230 Nrgn 1
66694297 26450 24680 Pfn2 1
66694298 26450 25680 1110008P14Rik 1

66694299 rows × 4 columns

tmp = genecounts_per_spot.query("gene == 'Gm42418'")

plt.scatter(tmp["x"], tmp["y"], c = tmp["counts"], s=1)

<matplotlib.collections.PathCollection at 0x58e0f8fa0>
../_images/2e3b1838de69357fe5533b6c243fd918b599e4a9a256165f6645a744f81d3f7f.png

041.cellcut derived data#

gef.get_counts_per_cell(name="C01337B3.cellbin.gef")
cell_id counts
0 0 9
1 1 51
2 2 102
3 3 48
4 4 35
... ... ...
95062 95062 11
95063 95063 49
95064 95064 70
95065 95065 21
95066 95066 32

95067 rows × 2 columns

gef.get_genecounts_per_cell(name="C01337B3.cellbin.gef")
gene cell_id counts
0 Agap1 0 3
1 Cdk8 0 1
2 Cirbp 0 1
3 Cpeb3 0 1
4 Cyhr1 0 1
... ... ... ...
13784635 Tagln3 95066 4
13784636 Tmem208 95066 1
13784637 Ubqln2 95066 1
13784638 Uqcc2 95066 1
13784639 Yipf5 95066 1

13784640 rows × 3 columns

gef.get_cell_borders(name="C01337B3.cellbin.gef", transformed=False)
cell_id x y border
0 0 13853 7165 [[5, -7], [-1, -7], [-4, -5], [-6, -5], [-8, -...
1 1 10235 7398 [[-6, -1], [-6, 1], [-5, 2], [-5, 3], [-3, 3],...
2 2 10210 7407 [[0, -9], [-1, -8], [-3, -8], [-6, -5], [-8, -...
3 3 10228 7415 [[4, -7], [2, -7], [1, -8], [-3, -6], [-7, 1],...
4 4 10165 7421 [[1, -4], [0, -4], [-1, -3], [-3, -3], [-3, -2...
... ... ... ... ...
95062 95062 17361 20474 [[-9, 2], [-9, 3], [9, 3], [8, 0], [6, -2], [4...
95063 95063 17309 20462 [[11, -10], [1, -11], [-11, -6], [-12, 4], [-8...
95064 95064 17363 20446 [[33, -14], [1, -14], [-5, -9], [-18, -7], [-2...
95065 95065 17283 20503 [[-2, -17], [-2, 16], [3, 15], [4, 13], [2, 5]...
95066 95066 17309 20493 [[-2, -11], [-5, -10], [-6, -8], [-6, -3], [-3...

95067 rows × 4 columns

gef.get_cell_borders(name="C01337B3.cellbin.gef", transformed=True)
cell_id border
0 0 [[13858, 7158], [13852, 7158], [13849, 7160], ...
1 1 [[10229, 7397], [10229, 7399], [10230, 7400], ...
2 2 [[10210, 7398], [10209, 7399], [10207, 7399], ...
3 3 [[10232, 7408], [10230, 7408], [10229, 7407], ...
4 4 [[10166, 7417], [10165, 7417], [10164, 7418], ...
... ... ...
95062 95062 [[17352, 20476], [17352, 20477], [17370, 20477...
95063 95063 [[17320, 20452], [17310, 20451], [17298, 20456...
95064 95064 [[17396, 20432], [17364, 20432], [17358, 20437...
95065 95065 [[17281, 20486], [17281, 20519], [17286, 20518...
95066 95066 [[17307, 20482], [17304, 20483], [17303, 20485...

95067 rows × 2 columns

gef.get_area_per_cell(name="C01337B3.cellbin.gef")
cell_id area
0 0 220
1 1 102
2 2 250
3 3 192
4 4 73
... ... ...
95062 95062 107
95063 95063 536
95064 95064 1111
95065 95065 195
95066 95066 265

95067 rows × 2 columns

adata = gef.read_cellcut(name="C01337B3.cellbin.gef")
adata.to_df()
x y offset geneCount expCount dnbCount area cellTypeID clusterID border
0 13853 7165 0 9 12 9 220 0 1 [[5, -7], [-1, -7], [-4, -5], [-6, -5], [-8, -...
1 10235 7398 9 51 67 56 102 0 6 [[-6, -1], [-6, 1], [-5, 2], [-5, 3], [-3, 3],...
2 10210 7407 60 102 144 116 250 0 6 [[0, -9], [-1, -8], [-3, -8], [-6, -5], [-8, -...
3 10228 7415 162 48 54 51 192 0 6 [[4, -7], [2, -7], [1, -8], [-3, -6], [-7, 1],...
4 10165 7421 210 35 45 38 73 0 6 [[1, -4], [0, -4], [-1, -3], [-3, -3], [-3, -2...
... ... ... ... ... ... ... ... ... ... ...
95062 17361 20474 13784457 11 18 13 107 0 1 [[-9, 2], [-9, 3], [9, 3], [8, 0], [6, -2], [4...
95063 17309 20462 13784468 49 77 57 536 0 11 [[11, -10], [1, -11], [-11, -6], [-12, 4], [-8...
95064 17363 20446 13784517 70 110 91 1111 0 11 [[33, -14], [1, -14], [-5, -9], [-18, -7], [-2...
95065 17283 20503 13784587 21 29 23 195 0 1 [[-2, -17], [-2, 16], [3, 15], [4, 13], [2, 5]...
95066 17309 20493 13784608 32 49 39 265 0 11 [[-2, -11], [-5, -10], [-6, -8], [-6, -3], [-3...

95067 rows × 10 columns