Hosuk:LabNotes/2014-8-19
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Decoding with Agi26k0gap Padlock Probe[edit]
- Image resolution : 20x obj, 4096 x 4086, z step = 1.04um
- Since how many features would be detected or how much dense rolonies in this sample, so image in wide field of view
- eventually 63x obj.
- Data is in '2014-08-13'
Analysis[edit]
- 1. Arrange data
- File name (decided at imaging) : [Sample]_[Resolution]_[Step#]_[Pos#]_z##_ch##.tif
- ex) PGP1F : PGP1F_Agi26k0gap_Decode_P20_S2_2014-08-09_20x_4k_Step1_Pos2_z01_ch01.tif
- ex) Tissue section : HBTissue_2014-08-12_RCAagain_20x_4k_Pos1_z00_ch00.tif
- Step# after confocal imaging
- Step0 : 1st Rolony --> ch00 : Cy3, ch01 : BF
- Step1~7 : ch00 : 488, ch01 : Cy3, ch02 : Cy5, ch03 : BF
- Maximum Intensity Projection(MIP) at each channel
- Re-format the Step1~Step7 --> Step1~21
- Step1 --> Step1:488, Step2:Cy3, Step3:Cy5
- Step2 --> Step4:488, Step5:Cy3, Step6:Cy5
- ...Step7 --> Step19:488, Step20:Cy3, Step21:Cy5
- Image aligning
- Step# in one cycle is an exact position, no shift happened --> Cycle N = Step[3(N-1)+1], Step[3(N-1)+2], Step[3(N-1)+3]
- Get offset using Bright field images (ch03) of each Cycle : Step1, Step4, Step7, ... Step19
- I've done by manually, but eventually need automation
- Shift, Crop of ch00, ch01, ch02 images(Image_Aligning_Crop_v5.m)
- Name : Crop_[file name]
- ex)Offset
- File name (decided at imaging) : [Sample]_[Resolution]_[Step#]_[Pos#]_z##_ch##.tif
Cycle | X | Y |
1 | 0 | 0 |
2 | -13 | +8 |
3 | -9 | +8 |
4 | -19 | +4 |
5 | -16 | +4 |
5 | -15 | +10 |
5 | -6 | -9 |
Average Offset | -11.14 | +0.71 |
Round | -11 | +1 |
New Offset | ||
1 | -11 | +1 |
2 | -2 | +7 |
3 | +2 | +7 |
4 | -8 | +3 |
5 | -5 | +3 |
5 | -4 | -11 |
5 | +5 | -10 |
Crop Size | 12 | 12 |
- 2. Run Script for detected barcodes and counts(RolonyDataInfo_v12_7StageDecoding.m, Rolony_Decoding_v18c_7StageDecoding.m)
- Run PISA with every image (Step1~21)
- PISA parameter
- PISAParam.log_radius = 2;
- PISAParam.log_upper = -2e-4;
- PISAParam.area_upper = 100;
- PISAParam.area_lower = 10;
- PISAParam.axratio_lower = 0.5;
- PISAParam.circ_upper = 1.6;
- PISAParam.circ_lower = 0.8;
- PISAParam.pconn = 8;
- PISAParam.bkgmult_lower = 3;
- PISAParam.FilterOption = 1;
- PISAParam.Deconvoluted = 0;
- PISAParam.DoMedflt = 1;
- PISAParam.nofindbkg = 0;
- eventually masking each image with MIP image will be used
- Result gemerated : Barcodes, Counts
- Run PISA with every image (Step1~21)
- 3. Mapping detected barcode string to gene
- Matt made master key table : barcode and gene name
- Allow 1 bit error (--> but these barcodes of 1 bit error were found in other genes...)
- Map detected barcode to gene name using VLOOKUP(E#, Sheet0Ex!$A$2:$B$24000, 2, 0)
Result[edit]
- Saved at 2014-08-18
- Total num : 2471
- Num detected only one dye at each digit : 1100
- Num detected 488 and Cy3 at each digit : 775
- Num detected 488 and Cy5 at each digit : 785
- Num detected Cy3 and Cy5 at each digit : 275
- Num detected 488, Cy3 and Cy5 at each digit : 99
- Mapped to gene : 348
- Need to consider features with two or three dyes to be mapped to gene --> need more to polish the data